System

Abstract

An object of the system according to the embodiment is to efficiently perform stock management and product development by utilizing settlement data and data of the same industry type.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a visualization unit, a proposal unit, and an inventory management unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The visualizing unit visualizes the analysis result obtained by the analyzing unit. The proposal unit makes a proposal based on the data visualized by the visualization unit. The stock management unit manages the stock based on the content proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to effectively utilize payment data and data from the same industry for inventory management and product development.

[0005] The system according to the embodiment aims to efficiently manage inventory and develop products by utilizing payment data and data from the same industry. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a visualization unit, a proposal unit, and an inventory management unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The visualization unit visualizes the analysis results obtained by the analysis unit. The proposal unit makes proposals based on the data visualized by the visualization unit. The inventory management unit manages inventory based on the proposals made by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage inventory and develop products by utilizing payment data and data from the same industry. [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 electronic payment system according to an embodiment of the present invention provides affiliated stores with data that easily visualizes and analyzes customer preferences, trends in popular products, and comparisons with other similar businesses using payment data and data from similar businesses in the surrounding area. The electronic payment system collects payment data from affiliated stores and data from similar businesses in the surrounding area and analyzes it using AI. For example, it can analyze the time of day a particular product sells well and the age group it is popular among. The analysis results are visualized and provided to affiliated stores. For example, it can visually display sales trends and sales data by customer attributes using graphs and charts. Furthermore, the electronic payment system makes product development proposals based on the analysis results that will increase sales at the store. For example, it can propose new products to be developed to coincide with the time periods when specific products are selling well or propose product lineups tailored to the hobbies and preferences of customers. It also provides consistent inventory monitoring. AI monitors inventory status in real time based on sales data and issues alerts when inventory is running low. This allows the electronic payment system to make data-driven management decisions and increase sales. This allows the electronic payment system to provide merchants with information they need to make data-based business decisions, thereby improving sales. For example, merchants can grasp their store's status at a glance and manage inventory more efficiently.

[0029] The electronic payment system according to the embodiment includes a collection unit, an analysis unit, a visualization unit, a proposal unit, and an inventory management unit. The collection unit collects data. The data includes, but is not limited to, sales data, customer data, and inventory data. The collection unit automatically collects data through the electronic payment system. The collection unit can also adjust the frequency and means of data collection. For example, the collection unit can optimize the frequency of data collection based on the frequency of use of the electronic payment system. The analysis unit analyzes the data collected by the collection unit. For example, the analysis can analyze the hobbies and tastes of customers and trends in popular products and compare them with products in the same industry, but is not limited to such examples. For example, the analysis unit analyzes preferences based on the purchase history and survey results of customers. The analysis unit can also analyze trends in popular products based on sales data and sales periods. The visualization unit visualizes the analysis results obtained by the analysis unit. For example, the visualization can visually display the analysis results using graphs and charts, but is not limited to such examples. For example, the visualization unit displays data using bar graphs, pie charts, line graphs, etc. The suggestion unit makes suggestions based on the data visualized by the visualization unit. The suggestions may include, but are not limited to, product development suggestions based on the analysis results that will lead to increased sales at the store. For example, the suggestion unit may propose new product ideas or improvements. The inventory management unit manages inventory based on the suggestions made by the suggestion unit. For example, inventory management may include, but is not limited to, monitoring inventory status in real time based on sales data and issuing an alert if inventory is about to run low. For example, the inventory management unit sets an inventory threshold and issues an alert if inventory falls below that threshold. This allows the electronic payment system according to the embodiment to consistently collect, analyze, visualize, suggest, and manage inventory.

[0030] The collection unit can collect data through an electronic payment system. Examples of electronic payment systems include, but are not limited to, credit card payments and mobile payments. For example, the collection unit collects sales data through a credit card payment system. The collection unit can also collect customer data through a mobile payment system. For example, the collection unit collects customer purchase histories through a mobile payment app. The collection unit can also collect inventory data through the electronic payment system. For example, the collection unit collects inventory data in cooperation with an inventory management system. This allows data to be automatically collected through the electronic payment system.

[0031] The analysis unit can analyze the preferences of customers and trends in popular products and compare them with data from neighboring businesses in the same industry. Preferences include, but are not limited to, purchase histories, survey results, and the like. For example, the analysis unit analyzes preferences based on the purchase histories of customers. The analysis unit can also analyze preferences based on survey results. For example, the analysis unit analyzes survey results from customers to identify preferences. Trends in popular products include, but are not limited to, sales data, sales periods, and the like. For example, the analysis unit analyzes trends in popular products based on sales data. The analysis unit can also analyze trends in popular products based on sales periods. For example, the analysis unit analyzes the time periods during which a specific product is sold. Data from neighboring businesses in the same industry includes, but is not limited to, sales data from competitors and industry reports. For example, the analysis unit performs analysis by referring to sales data from competitors. The analysis unit can also perform analysis by referring to industry reports. For example, the analysis unit performs comparison with neighboring businesses in the same industry based on industry reports. This allows us to analyze the hobbies and tastes of customers and trends in popular products, and compare them with other stores in the same industry.

[0032] The visualization unit can visually display the analysis results using graphs and charts. Graphs and charts include, but are not limited to, bar graphs, pie charts, and line graphs. For example, the visualization unit displays sales data using bar graphs. The visualization unit can also display attribute data of customers using pie charts. For example, the visualization unit displays sales data by age group of customers using a pie chart. The visualization unit can also display sales trends using a line graph. For example, the visualization unit displays sales trends by month using a line graph. This makes it easier to understand the data by visually displaying the analysis results.

[0033] The proposal unit can make product development proposals based on the analysis results that will lead to increased sales at the store. Product development proposals include, for example, new product ideas and improvements, but are not limited to these examples. For example, the proposal unit can make proposals to develop new products to suit the time periods when specific products are selling well. The proposal unit can also propose product lineups that match the hobbies and tastes of customers. For example, the proposal unit can propose new product ideas based on the preferences of customers. The proposal unit can also propose improvements to existing products based on sales data. For example, the proposal unit can propose improvements to products with low sales. In this way, by making product development proposals based on the analysis results, an increase in sales can be expected.

[0034] The inventory management unit monitors the inventory status in real time based on sales data and can issue an alert if there is a possibility of inventory shortage. Examples of real-time monitoring include, but are not limited to, monitoring tools and monitoring frequencies. For example, the inventory management unit monitors the inventory status in real time using a monitoring tool. The inventory management unit can also monitor the inventory status by adjusting the monitoring frequency. For example, the inventory management unit adjusts the monitoring frequency based on sales data. Examples of alerts include, but are not limited to, inventory thresholds and notification means. For example, the inventory management unit sets an inventory threshold and issues an alert when inventory falls below that threshold. The inventory management unit can also issue an alert using notification means. For example, the inventory management unit notifies the alert via email or SMS. This allows inventory status to be monitored in real time and prevents stockouts.

[0035] The collection unit can optimize the frequency of data collection based on the frequency of use of the electronic payment system. Examples of the frequency of data collection include, but are not limited to, adjustment methods based on the frequency of use. For example, the collection unit can increase the frequency of data collection during times when the electronic payment system is heavily used. The collection unit can also decrease the frequency of data collection during times when the electronic payment system is less used. For example, the collection unit dynamically adjusts the timing of data collection according to the frequency of use of the electronic payment system. By optimizing the frequency of data collection according to the frequency of use of the electronic payment system, efficient data collection is possible.

