system

Abstract

The system according to the embodiment aims to analyze POS data and inventory data and propose optimal order quantities and items. [Solution] The system according to the embodiment includes a data acquisition unit, a counting unit, a storage unit, an analysis unit, a calculation unit, and a proposal unit. The data acquisition unit acquires POS data and inventory data. The counting unit aggregates the data acquired by the data acquisition unit. The storage unit stores the data aggregated by the counting unit. The analysis unit analyzes the data accumulated by the storage unit. The calculation unit calculates the order quantity based on the data analyzed by the analysis unit. The proposal unit proposes the order quantity and items based on the order quantity calculated by the calculation unit and the latest trends.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to effectively utilize POS data and inventory data to propose optimal order quantities and items, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze POS data and inventory data and propose optimal order quantities and items. [Means for solving the problem]

[0006] The system according to the embodiment includes a data acquisition unit, a counting unit, a storage unit, an analysis unit, a calculation unit, and a proposal unit. The data acquisition unit acquires POS data and inventory data. The counting unit aggregates the data acquired by the data acquisition unit. The storage unit stores the data aggregated by the counting unit. The analysis unit analyzes the data stored by the storage unit. The calculation unit calculates the order quantity based on the data analyzed by the analysis unit. The proposal unit proposes the order quantity and items based on the order quantity calculated by the calculation unit and the latest trends. [Effects of the Invention]

[0007] The system according to the embodiment can analyze POS data and inventory data to propose optimal order quantities and items. [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 inventory management system according to an embodiment of the present invention incorporates POS data and inventory data into a generation AI, aggregates sales and inventory items and quantities on a daily and weekly basis, stores the data in historical data, and then calculates and proposes optimal order quantities and items (latest products) taking into account the latest trends. This inventory management system incorporates POS data and inventory data into a generation AI, aggregates sales items and quantities, and inventory items and quantities on a daily and weekly basis. The aggregated data is then stored as historical data, and the generation AI analyzes sales and inventory trends. Furthermore, the system calculates optimal order quantities based on the analysis results and proposes new and popular products taking into account the latest trends. This system enables efficient inventory management and prevents excess inventory and stockouts. Furthermore, product proposals that reflect the latest trends can be expected to increase sales. For example, by incorporating POS data and inventory data into a generation AI, sales items and quantities, and inventory items and quantities on a daily and weekly basis can be aggregated. For example, the system aggregates the quantity of product A sold on Monday and the quantity of product B received on Tuesday. The aggregated data is then stored as historical data. The generation AI analyzes sales trends and inventory trends based on the accumulated data. For example, it analyzes trends such as the tendency for a particular product to be sold in large quantities on a particular day of the week or the tendency for a particular product to be received in large quantities during a particular period. The generation AI then calculates the optimal order quantity based on the analysis results. For example, it calculates the number of product A needed to be ordered for the following Monday based on past sales data. It also takes into account the latest trends to suggest new and popular products. For example, it suggests ordering new product C based on recent trends. This system enables efficient inventory management and prevents overstocking and stockouts. Furthermore, product suggestions that reflect the latest trends are expected to increase sales. This allows the inventory management system to achieve efficient inventory management and prevent overstocking and stockouts. Furthermore, product suggestions that reflect the latest trends are expected to increase sales.

[0029] The inventory management system according to the embodiment includes a data acquisition unit, a compilation unit, a storage unit, an analysis unit, a calculation unit, and a proposal unit. The data acquisition unit acquires POS data and inventory data. The POS data includes, but is not limited to, sales data, customer data, and product data. The inventory data includes, but is not limited to, inventory data, shipping data, and receiving data. The data acquisition unit can acquire data directly from a POS system, for example. The data acquisition unit can also acquire data from an inventory management system. The data acquisition unit can also acquire data from an external system via an API. The compilation unit compiles sales items and quantities, and inventory items and quantities, on a daily and day-of-the-week basis. The compilation unit can, for example, compile daily sales data to understand sales trends by day of the week. The compilation unit can also compile inventory data to understand inventory trends. For example, the compilation unit can compile the quantity of product A sold on Monday and the quantity of product B received on Tuesday. The storage unit stores the aggregated data as past data. The storage unit can, for example, store the data in a database. The storage unit can also store the data in cloud storage. The storage unit can also back up the data. The analysis unit analyzes sales trends and inventory trends based on the stored data. The analysis unit can, for example, analyze the tendency for a specific product to be sold in large quantities on a specific day of the week. The analysis unit can also analyze the tendency for a specific product to be stocked in large quantities during a specific period. For example, the analysis unit calculates the number of product A to be ordered next Monday based on past sales data. The calculation unit calculates the order quantity based on the analysis results. The calculation unit can, for example, calculate the number of product A to be ordered next Monday based on past sales data. The calculation unit can also suggest new products or popular products taking into account the latest trends. For example, the calculation unit suggests ordering new product C based on recent trends. The suggestion unit suggests new products and popular products based on the latest trends. The suggestion unit can grasp the latest trends based on social media data and industry reports, for example.The suggestion unit can also suggest popular products based on the results of consumer surveys. This allows the inventory management system according to the embodiment to efficiently import POS data and inventory data, and aggregate, accumulate, analyze, calculate, and suggest products.

[0030] The data acquisition unit can acquire POS data and inventory data. The data acquisition unit can acquire data directly from a POS system, for example. The data acquisition unit can also acquire data from an inventory management system. The data acquisition unit can also acquire data from an external system via an API. POS data includes, but is not limited to, sales data, customer data, and product data. Inventory data includes, but is not limited to, inventory data, shipping data, and receiving data. This allows POS data and inventory data to be acquired efficiently.

[0031] The tallying unit can tally sales items and quantities, and incoming and outgoing items and quantities on a daily and day-of-the-week basis. The tallying unit can, for example, tally daily sales data to grasp sales trends by day of the week. The tallying unit can also tally incoming and outgoing data to grasp inventory trends. For example, the tallying unit tally the quantity of product A sold on Monday and the quantity of product B received on Tuesday. Daily and day-of-the-week units include, but are not limited to, calendar days, business days, and weekends. Sales items and quantities include, but are not limited to, product categories, SKUs, and sales units. Incoming and outgoing items and quantities include, but are not limited to, product categories, SKUs, and ingoing and outgoing units. This allows for efficient tallying of sales items and quantities, and incoming and outgoing items and quantities on a daily and day-of-the-week basis.

