system

Abstract

The system according to this embodiment aims to support decision-making that reflects quantitative evaluation in the selection of potential store locations. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a visualization unit. The data collection unit collects data related to potential store locations. The analysis unit analyzes the data collected by the data collection unit. The evaluation unit performs a location evaluation based on the data analyzed by the analysis unit. The visualization unit visualizes the evaluation results obtained by the evaluation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in the selection of store candidate locations, qualitative judgments are relied on, and quantitative evaluations are lacking, so there is a problem that it is difficult to select an optimal location.

[0005] The system according to the embodiment aims to assist in making a decision that reflects a quantitative evaluation in the selection of store candidate locations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a visualization unit. The data collection unit collects data related to potential store locations. The analysis unit analyzes the data collected by the data collection unit. The evaluation unit performs a location evaluation based on the data analyzed by the analysis unit. The visualization unit visualizes the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can support decision-making that reflects quantitative evaluation in the selection of potential store locations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The decision support system according to an embodiment of the present invention is a system that utilizes generative AI to evaluate shop locations and increase the success rate of business expansion. This decision support system does not rely on conventional qualitative judgments based on human experience and intuition, but uses generative AI to select the optimal location that reflects quantitative evaluation. First, a challenge faced by target groups such as shop planning departments and shop operators is a sales environment that does not justify operating costs. To solve this problem, the following steps are performed using generative AI. For example, in collecting data on potential store locations, data such as demographics, land prices, behavioral distribution, price per tsubo (unit of area), trading area, location information, competitor information, and company history are collected. This allows for a detailed understanding of the candidate locations. Next, the collected data is analyzed by generative AI to evaluate the location of each candidate location. For example, a numerical evaluation can be obtained, such as a location evaluation of 65 points for candidate location A and a location evaluation of 93 points for candidate location B. Furthermore, the evaluation results by generative AI are visualized and provided to shop planning departments and operators. This allows for a quick understanding of the optimal location with high customer attraction potential. Furthermore, it supports decision-making to maximize revenue and optimize operating costs. Specific examples of how the generative AI is used include the following types of data: For example, demographic data such as population density and age distribution around potential locations; land value data such as land prices and rents around potential locations; behavioral distribution data analyzing the behavioral patterns of people around potential locations; price per square meter data analyzing the price per square meter around potential locations; trade area data analyzing the trade area of ​​potential locations; location information analyzing the specific location of potential locations; competitor information analyzing information on competing stores around potential locations; and company history data analyzing past store openings and performance data. By analyzing this data with the generative AI and selecting the optimal store location, the success rate of expanding the shop's performance can be increased. Thus, the decision support system can evaluate store locations and increase the success rate of expanding the shop's performance.

[0029] The decision support system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a visualization unit. The data collection unit collects data related to potential store locations. For example, the data collection unit collects demographic data, land price data, behavioral distribution data, price per tsubo data, store trade area data, location information, competitor information, and company history data. For example, the data collection unit collects information such as population density and age groups around the candidate site. The data collection unit can also collect information on land prices and rents around the candidate site. Furthermore, the data collection unit can also collect behavioral patterns of people around the candidate site. For example, the data collection unit collects the price per tsubo of the candidate site. The data collection unit can also collect the trade area of ​​the candidate site. Furthermore, the data collection unit can also collect the specific location of the candidate site. For example, the data collection unit collects information on competing stores around the candidate site. The data collection unit can also collect past store opening history and performance data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data using a generation AI. For example, the analysis unit analyzes demographic data using generative AI. The analysis unit can also analyze land price data using generative AI. Furthermore, the analysis unit can analyze behavioral distribution data using generative AI. For example, the analysis unit analyzes price per tsubo (unit of area) data using generative AI. The analysis unit can also analyze store location area data using generative AI. Furthermore, the analysis unit can analyze location information using generative AI. For example, the analysis unit analyzes competitor information using generative AI. The analysis unit can also analyze its own historical data using generative AI. The evaluation unit performs location evaluations based on the data analyzed by the analysis unit. For example, the evaluation unit performs location evaluations for each candidate site based on the analysis results. For example, the evaluation unit assigns a location evaluation score of 65 points to candidate site A. The evaluation unit can also assign a location evaluation score of 93 points to candidate site B. Furthermore, the evaluation unit can perform numerical evaluations based on the analysis results. The visualization unit visualizes the evaluation results obtained by the evaluation unit. For example, the visualization unit visualizes the evaluation results in the form of graphs, maps, etc. For example, the visualization unit visualizes the evaluation results in graph format. The visualization unit can also visualize the evaluation results in map format.Furthermore, the visualization unit can visualize the evaluation results and provide them to the shop planning department and operating stores. This allows the decision support system according to the embodiment to assist in selecting the optimal location by collecting, analyzing, evaluating, and visualizing data on potential store locations.

[0030] The data collection department collects data on potential store locations. For example, it collects demographic data, land price data, behavioral distribution data, price per square meter data, market area data, location information, competitor information, and company history data. Specifically, the department collects information such as population density and age demographics around the potential location. This can utilize census data and local resident registration data. The department can also collect information on land prices and rents around the potential location, using data provided by real estate agents and publicly announced land price data. Furthermore, the department can collect behavioral patterns of people around the potential location, using smartphone location data and traffic survey data to understand people's movement patterns during specific time periods. To collect price per square meter data for the potential location, the department utilizes real estate transaction databases and information from local real estate agents. The department can also collect market area data for the potential location, using GIS (Geographic Information System) to analyze the distribution of population and facilities within a specific radius. Finally, the department can collect the specific location of the potential site. For example, precise location information is obtained using geographic coordinate data and address data. The data collection department conducts commercial databases and on-site surveys to collect information on competing stores around potential locations. The data collection department can also collect past store opening history and performance data. This utilizes the company's own database and past sales reports. As a result, the data collection department can gather a wide range of information from diverse data sources and understand the detailed situation of potential store locations.

