system

Abstract

The system according to the embodiment aims to analyze big data and identify the optimal location for opening a store. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a planning unit. The collection unit collects big data. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies the optimal store location based on the analysis results obtained by the analysis unit. The planning unit formulates a store opening plan based on the store location identified by the identification unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately analyze data to identify optimal store locations, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze big data and identify the optimal location for opening a store. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a planning unit. The collection unit collects big data. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies the optimal store location based on the analysis results obtained by the analysis unit. The planning unit formulates a store opening plan based on the store location identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze big data and identify the optimal location for opening a store. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI support system according to an embodiment of the present invention utilizes big data to identify optimal store locations for each industry and efficiently increase visitor numbers and sales. This AI support system collects and analyzes big data, identifies optimal store locations, and formulates store opening plans, thereby individually identifying optimal store locations while taking into account the characteristics of each industry. For example, big data related to each industry is collected, including data on demographics, traffic volume, locations of competing stores, and consumer purchasing history. Next, AI analyzes the collected data to identify optimal store locations for each industry. Based on the collected data, the AI ​​estimates optimal locations for maximizing visitor numbers and sales. Furthermore, a specific store opening plan is formulated based on the identified store locations. For example, the system considers the surrounding environment and competitive situation of the store location and proposes optimal store layouts and promotion strategies. This allows for efficient increase in visitor numbers and sales. Because this system individually identifies optimal store locations while taking into account the characteristics of each industry, it is applicable to a wide range of industries. For example, it can be applied to various industries, including restaurants, retail stores, and the service industry. Furthermore, because AI analyzes big data, it is possible to create scientifically based store opening plans without relying on traditional experience or intuition. This system allows companies to efficiently select store locations and maximize visitor numbers and sales. For example, a restaurant can efficiently increase visitor numbers by identifying and opening a store in an area with high population density and little competition. Similarly, a retail store can maximize sales by identifying an area where a particular product sells well based on consumer purchasing history. In this way, by utilizing big data and AI, we provide a system that identifies the optimal store location for each industry and efficiently increases visitor numbers and sales. This allows companies to efficiently select store locations and maximize visitor numbers and sales.

[0029] The AI ​​support system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a planning unit. The collection unit collects big data. Examples of big data include, but are not limited to, demographics, traffic volume, locations of competing stores, and consumer purchase history. For example, the collection unit acquires demographic data from a public government database. The collection unit can also collect traffic volume data using sensors or cameras. The collection unit can also acquire location information of competing stores using a geographic information system (GIS). For example, the collection unit acquires consumer purchase history data from a point card system or an online shopping site. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using AI to estimate the optimal location for maximizing store visits and sales. For example, the analysis unit analyzes the data using a machine learning algorithm. The analysis unit can also analyze the data using deep learning technology. The analysis unit can also analyze text data using natural language processing technology. The identification unit identifies the optimal store location based on the analysis results obtained by the analysis unit. The identification unit identifies the optimal store opening location using, for example, AI. The identification unit identifies the optimal store opening location using, for example, a geographic information system (GIS). The identification unit can also identify the optimal store opening location using a clustering algorithm. Furthermore, the identification unit can identify the optimal store opening location using regression analysis. The planning unit formulates a store opening plan based on the store opening location identified by the identification unit. The planning unit formulates the store opening plan using, for example, AI. The planning unit formulates the store opening plan using, for example, simulation technology. Furthermore, the planning unit can formulate the store opening plan using an optimization algorithm. Furthermore, the planning unit can formulate the store opening plan using a heuristic algorithm. As a result, the AI ​​support system according to the embodiment can collect and analyze big data, identify the optimal store opening location, and formulate a store opening plan.

[0030] The collection unit can collect data such as demographics, traffic volume, locations of competing stores, and consumer purchasing history. For example, the collection unit obtains demographic data from a public government database. For example, the collection unit can collect data such as age, gender, and income level. The collection unit can also collect traffic volume data using sensors or cameras. For example, the collection unit can collect data such as the number of vehicles, the number of pedestrians, and peak hours. The collection unit can also obtain location information of competing stores using a geographic information system (GIS). For example, the collection unit can collect data such as the number, distance, and business type of competing stores. The collection unit can also obtain consumer purchasing history data from a point card system or an online shopping site. For example, the collection unit can collect data such as purchased items, purchase frequency, and purchase amount. This enables more accurate analysis by collecting a variety of data. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input traffic volume data obtained by a sensor into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can estimate the optimal location for maximizing the number of store visitors and sales based on the collected data. The analysis unit, for example, uses AI to analyze the data and estimate the optimal location for maximizing the number of store visitors and sales. For example, the analysis unit can analyze the data using a machine learning algorithm. The analysis unit can also analyze the data using deep learning technology. For example, the analysis unit can analyze the data using a neural network to estimate the optimal store location. The analysis unit can also analyze text data using natural language processing technology. For example, the analysis unit can analyze consumer reviews using text mining technology to estimate the optimal store location. In this way, the optimal store location can be scientifically identified by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0032] The identification unit can individually identify the optimal store location by taking into account the characteristics of each industry. The identification unit can identify the optimal store location using, for example, AI. For example, the identification unit can identify the optimal store location using a geographic information system (GIS). The identification unit can also identify the optimal store location using a clustering algorithm. For example, the identification unit can cluster data using K-means clustering to identify the optimal store location. The identification unit can also identify the optimal store location using regression analysis. For example, the identification unit can analyze data using linear regression to identify the optimal store location. This makes it possible to identify a more appropriate store location by taking into account the characteristics of each industry. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the analyzed data to a generation AI and cause the generation AI to identify the optimal store location.