[0036] The collection unit can prioritize collecting data during specific time periods or days of the week. Examples of specific time periods or days of the week include, but are not limited to, peak hours and weekends. The collection unit can concentrate data collection during times when there are many customers, such as weekends and holidays. The collection unit can also prioritize collecting data during specific time periods on weekdays. For example, the collection unit can concentrate data collection during specific time periods or days of the week depending on the season or events. This allows important data to be collected efficiently by prioritizing the collection of data during specific time periods or days of the week.

[0037] The collection unit can select data to be collected based on the user's purchase history or store visit history. The purchase history includes, for example, but is not limited to, purchase date and time, purchased items, etc. The collection unit, for example, preferentially collects relevant data based on the user's past purchase history. The store visit history includes, for example, but is not limited to, store visit date and time, store visit frequency, etc. The collection unit can, for example, analyze the user's store visit history and preferentially collect data on frequently visited stores. For example, the collection unit combines the user's purchase history and store visit history to collect the most relevant data. This makes it possible to collect highly relevant data by taking the user's purchase history and store visit history into consideration.

[0038] The collection unit can prioritize collection of highly relevant data based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, location information services, etc. For example, when the user is in a specific area, the collection unit prioritizes collection of data related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collection of data related to the area to which the user is traveling. For example, the collection unit collects the most relevant data based on the user's geographical location information. This allows for efficient collection of highly relevant data by taking the user's geographical location information into consideration.

[0039] The collection unit can analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. The collection unit, for example, collects data related to the places where the user checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activity.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, survey results and reviews. The collection unit can adjust the data collection method, for example, based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and propose an optimal data collection method. For example, the collection unit can reflect the user's feedback to improve the accuracy of data collection. In this way, the optimal data collection method can be realized by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on sales data of a specific product. Sales data includes, for example, sales amount, sales quantity, etc., but is not limited to such examples. For example, the analysis unit analyzes data of products with high sales in detail. The analysis unit can also analyze data of products with low sales in a simplified manner. For example, the analysis unit dynamically adjusts the level of detail of the analysis based on the sales data. The level of detail of the analysis includes, for example, analysis depth, analysis items, etc., but is not limited to such examples. As a result, by adjusting the level of detail of the analysis based on sales data of a specific product, important products can be analyzed in detail.

[0042] The analysis unit can apply different analysis algorithms based on the attribute information of customers. Attribute information includes, but is not limited to, age, gender, and occupation. For example, the analysis unit applies different analysis algorithms to different age groups. The analysis unit can also apply different analysis algorithms to different genders. For example, the analysis unit selects an optimal analysis algorithm based on the attribute information of customers. Analysis algorithms include, but are not limited to, regression analysis and clustering. This improves the accuracy of the analysis by applying the optimal analysis algorithm based on the attribute information of customers.

[0043] The analysis unit can improve the accuracy of the analysis by referring to past analysis results. Past analysis results include, but are not limited to, past data sets and analysis reports, for example. The analysis unit, for example, adjusts the analysis algorithm based on the past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. For example, the analysis unit analyzes the past analysis results and proposes an optimal analysis method. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0044] The analysis unit can prioritize analyzing data from specific time periods or days of the week. Examples of specific time periods or days of the week include, but are not limited to, peak hours and weekends. The analysis unit prioritizes analyzing data from time periods with a high number of customers, such as weekends and holidays. The analysis unit can also prioritize analyzing data from specific time periods on weekdays. For example, the analysis unit prioritizes analyzing data from specific time periods or days of the week depending on the season or an event. This allows important data to be analyzed efficiently by prioritizing analysis of data from specific time periods or days of the week.

[0045] The analysis unit can improve the accuracy of the analysis by referring to data on neighboring businesses in the same industry. Data on neighboring businesses in the same industry includes, but is not limited to, sales data on competitors and industry reports, for example. The analysis unit performs analysis by referring to, for example, sales data on competitors. The analysis unit can also perform analysis by referring to industry reports. For example, the analysis unit compares with neighboring businesses in the same industry based on industry reports. By referring to data on neighboring businesses in the same industry, the accuracy of the analysis can be improved.

[0046] The analysis unit can perform analysis based on the market value of a specific product. Market value includes, but is not limited to, market price, demand forecast, and the like. For example, the analysis unit performs detailed analysis of data on products with high market value. The analysis unit can also perform simplified analysis of data on products with low market value. For example, the analysis unit dynamically adjusts the level of detail of the analysis based on the market value. This allows for detailed analysis of important products by taking into account the market value of the specific product.

[0047] The visualization unit can adjust the level of detail of the display based on sales data of a specific product. Sales data includes, for example, sales amount, sales quantity, etc., but is not limited to such examples. For example, the visualization unit displays data of products with high sales in detail. The visualization unit can also display data of products with low sales in a simplified manner. For example, the visualization unit dynamically adjusts the level of detail of the display based on the sales data. Examples of the level of detail of the display include, but are not limited to such examples. In this way, by adjusting the level of detail of the display based on the sales data of a specific product, data of important products can be displayed in detail.

[0048] The visualization unit can apply different display methods based on the attribute information of customers. Attribute information includes, but is not limited to, age, gender, and occupation. For example, the visualization unit can apply different display methods for different age groups. The visualization unit can also apply different display methods for different genders. For example, the visualization unit selects the optimal display method based on the attribute information of customers. Display methods include, but are not limited to, graph display and table display. This improves the accuracy of the display by applying the optimal display method based on the attribute information of customers.

[0049] The visualization unit can improve the accuracy of the display by referring to past visualization results. Past visualization results include, but are not limited to, past graphs, display reports, and the like. For example, the visualization unit adjusts the display method based on the past visualization results. The visualization unit can also improve the accuracy of the display by referring to the past visualization results. For example, the visualization unit analyzes the past visualization results and proposes an optimal display method. In this way, the accuracy of the display is improved by referring to the past visualization results.

[0050] The visualization unit can prioritize displaying data for specific time periods or days of the week. Examples of specific time periods or days of the week include, but are not limited to, peak hours and weekends. The visualization unit can prioritize displaying data for time periods with a high number of customers, such as weekends and holidays. The visualization unit can also prioritize displaying data for specific time periods on weekdays. For example, the visualization unit can prioritize displaying data for specific time periods or days of the week depending on the season or an event. This allows important data to be displayed efficiently by prioritized display of data for specific time periods or days of the week.

[0051] The visualization unit can improve the accuracy of the display by referring to data on neighboring businesses in the same industry. Data on neighboring businesses in the same industry includes, but is not limited to, sales data on competitors and industry reports, for example. The visualization unit performs the display by referring to, for example, the sales data of competitors. The visualization unit can also perform the display by referring to industry reports. For example, the visualization unit compares with neighboring businesses in the same industry based on industry reports. By referring to data on neighboring businesses in the same industry, the accuracy of the display can be improved.