[0031] The analysis unit analyzes data collected by the collection unit. For example, the analysis unit analyzes collected data using generative AI. Specifically, the analysis unit uses generative AI to analyze demographic data. The generative AI analyzes population density and age distribution to reveal consumer characteristics in a specific area. The analysis unit can also use generative AI to analyze land price data. The generative AI analyzes land price fluctuation patterns to predict future land price trends. Furthermore, the analysis unit can also use generative AI to analyze behavioral distribution data. The generative AI analyzes location data to identify people's movement patterns and length of stay. For example, the analysis unit uses generative AI to analyze price per square meter data. The generative AI analyzes fluctuations in price per square meter for each region to support optimal rent setting. The analysis unit can also use generative AI to analyze store trading area data. The generative AI analyzes population and the distribution of competing stores within a trading area to identify the optimal trading area. Furthermore, the analysis unit can also use generative AI to analyze location information. The generating AI analyzes geographic coordinate data to evaluate the accessibility and surrounding environment of potential locations. For example, the analysis unit uses the generating AI to analyze competitive information. The generating AI analyzes the locations and performance of competing stores to evaluate the competitive environment. The analysis unit can also use the generating AI to analyze its own historical data. The generating AI analyzes past store opening history and performance data to identify success and failure factors. This allows the analysis unit to perform advanced analysis of the collected data and provide the information necessary to evaluate potential store locations.

[0032] The evaluation unit conducts location evaluations based on data analyzed by the analysis unit. For example, the evaluation unit evaluates the location of each candidate site based on the analysis results. Specifically, the evaluation unit assigns a location evaluation score of 65 points to candidate site A. Based on the analysis results, the evaluation unit comprehensively evaluates factors such as population density, land price, competitive situation, and market area, and provides a quantified evaluation. The evaluation unit can also assign a location evaluation score of 93 points to candidate site B. Based on the analysis results, the evaluation unit weights each factor and calculates an overall evaluation. Furthermore, the evaluation unit can also provide a quantified evaluation based on the analysis results. The evaluation unit uses an algorithm to integrate the evaluation results of each factor and calculate an overall evaluation. For example, the evaluation unit assigns a population density evaluation score of 20 points, a land price evaluation score of 15 points, a competitive situation evaluation score of 10 points, and a market area evaluation score of 20 points, resulting in an overall evaluation score of 65 points. In addition, based on the analysis results, the evaluation unit can identify the strengths and weaknesses of each candidate site and propose specific improvement measures. This allows the evaluation department to conduct an objective and comprehensive location evaluation based on the analysis results, and to support the selection of the most suitable store location.

[0033] The visualization unit visualizes the evaluation results obtained by the evaluation unit. For example, the visualization unit visualizes the evaluation results in the form of graphs or maps. Specifically, the visualization unit visualizes the evaluation results in graph format. It displays the evaluation results in the form of bar graphs or pie charts, making it easier to compare the evaluation results of each candidate site. The visualization unit can also visualize the evaluation results in map format. It plots the evaluation results on a map, allowing for a quick overview of the location and evaluation results of each candidate site. Furthermore, the visualization unit can provide the visualized evaluation results to the shop planning department and operating stores. The visualization unit compiles the evaluation results in report format and provides them to stakeholders. The visualization unit also provides the evaluation results in an interactive dashboard format, allowing stakeholders to freely manipulate the data and conduct detailed analysis. In this way, the visualization unit can visualize the evaluation results in an easy-to-understand manner, supporting stakeholders in making quick and accurate decisions.

[0034] The data collection unit can collect demographic data, land price data, behavioral distribution data, price per square meter data, store location data, location information, competitor information, and company history data. For example, the data collection unit can collect demographic data such as age distribution, gender distribution, and number of households. It can also collect land price data such as fluctuations in land prices and historical land price data. Furthermore, it can collect behavioral distribution data such as commuting and school routes and shopping routes. For example, the data collection unit can collect price per square meter for commercial land and residential land. It can also collect store location data such as the extent of the trade area and the number of competing stores within the trade area. Furthermore, the data collection unit can collect location information such as addresses, geographical coordinates, and information on surrounding facilities. For example, the data collection unit can collect competitor information such as the location, sales data, and customer demographics of competing stores. It can also collect company history data such as past store opening history, sales data, and customer feedback. By collecting diverse data in this way, detailed location evaluation becomes possible. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input demographic data into the AI, and the AI ​​can collect the data.

[0035] The analysis unit can analyze collected data using a generative AI. For example, the analysis unit can analyze collected data using a generative AI. For example, the analysis unit can analyze demographic data using a generative AI. The analysis unit can also analyze land price data using a generative AI. Furthermore, the analysis unit can analyze behavioral distribution data using a generative AI. For example, the analysis unit can analyze price per square meter data using a generative AI. Furthermore, the analysis unit can analyze store location area data using a generative AI. Furthermore, the analysis unit can analyze location information using a generative AI. For example, the analysis unit can analyze competitor information using a generative AI. Furthermore, the analysis unit can analyze its own historical data using a generative AI. As a result, the accuracy of data analysis is improved by using a generative AI. The generative AI can use technologies such as deep learning and natural language processing. Some or all of the above-mentioned processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs collected data into the generative AI, and the generative AI can analyze the data.

[0036] The evaluation unit can perform a location evaluation for each candidate site based on the analysis results. For example, the evaluation unit can assign a location evaluation score of 65 points to candidate site A. It can also assign a location evaluation score of 93 points to candidate site B. Furthermore, the evaluation unit can perform a quantified evaluation based on the analysis results. This allows for an objective evaluation by performing the location evaluation based on the analysis results. The location evaluation is performed based on criteria such as evaluation items and methods for calculating evaluation scores. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the analysis results into AI, which can then perform the location evaluation.

[0037] The visualization unit can visualize the evaluation results and provide them to the shop planning department and the operating stores. The visualization unit can visualize the evaluation results in the form of graphs, maps, etc. For example, the visualization unit can visualize the evaluation results in the form of graphs. The visualization unit can also visualize the evaluation results in the form of maps. Furthermore, the visualization unit can visualize the evaluation results and provide them to the shop planning department and the operating stores. This makes the evaluation results easier to understand intuitively by visualizing them. Visualization is performed based on the tools used and the form of visualization (graphs, maps, etc.). Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input the evaluation results into AI, and the AI ​​can perform the visualization.