[0032] The storage unit can store the aggregated data as past data. The storage unit can store the data in a database, for example. The storage unit can also store the data in cloud storage. Furthermore, the storage unit can back up the data. Past data includes, for example, data from the past year, data from a specific period, etc., but is not limited to these examples. This allows the aggregated data to be stored efficiently.

[0033] The analysis unit can analyze sales trends and inventory trends based on the accumulated data. For example, the analysis unit can analyze the tendency for a particular product to be sold in large quantities on a particular day of the week. The analysis unit can also analyze the tendency for a particular product to be stocked in large quantities during a particular period. Sales trends include, but are not limited to, increases or decreases in sales, seasonality, and promotion effects. Stock-in / stock-out trends include, but are not limited to, inventory turnover, stock-in / stock-out frequency, and supply chain patterns. This allows for efficient analysis of sales trends and inventory-in / stock-out trends based on the accumulated data.

[0034] The calculation unit can calculate the order quantity based on the analysis results. For example, the calculation unit can calculate the order quantity of product A needed for the following Monday based on past sales data. The calculation unit can also suggest the latest products or popular products by taking the latest trends into account. The order quantity includes, but is not limited to, past sales data, inventory levels, demand forecasts, etc., for example. This makes it possible to efficiently calculate the optimal order quantity based on the analysis results.

[0035] The suggestion unit can suggest the latest products and popular products based on the latest trends. The suggestion unit can grasp the latest trends based on, for example, social media data and industry reports. The suggestion unit can also suggest popular products based on the results of consumer surveys. Examples of the latest trends include, but are not limited to, social media data, industry reports, consumer surveys, etc. Examples of the latest products and popular products include, but are not limited to, newly released products and top-selling products. This allows the latest products and popular products to be efficiently suggested taking the latest trends into account.

[0036] The data import unit can analyze past data import history and select an import method. For example, the data import unit can identify the most efficient import time period from the past data import history and import data during that time period. The data import unit can also select the optimal import method for a specific day of the week or time period based on the past data import history. Furthermore, the data import unit can analyze the past data import history to find improvements to the import method and optimize it. Past data import history includes, for example, history from the past year or history from a specific period, but is not limited to these examples. Import methods include, for example, API integration, file upload, manual input, etc., but are not limited to these examples. In this way, the optimal import method can be selected by analyzing the past data import history.

[0037] The data acquisition unit can acquire data based on specific events and campaign periods. For example, the data acquisition unit prioritizes acquiring related data during specific event periods. The data acquisition unit can also prioritize acquiring data on products related to campaigns during campaign periods. Furthermore, the data acquisition unit can identify data affected by events or campaigns and prioritize acquiring that data. Examples of specific event and campaign periods include, but are not limited to, sales periods, promotional events, and seasonal campaigns. This allows related data to be acquired efficiently by taking specific events and campaign periods into consideration.

[0038] When importing data, the data import unit can prioritize importing highly relevant data by taking geographical sales data into consideration. The data import unit, for example, prioritizes importing sales data from a specific region. The data import unit can also prioritize importing data that is highly geographically relevant. Furthermore, the data import unit can prioritize importing related data by taking into consideration sales trends for each region. Geographical sales data includes, for example, sales data by region and sales data by store, but is not limited to such examples. Highly relevant data includes, for example, products unique to a region, seasonal products, etc. This allows highly relevant data to be efficiently imported by taking geographical sales data into consideration.

[0039] The data acquisition unit can analyze social media trends and acquire related data when acquiring data. For example, the data acquisition unit prioritizes acquiring data related to products that are trending on social media. The data acquisition unit can also acquire related data based on trends. Furthermore, the data acquisition unit can analyze social media trends and acquire related data prioritized. Social media trends include, for example, hashtag frequency, post engagement, etc., but are not limited to such examples. Related data include, for example, product data related to the trend, consumer feedback, etc., but are not limited to such examples. In this way, related data can be efficiently acquired by analyzing social media trends.

[0040] The aggregation unit can adjust the level of detail of the aggregation based on the importance of the product when aggregating. For example, the aggregation unit performs detailed aggregation for products with high importance. The aggregation unit can also perform simplified aggregation for products with low importance. Furthermore, the aggregation unit can adjust the level of detail of the aggregation depending on the importance of the product. Examples of the importance of the product include, but are not limited to, sales revenue, profit margins, inventory turnover, etc. Examples of the level of detail of the aggregation include, but are not limited to, the number of detailed items, the granularity of the aggregation, etc. In this way, by adjusting the level of detail of the aggregation based on the importance of the product, detailed aggregation of important products can be performed.

[0041] The aggregation unit can apply different aggregation algorithms to specific categories and brands when aggregating. For example, the aggregation unit applies the optimal aggregation algorithm to each category. The aggregation unit can also apply different aggregation algorithms to each brand. Furthermore, the aggregation unit can select the optimal aggregation algorithm for a specific category or brand. Examples of specific categories and brands include, but are not limited to, product categories and brand names. Examples of different aggregation algorithms include, but are not limited to, weighted averages, moving averages, and regression analysis. This allows the accuracy of aggregation to be improved by applying the optimal aggregation algorithm to each specific category or brand.

[0042] The tallying unit can determine the priority of tallying based on the time of data submission during tallying. For example, the tallying unit prioritizes data submitted earlier. The tallying unit can also postpone tallying data submitted later. Furthermore, the tallying unit can determine the priority of tallying based on the time of data submission. Examples of data submission times include, but are not limited to, submission deadlines and submission frequency. Examples of tallying priorities include, but are not limited to, data importance, urgency, and submission time. In this way, by determining the priority of tallying based on the time of data submission, efficient tallying can be performed.

[0043] The aggregation unit can improve the accuracy of aggregation by referring to related market data when aggregating. For example, the aggregation unit can improve the accuracy of aggregation by referring to related market data. The aggregation unit can also correct the aggregation results based on the market data. Furthermore, the aggregation unit can improve the accuracy of aggregation by referring to market data. Examples of related market data include, but are not limited to, competitor data, industry reports, consumer surveys, etc. The accuracy of aggregation can include, but are not limited to, the accuracy, comprehensiveness, and real-timeness of the data. In this way, the accuracy of aggregation can be improved by referring to related market data.