[0052] The visualization unit can display data based on the market value of a specific product. Market value includes, but is not limited to, market price, demand forecast, and the like. For example, the visualization unit displays data for products with high market value in detail. The visualization unit can also display data for products with low market value in a simplified manner. For example, the visualization unit dynamically adjusts the level of detail of the display based on the market value. This allows important product data to be displayed in detail by taking into account the market value of the specific product.

[0053] The proposal unit can adjust the level of detail of the proposal based on sales data of a specific product. Sales data includes, for example, sales amount, sales quantity, etc., but is not limited to such examples. For example, the proposal unit makes detailed proposals for products with high sales. The proposal unit can also make simplified proposals for products with low sales. For example, the proposal unit dynamically adjusts the level of detail of the proposal based on the sales data. The level of detail of the proposal includes, for example, depth of the proposal, proposed items, etc., but is not limited to such examples. As a result, by adjusting the level of detail of the proposal based on the sales data of a specific product, important products can be proposed in detail.

[0054] The suggestion unit can apply different suggestion algorithms based on the attribute information of the customer. The attribute information includes, but is not limited to, for example, age, gender, occupation, etc. The suggestion unit can apply different suggestion algorithms for different age groups, for example. The suggestion unit can also apply different suggestion algorithms for different genders. For example, the suggestion unit selects an optimal suggestion algorithm based on the customer's attribute information. The suggestion algorithm includes, but is not limited to, for example, a recommendation system, a machine learning algorithm, etc. This improves the accuracy of suggestions by applying the optimal suggestion algorithm based on the customer's attribute information.

[0055] The proposal unit can improve the accuracy of proposals by referring to past proposal results. Past proposal results include, for example, past proposal content, proposal success rates, etc., but are not limited to these examples. The proposal unit, for example, adjusts the proposal algorithm based on the past proposal results. The proposal unit can also improve the accuracy of proposals by referring to the past proposal results. For example, the proposal unit analyzes the past proposal results and proposes an optimal proposal method. In this way, the accuracy of proposals is improved by referring to the past proposal results.

[0056] The suggestion unit can prioritize suggesting data for specific time periods or days of the week. Examples of specific time periods or days of the week include, but are not limited to, peak hours and weekends. For example, the suggestion unit prioritizes suggesting data for time periods with many customers, such as weekends and holidays. The suggestion unit can also prioritize suggesting data for specific time periods on weekdays. For example, the suggestion unit prioritizes suggesting data for specific time periods or days of the week depending on the season or an event. In this way, important data can be efficiently suggested by preferentially suggesting data for specific time periods or days of the week.

[0057] The proposal unit can improve the accuracy of proposals by referring to data on neighboring businesses in the same industry. Data on neighboring businesses in the same industry includes, but is not limited to, sales data on competitors and industry reports, for example. The proposal unit makes proposals by referring to, for example, the sales data of competitors. The proposal unit can also make proposals by referring to industry reports. For example, the proposal unit compares with neighboring businesses in the same industry based on industry reports. By referring to data on neighboring businesses in the same industry, the accuracy of proposals can be improved.

[0058] The proposal unit can make proposals based on the market value of a specific product. Market value includes, but is not limited to, market price, demand forecast, and the like. For example, the proposal unit makes detailed proposals for products with high market value. The proposal unit can also make simplified proposals for products with low market value. For example, the proposal unit dynamically adjusts the level of detail of the proposal based on the market value. This allows for detailed proposals for important products by taking into account the market value of the specific product.

[0059] The inventory management unit can adjust the level of detail of inventory management based on sales data of specific products. Sales data includes, for example, sales amount, sales quantity, etc., but is not limited to such examples. For example, the inventory management unit manages inventory of products with high sales in detail. The inventory management unit can also manage inventory of products with low sales in a simplified manner. For example, the inventory management unit dynamically adjusts the level of detail of inventory management based on sales data. The level of detail of inventory management includes, for example, management depth, management items, etc., but is not limited to such examples. In this way, by adjusting the level of detail of inventory management based on sales data of specific products, inventory of important products can be managed in detail.

[0060] The inventory management unit can apply different inventory management algorithms based on the attribute information of customers. Attribute information includes, but is not limited to, age, gender, and occupation. For example, the inventory management unit applies different inventory management algorithms for different age groups. The inventory management unit can also apply different inventory management algorithms for different genders. For example, the inventory management unit selects an optimal inventory management algorithm based on the attribute information of customers. Inventory management algorithms include, but are not limited to, demand forecasting algorithms and optimization algorithms. This improves the accuracy of inventory management by applying the optimal inventory management algorithm based on the attribute information of customers.

[0061] The inventory management department can improve the accuracy of inventory management by referring to past inventory management results. Past inventory management results include, but are not limited to, past inventory data, management reports, etc. For example, the inventory management department adjusts the inventory management algorithm based on the past inventory management results. The inventory management department can also improve the accuracy of inventory management by referring to the past inventory management results. For example, the inventory management department analyzes the past inventory management results and proposes an optimal inventory management method. In this way, the accuracy of inventory management is improved by referring to the past inventory management results.

[0062] The inventory management unit can prioritize managing data during specific time periods or days of the week. Examples of specific time periods or days of the week include, but are not limited to, peak hours and weekends. The inventory management unit prioritizes managing inventory during times when there are many customers, such as weekends and holidays. The inventory management unit can also prioritize managing inventory during specific time periods on weekdays. For example, the inventory management unit prioritizes managing inventory during specific time periods or days of the week depending on the season or events. This allows important data to be managed efficiently by prioritizing management of data during specific time periods or days of the week.

[0063] The inventory management unit can improve the accuracy of inventory management by referring to data from neighboring businesses in the same industry. Data from neighboring businesses in the same industry includes, but is not limited to, sales data from competitors and industry reports. For example, the inventory management unit performs inventory management by referring to the sales data from competitors. The inventory management unit can also perform inventory management by referring to industry reports. For example, the inventory management unit compares with neighboring businesses in the same industry based on industry reports. In this way, the accuracy of inventory management is improved by referring to data from neighboring businesses in the same industry.

[0064] The inventory management unit can manage inventory based on the market value of specific products. Market values ​​include, but are not limited to, market prices, demand forecasts, and the like. For example, the inventory management unit manages inventory of products with high market values ​​in detail. The inventory management unit can also manage inventory of products with low market values ​​in a simplified manner. For example, the inventory management unit dynamically adjusts the level of detail of inventory management based on market values. This allows for detailed inventory management of important products by taking into account the market values ​​of specific products.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The collection unit can analyze the purchase frequency of specific products based on the user's purchase history and prioritize collecting data on products with high purchase frequencies. For example, the collection unit prioritizes collecting data on products frequently purchased by the user and performs detailed analysis. The collection unit can also collect data on related products based on the user's purchase history. For example, if the user purchases many products from a specific category, the collection unit collects data on products related to that category. This makes it possible to efficiently collect highly relevant data based on the user's purchase history.

[0067] The visualization unit can adjust the level of detail of the display based on sales data of a specific product. For example, the visualization unit can display data of products with high sales in detail. The visualization unit can also display data of products with low sales in a simplified manner. For example, the visualization unit dynamically adjusts the level of detail of the display based on the sales data. In this way, by adjusting the level of detail of the display based on sales data of a specific product, data of important products can be displayed in detail.