[0038] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method from past data collection history. The data collection unit can also optimize the frequency of data collection based on past data collection history. Furthermore, the data collection unit can analyze past data collection history and optimize the timing of data collection. This enables efficient data collection by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, which can then select the optimal data collection method.

[0039] The data collection unit can adjust the data collection frequency based on specific time periods or days of the week. For example, the unit can concentrate data collection during weekday daytime hours and reduce the frequency at night and on weekends. It can also concentrate data collection on specific days of the week and reduce the frequency on other days. Furthermore, it can concentrate data collection during specific time periods and reduce the frequency at other times. This allows for efficient data collection by adjusting the data collection frequency based on specific time periods or days of the week. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for specific time periods or days of the week into the AI, which can then adjust the data collection frequency.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of information about the surrounding area of ​​a potential store location. It can also prioritize the collection of location information of competing stores. Furthermore, the data collection unit can prioritize the collection of demographic data within the trade area. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze trending topics on social media and collect relevant data. It can also analyze user behavior patterns on social media and collect relevant data. Furthermore, the data collection unit can analyze competitor store ratings on social media and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into AI, which can then collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. Furthermore, the analysis unit can apply multiple analysis methods to important data and a single analysis method to less important data. In addition, the analysis unit can generate a detailed report for important data and a simplified report for less important data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs the importance of the data into the generative AI, which can then adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a clustering algorithm to demographic data. It can also apply a regression analysis algorithm to land price data. Furthermore, it can apply a pattern recognition algorithm to behavioral distribution data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the data category into the generative AI, which can then apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, the analysis unit can determine the order of analysis based on the data collection timing. This allows for the prioritization of analysis based on the data collection timing, thereby ensuring that the latest data is analyzed first. Some or all of the above-described processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs the data collection timing into the generation AI, which then determines the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the relevance of the data into the generative AI, which can then adjust the order of analysis.

[0046] The evaluation unit can improve the accuracy of its evaluations by considering the interrelationships between data. For example, the evaluation unit can perform evaluations by considering the interrelationships between demographic data and behavioral distribution data. It can also perform evaluations by considering the interrelationships between land price data and price per square meter data. Furthermore, the evaluation unit can perform evaluations by considering the interrelationships between competitor information and the company's own historical data. This improves the accuracy of the evaluation by considering the interrelationships between the data. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the interrelationships between the data into the AI, which can then improve the accuracy of the evaluation.

[0047] The evaluation unit can consider the attribute information of the data submitter when performing the evaluation. For example, if the data submitter is an expert, the evaluation unit will give more weight to that. If the data submitter is a general user, the evaluation unit may also use that evaluation only as a reference. Furthermore, the evaluation unit can weight the evaluation based on the attribute information of the data submitter. This improves the accuracy of the evaluation by considering the attribute information of the data submitter. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the attribute information of the data submitter into AI, and the AI ​​can perform the evaluation.

[0048] The evaluation unit can perform evaluations while considering geographical distribution. For example, the evaluation unit can perform evaluations while considering the geographical distribution of potential store locations. It can also perform evaluations while considering the geographical distribution of competing stores. Furthermore, the evaluation unit can perform evaluations while considering the geographical distribution within the trading area. This allows for more accurate evaluations by considering geographical distribution. Some or all of the above processing in the evaluation unit may be performed using AI, or it may be performed without AI. For example, the evaluation unit can input geographical distribution data into AI, and the AI ​​can perform the evaluation.

[0049] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature during the evaluation process. For example, the evaluation unit can revise its evaluation criteria by referring to relevant literature. It can also improve its evaluation methods by referring to relevant literature. Furthermore, the evaluation unit can supplement its evaluation results by referring to relevant literature. In this way, the accuracy of the evaluation is improved by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input relevant literature into the AI, which can then improve the accuracy of the evaluation.

[0050] The visualization unit can select the optimal visualization method by referring to past visualization data during visualization. For example, the visualization unit selects the optimal visualization method based on past visualization data. The visualization unit can also improve the accuracy of visualization by referring to past visualization data. Furthermore, the visualization unit can analyze past visualization data and improve the visualization method. As a result, the accuracy of visualization is improved by referring to past visualization data. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input past visualization data into AI, and the AI ​​can select the optimal visualization method.

[0051] The visualization unit can apply different visualization methods to each data category during visualization. For example, the visualization unit can apply a heatmap to demographic data. It can also apply a line graph to land price data. Furthermore, it can apply a scatter plot to behavioral distribution data. This improves the accuracy of visualization by applying the appropriate visualization method according to the data category. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input the data categories into the AI, and the AI ​​can apply the appropriate visualization method.

[0052] The visualization unit can analyze changes in visualization based on the data collection timing during visualization. For example, the visualization unit prioritizes visualizing the most recent data. It can also visualize the latest data while referring to past data. Furthermore, the visualization unit can determine the order of visualization based on the data collection timing. This allows for prioritizing the visualization of the latest data by analyzing changes in visualization based on the data collection timing. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input the data collection timing into the AI, which can then analyze changes in visualization.

[0053] The visualization unit can perform visualizations by referring to relevant market data during the visualization process. For example, the visualization unit can improve the accuracy of the visualization by referring to relevant market data. The visualization unit can also improve the visualization method based on relevant market data. Furthermore, the visualization unit can analyze relevant market data and supplement the content of the visualization. This improves the accuracy of the visualization by referring to relevant market data. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input relevant market data into AI, and the AI ​​can perform the visualization.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The decision support system, in its data collection unit, can acquire environmental data in real time during data collection and dynamically adjust the data collection method in response to environmental changes. For example, it can collect weather data and prioritize indoor data collection during bad weather. It can also collect traffic data and, if traffic congestion is occurring, collect data during times of low traffic. Furthermore, it can collect event information and, if a large-scale event is being held, consider its impact when collecting data. By dynamically adjusting the data collection method in response to environmental changes, it is possible to collect more accurate data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input environmental data into the AI, which can then adjust the data collection method.