[0044] The storage unit can select a storage method by referring to past storage data when storing data. The storage unit, for example, selects the optimal storage method based on the past storage data. The storage unit can also refer to the past storage data to find improvements to the storage method and optimize it. Furthermore, the storage unit can analyze the past storage data and select the optimal storage method. Past storage data includes, for example, data from the past year or data for a specific period, but is not limited to such examples. Storage methods include, for example, database type, storage format, backup method, etc., but are not limited to such examples. In this way, the optimal storage method can be selected by referring to the past storage data.

[0045] The storage unit can determine the storage priority based on the importance of the data when storing the data. For example, the storage unit preferentially stores data with high importance. The storage unit can also store data with low importance later. Furthermore, the storage unit can also determine the storage priority based on the importance of the data. Examples of the importance of data include, but are not limited to, the impact on business operations and the frequency of data use. Examples of the storage priority include, but are not limited to, the importance, urgency, and storage period of the data. In this way, by determining the storage priority based on the importance of the data, important data can be preferentially stored.

[0046] The storage unit can prioritize data that is highly relevant based on geographical sales data during storage. For example, the storage unit prioritizes storage of sales data in a specific region. The storage unit can also prioritize storage of data that is highly geographically relevant. Furthermore, the storage unit can prioritize storage of relevant data taking into account sales trends for each region. Examples of geographical sales data include, but are not limited to, sales data by region and sales data by store. Examples of highly relevant data include, but are not limited to, products specific to a region and seasonal products. This allows for efficient storage of highly relevant data by taking into account geographical sales data.

[0047] The storage unit can store related data based on social media trends during storage. For example, the storage unit preferentially stores data related to products that are trending on social media. The storage unit can also store related data based on trends. Furthermore, the storage unit can analyze social media trends and preferentially store related data. Examples of social media trends include, but are not limited to, hashtag frequency and post engagement. Examples of related data include, but are not limited to, product data related to trends, consumer feedback, and the like. This allows related data to be efficiently stored by analyzing social media trends.

[0048] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between data. For example, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships between data. The analysis unit can also correct the analysis results based on the interrelationships between data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the interrelationships between data. Examples of the interrelationships between data include, but are not limited to, correlation analysis and identification of causal relationships. Examples of the accuracy of the analysis include, but are not limited to, accuracy, comprehensiveness, real-timeness, etc. of data. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships between data.

[0049] During analysis, the analysis unit can apply different analysis methods based on specific periods and events. For example, the analysis unit applies an optimal analysis method based on a specific period. The analysis unit can also apply different analysis methods based on an event. Furthermore, the analysis unit can select an optimal analysis method depending on a specific period or event. Examples of specific periods and events include, but are not limited to, sales periods, promotional events, and seasonal campaigns. Examples of different analysis methods include, but are not limited to, regression analysis, clustering, and time series analysis. This allows the accuracy of the analysis to be improved by applying an optimal analysis method based on a specific period or event.

[0050] The analysis unit can perform analysis based on geographical sales data during analysis. For example, the analysis unit analyzes sales trends by region based on the geographical sales data. The analysis unit can also analyze sales strategies in specific regions by taking the geographical sales data into consideration. Furthermore, the analysis unit can analyze sales performance by region by referring to the geographical sales data. Geographical sales data includes, for example, sales data by region and sales data by store, but is not limited to these examples. Analysis can include, for example, data accuracy, comprehensiveness, real-timeness, etc., but is not limited to these examples. As a result, by taking the geographical sales data into consideration, sales trends by region can be efficiently analyzed.

[0051] During analysis, the analysis unit can improve the accuracy of the analysis based on related market data. For example, the analysis unit improves the accuracy of the analysis by referring to related market data. The analysis unit can also correct the analysis results based on the market data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to market data. Examples of related market data include, but are not limited to, competitor data, industry reports, consumer surveys, etc. The accuracy of the analysis includes, but is not limited to, the accuracy, comprehensiveness, and real-timeness of the data. In this way, the accuracy of the analysis can be improved by referring to related market data.

[0052] The calculation unit can select a calculation method by referring to past sales data during calculation. The calculation unit, for example, selects an optimal calculation method based on past sales data. The calculation unit can also refer to past sales data to find improvements to the calculation method and optimize it. The calculation unit can also analyze past sales data to select an optimal calculation method. Past sales data includes, for example, data from the past year or data for a specific period, but is not limited to these examples. Calculation methods include, for example, demand forecasting algorithms, consideration of inventory levels, etc., but are not limited to these examples. In this way, the optimal calculation method can be selected by referring to past sales data.

[0053] The calculation unit may perform calculations based on specific trends and events. For example, the calculation unit may perform optimal calculations by taking specific trends into consideration. The calculation unit may also perform calculations by taking into consideration the influence of events. Furthermore, the calculation unit may select an optimal calculation method based on trends and events. Examples of specific trends and events include, but are not limited to, social media trends, industry events, and seasonal campaigns. Examples of calculations include, but are not limited to, data accuracy, comprehensiveness, and real-timeness. This allows optimal calculations to be performed by taking specific trends and events into consideration.

[0054] The calculation unit can select a calculation method based on geographical sales data during calculation. For example, the calculation unit selects an optimal calculation method for each region based on the geographical sales data. The calculation unit can also select an optimal calculation method for a specific region by taking the geographical sales data into consideration. Furthermore, the calculation unit can select an optimal calculation method for each region by referring to the geographical sales data. The geographical sales data includes, for example, sales data by region and sales data by store, but is not limited to these examples. The calculation method can include, for example, a demand forecasting algorithm, consideration of inventory levels, etc., but is not limited to these examples. In this way, the optimal calculation method for each region can be selected by taking the geographical sales data into consideration.

[0055] The calculation unit can analyze social media trends and reflect related data in the calculation when performing the calculation. The calculation unit can reflect related data in the calculation based on, for example, social media trends. The calculation unit can also reflect related data in the calculation based on the trends. The calculation unit can also analyze social media trends and reflect related data in the calculation. Social media trends include, for example, hashtag frequency, post engagement, etc., but are not limited to these examples. Related data include, for example, product data related to the trend, consumer feedback, etc., but are not limited to these examples. In this way, by analyzing social media trends, related data can be efficiently reflected in the calculation.

[0056] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the product. For example, the proposal unit makes a detailed proposal for a product with a high importance. The proposal unit can also make a simplified proposal for a product with a low importance. Furthermore, the proposal unit can adjust the level of detail of the proposal depending on the importance of the product. Examples of the importance of the product include, but are not limited to, sales revenue, profit margins, and inventory turnover rates. Examples of the level of detail of the proposal include, but are not limited to, the number of detailed items and the granularity of the proposal. In this way, by adjusting the level of detail of the proposal based on the importance of the product, detailed proposals can be made for important products.