[0033] The planning unit can propose an optimal store layout and promotion strategy by taking into account the surrounding environment and competitive situation of the identified store location. The planning unit, for example, uses AI to formulate the store opening plan. For example, the planning unit can formulate the store opening plan using simulation technology. The planning unit can also formulate the store opening plan using an optimization algorithm. For example, the planning unit can propose an optimal store layout using a genetic algorithm. Furthermore, the planning unit can also formulate the store opening plan using a heuristic algorithm. For example, the planning unit can propose an optimal promotion strategy using a heuristic algorithm. This allows for the formulation of a more effective store opening plan by taking into account the surrounding environment and competitive situation. Some or all of the above-mentioned processing in the planning unit may be performed using AI, for example, or may be performed without using AI. For example, the planning unit can input data of the identified store location into the generation AI and cause the generation AI to formulate the store opening plan.

[0034] The collection unit can analyze past data collection history and select the optimal data collection method. For example, the collection unit can discover from the past data collection history that data collection is effective during a specific time period and focus collection on that time period. For example, the collection unit can confirm that a specific data source is highly reliable based on the past data collection history and prioritize the use of that data source. The collection unit can also analyze the past data collection history and discover that data collection in a specific region is effective and focus on that region. For example, the collection unit can optimize data collection in a specific time period or region based on the past data collection history. This allows a more effective data collection method to be selected by analyzing the past data collection history. Some or all of the above-described processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input the past data collection history into the generation AI and have the generation AI select the optimal data collection method.

[0035] When collecting data, the collection unit can prioritize collecting data specialized for a specific industry or region. For example, when collecting data specialized for the food and beverage industry, the collection unit prioritizes collecting population density and location information of competing stores. For example, when collecting data specialized for the retail industry, the collection unit can prioritize collecting consumer purchase history and traffic volume data. Furthermore, when collecting data specialized for the service industry, the collection unit can prioritize collecting information on surrounding facilities and consumer reviews. For example, the collection unit prioritizes collecting data specialized for a specific industry or region, enabling more accurate analysis. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data specialized for a specific industry or region into the generation AI and cause the generation AI to collect data.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting demographic data related to that area. For example, if the user is interested in a specific industry, the collection unit can prioritize collecting location information of competing stores related to that industry. Furthermore, if the user is active during a specific time period, the collection unit can prioritize collecting traffic volume data related to that time period. For example, the collection unit can collect more relevant data by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0037] The collection unit can analyze the user's social media activities and collect relevant data when collecting data. For example, if the user mentions a specific region on social media, the collection unit can prioritize collecting data related to that region. For example, if the user mentions a specific industry on social media, the collection unit can prioritize collecting data related to that industry. Furthermore, if the user is active on social media during a specific time period, the collection unit can prioritize collecting data related to that time period. For example, the collection unit can collect more relevant data by analyzing the user's social media activities and collecting relevant data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect relevant data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance to provide highly accurate results. For example, the analysis unit can perform a simplified analysis on data of low importance to provide results quickly. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance to provide balanced results. For example, the analysis unit can evaluate the importance of the data and adjust the level of detail of the analysis to enable more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a statistical analysis algorithm to demographic data to provide highly accurate results. For example, the analysis unit can apply a time series analysis algorithm to traffic volume data to make future predictions. The analysis unit can also apply a clustering algorithm to consumer purchase history data to analyze consumer behavior patterns. For example, the analysis unit can apply different analysis algorithms depending on the data category to enable more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit can prioritize analysis of the most recent data and provide real-time results. For example, the analysis unit can analyze long-term trends based on past data. The analysis unit can also prioritize analysis of data collected during a specific period to clarify the characteristics of that period. For example, the analysis unit can determine the priority of analysis taking into account the time when the data was collected, thereby enabling more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the priority of analysis.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize analysis of data with high relevance to provide highly accurate results. For example, the analysis unit may analyze data with medium relevance next to achieve overall balance. The analysis unit may also analyze data with low relevance last to provide complementary information. For example, the analysis unit may evaluate the relevance of the data and adjust the order of analysis, thereby enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the relevance of the data into the generation AI and cause the generation AI to adjust the order of analysis.

[0042] The identification unit can improve the accuracy of identification by taking into account the interrelationships between data during identification. The identification unit can improve the accuracy of identification by taking into account, for example, the interrelationships between demographic data and traffic volume data. For example, the identification unit can improve the accuracy of identification by taking into account the interrelationships between location information of competing stores and consumer purchasing history. The identification unit can also improve the accuracy of identification by taking into account the interrelationships between surrounding environment data and consumer reviews. For example, the identification unit can evaluate the interrelationships between data and improve the accuracy of identification, thereby enabling more appropriate identification. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the interrelationships between data into a generation AI and cause the generation AI to improve the accuracy of identification.