[0056] The decision support system can further perform evaluations in its evaluation unit, taking data reliability into consideration. For example, it can assign a high rating to highly reliable data and a low rating to unreliable data. It can also weight the evaluation based on data reliability. Furthermore, data reliability can be incorporated as part of the evaluation criteria. This allows for more accurate evaluations by considering data reliability. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input data reliability into the AI, and the AI ​​can perform the evaluation.

[0057] The decision support system can further collect user feedback in real time during data collection and dynamically adjust the data collection method based on the collected feedback. For example, if a user provides feedback that certain data is missing, that data can be prioritized for collection. Alternatively, if a user provides feedback that certain data is unnecessary, the collection of that data can be stopped. Furthermore, the frequency of data collection can be adjusted based on user feedback. This allows for efficient data collection by reflecting user feedback. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user feedback into the AI, which can then adjust the data collection method.

[0058] The decision support system can further improve the accuracy of its analysis by considering data correlations during the analysis process. For example, it can analyze the correlation between demographic data and behavioral distribution data to obtain more accurate results. It can also analyze the correlation between land price data and price per square meter data. Furthermore, it can analyze the correlation between competitor information and the company's own historical data. By considering data correlations, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs data correlations into the generation AI, which can then improve the accuracy of the analysis.

[0059] The decision support system can further prioritize visualizations based on data importance in its visualization unit. For example, it can prioritize visualizing important data and postpone visualizations of less important data. It can also perform detailed visualizations for important data and simplified visualizations for less important data. Furthermore, it can apply multiple visualization methods to important data and a single visualization method to less important data. This enables efficient visualization by prioritizing visualizations based on data importance. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input data importance into the AI, which can then determine the visualization priority.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data on potential store locations. The data collection unit collects, for example, demographic data, land price data, behavioral distribution data, price per square meter data, store trading area data, location information, competitor information, and company history data. Specifically, it collects data such as population density and age demographics around the candidate site, land prices and rents, people's behavioral patterns, price per square meter, trading area, specific location, information on competing stores, past store opening history and performance data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses a generation AI to analyze demographic data, land price data, behavioral distribution data, price per square meter data, store location area data, location information, competitor information, and company history data. Step 3: The evaluation unit performs a site evaluation based on the data analyzed by the analysis unit. For example, the evaluation unit quantifies the site evaluation of each candidate site based on the analysis results, assigning a site evaluation score of 65 points to candidate site A and 93 points to candidate site B. Step 4: The visualization unit visualizes the evaluation results obtained by the evaluation unit. The visualization unit visualizes the evaluation results in a format such as a graph or map and provides it to the shop planning department or the operating store.

[0062] (Example of form 2) The decision support system according to an embodiment of the present invention is a system that utilizes generative AI to evaluate shop locations and increase the success rate of business expansion. This decision support system does not rely on conventional qualitative judgments based on human experience and intuition, but uses generative AI to select the optimal location that reflects quantitative evaluation. First, a challenge faced by target groups such as shop planning departments and shop operators is a sales environment that does not justify operating costs. To solve this problem, the following steps are performed using generative AI. For example, in collecting data on potential store locations, data such as demographics, land prices, behavioral distribution, price per tsubo (unit of area), trading area, location information, competitor information, and company history are collected. This allows for a detailed understanding of the candidate locations. Next, the collected data is analyzed by generative AI to evaluate the location of each candidate location. For example, a numerical evaluation can be obtained, such as a location evaluation of 65 points for candidate location A and a location evaluation of 93 points for candidate location B. Furthermore, the evaluation results by generative AI are visualized and provided to shop planning departments and operators. This allows for a quick understanding of the optimal location with high customer attraction potential. Furthermore, it supports decision-making to maximize revenue and optimize operating costs. Specific examples of how the generative AI is used include the following types of data: For example, demographic data such as population density and age distribution around potential locations; land value data such as land prices and rents around potential locations; behavioral distribution data analyzing the behavioral patterns of people around potential locations; price per square meter data analyzing the price per square meter around potential locations; trade area data analyzing the trade area of ​​potential locations; location information analyzing the specific location of potential locations; competitor information analyzing information on competing stores around potential locations; and company history data analyzing past store openings and performance data. By analyzing this data with the generative AI and selecting the optimal store location, the success rate of expanding the shop's performance can be increased. Thus, the decision support system can evaluate store locations and increase the success rate of expanding the shop's performance.

[0063] The decision support system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a visualization unit. The data collection unit collects data related to potential store locations. For example, the data collection unit collects demographic data, land price data, behavioral distribution data, price per tsubo data, store trade area data, location information, competitor information, and company history data. For example, the data collection unit collects information such as population density and age groups around the candidate site. The data collection unit can also collect information on land prices and rents around the candidate site. Furthermore, the data collection unit can also collect behavioral patterns of people around the candidate site. For example, the data collection unit collects the price per tsubo of the candidate site. The data collection unit can also collect the trade area of ​​the candidate site. Furthermore, the data collection unit can also collect the specific location of the candidate site. For example, the data collection unit collects information on competing stores around the candidate site. The data collection unit can also collect past store opening history and performance data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data using a generation AI. For example, the analysis unit analyzes demographic data using generative AI. The analysis unit can also analyze land price data using generative AI. Furthermore, the analysis unit can analyze behavioral distribution data using generative AI. For example, the analysis unit analyzes price per tsubo (unit of area) data using generative AI. The analysis unit can also analyze store location area data using generative AI. Furthermore, the analysis unit can analyze location information using generative AI. For example, the analysis unit analyzes competitor information using generative AI. The analysis unit can also analyze its own historical data using generative AI. The evaluation unit performs location evaluations based on the data analyzed by the analysis unit. For example, the evaluation unit performs location evaluations for each candidate site based on the analysis results. For example, the evaluation unit assigns a location evaluation score of 65 points to candidate site A. The evaluation unit can also assign a location evaluation score of 93 points to candidate site B. Furthermore, the evaluation unit can perform numerical evaluations based on the analysis results. The visualization unit visualizes the evaluation results obtained by the evaluation unit. For example, the visualization unit visualizes the evaluation results in the form of graphs, maps, etc. For example, the visualization unit visualizes the evaluation results in graph format. The visualization unit can also visualize the evaluation results in map format.Furthermore, the visualization unit can visualize the evaluation results and provide them to the shop planning department and operating stores. This allows the decision support system according to the embodiment to assist in selecting the optimal location by collecting, analyzing, evaluating, and visualizing data on potential store locations.