[0057] When making a suggestion, the suggestion unit can apply different suggestion algorithms to each specific category and brand. For example, the suggestion unit applies an optimal suggestion algorithm to each category. The suggestion unit can also apply a different suggestion algorithm to each brand. Furthermore, the suggestion unit can select an optimal suggestion algorithm for each specific category or brand. Examples of specific categories and brands include, but are not limited to, product categories and brand names. Examples of different suggestion algorithms include, but are not limited to, collaborative filtering and content-based filtering. This allows the accuracy of suggestions to be improved by applying an optimal suggestion algorithm to each specific category or brand.

[0058] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the product. For example, the proposal unit preferentially proposes products that are submitted earlier. The proposal unit can also postpone the proposal of products that are submitted later. Furthermore, the proposal unit can determine the priority of the proposal based on the submission time of the product. The submission time of the product includes, for example, a submission deadline, a submission frequency, etc., but is not limited to such examples. The priority of the proposal includes, for example, the importance, urgency, and submission time of the product, but is not limited to such examples. In this way, by determining the priority of the proposal based on the submission time of the product, efficient proposals can be made.

[0059] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit preferentially suggests highly relevant products. The suggestion unit can also postpone suggesting less relevant products. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the products. Examples of product relevance include, but are not limited to, commonality of categories and similarity of purchase history. Examples of the order of suggestions include, but are not limited to, an order of relevance or importance. In this way, by adjusting the order of suggestions based on the relevance of the products, highly relevant products can be preferentially suggested.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The data import unit can analyze past data import history and select an import method. For example, it can identify the most efficient import time period from past data import history and import data during that time period. It can also select the optimal import method for a specific day of the week or time period. It can also analyze past data import history to find areas for improvement in the import method and optimize it. This makes it possible to select the optimal import method by analyzing past data import history.

[0062] The counting unit can adjust the level of detail of the counting based on the importance of the products when counting. For example, detailed counting can be performed for products with high importance. Also, simplified counting can be performed for products with low importance. Furthermore, the level of detail of the counting can be adjusted according to the importance of the products. In this way, by adjusting the level of detail of the counting based on the importance of the products, it is possible to perform detailed counting of important products.

[0063] When storing data, the storage unit can select a storage method by referring to past storage data. For example, it can select the optimal storage method based on past storage data. It can also refer to past storage data to find improvements to the storage method and optimize it. It can also analyze past storage data and select the optimal storage method. This makes it possible to select the optimal storage method by referring to past storage data.

[0064] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships of data. For example, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of data. The analysis results can also be corrected based on the interrelationships of data. Furthermore, the accuracy of the analysis can be improved by referring to the interrelationships of data. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships of data.

[0065] The calculation unit can select a calculation method by referring to past sales data when performing calculations. For example, the calculation unit can select the optimal calculation method based on past sales data. It can also refer to past sales data to find areas for improvement in the calculation method and optimize it. It can also analyze past sales data and select the optimal calculation method. In this way, it is possible to select the optimal calculation method by referring to past sales data.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The data acquisition unit acquires POS data and inventory data. POS data includes sales data, customer data, product data, etc., while inventory data includes inventory data, shipping data, and receiving data. The data acquisition unit can acquire data directly from the POS system and inventory management system, and can also acquire data from external systems via API. Step 2: The aggregation unit aggregates the sales items and quantities, as well as the items and quantities received and shipped by day and day of the week. For example, by aggregating daily sales data, it is possible to grasp sales trends by day of the week. It is also possible to aggregate incoming and outgoing data to grasp inventory trends. Step 3: The storage unit stores the aggregated data as past data. The storage unit can also save the data in a database or cloud storage, and back up the data. Step 4: The analysis unit analyzes sales trends and inventory trends based on the accumulated data. For example, it can analyze the tendency for certain products to be sold in large quantities on certain days of the week, or the tendency for certain products to be stocked in large quantities during certain periods. Step 5: The calculation unit calculates the order quantity based on the analysis results. For example, it can calculate the order quantity of product A needed for the following Monday based on past sales data. It can also suggest the latest products or popular products by taking into account the latest trends. Step 6: The proposal department proposes the latest and most popular products based on the latest trends. For example, they can identify the latest trends based on social media data and industry reports, and propose popular products based on the results of consumer surveys.

[0068] (Example 2) An inventory management system according to an embodiment of the present invention incorporates POS data and inventory data into a generation AI, aggregates sales and inventory items and quantities on a daily and weekly basis, stores the data in historical data, and then calculates and proposes optimal order quantities and items (latest products) taking into account the latest trends. This inventory management system incorporates POS data and inventory data into a generation AI, aggregates sales items and quantities, and inventory items and quantities on a daily and weekly basis. The aggregated data is then stored as historical data, and the generation AI analyzes sales and inventory trends. Furthermore, the system calculates optimal order quantities based on the analysis results and proposes new and popular products taking into account the latest trends. This system enables efficient inventory management and prevents excess inventory and stockouts. Furthermore, product proposals that reflect the latest trends can be expected to increase sales. For example, by incorporating POS data and inventory data into a generation AI, sales items and quantities, and inventory items and quantities on a daily and weekly basis can be aggregated. For example, the system aggregates the quantity of product A sold on Monday and the quantity of product B received on Tuesday. The aggregated data is then stored as historical data. The generation AI analyzes sales trends and inventory trends based on the accumulated data. For example, it analyzes trends such as the tendency for a particular product to be sold in large quantities on a particular day of the week or the tendency for a particular product to be received in large quantities during a particular period. The generation AI then calculates the optimal order quantity based on the analysis results. For example, it calculates the number of product A needed to be ordered for the following Monday based on past sales data. It also takes into account the latest trends to suggest new and popular products. For example, it suggests ordering new product C based on recent trends. This system enables efficient inventory management and prevents overstocking and stockouts. Furthermore, product suggestions that reflect the latest trends are expected to increase sales. This allows the inventory management system to achieve efficient inventory management and prevent overstocking and stockouts. Furthermore, product suggestions that reflect the latest trends are expected to increase sales.