[0068] The inventory management unit can adjust the level of detail of inventory management based on sales data for specific products. For example, the inventory management unit manages inventory for high-sales products in detail. The inventory management unit can also manage inventory for low-sales products in a simplified manner. For example, the inventory management unit dynamically adjusts the level of detail of inventory management based on sales data. In this way, by adjusting the level of detail of inventory management based on sales data for specific products, it is possible to manage inventory for important products in detail.

[0069] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting data related to the area to which the user is traveling. For example, the collection unit collects the most relevant data based on the user's geographical location information. This allows for efficient collection of highly relevant data by taking the user's geographical location information into consideration.

[0070] The analysis unit can perform analysis based on the market value of a specific product. For example, the analysis unit can perform a detailed analysis of data on products with high market values. The analysis unit can also perform a simplified analysis of data on products with low market values. For example, the analysis unit can dynamically adjust the level of detail of the analysis based on the market value. This allows for detailed analysis of important products by taking into account the market value of the specific product.

[0071] The suggestion unit can prioritize suggesting data for specific time periods or days of the week. For example, the suggestion unit prioritizes suggesting data for time periods with a high number of customers, such as weekends and holidays. The suggestion unit can also prioritize suggesting data for specific time periods on weekdays. For example, the suggestion unit prioritizes suggesting data for specific time periods or days of the week depending on the season or an event. In this way, important data can be efficiently suggested by preferentially suggesting data for specific time periods or days of the week.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects data. The data includes sales data, customer data, inventory data, etc. The collection unit automatically collects data through the electronic payment system and can adjust the frequency and means of data collection. For example, the frequency of data collection can be optimized based on the frequency of use of the electronic payment system. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis involves analyzing the hobbies and tastes of customers and trends in popular products, and comparing them with other products in the same industry. For example, it analyzes preferences based on customers' purchase history and survey results, and analyzes trends in popular products based on sales data and sales periods. Step 3: The visualization unit visualizes the analysis results obtained by the analysis unit. Visualization visually displays the analysis results using graphs and charts. For example, data is displayed using bar graphs, pie charts, line graphs, etc. Step 4: The proposal department makes proposals based on the data visualized by the visualization department. Based on the analysis results, the proposals are product development proposals that will lead to increased sales for the store. For example, they propose ideas for new products and improvements. Step 5: The inventory management department manages inventory based on the proposals made by the proposal department. Inventory management monitors the inventory status in real time based on sales data and issues an alert if inventory is about to run low. For example, an inventory threshold can be set and an alert issued if inventory falls below that threshold.

[0074] (Example 2) An electronic payment system according to an embodiment of the present invention provides affiliated stores with data that easily visualizes and analyzes customer preferences, trends in popular products, and comparisons with other similar businesses using payment data and data from similar businesses in the surrounding area. The electronic payment system collects payment data from affiliated stores and data from similar businesses in the surrounding area and analyzes it using AI. For example, it can analyze the time of day a particular product sells well and the age group it is popular among. The analysis results are visualized and provided to affiliated stores. For example, it can visually display sales trends and sales data by customer attributes using graphs and charts. Furthermore, the electronic payment system makes product development proposals based on the analysis results that will increase sales at the store. For example, it can propose new products to be developed to coincide with the time periods when specific products are selling well or propose product lineups tailored to the hobbies and preferences of customers. It also provides consistent inventory monitoring. AI monitors inventory status in real time based on sales data and issues alerts when inventory is running low. This allows the electronic payment system to make data-driven management decisions and increase sales. This allows the electronic payment system to provide merchants with information they need to make data-based business decisions, thereby improving sales. For example, merchants can grasp their store's status at a glance and manage inventory more efficiently.

[0075] The electronic payment system according to the embodiment includes a collection unit, an analysis unit, a visualization unit, a proposal unit, and an inventory management unit. The collection unit collects data. The data includes, but is not limited to, sales data, customer data, and inventory data. The collection unit automatically collects data through the electronic payment system. The collection unit can also adjust the frequency and means of data collection. For example, the collection unit can optimize the frequency of data collection based on the frequency of use of the electronic payment system. The analysis unit analyzes the data collected by the collection unit. For example, the analysis can analyze the hobbies and tastes of customers and trends in popular products and compare them with products in the same industry, but is not limited to such examples. For example, the analysis unit analyzes preferences based on the purchase history and survey results of customers. The analysis unit can also analyze trends in popular products based on sales data and sales periods. The visualization unit visualizes the analysis results obtained by the analysis unit. For example, the visualization can visually display the analysis results using graphs and charts, but is not limited to such examples. For example, the visualization unit displays data using bar graphs, pie charts, line graphs, etc. The suggestion unit makes suggestions based on the data visualized by the visualization unit. The suggestions may include, but are not limited to, product development suggestions based on the analysis results that will lead to increased sales at the store. For example, the suggestion unit may propose new product ideas or improvements. The inventory management unit manages inventory based on the suggestions made by the suggestion unit. For example, inventory management may include, but is not limited to, monitoring inventory status in real time based on sales data and issuing an alert if inventory is about to run low. For example, the inventory management unit sets an inventory threshold and issues an alert if inventory falls below that threshold. This allows the electronic payment system according to the embodiment to consistently collect, analyze, visualize, suggest, and manage inventory.

[0076] The collection unit can collect data through an electronic payment system. Examples of electronic payment systems include, but are not limited to, credit card payments and mobile payments. For example, the collection unit collects sales data through a credit card payment system. The collection unit can also collect customer data through a mobile payment system. For example, the collection unit collects customer purchase histories through a mobile payment app. The collection unit can also collect inventory data through the electronic payment system. For example, the collection unit collects inventory data in cooperation with an inventory management system. This allows data to be automatically collected through the electronic payment system.

[0077] The analysis unit can analyze the preferences of customers and trends in popular products and compare them with data from neighboring businesses in the same industry. Preferences include, but are not limited to, purchase histories, survey results, and the like. For example, the analysis unit analyzes preferences based on the purchase histories of customers. The analysis unit can also analyze preferences based on survey results. For example, the analysis unit analyzes survey results from customers to identify preferences. Trends in popular products include, but are not limited to, sales data, sales periods, and the like. For example, the analysis unit analyzes trends in popular products based on sales data. The analysis unit can also analyze trends in popular products based on sales periods. For example, the analysis unit analyzes the time periods during which a specific product is sold. Data from neighboring businesses in the same industry includes, but is not limited to, sales data from competitors and industry reports. For example, the analysis unit performs analysis by referring to sales data from competitors. The analysis unit can also perform analysis by referring to industry reports. For example, the analysis unit performs comparison with neighboring businesses in the same industry based on industry reports. This allows us to analyze the hobbies and tastes of customers and trends in popular products, and compare them with other stores in the same industry.

[0078] The visualization unit can visually display the analysis results using graphs and charts. Graphs and charts include, but are not limited to, bar graphs, pie charts, and line graphs. For example, the visualization unit displays sales data using bar graphs. The visualization unit can also display attribute data of customers using pie charts. For example, the visualization unit displays sales data by age group of customers using a pie chart. The visualization unit can also display sales trends using a line graph. For example, the visualization unit displays sales trends by month using a line graph. This makes it easier to understand the data by visually displaying the analysis results.