[0064] The data collection department collects data on potential store locations. For example, it collects demographic data, land price data, behavioral distribution data, price per square meter data, market area data, location information, competitor information, and company history data. Specifically, the department collects information such as population density and age demographics around the potential location. This can utilize census data and local resident registration data. The department can also collect information on land prices and rents around the potential location, using data provided by real estate agents and publicly announced land price data. Furthermore, the department can collect behavioral patterns of people around the potential location, using smartphone location data and traffic survey data to understand people's movement patterns during specific time periods. To collect price per square meter data for the potential location, the department utilizes real estate transaction databases and information from local real estate agents. The department can also collect market area data for the potential location, using GIS (Geographic Information System) to analyze the distribution of population and facilities within a specific radius. Finally, the department can collect the specific location of the potential site. For example, precise location information is obtained using geographic coordinate data and address data. The data collection department conducts commercial databases and on-site surveys to collect information on competing stores around potential locations. The data collection department can also collect past store opening history and performance data. This utilizes the company's own database and past sales reports. As a result, the data collection department can gather a wide range of information from diverse data sources and understand the detailed situation of potential store locations.

[0065] The analysis unit analyzes data collected by the collection unit. For example, the analysis unit analyzes collected data using generative AI. Specifically, the analysis unit uses generative AI to analyze demographic data. The generative AI analyzes population density and age distribution to reveal consumer characteristics in a specific area. The analysis unit can also use generative AI to analyze land price data. The generative AI analyzes land price fluctuation patterns to predict future land price trends. Furthermore, the analysis unit can also use generative AI to analyze behavioral distribution data. The generative AI analyzes location data to identify people's movement patterns and length of stay. For example, the analysis unit uses generative AI to analyze price per square meter data. The generative AI analyzes fluctuations in price per square meter for each region to support optimal rent setting. The analysis unit can also use generative AI to analyze store trading area data. The generative AI analyzes population and the distribution of competing stores within a trading area to identify the optimal trading area. Furthermore, the analysis unit can also use generative AI to analyze location information. The generating AI analyzes geographic coordinate data to evaluate the accessibility and surrounding environment of potential locations. For example, the analysis unit uses the generating AI to analyze competitive information. The generating AI analyzes the locations and performance of competing stores to evaluate the competitive environment. The analysis unit can also use the generating AI to analyze its own historical data. The generating AI analyzes past store opening history and performance data to identify success and failure factors. This allows the analysis unit to perform advanced analysis of the collected data and provide the information necessary to evaluate potential store locations.

[0066] The evaluation unit conducts location evaluations based on data analyzed by the analysis unit. For example, the evaluation unit evaluates the location of each candidate site based on the analysis results. Specifically, the evaluation unit assigns a location evaluation score of 65 points to candidate site A. Based on the analysis results, the evaluation unit comprehensively evaluates factors such as population density, land price, competitive situation, and market area, and provides a quantified evaluation. The evaluation unit can also assign a location evaluation score of 93 points to candidate site B. Based on the analysis results, the evaluation unit weights each factor and calculates an overall evaluation. Furthermore, the evaluation unit can also provide a quantified evaluation based on the analysis results. The evaluation unit uses an algorithm to integrate the evaluation results of each factor and calculate an overall evaluation. For example, the evaluation unit assigns a population density evaluation score of 20 points, a land price evaluation score of 15 points, a competitive situation evaluation score of 10 points, and a market area evaluation score of 20 points, resulting in an overall evaluation score of 65 points. In addition, based on the analysis results, the evaluation unit can identify the strengths and weaknesses of each candidate site and propose specific improvement measures. This allows the evaluation department to conduct an objective and comprehensive location evaluation based on the analysis results, and to support the selection of the most suitable store location.

[0067] The visualization unit visualizes the evaluation results obtained by the evaluation unit. For example, the visualization unit visualizes the evaluation results in the form of graphs or maps. Specifically, the visualization unit visualizes the evaluation results in graph format. It displays the evaluation results in the form of bar graphs or pie charts, making it easier to compare the evaluation results of each candidate site. The visualization unit can also visualize the evaluation results in map format. It plots the evaluation results on a map, allowing for a quick overview of the location and evaluation results of each candidate site. Furthermore, the visualization unit can provide the visualized evaluation results to the shop planning department and operating stores. The visualization unit compiles the evaluation results in report format and provides them to stakeholders. The visualization unit also provides the evaluation results in an interactive dashboard format, allowing stakeholders to freely manipulate the data and conduct detailed analysis. In this way, the visualization unit can visualize the evaluation results in an easy-to-understand manner, supporting stakeholders in making quick and accurate decisions.

[0068] The data collection unit can collect demographic data, land price data, behavioral distribution data, price per square meter data, store location data, location information, competitor information, and company history data. For example, the data collection unit can collect demographic data such as age distribution, gender distribution, and number of households. It can also collect land price data such as fluctuations in land prices and historical land price data. Furthermore, it can collect behavioral distribution data such as commuting and school routes and shopping routes. For example, the data collection unit can collect price per square meter for commercial land and residential land. It can also collect store location data such as the extent of the trade area and the number of competing stores within the trade area. Furthermore, the data collection unit can collect location information such as addresses, geographical coordinates, and information on surrounding facilities. For example, the data collection unit can collect competitor information such as the location, sales data, and customer demographics of competing stores. It can also collect company history data such as past store opening history, sales data, and customer feedback. By collecting diverse data in this way, detailed location evaluation becomes possible. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input demographic data into the AI, and the AI ​​can collect the data.