[0043] The identification unit can perform the identification taking into consideration the characteristics of each industry. For example, the identification unit can use identification criteria specialized for the food and beverage industry to identify the optimal store location. For example, the identification unit can use identification criteria specialized for the retail industry to identify the optimal store location. The identification unit can also use identification criteria specialized for the service industry to identify the optimal store location. For example, the identification unit can perform the identification taking into consideration the characteristics of each industry, thereby enabling more appropriate identification. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the characteristics of each industry into the generation AI and cause the generation AI to execute the identification.

[0044] The identification unit can perform the identification by taking into account the geographic distribution of the data. For example, the identification unit can identify the optimal store location by taking into account the population density in a specific area. For example, the identification unit can identify the optimal store location by taking into account the traffic volume in a specific area. The identification unit can also identify the optimal store location by taking into account the locations of competing stores in a specific area. For example, the identification unit can perform the identification by taking into account the geographic distribution of the data, thereby enabling more appropriate identification. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the geographic distribution of the data into a generation AI and cause the generation AI to execute the identification.

[0045] During identification, the identification unit can improve the accuracy of the identification by referring to literature related to the data. For example, the identification unit can improve the accuracy of demographic data by referring to related literature. For example, the identification unit can improve the accuracy of traffic volume data by referring to related literature. The identification unit can also improve the accuracy of consumer purchase history data by referring to related literature. For example, the identification unit can improve the accuracy of the identification by referring to literature related to the data, thereby enabling more appropriate identification. Some or all of the above-mentioned processing in the identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit can input related literature into the generation AI and cause the generation AI to improve the accuracy of the identification.

[0046] When formulating a plan, the planning unit can optimize the current plan by referring to past plan data. The planning unit, for example, refers to past successful store opening plans and reflects them in the current plan. For example, the planning unit can analyze past unsuccessful store opening plans and take measures to avoid the same mistakes. The planning unit can also formulate a plan suited to current market conditions based on past plan data. For example, the planning unit can optimize the current plan by referring to past plan data, thereby enabling more effective plan formulation. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input past plan data into a generation AI and have the generation AI optimize the current plan.

[0047] When formulating a plan, the planning unit can apply different planning methods taking into account the characteristics of each industry. The planning unit, for example, uses a planning method specialized for the food and beverage industry to formulate an optimal store opening plan. For example, the planning unit can use a planning method specialized for the retail industry to formulate an optimal store opening plan. The planning unit can also use a planning method specialized for the service industry to formulate an optimal store opening plan. For example, the planning unit can apply different planning methods taking into account the characteristics of each industry to formulate a more appropriate plan. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the characteristics of each industry into a generation AI and have the generation AI formulate a plan.

[0048] When formulating a plan, the planning unit can analyze changes in the plan based on the time when data was collected. The planning unit, for example, updates the plan in real time based on the latest data. For example, the planning unit can analyze long-term trends based on past data and reflect the results in the plan. The planning unit can also formulate a plan that takes into account the characteristics of a specific period based on data collected during that period. For example, the planning unit can analyze changes in the plan taking into account the time when data was collected, thereby enabling more effective planning. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the time when data was collected into the generation AI and cause the generation AI to analyze changes in the plan.

[0049] When formulating a plan, the planning unit can analyze the plan by referring to market data related to the data. The planning unit, for example, refers to the relevant market data, analyzes the competitive situation, and reflects the results in the plan. For example, the planning unit can analyze consumer purchasing trends based on the relevant market data and reflect the results in the plan. The planning unit can also refer to the relevant market data and formulate a plan that takes regional characteristics into consideration. For example, the planning unit can formulate a more effective plan by analyzing the plan by referring to the relevant market data of the data. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the relevant market data into a generation AI and have the generation AI analyze the plan.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can analyze past data collection history and select the optimal data collection method. For example, it can discover from past data collection history that data collection is effective during a specific time period and concentrate collection on that time period. The collection unit can confirm that a specific data source is highly reliable based on past data collection history and use that data source preferentially. It can also analyze past data collection history and discover that data collection in a specific region is effective and focus on that region. The collection unit can optimize data collection in a specific time period or region based on past data collection history. In this way, by analyzing past data collection history, it is possible to select a more effective data collection method.

[0052] When collecting data, the collection unit can prioritize collecting data specialized in a specific industry or region. For example, when collecting data specialized in the food and beverage industry, it can prioritize collecting population density and location information of competing stores. When collecting data specialized in the retail industry, the collection unit can prioritize collecting consumer purchase history and traffic volume data. Furthermore, when collecting data specialized in the service industry, it can also prioritize collecting information on surrounding facilities and consumer reviews. By prioritizing the collection of data specialized in a specific industry or region, the collection unit can perform more accurate analysis.