[0079] The proposal unit can make product development proposals based on the analysis results that will lead to increased sales at the store. Product development proposals include, for example, new product ideas and improvements, but are not limited to these examples. For example, the proposal unit can make proposals to develop new products to suit the time periods when specific products are selling well. The proposal unit can also propose product lineups that match the hobbies and tastes of customers. For example, the proposal unit can propose new product ideas based on the preferences of customers. The proposal unit can also propose improvements to existing products based on sales data. For example, the proposal unit can propose improvements to products with low sales. In this way, by making product development proposals based on the analysis results, an increase in sales can be expected.

[0080] The inventory management unit monitors the inventory status in real time based on sales data and can issue an alert if there is a possibility of inventory shortage. Examples of real-time monitoring include, but are not limited to, monitoring tools and monitoring frequencies. For example, the inventory management unit monitors the inventory status in real time using a monitoring tool. The inventory management unit can also monitor the inventory status by adjusting the monitoring frequency. For example, the inventory management unit adjusts the monitoring frequency based on sales data. Examples of alerts include, but are not limited to, inventory thresholds and notification means. For example, the inventory management unit sets an inventory threshold and issues an alert when inventory falls below that threshold. The inventory management unit can also issue an alert using notification means. For example, the inventory management unit notifies the alert via email or SMS. This allows inventory status to be monitored in real time and prevents stockouts.

[0081] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. Estimating emotions includes, but is not limited to, facial expression recognition and voice analysis. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The timing of data collection includes, but is not limited to, collection frequency and collection time period. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the collection unit can shorten the timing of data collection and quickly acquire data. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions.

[0082] The collection unit can optimize the frequency of data collection based on the frequency of use of the electronic payment system. Examples of the frequency of data collection include, but are not limited to, adjustment methods based on the frequency of use. For example, the collection unit can increase the frequency of data collection during times when the electronic payment system is heavily used. The collection unit can also decrease the frequency of data collection during times when the electronic payment system is less used. For example, the collection unit dynamically adjusts the timing of data collection according to the frequency of use of the electronic payment system. By optimizing the frequency of data collection according to the frequency of use of the electronic payment system, efficient data collection is possible.

[0083] The collection unit can prioritize collecting data during specific time periods or days of the week. Examples of specific time periods or days of the week include, but are not limited to, peak hours and weekends. The collection unit can concentrate data collection during times when there are many customers, such as weekends and holidays. The collection unit can also prioritize collecting data during specific time periods on weekdays. For example, the collection unit can concentrate data collection during specific time periods or days of the week depending on the season or events. This allows important data to be collected efficiently by prioritizing the collection of data during specific time periods or days of the week.

[0084] The collection unit can select data to be collected based on the user's purchase history or store visit history. The purchase history includes, for example, but is not limited to, purchase date and time, purchased items, etc. The collection unit, for example, preferentially collects relevant data based on the user's past purchase history. The store visit history includes, for example, but is not limited to, store visit date and time, store visit frequency, etc. The collection unit can, for example, analyze the user's store visit history and preferentially collect data on frequently visited stores. For example, the collection unit combines the user's purchase history and store visit history to collect the most relevant data. This makes it possible to collect highly relevant data by taking the user's purchase history and store visit history into consideration.

[0085] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. Estimating emotions includes, but is not limited to, facial expression recognition and voice analysis. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotion. Data prioritization includes, but is not limited to, importance and urgency. For example, when the user is excited, the collection unit can prioritize collecting highly relevant data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. For example, when the user is stressed, the collection unit prioritizes collecting only important data. Thus, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially.

[0086] The collection unit can prioritize collection of highly relevant data based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, location information services, etc. For example, when the user is in a specific area, the collection unit prioritizes collection of data related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collection of data related to the area to which the user is traveling. For example, the collection unit collects the most relevant data based on the user's geographical location information. This allows for efficient collection of highly relevant data by taking the user's geographical location information into consideration.

[0087] The collection unit can analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. The collection unit, for example, collects data related to the places where the user checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activity.

[0088] The collection unit can customize the collection method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, survey results and reviews. The collection unit can adjust the data collection method, for example, based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and propose an optimal data collection method. For example, the collection unit can reflect the user's feedback to improve the accuracy of data collection. In this way, the optimal data collection method can be realized by reflecting the user's past feedback.

[0089] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. Estimating the emotion includes, but is not limited to, facial expression recognition, voice analysis, and the like. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit can estimate the emotion by analyzing the tone and speed of the user's voice. The presentation method of the analysis includes, but is not limited to, types of graphs, report formats, and the like. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. For example, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows the analysis presentation method to be adjusted according to the user's emotion, thereby providing optimal analysis results for the user.

[0090] The analysis unit can adjust the level of detail of the analysis based on sales data of a specific product. Sales data includes, for example, sales amount, sales quantity, etc., but is not limited to such examples. For example, the analysis unit analyzes data of products with high sales in detail. The analysis unit can also analyze data of products with low sales in a simplified manner. For example, the analysis unit dynamically adjusts the level of detail of the analysis based on the sales data. The level of detail of the analysis includes, for example, analysis depth, analysis items, etc., but is not limited to such examples. As a result, by adjusting the level of detail of the analysis based on sales data of a specific product, important products can be analyzed in detail.

[0091] The analysis unit can apply different analysis algorithms based on the attribute information of customers. Attribute information includes, but is not limited to, age, gender, and occupation. For example, the analysis unit applies different analysis algorithms to different age groups. The analysis unit can also apply different analysis algorithms to different genders. For example, the analysis unit selects an optimal analysis algorithm based on the attribute information of customers. Analysis algorithms include, but are not limited to, regression analysis and clustering. This improves the accuracy of the analysis by applying the optimal analysis algorithm based on the attribute information of customers.

[0092] The analysis unit can improve the accuracy of the analysis by referring to past analysis results. Past analysis results include, but are not limited to, past data sets and analysis reports, for example. The analysis unit, for example, adjusts the analysis algorithm based on the past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. For example, the analysis unit analyzes the past analysis results and proposes an optimal analysis method. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0093] The analysis unit can estimate the user's emotions and determine an analysis priority based on the estimated user's emotions. Estimating emotions includes, but is not limited to, facial expression recognition and voice analysis. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. Analysis priorities include, but are not limited to, importance and urgency. For example, if the user is excited, the analysis unit can prioritize analyzing important data. Also, if the user is relaxed, the analysis unit can prioritize analyzing detailed data. For example, if the user is stressed, the analysis unit can prioritize analyzing concise data. Thus, by determining the analysis priority according to the user's emotions, important data can be prioritized.

[0094] The analysis unit can prioritize analyzing data from specific time periods or days of the week. Examples of specific time periods or days of the week include, but are not limited to, peak hours and weekends. The analysis unit prioritizes analyzing data from time periods with a high number of customers, such as weekends and holidays. The analysis unit can also prioritize analyzing data from specific time periods on weekdays. For example, the analysis unit prioritizes analyzing data from specific time periods or days of the week depending on the season or an event. This allows important data to be analyzed efficiently by prioritizing analysis of data from specific time periods or days of the week.