[0069] The inventory management system according to the embodiment includes a data acquisition unit, a compilation unit, a storage unit, an analysis unit, a calculation unit, and a proposal unit. The data acquisition unit acquires POS data and inventory data. The POS data includes, but is not limited to, sales data, customer data, and product data. The inventory data includes, but is not limited to, inventory data, shipping data, and receiving data. The data acquisition unit can acquire data directly from a POS system, for example. The data acquisition unit can also acquire data from an inventory management system. The data acquisition unit can also acquire data from an external system via an API. The compilation unit compiles sales items and quantities, and inventory items and quantities, on a daily and day-of-the-week basis. The compilation unit can, for example, compile daily sales data to understand sales trends by day of the week. The compilation unit can also compile inventory data to understand inventory trends. For example, the compilation unit can compile the quantity of product A sold on Monday and the quantity of product B received on Tuesday. The storage unit stores the aggregated data as past data. The storage unit can, for example, store the data in a database. The storage unit can also store the data in cloud storage. The storage unit can also back up the data. The analysis unit analyzes sales trends and inventory trends based on the stored data. The analysis unit can, for example, analyze the tendency for a specific product to be sold in large quantities on a specific day of the week. The analysis unit can also analyze the tendency for a specific product to be stocked in large quantities during a specific period. For example, the analysis unit calculates the number of product A to be ordered next Monday based on past sales data. The calculation unit calculates the order quantity based on the analysis results. The calculation unit can, for example, calculate the number of product A to be ordered next Monday based on past sales data. The calculation unit can also suggest new products or popular products taking into account the latest trends. For example, the calculation unit suggests ordering new product C based on recent trends. The suggestion unit suggests new products and popular products based on the latest trends. The suggestion unit can grasp the latest trends based on social media data and industry reports, for example.The suggestion unit can also suggest popular products based on the results of consumer surveys. This allows the inventory management system according to the embodiment to efficiently import POS data and inventory data, and aggregate, accumulate, analyze, calculate, and suggest products.

[0070] The data acquisition unit can acquire POS data and inventory data. The data acquisition unit can acquire data directly from a POS system, for example. The data acquisition unit can also acquire data from an inventory management system. The data acquisition unit can also acquire data from an external system via an API. POS data includes, but is not limited to, sales data, customer data, and product data. Inventory data includes, but is not limited to, inventory data, shipping data, and receiving data. This allows POS data and inventory data to be acquired efficiently.

[0071] The tallying unit can tally sales items and quantities, and incoming and outgoing items and quantities on a daily and day-of-the-week basis. The tallying unit can, for example, tally daily sales data to grasp sales trends by day of the week. The tallying unit can also tally incoming and outgoing data to grasp inventory trends. For example, the tallying unit tally the quantity of product A sold on Monday and the quantity of product B received on Tuesday. Daily and day-of-the-week units include, but are not limited to, calendar days, business days, and weekends. Sales items and quantities include, but are not limited to, product categories, SKUs, and sales units. Incoming and outgoing items and quantities include, but are not limited to, product categories, SKUs, and ingoing and outgoing units. This allows for efficient tallying of sales items and quantities, and incoming and outgoing items and quantities on a daily and day-of-the-week basis.

[0072] The storage unit can store the aggregated data as past data. The storage unit can store the data in a database, for example. The storage unit can also store the data in cloud storage. Furthermore, the storage unit can back up the data. Past data includes, for example, data from the past year, data from a specific period, etc., but is not limited to these examples. This allows the aggregated data to be stored efficiently.

[0073] The analysis unit can analyze sales trends and inventory trends based on the accumulated data. For example, the analysis unit can analyze the tendency for a particular product to be sold in large quantities on a particular day of the week. The analysis unit can also analyze the tendency for a particular product to be stocked in large quantities during a particular period. Sales trends include, but are not limited to, increases or decreases in sales, seasonality, and promotion effects. Stock-in / stock-out trends include, but are not limited to, inventory turnover, stock-in / stock-out frequency, and supply chain patterns. This allows for efficient analysis of sales trends and inventory-in / stock-out trends based on the accumulated data.

[0074] The calculation unit can calculate the order quantity based on the analysis results. For example, the calculation unit can calculate the order quantity of product A needed for the following Monday based on past sales data. The calculation unit can also suggest the latest products or popular products by taking the latest trends into account. The order quantity includes, but is not limited to, past sales data, inventory levels, demand forecasts, etc., for example. This makes it possible to efficiently calculate the optimal order quantity based on the analysis results.

[0075] The suggestion unit can suggest the latest products and popular products based on the latest trends. The suggestion unit can grasp the latest trends based on, for example, social media data and industry reports. The suggestion unit can also suggest popular products based on the results of consumer surveys. Examples of the latest trends include, but are not limited to, social media data, industry reports, consumer surveys, etc. Examples of the latest products and popular products include, but are not limited to, newly released products and top-selling products. This allows the latest products and popular products to be efficiently suggested taking the latest trends into account.

[0076] The data acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the data acquisition unit can reduce the frequency of data acquisition to reduce the burden on the user. Furthermore, if the user is relaxed, the data acquisition unit can increase the frequency of data acquisition to collect detailed data. Furthermore, if the user is in a hurry, the data acquisition unit can quickly acquire data and prioritize the acquisition of the minimum necessary data. Examples of user emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of data acquisition timing include, but are not limited to, real-time, regular schedules, and user activity patterns. This allows the burden on the user to be reduced by adjusting the timing of data acquisition according to the user's emotions.

[0077] The data import unit can analyze past data import history and select an import method. For example, the data import unit can identify the most efficient import time period from the past data import history and import data during that time period. The data import unit can also select the optimal import method for a specific day of the week or time period based on the past data import history. Furthermore, the data import unit can analyze the past data import history to find improvements to the import method and optimize it. Past data import history includes, for example, history from the past year or history from a specific period, but is not limited to these examples. Import methods include, for example, API integration, file upload, manual input, etc., but are not limited to these examples. In this way, the optimal import method can be selected by analyzing the past data import history.

[0078] The data acquisition unit can acquire data based on specific events and campaign periods. For example, the data acquisition unit prioritizes acquiring related data during specific event periods. The data acquisition unit can also prioritize acquiring data on products related to campaigns during campaign periods. Furthermore, the data acquisition unit can identify data affected by events or campaigns and prioritize acquiring that data. Examples of specific event and campaign periods include, but are not limited to, sales periods, promotional events, and seasonal campaigns. This allows related data to be acquired efficiently by taking specific events and campaign periods into consideration.