[0053] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, for data of high importance, a detailed analysis is performed to provide highly accurate results. For data of low importance, the analysis unit can perform a simplified analysis to provide quick results. In addition, for data of medium importance, the analysis unit can perform an analysis with an appropriate level of detail to provide balanced results. By evaluating the importance of data and adjusting the level of detail of the analysis, the analysis unit can perform more effective analysis.

[0054] The identification unit can improve the accuracy of identification by taking into account the correlation of data during identification. For example, the accuracy of identification can be improved by taking into account the correlation of demographic data and traffic volume data. The identification unit can improve the accuracy of identification by taking into account the correlation of location information of competing stores and consumer purchasing history. The identification unit can also improve the accuracy of identification by taking into account the correlation of surrounding environment data and consumer reviews. The identification unit can improve the accuracy of identification by evaluating the correlation of data and improving the accuracy of identification, thereby enabling more appropriate identification.

[0055] When formulating a plan, the planning department can refer to past planning data to optimize the current plan. For example, it can refer to past successful store opening plans and reflect them in the current plan. The planning department can analyze past unsuccessful store opening plans and take measures to avoid the same mistakes. It can also use past planning data to formulate a plan that is appropriate for the current market situation. By optimizing the current plan by referring to past planning data, the planning department can formulate more effective plans.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects big data. Big data includes, for example, demographic data, traffic volume, locations of competing stores, and consumer purchasing history. The collection unit obtains demographic data from public government databases and traffic volume data using sensors and cameras. It also obtains location information of competing stores using a geographic information system (GIS) and obtains consumer purchasing history data from point card systems and online shopping sites. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI, machine learning algorithms, deep learning technology, and natural language processing technology to analyze the data and estimate the optimal locations to maximize store visits and sales. Step 3: The identification unit identifies the optimal store location based on the analysis results obtained by the analysis unit. The identification unit identifies the optimal store location using AI, geographic information systems (GIS), clustering algorithms, and regression analysis. Step 4: The Planning Department formulates a store opening plan based on the store locations identified by the Identification Department. The Planning Department formulates the store opening plan using AI, simulation technology, optimization algorithms, and heuristic algorithms.

[0058] (Example 2) An AI support system according to an embodiment of the present invention utilizes big data to identify optimal store locations for each industry and efficiently increase visitor numbers and sales. This AI support system collects and analyzes big data, identifies optimal store locations, and formulates store opening plans, thereby individually identifying optimal store locations while taking into account the characteristics of each industry. For example, big data related to each industry is collected, including data on demographics, traffic volume, locations of competing stores, and consumer purchasing history. Next, AI analyzes the collected data to identify optimal store locations for each industry. Based on the collected data, the AI ​​estimates optimal locations for maximizing visitor numbers and sales. Furthermore, a specific store opening plan is formulated based on the identified store locations. For example, the system considers the surrounding environment and competitive situation of the store location and proposes optimal store layouts and promotion strategies. This allows for efficient increase in visitor numbers and sales. Because this system individually identifies optimal store locations while taking into account the characteristics of each industry, it is applicable to a wide range of industries. For example, it can be applied to various industries, including restaurants, retail stores, and the service industry. Furthermore, because AI analyzes big data, it is possible to create scientifically based store opening plans without relying on traditional experience or intuition. This system allows companies to efficiently select store locations and maximize visitor numbers and sales. For example, a restaurant can efficiently increase visitor numbers by identifying and opening a store in an area with high population density and little competition. Similarly, a retail store can maximize sales by identifying an area where a particular product sells well based on consumer purchasing history. In this way, by utilizing big data and AI, we provide a system that identifies the optimal store location for each industry and efficiently increases visitor numbers and sales. This allows companies to efficiently select store locations and maximize visitor numbers and sales.

[0059] The AI ​​support system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a planning unit. The collection unit collects big data. Examples of big data include, but are not limited to, demographics, traffic volume, locations of competing stores, and consumer purchase history. For example, the collection unit acquires demographic data from a public government database. The collection unit can also collect traffic volume data using sensors or cameras. The collection unit can also acquire location information of competing stores using a geographic information system (GIS). For example, the collection unit acquires consumer purchase history data from a point card system or an online shopping site. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using AI to estimate the optimal location for maximizing store visits and sales. For example, the analysis unit analyzes the data using a machine learning algorithm. The analysis unit can also analyze the data using deep learning technology. The analysis unit can also analyze text data using natural language processing technology. The identification unit identifies the optimal store location based on the analysis results obtained by the analysis unit. The identification unit identifies the optimal store opening location using, for example, AI. The identification unit identifies the optimal store opening location using, for example, a geographic information system (GIS). The identification unit can also identify the optimal store opening location using a clustering algorithm. Furthermore, the identification unit can identify the optimal store opening location using regression analysis. The planning unit formulates a store opening plan based on the store opening location identified by the identification unit. The planning unit formulates the store opening plan using, for example, AI. The planning unit formulates the store opening plan using, for example, simulation technology. Furthermore, the planning unit can formulate the store opening plan using an optimization algorithm. Furthermore, the planning unit can formulate the store opening plan using a heuristic algorithm. As a result, the AI ​​support system according to the embodiment can collect and analyze big data, identify the optimal store opening location, and formulate a store opening plan.