[0095] The analysis unit can improve the accuracy of the analysis by referring to data on neighboring businesses in the same industry. Data on neighboring businesses in the same industry includes, but is not limited to, sales data on competitors and industry reports, for example. The analysis unit performs analysis by referring to, for example, sales data on competitors. The analysis unit can also perform analysis by referring to industry reports. For example, the analysis unit compares with neighboring businesses in the same industry based on industry reports. By referring to data on neighboring businesses in the same industry, the accuracy of the analysis can be improved.

[0096] The analysis unit can perform analysis based on the market value of a specific product. Market value includes, but is not limited to, market price, demand forecast, and the like. For example, the analysis unit performs detailed analysis of data on products with high market value. The analysis unit can also perform simplified analysis of data on products with low market value. For example, the analysis unit dynamically adjusts the level of detail of the analysis based on the market value. This allows for detailed analysis of important products by taking into account the market value of the specific product.

[0097] The visualization unit can estimate the user's emotion and adjust the visualization display method based on the estimated user's emotion. Estimating the emotion includes, but is not limited to, facial expression recognition, voice analysis, and the like. For example, the visualization unit estimates the user's emotion using facial expression recognition technology. The visualization unit can also estimate the user's emotion using voice analysis technology. For example, the visualization unit can estimate the emotion by analyzing the tone and speed of the user's voice. The visualization display method includes, but is not limited to, the type of graph, display format, and the like. For example, the visualization unit can display detailed graphs and charts when the user is relaxed. Furthermore, the visualization unit can display concise graphs and charts that focus on the main points when the user is in a hurry. For example, the visualization unit can display graphs and charts with visually stimulating effects when the user is excited. This allows the visualization display method to be adjusted according to the user's emotion, thereby providing an optimal display for the user.

[0098] The visualization unit can adjust the level of detail of the display based on sales data of a specific product. Sales data includes, for example, sales amount, sales quantity, etc., but is not limited to such examples. For example, the visualization unit displays data of products with high sales in detail. The visualization unit can also display data of products with low sales in a simplified manner. For example, the visualization unit dynamically adjusts the level of detail of the display based on the sales data. Examples of the level of detail of the display include, but are not limited to such examples. In this way, by adjusting the level of detail of the display based on the sales data of a specific product, data of important products can be displayed in detail.

[0099] The visualization unit can apply different display methods based on the attribute information of customers. Attribute information includes, but is not limited to, age, gender, and occupation. For example, the visualization unit can apply different display methods for different age groups. The visualization unit can also apply different display methods for different genders. For example, the visualization unit selects the optimal display method based on the attribute information of customers. Display methods include, but are not limited to, graph display and table display. This improves the accuracy of the display by applying the optimal display method based on the attribute information of customers.

[0100] The visualization unit can improve the accuracy of the display by referring to past visualization results. Past visualization results include, but are not limited to, past graphs, display reports, and the like. For example, the visualization unit adjusts the display method based on the past visualization results. The visualization unit can also improve the accuracy of the display by referring to the past visualization results. For example, the visualization unit analyzes the past visualization results and proposes an optimal display method. In this way, the accuracy of the display is improved by referring to the past visualization results.

[0101] The visualization unit can estimate the user's emotions and determine a visualization priority based on the estimated user's emotions. Estimating emotions includes, but is not limited to, facial expression recognition and voice analysis. For example, the visualization unit can estimate the user's emotions using facial expression recognition technology. The visualization unit can also estimate the user's emotions using voice analysis technology. For example, the visualization unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The visualization unit can prioritize visualization based on, but is not limited to, importance and urgency. For example, if the user is excited, the visualization unit can prioritize displaying important data. For example, if the user is relaxed, the visualization unit can prioritize displaying detailed data. For example, if the user is stressed, the visualization unit can prioritize displaying concise data. Thus, by determining the visualization priority based on the user's emotions, important data can be prioritized.

[0102] The visualization unit can prioritize displaying data for specific time periods or days of the week. Examples of specific time periods or days of the week include, but are not limited to, peak hours and weekends. The visualization unit can prioritize displaying data for time periods with a high number of customers, such as weekends and holidays. The visualization unit can also prioritize displaying data for specific time periods on weekdays. For example, the visualization unit can prioritize displaying data for specific time periods or days of the week depending on the season or an event. This allows important data to be displayed efficiently by prioritized display of data for specific time periods or days of the week.

[0103] The visualization unit can improve the accuracy of the display by referring to data on neighboring businesses in the same industry. Data on neighboring businesses in the same industry includes, but is not limited to, sales data on competitors and industry reports, for example. The visualization unit performs the display by referring to, for example, the sales data of competitors. The visualization unit can also perform the display by referring to industry reports. For example, the visualization unit compares with neighboring businesses in the same industry based on industry reports. By referring to data on neighboring businesses in the same industry, the accuracy of the display can be improved.

[0104] The visualization unit can display data based on the market value of a specific product. Market value includes, but is not limited to, market price, demand forecast, and the like. For example, the visualization unit displays data for products with high market value in detail. The visualization unit can also display data for products with low market value in a simplified manner. For example, the visualization unit dynamically adjusts the level of detail of the display based on the market value. This allows important product data to be displayed in detail by taking into account the market value of the specific product.

[0105] The suggestion unit can estimate the user's emotion and adjust the way in which the suggestion is presented based on the estimated user's emotion. Estimating the emotion includes, but is not limited to, facial expression recognition, voice analysis, and the like. For example, the suggestion unit can estimate the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit can estimate the emotion by analyzing the tone and speed of the user's voice. The way in which the suggestion is presented includes, but is not limited to, the form and content of the suggestion. For example, the suggestion unit can provide a detailed suggestion when the user is relaxed. Furthermore, the suggestion unit can provide a concise suggestion that focuses on the main points when the user is in a hurry. For example, the suggestion unit can provide a visually stimulating suggestion when the user is excited. This allows the way in which the suggestion is presented to be adjusted according to the user's emotion, thereby enabling the user to receive the most appropriate suggestion.

[0106] The proposal unit can adjust the level of detail of the proposal based on sales data of a specific product. Sales data includes, for example, sales amount, sales quantity, etc., but is not limited to such examples. For example, the proposal unit makes detailed proposals for products with high sales. The proposal unit can also make simplified proposals for products with low sales. For example, the proposal unit dynamically adjusts the level of detail of the proposal based on the sales data. The level of detail of the proposal includes, for example, depth of the proposal, proposed items, etc., but is not limited to such examples. As a result, by adjusting the level of detail of the proposal based on the sales data of a specific product, important products can be proposed in detail.

[0107] The suggestion unit can apply different suggestion algorithms based on the attribute information of the customer. The attribute information includes, but is not limited to, for example, age, gender, occupation, etc. The suggestion unit can apply different suggestion algorithms for different age groups, for example. The suggestion unit can also apply different suggestion algorithms for different genders. For example, the suggestion unit selects an optimal suggestion algorithm based on the customer's attribute information. The suggestion algorithm includes, but is not limited to, for example, a recommendation system, a machine learning algorithm, etc. This improves the accuracy of suggestions by applying the optimal suggestion algorithm based on the customer's attribute information.