[0079] The data acquisition unit can estimate the user's emotions and determine the priority of the acquired data based on the estimated user's emotions. For example, when the user is feeling stressed, the data acquisition unit can prioritize acquiring data with high importance. Furthermore, when the user is relaxed, the data acquisition unit can prioritize acquiring detailed data. Furthermore, when the user is in a hurry, the data acquisition unit can prioritize acquiring data that can be obtained quickly. Examples of the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of the priority of the acquired data include, but are not limited to, the importance, urgency, and user interest in the data. In this way, by determining the priority of the acquired data according to the user's emotions, important data can be acquired preferentially.

[0080] When importing data, the data import unit can prioritize importing highly relevant data by taking geographical sales data into consideration. The data import unit, for example, prioritizes importing sales data from a specific region. The data import unit can also prioritize importing data that is highly geographically relevant. Furthermore, the data import unit can prioritize importing related data by taking into consideration sales trends for each region. Geographical sales data includes, for example, sales data by region and sales data by store, but is not limited to such examples. Highly relevant data includes, for example, products unique to a region, seasonal products, etc. This allows highly relevant data to be efficiently imported by taking geographical sales data into consideration.

[0081] The data acquisition unit can analyze social media trends and acquire related data when acquiring data. For example, the data acquisition unit prioritizes acquiring data related to products that are trending on social media. The data acquisition unit can also acquire related data based on trends. Furthermore, the data acquisition unit can analyze social media trends and acquire related data prioritized. Social media trends include, for example, hashtag frequency, post engagement, etc., but are not limited to such examples. Related data include, for example, product data related to the trend, consumer feedback, etc., but are not limited to such examples. In this way, related data can be efficiently acquired by analyzing social media trends.

[0082] The tallying unit can estimate the user's emotions and adjust the presentation method of the tally based on the estimated user's emotions. For example, if the user is nervous, the tallying unit can provide a simple, highly visible summary table. Furthermore, if the user is relaxed, the tallying unit can provide a summary table including detailed information. Furthermore, if the user is in a hurry, the tallying unit can provide a summary table that focuses on the main points. Examples of the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of the presentation method of the tally include, but are not limited to, graph format, table format, dashboard format, etc. In this way, by adjusting the presentation method of the tally according to the user's emotions, it is possible to provide a summary table that is easy for the user to read.

[0083] The aggregation unit can adjust the level of detail of the aggregation based on the importance of the product when aggregating. For example, the aggregation unit performs detailed aggregation for products with high importance. The aggregation unit can also perform simplified aggregation for products with low importance. Furthermore, the aggregation unit can adjust the level of detail of the aggregation depending on the importance of the product. Examples of the importance of the product include, but are not limited to, sales revenue, profit margins, inventory turnover, etc. Examples of the level of detail of the aggregation include, but are not limited to, the number of detailed items, the granularity of the aggregation, etc. In this way, by adjusting the level of detail of the aggregation based on the importance of the product, detailed aggregation of important products can be performed.

[0084] The aggregation unit can apply different aggregation algorithms to specific categories and brands when aggregating. For example, the aggregation unit applies the optimal aggregation algorithm to each category. The aggregation unit can also apply different aggregation algorithms to each brand. Furthermore, the aggregation unit can select the optimal aggregation algorithm for a specific category or brand. Examples of specific categories and brands include, but are not limited to, product categories and brand names. Examples of different aggregation algorithms include, but are not limited to, weighted averages, moving averages, and regression analysis. This allows the accuracy of aggregation to be improved by applying the optimal aggregation algorithm to each specific category or brand.

[0085] The tallying unit can estimate the user's emotions and adjust the length of the tally based on the estimated user's emotions. For example, if the user is in a hurry, the tallying unit can perform a short, to-the-point tally. Furthermore, if the user is relaxed, the tallying unit can perform a longer tally including detailed information. Furthermore, if the user is excited, the tallying unit can perform a tally with visually stimulating effects. Examples of user emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of the length of the tally include, but are not limited to, the tally period and the number of display items. This allows the length of the tally to be adjusted according to the user's emotions, thereby providing a tally of an appropriate length for the user.

[0086] The tallying unit can determine the priority of tallying based on the time of data submission during tallying. For example, the tallying unit prioritizes data submitted earlier. The tallying unit can also postpone tallying data submitted later. Furthermore, the tallying unit can determine the priority of tallying based on the time of data submission. Examples of data submission times include, but are not limited to, submission deadlines and submission frequency. Examples of tallying priorities include, but are not limited to, data importance, urgency, and submission time. In this way, by determining the priority of tallying based on the time of data submission, efficient tallying can be performed.

[0087] The aggregation unit can improve the accuracy of aggregation by referring to related market data when aggregating. For example, the aggregation unit can improve the accuracy of aggregation by referring to related market data. The aggregation unit can also correct the aggregation results based on the market data. Furthermore, the aggregation unit can improve the accuracy of aggregation by referring to market data. Examples of related market data include, but are not limited to, competitor data, industry reports, consumer surveys, etc. The accuracy of aggregation can include, but are not limited to, the accuracy, comprehensiveness, and real-timeness of the data. In this way, the accuracy of aggregation can be improved by referring to related market data.

[0088] The storage unit can estimate the user's emotions and select stored data based on the estimated user emotions. For example, when the user is feeling stressed, the storage unit prioritizes storing data of high importance. Furthermore, when the user is relaxed, the storage unit can also prioritize storing detailed data. Furthermore, when the user is in a hurry, the storage unit can prioritize storing data that can be obtained quickly. Examples of user emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of data selection based on, but are not limited to, data importance, relevance, and storage period. In this way, by selecting stored data according to the user's emotions, important data can be preferentially stored.

[0089] The storage unit can select a storage method by referring to past storage data when storing data. The storage unit, for example, selects the optimal storage method based on the past storage data. The storage unit can also refer to the past storage data to find improvements to the storage method and optimize it. Furthermore, the storage unit can analyze the past storage data and select the optimal storage method. Past storage data includes, for example, data from the past year or data for a specific period, but is not limited to such examples. Storage methods include, for example, database type, storage format, backup method, etc., but are not limited to such examples. In this way, the optimal storage method can be selected by referring to the past storage data.