[0060] The collection unit can collect data such as demographics, traffic volume, locations of competing stores, and consumer purchasing history. For example, the collection unit obtains demographic data from a public government database. For example, the collection unit can collect data such as age, gender, and income level. The collection unit can also collect traffic volume data using sensors or cameras. For example, the collection unit can collect data such as the number of vehicles, the number of pedestrians, and peak hours. The collection unit can also obtain location information of competing stores using a geographic information system (GIS). For example, the collection unit can collect data such as the number, distance, and business type of competing stores. The collection unit can also obtain consumer purchasing history data from a point card system or an online shopping site. For example, the collection unit can collect data such as purchased items, purchase frequency, and purchase amount. This enables more accurate analysis by collecting a variety of data. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input traffic volume data obtained by a sensor into a generation AI and have the generation AI analyze the data.

[0061] The analysis unit can estimate the optimal location for maximizing the number of store visitors and sales based on the collected data. The analysis unit, for example, uses AI to analyze the data and estimate the optimal location for maximizing the number of store visitors and sales. For example, the analysis unit can analyze the data using a machine learning algorithm. The analysis unit can also analyze the data using deep learning technology. For example, the analysis unit can analyze the data using a neural network to estimate the optimal store location. The analysis unit can also analyze text data using natural language processing technology. For example, the analysis unit can analyze consumer reviews using text mining technology to estimate the optimal store location. In this way, the optimal store location can be scientifically identified by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0062] The identification unit can individually identify the optimal store location by taking into account the characteristics of each industry. The identification unit can identify the optimal store location using, for example, AI. For example, the identification unit can identify the optimal store location using a geographic information system (GIS). The identification unit can also identify the optimal store location using a clustering algorithm. For example, the identification unit can cluster data using K-means clustering to identify the optimal store location. The identification unit can also identify the optimal store location using regression analysis. For example, the identification unit can analyze data using linear regression to identify the optimal store location. This makes it possible to identify a more appropriate store location by taking into account the characteristics of each industry. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the analyzed data to a generation AI and cause the generation AI to identify the optimal store location.

[0063] The planning unit can propose an optimal store layout and promotion strategy by taking into account the surrounding environment and competitive situation of the identified store location. The planning unit, for example, uses AI to formulate the store opening plan. For example, the planning unit can formulate the store opening plan using simulation technology. The planning unit can also formulate the store opening plan using an optimization algorithm. For example, the planning unit can propose an optimal store layout using a genetic algorithm. Furthermore, the planning unit can also formulate the store opening plan using a heuristic algorithm. For example, the planning unit can propose an optimal promotion strategy using a heuristic algorithm. This allows for the formulation of a more effective store opening plan by taking into account the surrounding environment and competitive situation. Some or all of the above-mentioned processing in the planning unit may be performed using AI, for example, or may be performed without using AI. For example, the planning unit can input data of the identified store location into the generation AI and cause the generation AI to formulate the store opening plan.

[0064] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit temporarily stops data collection and resumes it when the user is relaxed. For example, the collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can actively collect data and collect detailed data. For example, the collection unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data and process it quickly. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0065] The collection unit can analyze past data collection history and select the optimal data collection method. For example, the collection unit can discover from the past data collection history that data collection is effective during a specific time period and focus collection on that time period. For example, the collection unit can confirm that a specific data source is highly reliable based on the past data collection history and prioritize the use of that data source. The collection unit can also analyze the past data collection history and discover that data collection in a specific region is effective and focus on that region. For example, the collection unit can optimize data collection in a specific time period or region based on the past data collection history. This allows a more effective data collection method to be selected by analyzing the past data collection history. Some or all of the above-described processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input the past data collection history into the generation AI and have the generation AI select the optimal data collection method.

[0066] When collecting data, the collection unit can prioritize collecting data specialized for a specific industry or region. For example, when collecting data specialized for the food and beverage industry, the collection unit prioritizes collecting population density and location information of competing stores. For example, when collecting data specialized for the retail industry, the collection unit can prioritize collecting consumer purchase history and traffic volume data. Furthermore, when collecting data specialized for the service industry, the collection unit can prioritize collecting information on surrounding facilities and consumer reviews. For example, the collection unit prioritizes collecting data specialized for a specific industry or region, enabling more accurate analysis. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data specialized for a specific industry or region into the generation AI and cause the generation AI to collect data.