[0108] The proposal unit can improve the accuracy of proposals by referring to past proposal results. Past proposal results include, for example, past proposal content, proposal success rates, etc., but are not limited to these examples. The proposal unit, for example, adjusts the proposal algorithm based on the past proposal results. The proposal unit can also improve the accuracy of proposals by referring to the past proposal results. For example, the proposal unit analyzes the past proposal results and proposes an optimal proposal method. In this way, the accuracy of proposals is improved by referring to the past proposal results.

[0109] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. Estimating the emotion can include, but is not limited to, facial expression recognition, voice analysis, and the like. The suggestion unit can estimate the user's emotion using, for example, facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit can estimate the emotion by analyzing the tone and speed of the user's voice. The priority of suggestions can include, but is not limited to, importance, urgency, and the like. For example, if the user is excited, the suggestion unit can prioritize important suggestions. Also, if the user is relaxed, the suggestion unit can prioritize detailed suggestions. For example, if the user is stressed, the suggestion unit can prioritize concise suggestions. Thus, by determining the priority of suggestions according to the user's emotion, important suggestions can be prioritized.

[0110] The suggestion unit can prioritize suggesting data for specific time periods or days of the week. Examples of specific time periods or days of the week include, but are not limited to, peak hours and weekends. For example, the suggestion unit prioritizes suggesting data for time periods with many customers, such as weekends and holidays. The suggestion unit can also prioritize suggesting data for specific time periods on weekdays. For example, the suggestion unit prioritizes suggesting data for specific time periods or days of the week depending on the season or an event. In this way, important data can be efficiently suggested by preferentially suggesting data for specific time periods or days of the week.

[0111] The proposal unit can improve the accuracy of proposals by referring to data on neighboring businesses in the same industry. Data on neighboring businesses in the same industry includes, but is not limited to, sales data on competitors and industry reports, for example. The proposal unit makes proposals by referring to, for example, the sales data of competitors. The proposal unit can also make proposals by referring to industry reports. For example, the proposal unit compares with neighboring businesses in the same industry based on industry reports. By referring to data on neighboring businesses in the same industry, the accuracy of proposals can be improved.

[0112] The proposal unit can make proposals based on the market value of a specific product. Market value includes, but is not limited to, market price, demand forecast, and the like. For example, the proposal unit makes detailed proposals for products with high market value. The proposal unit can also make simplified proposals for products with low market value. For example, the proposal unit dynamically adjusts the level of detail of the proposal based on the market value. This allows for detailed proposals for important products by taking into account the market value of the specific product.

[0113] The inventory management unit can estimate the user's emotions and adjust the inventory management method based on the estimated user's emotions. Estimating emotions includes, but is not limited to, facial expression recognition, voice analysis, and the like. For example, the inventory management unit estimates the user's emotions using facial expression recognition technology. The inventory management unit can also estimate the user's emotions using voice analysis technology. For example, the inventory management unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. Inventory management methods include, but are not limited to, inventory monitoring methods and management standards. For example, the inventory management unit can perform detailed inventory management when the user is relaxed. Furthermore, the inventory management unit can perform concise inventory management that focuses on the main points when the user is in a hurry. For example, the inventory management unit can perform visually stimulating inventory management when the user is excited. This allows the inventory management method to be adjusted according to the user's emotions, thereby enabling optimal inventory management for the user.

[0114] The inventory management unit can adjust the level of detail of inventory management based on sales data of specific products. Sales data includes, for example, sales amount, sales quantity, etc., but is not limited to such examples. For example, the inventory management unit manages inventory of products with high sales in detail. The inventory management unit can also manage inventory of products with low sales in a simplified manner. For example, the inventory management unit dynamically adjusts the level of detail of inventory management based on sales data. The level of detail of inventory management includes, for example, management depth, management items, etc., but is not limited to such examples. In this way, by adjusting the level of detail of inventory management based on sales data of specific products, inventory of important products can be managed in detail.

[0115] The inventory management unit can apply different inventory management algorithms based on the attribute information of customers. Attribute information includes, but is not limited to, age, gender, and occupation. For example, the inventory management unit applies different inventory management algorithms for different age groups. The inventory management unit can also apply different inventory management algorithms for different genders. For example, the inventory management unit selects an optimal inventory management algorithm based on the attribute information of customers. Inventory management algorithms include, but are not limited to, demand forecasting algorithms and optimization algorithms. This improves the accuracy of inventory management by applying the optimal inventory management algorithm based on the attribute information of customers.

[0116] The inventory management department can improve the accuracy of inventory management by referring to past inventory management results. Past inventory management results include, but are not limited to, past inventory data, management reports, etc. For example, the inventory management department adjusts the inventory management algorithm based on the past inventory management results. The inventory management department can also improve the accuracy of inventory management by referring to the past inventory management results. For example, the inventory management department analyzes the past inventory management results and proposes an optimal inventory management method. In this way, the accuracy of inventory management is improved by referring to the past inventory management results.

[0117] The inventory management unit can estimate the user's emotions and determine inventory management priorities based on the estimated user emotions. Estimating emotions includes, but is not limited to, facial expression recognition and voice analysis. For example, the inventory management unit can estimate the user's emotions using facial expression recognition technology. The inventory management unit can also estimate the user's emotions using voice analysis technology. For example, the inventory management unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. Inventory management priorities include, but are not limited to, importance and urgency. For example, if the user is excited, the inventory management unit can prioritize inventory management of important items. For example, if the user is relaxed, the inventory management unit can prioritize detailed inventory management. For example, if the user is stressed, the inventory management unit can prioritize simple inventory management. In this way, by determining inventory management priorities according to the user's emotions, it is possible to prioritize inventory management of important items.

[0118] The inventory management unit can prioritize managing data during specific time periods or days of the week. Examples of specific time periods or days of the week include, but are not limited to, peak hours and weekends. The inventory management unit prioritizes managing inventory during times when there are many customers, such as weekends and holidays. The inventory management unit can also prioritize managing inventory during specific time periods on weekdays. For example, the inventory management unit prioritizes managing inventory during specific time periods or days of the week depending on the season or events. This allows important data to be managed efficiently by prioritizing management of data during specific time periods or days of the week.

[0119] The inventory management unit can improve the accuracy of inventory management by referring to data from neighboring businesses in the same industry. Data from neighboring businesses in the same industry includes, but is not limited to, sales data from competitors and industry reports. For example, the inventory management unit performs inventory management by referring to the sales data from competitors. The inventory management unit can also perform inventory management by referring to industry reports. For example, the inventory management unit compares with neighboring businesses in the same industry based on industry reports. In this way, the accuracy of inventory management is improved by referring to data from neighboring businesses in the same industry.