[0069] The analysis unit can analyze collected data using a generative AI. For example, the analysis unit can analyze collected data using a generative AI. For example, the analysis unit can analyze demographic data using a generative AI. The analysis unit can also analyze land price data using a generative AI. Furthermore, the analysis unit can analyze behavioral distribution data using a generative AI. For example, the analysis unit can analyze price per square meter data using a generative AI. Furthermore, the analysis unit can analyze store location area data using a generative AI. Furthermore, the analysis unit can analyze location information using a generative AI. For example, the analysis unit can analyze competitor information using a generative AI. Furthermore, the analysis unit can analyze its own historical data using a generative AI. As a result, the accuracy of data analysis is improved by using a generative AI. The generative AI can use technologies such as deep learning and natural language processing. Some or all of the above-mentioned processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs collected data into the generative AI, and the generative AI can analyze the data.

[0070] The evaluation unit can perform a location evaluation for each candidate site based on the analysis results. For example, the evaluation unit can assign a location evaluation score of 65 points to candidate site A. It can also assign a location evaluation score of 93 points to candidate site B. Furthermore, the evaluation unit can perform a quantified evaluation based on the analysis results. This allows for an objective evaluation by performing the location evaluation based on the analysis results. The location evaluation is performed based on criteria such as evaluation items and methods for calculating evaluation scores. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the analysis results into AI, which can then perform the location evaluation.

[0071] The visualization unit can visualize the evaluation results and provide them to the shop planning department and the operating stores. The visualization unit can visualize the evaluation results in the form of graphs, maps, etc. For example, the visualization unit can visualize the evaluation results in the form of graphs. The visualization unit can also visualize the evaluation results in the form of maps. Furthermore, the visualization unit can visualize the evaluation results and provide them to the shop planning department and the operating stores. This makes the evaluation results easier to understand intuitively by visualizing them. Visualization is performed based on the tools used and the form of visualization (graphs, maps, etc.). Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input the evaluation results into AI, and the AI ​​can perform the visualization.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can automatically collect data to reduce the user's burden. The data collection unit can also allow the user to choose the timing of data collection if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can collect data quickly and provide results immediately. This reduces the user's burden by adjusting the timing of data collection according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of data collection.

[0073] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method from past data collection history. The data collection unit can also optimize the frequency of data collection based on past data collection history. Furthermore, the data collection unit can analyze past data collection history and optimize the timing of data collection. This enables efficient data collection by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, which can then select the optimal data collection method.

[0074] The data collection unit can adjust the data collection frequency based on specific time periods or days of the week. For example, the unit can concentrate data collection during weekday daytime hours and reduce the frequency at night and on weekends. It can also concentrate data collection on specific days of the week and reduce the frequency on other days. Furthermore, it can concentrate data collection during specific time periods and reduce the frequency at other times. This allows for efficient data collection by adjusting the data collection frequency based on specific time periods or days of the week. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for specific time periods or days of the week into the AI, which can then adjust the data collection frequency.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. It can also prioritize collecting detailed data if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the data priority.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of information about the surrounding area of ​​a potential store location. It can also prioritize the collection of location information of competing stores. Furthermore, the data collection unit can prioritize the collection of demographic data within the trade area. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze trending topics on social media and collect relevant data. It can also analyze user behavior patterns on social media and collect relevant data. Furthermore, the data collection unit can analyze competitor store ratings on social media and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into AI, which can then collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI can adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. Furthermore, the analysis unit can apply multiple analysis methods to important data and a single analysis method to less important data. In addition, the analysis unit can generate a detailed report for important data and a simplified report for less important data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs the importance of the data into the generative AI, which can then adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a clustering algorithm to demographic data. It can also apply a regression analysis algorithm to land price data. Furthermore, it can apply a pattern recognition algorithm to behavioral distribution data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the data category into the generative AI, which can then apply an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs user emotion data into the generative AI, which can then adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, the analysis unit can determine the order of analysis based on the data collection timing. This allows for the prioritization of analysis based on the data collection timing, thereby ensuring that the latest data is analyzed first. Some or all of the above-described processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs the data collection timing into the generation AI, which then determines the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the relevance of the data into the generative AI, which can then adjust the order of analysis.

[0084] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is nervous, the evaluation unit can provide simple and easy-to-understand evaluation criteria. If the user is relaxed, the evaluation unit can also provide detailed evaluation criteria. Furthermore, if the user is in a hurry, the evaluation unit can provide concise evaluation criteria. By adjusting the evaluation criteria according to the user's emotions, the evaluation results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI, and the generative AI can adjust the evaluation criteria.

[0085] The evaluation unit can improve the accuracy of its evaluations by considering the interrelationships between data. For example, the evaluation unit can perform evaluations by considering the interrelationships between demographic data and behavioral distribution data. It can also perform evaluations by considering the interrelationships between land price data and price per square meter data. Furthermore, the evaluation unit can perform evaluations by considering the interrelationships between competitor information and the company's own historical data. This improves the accuracy of the evaluation by considering the interrelationships between the data. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the interrelationships between the data into the AI, which can then improve the accuracy of the evaluation.

[0086] The evaluation unit can consider the attribute information of the data submitter when performing the evaluation. For example, if the data submitter is an expert, the evaluation unit will give more weight to that. If the data submitter is a general user, the evaluation unit may also use that evaluation only as a reference. Furthermore, the evaluation unit can weight the evaluation based on the attribute information of the data submitter. This improves the accuracy of the evaluation by considering the attribute information of the data submitter. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the attribute information of the data submitter into AI, and the AI ​​can perform the evaluation.

[0087] The evaluation unit can estimate the user's emotions and adjust the display order of the evaluation results based on the estimated emotions. For example, if the user is nervous, the evaluation unit can prioritize displaying important evaluation results. It can also display detailed evaluation results if the user is relaxed. Furthermore, if the user is in a hurry, the evaluation unit can display concise evaluation results. By adjusting the display order of evaluation results according to the user's emotions, the evaluation unit can provide evaluation results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI, which can then adjust the display order of the evaluation results.