[0067] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting only important data, thereby reducing the user's burden. For example, when the user is relaxed, the collection unit can prioritize collecting detailed data and perform highly accurate analysis. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly, thereby improving processing efficiency. For example, the collection unit can capture the user's facial expressions with a camera, estimate the emotions using an emotion estimation algorithm, and prioritize the data to be collected. This enables more appropriate data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0068] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting demographic data related to that area. For example, if the user is interested in a specific industry, the collection unit can prioritize collecting location information of competing stores related to that industry. Furthermore, if the user is active during a specific time period, the collection unit can prioritize collecting traffic volume data related to that time period. For example, the collection unit can collect more relevant data by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0069] The collection unit can analyze the user's social media activities and collect relevant data when collecting data. For example, if the user mentions a specific region on social media, the collection unit can prioritize collecting data related to that region. For example, if the user mentions a specific industry on social media, the collection unit can prioritize collecting data related to that industry. Furthermore, if the user is active on social media during a specific time period, the collection unit can prioritize collecting data related to that time period. For example, the collection unit can collect more relevant data by analyzing the user's social media activities and collecting relevant data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect relevant data.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to allow the user to understand more deeply. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, the analysis unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and adjust the presentation method of the analysis. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the user captured with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance to provide highly accurate results. For example, the analysis unit can perform a simplified analysis on data of low importance to provide results quickly. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance to provide balanced results. For example, the analysis unit can evaluate the importance of the data and adjust the level of detail of the analysis to enable more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a statistical analysis algorithm to demographic data to provide highly accurate results. For example, the analysis unit can apply a time series analysis algorithm to traffic volume data to make future predictions. The analysis unit can also apply a clustering algorithm to consumer purchase history data to analyze consumer behavior patterns. For example, the analysis unit can apply different analysis algorithms depending on the data category to enable more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis result. For example, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, the analysis unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and adjust the length of the analysis. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the user captured with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit can prioritize analysis of the most recent data and provide real-time results. For example, the analysis unit can analyze long-term trends based on past data. The analysis unit can also prioritize analysis of data collected during a specific period to clarify the characteristics of that period. For example, the analysis unit can determine the priority of analysis taking into account the time when the data was collected, thereby enabling more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the priority of analysis.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize analysis of data with high relevance to provide highly accurate results. For example, the analysis unit may analyze data with medium relevance next to achieve overall balance. The analysis unit may also analyze data with low relevance last to provide complementary information. For example, the analysis unit may evaluate the relevance of the data and adjust the order of analysis, thereby enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the relevance of the data into the generation AI and cause the generation AI to adjust the order of analysis.

[0076] The identification unit can estimate the user's emotions and adjust the specific criteria based on the estimated user emotions. For example, if the user is nervous, the identification unit provides simple, highly visible identification criteria. For example, if the user is relaxed, the identification unit can provide detailed identification criteria to allow the user to understand them more deeply. Furthermore, if the user is in a hurry, the identification unit can provide concise identification criteria that focus on the main points. For example, the identification unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and adjust the specific criteria. This enables more appropriate identification by adjusting the specific criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit can be performed using AI, for example, or without AI. For example, the identification unit can input image data of the user captured with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0077] The identification unit can improve the accuracy of identification by taking into account the interrelationships between data during identification. The identification unit can improve the accuracy of identification by taking into account, for example, the interrelationships between demographic data and traffic volume data. For example, the identification unit can improve the accuracy of identification by taking into account the interrelationships between location information of competing stores and consumer purchasing history. The identification unit can also improve the accuracy of identification by taking into account the interrelationships between surrounding environment data and consumer reviews. For example, the identification unit can evaluate the interrelationships between data and improve the accuracy of identification, thereby enabling more appropriate identification. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the interrelationships between data into a generation AI and cause the generation AI to improve the accuracy of identification.

[0078] The identification unit can perform the identification taking into consideration the characteristics of each industry. For example, the identification unit can use identification criteria specialized for the food and beverage industry to identify the optimal store location. For example, the identification unit can use identification criteria specialized for the retail industry to identify the optimal store location. The identification unit can also use identification criteria specialized for the service industry to identify the optimal store location. For example, the identification unit can perform the identification taking into consideration the characteristics of each industry, thereby enabling more appropriate identification. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the characteristics of each industry into the generation AI and cause the generation AI to execute the identification.

[0079] The identification unit can estimate the user's emotions and adjust the order in which specific results are displayed based on the estimated user emotions. For example, if the user is nervous, the identification unit can display results in a simple, highly visible order. For example, if the user is relaxed, the identification unit can display results in a detailed order to allow the user to understand more deeply. Furthermore, if the user is in a hurry, the identification unit can display results in a concise order that focuses on the main points. For example, the identification unit can capture the user's facial expressions with a camera, estimate the emotions using an emotion estimation algorithm, and adjust the order in which specific results are displayed. This allows for more appropriate result display by adjusting the order in which specific results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit can be performed, for example, using AI or without AI. For example, the identification unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0080] The identification unit can perform the identification by taking into account the geographic distribution of the data. For example, the identification unit can identify the optimal store location by taking into account the population density in a specific area. For example, the identification unit can identify the optimal store location by taking into account the traffic volume in a specific area. The identification unit can also identify the optimal store location by taking into account the locations of competing stores in a specific area. For example, the identification unit can perform the identification by taking into account the geographic distribution of the data, thereby enabling more appropriate identification. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the geographic distribution of the data into a generation AI and cause the generation AI to execute the identification.