[0120] The inventory management unit can manage inventory based on the market value of specific products. Market values ​​include, but are not limited to, market prices, demand forecasts, and the like. For example, the inventory management unit manages inventory of products with high market values ​​in detail. The inventory management unit can also manage inventory of products with low market values ​​in a simplified manner. For example, the inventory management unit dynamically adjusts the level of detail of inventory management based on market values. This allows for detailed inventory management of important products by taking into account the market values ​​of specific products. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, visualization unit, proposal unit, and inventory management 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 is realized by the control unit 46A of the smart device 14 and collects sales data and customer data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The visualization unit is realized by the control unit 46A of the smart device 14 and displays the analysis results in graphs and charts. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes product development proposals based on the analysis results. The inventory management unit is realized by the control unit 46A of the smart device 14 and monitors inventory status in real time and issues alerts. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, visualization unit, proposal unit, and inventory management 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 is realized by the control unit 46A of the smart glasses 214 and collects sales data and customer data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The visualization unit is realized by the control unit 46A of the smart glasses 214 and displays the analysis results in graphs and charts. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes product development proposals based on the analysis results. The inventory management unit is realized by the control unit 46A of the smart glasses 214 and monitors the inventory status in real time and issues alerts. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, visualization unit, proposal unit, and inventory management 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 is realized by the control unit 46A of the headset terminal 314 and collects sales data and customer data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The visualization unit is realized by the control unit 46A of the headset terminal 314 and displays the analysis results in graphs and charts. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes product development proposals based on the analysis results. The inventory management unit is realized by the control unit 46A of the headset terminal 314 and monitors the inventory status in real time and issues alerts. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, visualization unit, proposal unit, and inventory management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects sales data and customer data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The visualization unit is realized, for example, by the control unit 46A of the robot 414 and displays the analysis results in graphs and charts. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes product development proposals based on the analysis results. The inventory management unit is realized, for example, by the control unit 46A of the robot 414 and monitors the inventory status in real time and issues alerts.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The collection unit can analyze the purchase frequency of specific products based on the user's purchase history and prioritize collecting data on products with high purchase frequencies. For example, the collection unit prioritizes collecting data on products frequently purchased by the user and performs detailed analysis. The collection unit can also collect data on related products based on the user's purchase history. For example, if the user purchases many products from a specific category, the collection unit collects data on products related to that category. This makes it possible to efficiently collect highly relevant data based on the user's purchase history.

[0123] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is excited, the analysis unit prioritizes analyzing important data. The analysis unit can also prioritize analyzing detailed data if the user is relaxed. For example, if the user is stressed, the analysis unit prioritizes analyzing concise data. In this way, by determining the analysis priority according to the user's emotions, important data can be analyzed preferentially.

[0124] The visualization unit can adjust the level of detail of the display based on sales data of a specific product. For example, the visualization unit can display data of products with high sales in detail. The visualization unit can also display data of products with low sales in a simplified manner. For example, the visualization unit dynamically adjusts the level of detail of the display based on the sales data. In this way, by adjusting the level of detail of the display based on sales data of a specific product, data of important products can be displayed in detail.

[0125] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, the suggestion unit can make detailed suggestions when the user is relaxed. Also, the suggestion unit can make concise suggestions that focus on the main points when the user is in a hurry. For example, the suggestion unit can make visually stimulating suggestions when the user is excited. In this way, by adjusting the way suggestions are expressed according to the user's emotions, it is possible to provide optimal suggestions for the user.

[0126] The inventory management unit can adjust the level of detail of inventory management based on sales data for specific products. For example, the inventory management unit manages inventory for high-sales products in detail. The inventory management unit can also manage inventory for low-sales products in a simplified manner. For example, the inventory management unit dynamically adjusts the level of detail of inventory management based on sales data. In this way, by adjusting the level of detail of inventory management based on sales data for specific products, it is possible to manage inventory for important products in detail.

[0127] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting data related to the area to which the user is traveling. For example, the collection unit collects the most relevant data based on the user's geographical location information. This allows for efficient collection of highly relevant data by taking the user's geographical location information into consideration.

[0128] The analysis unit can perform analysis based on the market value of a specific product. For example, the analysis unit can perform a detailed analysis of data on products with high market values. The analysis unit can also perform a simplified analysis of data on products with low market values. For example, the analysis unit can dynamically adjust the level of detail of the analysis based on the market value. This allows for detailed analysis of important products by taking into account the market value of the specific product.

[0129] The visualization unit can estimate the user's emotions and adjust the visualization display method based on the estimated user's emotions. For example, if the user is relaxed, the visualization unit can display detailed graphs and charts. Alternatively, if the user is in a hurry, the visualization unit can display concise graphs and charts that focus on the main points. For example, if the user is excited, the visualization unit can display graphs and charts with visually stimulating effects. In this way, by adjusting the visualization display method according to the user's emotions, it is possible to provide an optimal display for the user.

[0130] The suggestion unit can prioritize suggesting data for specific time periods or days of the week. For example, the suggestion unit prioritizes suggesting data for time periods with a high number of customers, such as weekends and holidays. The suggestion unit can also prioritize suggesting data for specific time periods on weekdays. For example, the suggestion unit prioritizes suggesting data for specific time periods or days of the week depending on the season or an event. In this way, important data can be efficiently suggested by preferentially suggesting data for specific time periods or days of the week.

[0131] The inventory management unit can estimate the user's emotions and adjust the inventory management method based on the estimated user's emotions. For example, if the user is relaxed, the inventory management unit performs detailed inventory management. Also, if the user is in a hurry, the inventory management unit can perform simple inventory management that focuses on the main points. For example, if the user is excited, the inventory management unit performs visually stimulating inventory management. In this way, by adjusting the inventory management method according to the user's emotions, optimal inventory management for the user is possible.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The collection unit collects data. The data includes sales data, customer data, inventory data, etc. The collection unit automatically collects data through the electronic payment system and can adjust the frequency and means of data collection. For example, the frequency of data collection can be optimized based on the frequency of use of the electronic payment system. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis involves analyzing the hobbies and tastes of customers and trends in popular products, and comparing them with other products in the same industry. For example, it analyzes preferences based on customers' purchase history and survey results, and analyzes trends in popular products based on sales data and sales periods. Step 3: The visualization unit visualizes the analysis results obtained by the analysis unit. Visualization visually displays the analysis results using graphs and charts. For example, data is displayed using bar graphs, pie charts, line graphs, etc. Step 4: The proposal department makes proposals based on the data visualized by the visualization department. Based on the analysis results, the proposals are product development proposals that will lead to increased sales for the store. For example, they propose ideas for new products and improvements. Step 5: The inventory management department manages inventory based on the proposals made by the proposal department. Inventory management monitors the inventory status in real time based on sales data and issues an alert if inventory is about to run low. For example, an inventory threshold can be set and an alert issued if inventory falls below that threshold.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[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 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.

[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 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.

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

[0152] 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.

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 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 data; an analysis unit that analyzes the data collected by the collection unit; a visualization unit that visualizes the analysis results obtained by the analysis unit; a suggestion unit that makes a suggestion based on the data visualized by the visualization unit; an inventory management unit that manages inventory based on the content proposed by the proposal unit; A system characterized by:

2. The collecting unit Collecting data through electronic payment systems 2. The system of claim 1.

3. The analysis unit Analyze customer preferences and trends in popular products, and compare them with data from nearby businesses in the same industry 2. The system of claim 1.

4. The visualization unit Visually display analysis results using graphs and charts 2. The system of claim 1.

5. The proposal unit Based on the analysis results, make product development proposals that will lead to increased sales at your store.

2. The system of claim 1.

6. The inventory management unit Monitor inventory status in real time based on sales data and issue alerts when inventory is running low 2. The system of claim 1.

7. 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.

8. The collecting unit Optimize the frequency of data collection based on the frequency of use of electronic payment systems 2. The system of claim 1.

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