[0088] The evaluation unit can perform evaluations while considering geographical distribution. For example, the evaluation unit can perform evaluations while considering the geographical distribution of potential store locations. It can also perform evaluations while considering the geographical distribution of competing stores. Furthermore, the evaluation unit can perform evaluations while considering the geographical distribution within the trading area. This allows for more accurate evaluations by considering geographical distribution. Some or all of the above processing in the evaluation unit may be performed using AI, or it may be performed without AI. For example, the evaluation unit can input geographical distribution data into AI, and the AI ​​can perform the evaluation.

[0089] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature during the evaluation process. For example, the evaluation unit can revise its evaluation criteria by referring to relevant literature. It can also improve its evaluation methods by referring to relevant literature. Furthermore, the evaluation unit can supplement its evaluation results by referring to relevant literature. In this way, the accuracy of the evaluation is improved by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input relevant literature into the AI, which can then improve the accuracy of the evaluation.

[0090] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is tense, the visualization unit can provide a simple and highly visible visualization method. It can also provide a detailed visualization method if the user is relaxed. Furthermore, if the user is in a hurry, the visualization unit can provide a concise visualization method. By adjusting the visualization method according to the user's emotions, the system can provide visualization results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input user emotion data into the generative AI, which can then adjust the visualization method.

[0091] The visualization unit can select the optimal visualization method by referring to past visualization data during visualization. For example, the visualization unit selects the optimal visualization method based on past visualization data. The visualization unit can also improve the accuracy of visualization by referring to past visualization data. Furthermore, the visualization unit can analyze past visualization data and improve the visualization method. As a result, the accuracy of visualization is improved by referring to past visualization data. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input past visualization data into AI, and the AI ​​can select the optimal visualization method.

[0092] The visualization unit can apply different visualization methods to each data category during visualization. For example, the visualization unit can apply a heatmap to demographic data. It can also apply a line graph to land price data. Furthermore, it can apply a scatter plot to behavioral distribution data. This improves the accuracy of visualization by applying the appropriate visualization method according to the data category. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input the data categories into the AI, and the AI ​​can apply the appropriate visualization method.

[0093] The visualization unit can estimate the user's emotions and determine visualization priorities based on the estimated emotions. For example, if the user is stressed, the visualization unit can prioritize visualizing important data. If the user is relaxed, the visualization unit can also visualize detailed data. Furthermore, if the user is in a hurry, the visualization unit can prioritize visualizing concise data. This allows for the prioritization of important data by determining visualization priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input user emotion data into a generative AI, which can then determine visualization priorities.

[0094] The visualization unit can analyze changes in visualization based on the data collection timing during visualization. For example, the visualization unit prioritizes visualizing the most recent data. It can also visualize the latest data while referring to past data. Furthermore, the visualization unit can determine the order of visualization based on the data collection timing. This allows for prioritizing the visualization of the latest data by analyzing changes in visualization based on the data collection timing. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input the data collection timing into the AI, which can then analyze changes in visualization.

[0095] The visualization unit can perform visualizations by referring to relevant market data during the visualization process. For example, the visualization unit can improve the accuracy of the visualization by referring to relevant market data. The visualization unit can also improve the visualization method based on relevant market data. Furthermore, the visualization unit can analyze relevant market data and supplement the content of the visualization. This improves the accuracy of the visualization by referring to relevant market data. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input relevant market data into AI, and the AI ​​can perform the visualization.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The decision support system can further estimate the user's emotions and dynamically adjust the evaluation criteria for potential store locations based on those estimated emotions. For example, if the user is feeling anxious, the evaluation criteria can be made stricter, prioritizing low-risk locations. Conversely, if the user is confident, the evaluation criteria can be relaxed, and locations with higher risk can be evaluated. Furthermore, if the user is in a hurry, the evaluation criteria can be simplified, and evaluation results can be provided quickly. In this way, by dynamically adjusting the evaluation criteria according to the user's emotions, the system can provide the optimal evaluation results for the user. Emotion estimation can be achieved, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into the generative AI, which can then adjust the evaluation criteria.

[0098] The decision support system, in its data collection unit, can acquire environmental data in real time during data collection and dynamically adjust the data collection method in response to environmental changes. For example, it can collect weather data and prioritize indoor data collection during bad weather. It can also collect traffic data and, if traffic congestion is occurring, collect data during times of low traffic. Furthermore, it can collect event information and, if a large-scale event is being held, consider its impact when collecting data. By dynamically adjusting the data collection method in response to environmental changes, it is possible to collect more accurate data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input environmental data into the AI, which can then adjust the data collection method.

[0099] The decision support system can further estimate the user's emotions in its analysis unit and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis results can be summarized concisely, providing only the essential points. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, the analysis results can be visually represented for quick understanding. By adjusting the level of detail in the analysis results according to the user's emotions, the system can provide analysis results that are easy for the user to understand. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI, which can then adjust the level of detail in the analysis results.

[0100] The decision support system can further perform evaluations in its evaluation unit, taking data reliability into consideration. For example, it can assign a high rating to highly reliable data and a low rating to unreliable data. It can also weight the evaluation based on data reliability. Furthermore, data reliability can be incorporated as part of the evaluation criteria. This allows for more accurate evaluations by considering data reliability. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input data reliability into the AI, and the AI ​​can perform the evaluation.

[0101] The decision support system can further estimate the user's emotions in its visualization unit and adjust the visualization format based on the estimated emotions. For example, if the user is stressed, a simple and easy-to-read graph can be provided. If the user is relaxed, a complex graph containing detailed data can be provided. Furthermore, if the user is in a hurry, a concise dashboard format can be used for visualization. By adjusting the visualization format according to the user's emotions, the system can provide visualization results that are easy for the user to understand. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input user emotion data into the generative AI, which can then adjust the visualization format.