[0090] The storage unit can determine the storage priority based on the importance of the data when storing the data. For example, the storage unit preferentially stores data with high importance. The storage unit can also store data with low importance later. Furthermore, the storage unit can also determine the storage priority based on the importance of the data. Examples of the importance of data include, but are not limited to, the impact on business operations and the frequency of data use. Examples of the storage priority include, but are not limited to, the importance, urgency, and storage period of the data. In this way, by determining the storage priority based on the importance of the data, important data can be preferentially stored.

[0091] The storage unit can estimate the user's emotions and adjust the display method of the stored data based on the estimated user emotions. For example, when the user is nervous, the storage unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the storage unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the storage unit can provide a display method that focuses on the main points. Examples of the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of the display method of the stored data include, but are not limited to, graph format, table format, dashboard format, etc. In this way, by adjusting the display method of the stored data according to the user's emotions, it is possible to provide a display method that is easy for the user to view.

[0092] The storage unit can prioritize data that is highly relevant based on geographical sales data during storage. For example, the storage unit prioritizes storage of sales data in a specific region. The storage unit can also prioritize storage of data that is highly geographically relevant. Furthermore, the storage unit can prioritize storage of relevant data taking into account sales trends for each region. Examples of geographical sales data include, but are not limited to, sales data by region and sales data by store. Examples of highly relevant data include, but are not limited to, products specific to a region and seasonal products. This allows for efficient storage of highly relevant data by taking into account geographical sales data.

[0093] The storage unit can store related data based on social media trends during storage. For example, the storage unit preferentially stores data related to products that are trending on social media. The storage unit can also store related data based on trends. Furthermore, the storage unit can analyze social media trends and preferentially store related data. Examples of social media trends include, but are not limited to, hashtag frequency and post engagement. Examples of related data include, but are not limited to, product data related to trends, consumer feedback, and the like. This allows related data to be efficiently stored by analyzing social media trends.

[0094] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide analysis results including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. Examples of the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of the analysis criteria include, but are not limited to, the accuracy of the analysis, the scope of the analysis, and the frequency of the analysis. In this way, by adjusting the analysis criteria according to the user's emotions, it is possible to provide analysis results that are easy for the user to read.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between data. For example, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships between data. The analysis unit can also correct the analysis results based on the interrelationships between data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the interrelationships between data. Examples of the interrelationships between data include, but are not limited to, correlation analysis and identification of causal relationships. Examples of the accuracy of the analysis include, but are not limited to, accuracy, comprehensiveness, real-timeness, etc. of data. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships between data.

[0096] During analysis, the analysis unit can apply different analysis methods based on specific periods and events. For example, the analysis unit applies an optimal analysis method based on a specific period. The analysis unit can also apply different analysis methods based on an event. Furthermore, the analysis unit can select an optimal analysis method depending on a specific period or event. Examples of specific periods and events include, but are not limited to, sales periods, promotional events, and seasonal campaigns. Examples of different analysis methods include, but are not limited to, regression analysis, clustering, and time series analysis. This allows the accuracy of the analysis to be improved by applying an optimal analysis method based on a specific period or event.

[0097] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user's emotions. For example, when the user is nervous, the analysis unit can prioritize displaying important analysis results. Furthermore, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying analysis results that focus on the main points. Examples of the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of the display order of the analysis results include, but are not limited to, order of importance, chronological order, and by category. In this way, by adjusting the display order of the analysis results according to the user's emotions, important analysis results can be prioritized.

[0098] The analysis unit can perform analysis based on geographical sales data during analysis. For example, the analysis unit analyzes sales trends by region based on the geographical sales data. The analysis unit can also analyze sales strategies in specific regions by taking the geographical sales data into consideration. Furthermore, the analysis unit can analyze sales performance by region by referring to the geographical sales data. Geographical sales data includes, for example, sales data by region and sales data by store, but is not limited to these examples. Analysis can include, for example, data accuracy, comprehensiveness, real-timeness, etc., but is not limited to these examples. As a result, by taking the geographical sales data into consideration, sales trends by region can be efficiently analyzed.

[0099] During analysis, the analysis unit can improve the accuracy of the analysis based on related market data. For example, the analysis unit improves the accuracy of the analysis by referring to related market data. The analysis unit can also correct the analysis results based on the market data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to market data. Examples of related market data include, but are not limited to, competitor data, industry reports, consumer surveys, etc. The accuracy of the analysis includes, but is not limited to, the accuracy, comprehensiveness, and real-timeness of the data. In this way, the accuracy of the analysis can be improved by referring to related market data.

[0100] The calculation unit can estimate the user's emotion and adjust the calculation method based on the estimated user's emotion. For example, when the user is nervous, the calculation unit can provide a simple and highly visible calculation method. Furthermore, when the user is relaxed, the calculation unit can provide a calculation method that includes detailed information. Furthermore, when the user is in a hurry, the calculation unit can provide a calculation method that focuses on the main points. Examples of the user's emotion include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of the calculation method include, but are not limited to, calculation algorithms, data weighting, and calculation frequency. In this way, by adjusting the calculation method according to the user's emotion, it is possible to provide calculation results that are easy for the user to see.

[0101] The calculation unit can select a calculation method by referring to past sales data during calculation. The calculation unit, for example, selects an optimal calculation method based on past sales data. The calculation unit can also refer to past sales data to find improvements to the calculation method and optimize it. The calculation unit can also analyze past sales data to select an optimal calculation method. Past sales data includes, for example, data from the past year or data for a specific period, but is not limited to these examples. Calculation methods include, for example, demand forecasting algorithms, consideration of inventory levels, etc., but are not limited to these examples. In this way, the optimal calculation method can be selected by referring to past sales data.

[0102] The calculation unit may perform calculations based on specific trends and events. For example, the calculation unit may perform optimal calculations by taking specific trends into consideration. The calculation unit may also perform calculations by taking into consideration the influence of events. Furthermore, the calculation unit may select an optimal calculation method based on trends and events. Examples of specific trends and events include, but are not limited to, social media trends, industry events, and seasonal campaigns. Examples of calculations include, but are not limited to, data accuracy, comprehensiveness, and real-timeness. This allows optimal calculations to be performed by taking specific trends and events into consideration.

[0103] The calculation unit can estimate the user's emotions and determine the priority of the calculation results based on the estimated user's emotions. For example, when the user is nervous, the calculation unit can prioritize displaying important calculation results. Furthermore, when the user is relaxed, the calculation unit can prioritize displaying detailed calculation results. Furthermore, when the user is in a hurry, the calculation unit can prioritize displaying calculation results that focus on the main points. Examples of the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of the priority of the calculation results include, but are not limited to, the importance of data, urgency, and user interest. In this way, by determining the priority of the calculation results according to the user's emotions, important calculation results can be prioritized and displayed.