[0081] During identification, the identification unit can improve the accuracy of the identification by referring to literature related to the data. For example, the identification unit can improve the accuracy of demographic data by referring to related literature. For example, the identification unit can improve the accuracy of traffic volume data by referring to related literature. The identification unit can also improve the accuracy of consumer purchase history data by referring to related literature. For example, the identification unit can improve the accuracy of the identification by referring to literature related to the data, thereby enabling more appropriate identification. Some or all of the above-mentioned processing in the identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit can input related literature into the generation AI and cause the generation AI to improve the accuracy of the identification.

[0082] The planning unit can estimate the user's emotions and adjust the display method of the plan based on the estimated user emotions. For example, if the user is nervous, the planning unit can provide a simple, highly visible display method. For example, if the user is relaxed, the planning unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the planning unit can provide a display method that focuses on the main points. For example, the planning unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and adjust the display method of the plan. This enables a more appropriate plan display by adjusting the display method of the plan according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the planning unit can be performed using AI, for example, or without AI. For example, the planning unit can input image data of the user captured with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0083] When formulating a plan, the planning unit can optimize the current plan by referring to past plan data. The planning unit, for example, refers to past successful store opening plans and reflects them in the current plan. For example, the planning unit can analyze past unsuccessful store opening plans and take measures to avoid the same mistakes. The planning unit can also formulate a plan suited to current market conditions based on past plan data. For example, the planning unit can optimize the current plan by referring to past plan data, thereby enabling more effective plan formulation. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input past plan data into a generation AI and have the generation AI optimize the current plan.

[0084] When formulating a plan, the planning unit can apply different planning methods taking into account the characteristics of each industry. The planning unit, for example, uses a planning method specialized for the food and beverage industry to formulate an optimal store opening plan. For example, the planning unit can use a planning method specialized for the retail industry to formulate an optimal store opening plan. The planning unit can also use a planning method specialized for the service industry to formulate an optimal store opening plan. For example, the planning unit can apply different planning methods taking into account the characteristics of each industry to formulate a more appropriate plan. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the characteristics of each industry into a generation AI and have the generation AI formulate a plan.

[0085] The planning unit can estimate the user's emotions and adjust the importance of plans based on the estimated user emotions. For example, if the user is nervous, the planning unit can prioritize displaying only important plans. For example, if the user is relaxed, the planning unit can display detailed plans to allow the user to understand them more deeply. Furthermore, if the user is in a hurry, the planning unit can display concise plans that focus on the main points. For example, the planning unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and adjust the importance of the plans. This enables more appropriate planning by adjusting the importance of plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the planning unit can be performed using AI, for example, or without AI. For example, the planning unit can input image data of the user captured with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0086] When formulating a plan, the planning unit can analyze changes in the plan based on the time when data was collected. The planning unit, for example, updates the plan in real time based on the latest data. For example, the planning unit can analyze long-term trends based on past data and reflect the results in the plan. The planning unit can also formulate a plan that takes into account the characteristics of a specific period based on data collected during that period. For example, the planning unit can analyze changes in the plan taking into account the time when data was collected, thereby enabling more effective planning. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the time when data was collected into the generation AI and cause the generation AI to analyze changes in the plan.

[0087] When formulating a plan, the planning unit can analyze the plan by referring to market data related to the data. The planning unit, for example, refers to the relevant market data, analyzes the competitive situation, and reflects the results in the plan. For example, the planning unit can analyze consumer purchasing trends based on the relevant market data and reflect the results in the plan. The planning unit can also refer to the relevant market data and formulate a plan that takes regional characteristics into consideration. For example, the planning unit can formulate a more effective plan by analyzing the plan by referring to the relevant market data of the data. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the relevant market data into a generation AI and have the generation AI analyze the plan. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and planning unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 or a sensor of the smart device 14, and the data is analyzed by the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies an optimal store opening location based on the analysis results. The planning unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and formulates a store opening plan based on the identified store opening location. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and planning unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 or a sensor of the smart glasses 214, and the data is analyzed by the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies an optimal store opening location based on the analysis results. The planning unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and formulates a store opening plan based on the identified store opening location. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and planning unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 or a sensor of the headset terminal 314, and the data is analyzed by the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies an optimal store opening location based on the analysis results. The planning unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and formulates a store opening plan based on the identified store opening location. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, identification unit, and planning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 or sensors of the robot 414, and the data is analyzed by the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies an optimal store opening location based on the analysis results. The planning unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and formulates a store opening plan based on the identified store opening location.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, data collection can be temporarily stopped and resumed when the user is relaxed. The collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can actively collect data and collect detailed data. The collection unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can prioritize the collection of only important data and process it quickly. The collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions.

[0090] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided, allowing the user to understand it in depth. Also, if the user is in a hurry, a concise analysis result that focuses on the main points can be provided. The analysis unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and adjust the way the analysis is presented. This allows the system to provide more appropriate analysis results by adjusting the way the analysis is presented according to the user's emotions.

[0091] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated user emotions. For example, if the user is nervous, simple and highly visible identification criteria can be provided. If the user is relaxed, detailed identification criteria can be provided to allow the user to understand them in depth. Also, if the user is in a hurry, concise identification criteria that focus on the main points can be provided. The identification unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and adjust the identification criteria. This allows for more appropriate identification by adjusting the identification criteria according to the user's emotions.