[0102] The decision support system can further collect user feedback in real time during data collection and dynamically adjust the data collection method based on the collected feedback. For example, if a user provides feedback that certain data is missing, that data can be prioritized for collection. Alternatively, if a user provides feedback that certain data is unnecessary, the collection of that data can be stopped. Furthermore, the frequency of data collection can be adjusted based on user feedback. This allows for efficient data collection by reflecting user feedback. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user feedback into the AI, which can then adjust the data collection method.

[0103] The decision support system can further improve the accuracy of its analysis by considering data correlations during the analysis process. For example, it can analyze the correlation between demographic data and behavioral distribution data to obtain more accurate results. It can also analyze the correlation between land price data and price per square meter data. Furthermore, it can analyze the correlation between competitor information and the company's own historical data. By considering data correlations, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs data correlations into the generation AI, which can then improve the accuracy of the analysis.

[0104] The decision support system can further estimate the user's emotions in its evaluation unit and adjust the display method of the evaluation results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a detailed evaluation result can be provided. Furthermore, if the user is in a hurry, a concise display method can be provided. In this way, by adjusting the display method of the evaluation results according to the user's emotions, the system can provide evaluation results that are easy for the user to understand. Emotion estimation can be achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into the generative AI, and the generative AI can adjust the display method of the evaluation results.

[0105] The decision support system can further prioritize visualizations based on data importance in its visualization unit. For example, it can prioritize visualizing important data and postpone visualizations of less important data. It can also perform detailed visualizations for important data and simplified visualizations for less important data. Furthermore, it can apply multiple visualization methods to important data and a single visualization method to less important data. This enables efficient visualization by prioritizing visualizations based on data importance. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input data importance into the AI, which can then determine the visualization priority.

[0106] The decision support system can further estimate the user's emotions during data collection in its data collection unit and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data can be collected quickly to provide results immediately. In this way, the user's burden can be reduced by adjusting the frequency of data collection according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into the generative AI, which can then adjust the frequency of data collection.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects data on potential store locations. The data collection unit collects, for example, demographic data, land price data, behavioral distribution data, price per square meter data, store trading area data, location information, competitor information, and company history data. Specifically, it collects data such as population density and age demographics around the candidate site, land prices and rents, people's behavioral patterns, price per square meter, trading area, specific location, information on competing stores, past store opening history and performance data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses a generation AI to analyze demographic data, land price data, behavioral distribution data, price per square meter data, store location area data, location information, competitor information, and company history data. Step 3: The evaluation unit performs a site evaluation based on the data analyzed by the analysis unit. For example, the evaluation unit quantifies the site evaluation of each candidate site based on the analysis results, assigning a site evaluation score of 65 points to candidate site A and 93 points to candidate site B. Step 4: The visualization unit visualizes the evaluation results obtained by the evaluation unit. The visualization unit visualizes the evaluation results in a format such as a graph or map and provides it to the shop planning department or the operating store.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and visualization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data on potential store locations using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit analyzes the collected data using AI generated by the specific processing unit 290 of the data processing unit 12. The evaluation unit performs a location evaluation based on the analysis results using the specific processing unit 290 of the data processing unit 12. The visualization unit visualizes the evaluation results using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and visualization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data on potential store locations using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit analyzes the collected data using AI generated by the identification processing unit 290 of the data processing unit 12. The evaluation unit performs a location evaluation based on the analysis results using the identification processing unit 290 of the data processing unit 12. The visualization unit visualizes the evaluation results using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and visualization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data on potential store locations using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit analyzes the collected data using AI generated by the identification processing unit 290 of the data processing unit 12. The evaluation unit performs a location evaluation based on the analysis results using the identification processing unit 290 of the data processing unit 12. The visualization unit visualizes the evaluation results using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and visualization unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data on potential store locations using the camera 42 and communication I / F 44 of the robot 414. The analysis unit analyzes the collected data using AI generated by the specific processing unit 290 of the data processing unit 12. The evaluation unit performs a location evaluation based on the analysis results using the specific processing unit 290 of the data processing unit 12. The visualization unit visualizes the evaluation results using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The data collection department collects data on potential store locations, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that performs a site evaluation based on the data analyzed by the aforementioned analysis unit, The system includes a visualization unit that visualizes the evaluation results obtained by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect demographic data, land price data, behavioral distribution data, price per square meter data, store location data, location information, competitor information, and our own historical data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed by generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Based on the analysis results, a site evaluation will be conducted for each candidate location. The system described in Appendix 1, characterized by the features described herein. (Note 5) The visualization unit is, Visualize the evaluation results and provide them to the shop planning department and the operating stores. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past data collection history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, adjust the frequency of data collection based on specific time periods or days of the week. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During evaluation, consider the interrelationships between data to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During the evaluation process, the attribute information of the data submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, The system estimates the user's emotions and adjusts the display order of evaluation results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation, the geographical distribution will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation process, we refer to relevant literature to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The visualization unit is, It estimates the user's emotions and adjusts the visualization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The visualization unit is, When creating visualizations, the optimal visualization method is selected by referring to past visualization data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The visualization unit is, When visualizing data, apply different visualization techniques to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The visualization unit is, It estimates the user's emotions and determines the priority of visualizations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The visualization unit is, When creating visualizations, analyze how the visualization changes based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 29) The visualization unit is, When creating visualizations, relevant market data is referenced. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data on potential store locations, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that performs a site evaluation based on the data analyzed by the aforementioned analysis unit, The system includes a visualization unit that visualizes the evaluation results obtained by the evaluation unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect demographic data, land price data, behavioral distribution data, price per square meter data, store location data, location information, competitor information, and our own historical data. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed using a generating AI. The system according to feature 1.

4. The evaluation unit, Based on the analysis results, a site evaluation will be conducted for each candidate location. The system according to feature 1.

5. The visualization unit, Visualize the evaluation results and provide them to the shop planning department and the operating stores. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past data collection history to select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, adjust the frequency of data collection based on specific time periods or days of the week. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system according to feature 1.

Images