[0092] The planning unit can estimate the user's emotions and adjust the display method of the plan based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Also, if the user is in a hurry, a display method that focuses on the main points can be provided. The planning unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and adjust the display method of the plan. This makes it possible to display a more appropriate plan by adjusting the display method of the plan according to the user's emotions.

[0093] The planning unit can estimate the user's emotions and adjust the importance of plans based on the estimated user emotions. For example, if the user is nervous, only important plans can be displayed preferentially. If the user is relaxed, detailed plans can be displayed to allow the user to understand them in depth. Also, if the user is in a hurry, a concise plan that focuses on the main points can be displayed. The planning unit can capture the user's facial expressions with a camera, estimate the emotions using an emotion estimation algorithm, and adjust the importance of plans. This allows for more appropriate planning by adjusting the importance of plans according to the user's emotions.

[0094] The collection unit can analyze past data collection history and select the optimal data collection method. For example, it can discover from past data collection history that data collection is effective during a specific time period and concentrate collection on that time period. The collection unit can confirm that a specific data source is highly reliable based on past data collection history and use that data source preferentially. It can also analyze past data collection history and discover that data collection in a specific region is effective and focus on that region. The collection unit can optimize data collection in a specific time period or region based on past data collection history. In this way, by analyzing past data collection history, it is possible to select a more effective data collection method.

[0095] When collecting data, the collection unit can prioritize collecting data specialized in a specific industry or region. For example, when collecting data specialized in the food and beverage industry, it can prioritize collecting population density and location information of competing stores. When collecting data specialized in the retail industry, the collection unit can prioritize collecting consumer purchase history and traffic volume data. Furthermore, when collecting data specialized in the service industry, it can also prioritize collecting information on surrounding facilities and consumer reviews. By prioritizing the collection of data specialized in a specific industry or region, the collection unit can perform more accurate analysis.

[0096] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, for data of high importance, a detailed analysis is performed to provide highly accurate results. For data of low importance, the analysis unit can perform a simplified analysis to provide quick results. In addition, for data of medium importance, the analysis unit can perform an analysis with an appropriate level of detail to provide balanced results. By evaluating the importance of data and adjusting the level of detail of the analysis, the analysis unit can perform more effective analysis.

[0097] The identification unit can improve the accuracy of identification by taking into account the correlation of data during identification. For example, the accuracy of identification can be improved by taking into account the correlation of demographic data and traffic volume data. The identification unit can improve the accuracy of identification by taking into account the correlation of location information of competing stores and consumer purchasing history. The identification unit can also improve the accuracy of identification by taking into account the correlation of surrounding environment data and consumer reviews. The identification unit can improve the accuracy of identification by evaluating the correlation of data and improving the accuracy of identification, thereby enabling more appropriate identification.

[0098] When formulating a plan, the planning department can refer to past planning data to optimize the current plan. For example, it can refer to past successful store opening plans and reflect them in the current plan. The planning department can analyze past unsuccessful store opening plans and take measures to avoid the same mistakes. It can also use past planning data to formulate a plan that is appropriate for the current market situation. By optimizing the current plan by referring to past planning data, the planning department can formulate more effective plans.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects big data. Big data includes, for example, demographic data, traffic volume, locations of competing stores, and consumer purchasing history. The collection unit obtains demographic data from public government databases and traffic volume data using sensors and cameras. It also obtains location information of competing stores using a geographic information system (GIS) and obtains consumer purchasing history data from point card systems and online shopping sites. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI, machine learning algorithms, deep learning technology, and natural language processing technology to analyze the data and estimate the optimal locations to maximize store visits and sales. Step 3: The identification unit identifies the optimal store location based on the analysis results obtained by the analysis unit. The identification unit identifies the optimal store location using AI, geographic information systems (GIS), clustering algorithms, and regression analysis. Step 4: The Planning Department formulates a store opening plan based on the store locations identified by the Identification Department. The Planning Department formulates the store opening plan using AI, simulation technology, optimization algorithms, and heuristic algorithms.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects big data; an analysis unit that analyzes the data collected by the collection unit; an identification unit that identifies an optimal store location based on the analysis results obtained by the analysis unit; a planning unit that formulates a store opening plan based on the store opening location identified by the identification unit. A system characterized by:

2. The collecting unit Collect data such as demographics, traffic volume, competitor locations, and consumer purchasing history 2. The system of claim 1.

3. The analysis unit Based on the collected data, estimate the optimal location to maximize store traffic and sales.

2. The system of claim 1.

4. The identification unit Identify the best location for each store, taking into account the characteristics of each industry 2. The system of claim 1.

5. The planning unit Propose optimal store layouts and promotion strategies, taking into consideration the surrounding environment and competitive situation of the identified store locations.

2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze past data collection history and select the optimal data collection method 2. The system of claim 1.

8. The collecting unit When collecting data, prioritize collecting data specific to a particular industry or region.

2. The system of claim 1.

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