[0104] The calculation unit can select a calculation method based on geographical sales data during calculation. For example, the calculation unit selects an optimal calculation method for each region based on the geographical sales data. The calculation unit can also select an optimal calculation method for a specific region by taking the geographical sales data into consideration. Furthermore, the calculation unit can select an optimal calculation method for each region by referring to the geographical sales data. The geographical sales data includes, for example, sales data by region and sales data by store, but is not limited to these examples. The calculation method can include, for example, a demand forecasting algorithm, consideration of inventory levels, etc., but is not limited to these examples. In this way, the optimal calculation method for each region can be selected by taking the geographical sales data into consideration.

[0105] The calculation unit can analyze social media trends and reflect related data in the calculation when performing the calculation. The calculation unit can reflect related data in the calculation based on, for example, social media trends. The calculation unit can also reflect related data in the calculation based on the trends. The calculation unit can also analyze social media trends and reflect related data in the calculation. Social media trends include, for example, hashtag frequency, post engagement, etc., but are not limited to these examples. Related data include, for example, product data related to the trend, consumer feedback, etc., but are not limited to these examples. In this way, by analyzing social media trends, related data can be efficiently reflected in the calculation.

[0106] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can make simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can make suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make suggestions that focus on the main points. Examples of the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of the way in which suggestions are expressed include, but are not limited to, text format, graphical format, and audio format. In this way, by adjusting the way in which suggestions are expressed according to the user's emotions, it is possible to provide suggestions that are easy for the user to read.

[0107] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the product. For example, the proposal unit makes a detailed proposal for a product with a high importance. The proposal unit can also make a simplified proposal for a product with a low importance. Furthermore, the proposal unit can adjust the level of detail of the proposal depending on the importance of the product. Examples of the importance of the product include, but are not limited to, sales revenue, profit margins, and inventory turnover rates. Examples of the level of detail of the proposal include, but are not limited to, the number of detailed items and the granularity of the proposal. In this way, by adjusting the level of detail of the proposal based on the importance of the product, detailed proposals can be made for important products.

[0108] When making a suggestion, the suggestion unit can apply different suggestion algorithms to each specific category and brand. For example, the suggestion unit applies an optimal suggestion algorithm to each category. The suggestion unit can also apply a different suggestion algorithm to each brand. Furthermore, the suggestion unit can select an optimal suggestion algorithm for each specific category or brand. Examples of specific categories and brands include, but are not limited to, product categories and brand names. Examples of different suggestion algorithms include, but are not limited to, collaborative filtering and content-based filtering. This allows the accuracy of suggestions to be improved by applying an optimal suggestion algorithm to each specific category or brand.

[0109] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. Examples of the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of the length of the suggestions include, but are not limited to, the amount of text in the suggestions and the number of display items. In this way, by adjusting the length of the suggestions according to the user's emotions, it is possible to provide suggestions of an appropriate length for the user.

[0110] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the product. For example, the proposal unit preferentially proposes products that are submitted earlier. The proposal unit can also postpone the proposal of products that are submitted later. Furthermore, the proposal unit can determine the priority of the proposal based on the submission time of the product. The submission time of the product includes, for example, a submission deadline, a submission frequency, etc., but is not limited to such examples. The priority of the proposal includes, for example, the importance, urgency, and submission time of the product, but is not limited to such examples. In this way, by determining the priority of the proposal based on the submission time of the product, efficient proposals can be made.

[0111] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit preferentially suggests highly relevant products. The suggestion unit can also postpone suggesting less relevant products. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the products. Examples of product relevance include, but are not limited to, commonality of categories and similarity of purchase history. Examples of the order of suggestions include, but are not limited to, an order of relevance or importance. In this way, by adjusting the order of suggestions based on the relevance of the products, highly relevant products can be preferentially suggested. === Hard Collateral 1-1 === Each of the multiple elements, including the data acquisition unit, tallying unit, storage unit, analysis unit, calculation unit, and suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data acquisition unit can acquire POS data and inventory data via the communication I / F 44 of the smart device 14. The tallying unit can tally the items and quantities sold and the items and quantities received and sent by day and day of the week using the processor 46 of the smart device 14. The storage unit can store the tally data in the database 24 of the data processing device 12. The analysis unit can analyze sales trends and inventory trends based on the data accumulated by the specific processing unit 290 of the data processing device 12. The calculation unit can calculate the order quantity based on the analysis results using the specific processing unit 290 of the data processing device 12. The suggestion unit can suggest the latest products and popular products based on the latest trends using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the data acquisition unit, tallying unit, storage unit, analysis unit, calculation unit, and suggestion 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 data acquisition unit can acquire POS data and inventory data via the communication I / F 44 of the smart glasses 214. The tallying unit can tally the items and quantities sold, and the items and quantities received and sent by day and day of the week, using the processor 46 of the smart glasses 214. The storage unit can store the tally data in the database 24 of the data processing device 12. The analysis unit can analyze sales trends and inventory trends based on the data accumulated by the specific processing unit 290 of the data processing device 12. The calculation unit can calculate the order quantity based on the analysis results by the specific processing unit 290 of the data processing device 12. The suggestion unit can suggest the latest products and popular products based on the latest trends by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the data acquisition unit, tallying unit, storage unit, analysis unit, calculation unit, and suggestion unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the data acquisition unit can acquire POS data and inventory data via the communication I / F 44 of the headset terminal 314. The tallying unit can tally the items and quantities sold and the items and quantities received and sent by day and day of the week using the processor 46 of the headset terminal 314. The storage unit can save the tally data in the database 24 of the data processing device 12. The analysis unit can analyze sales trends and inventory trends based on the data stored by the specific processing unit 290 of the data processing device 12. The calculation unit can calculate the order quantity based on the analysis results using the specific processing unit 290 of the data processing device 12. The suggestion unit can suggest the latest products and popular products based on the latest trends using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the data acquisition unit, tallying unit, storage unit, analysis unit, calculation unit, and suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data acquisition unit can acquire POS data and inventory data via the communication I / F 44 of the robot 414. The tallying unit can tally the items and quantities sold and the items and quantities received and sent by day and day of the week using the processor 46 of the robot 414. The storage unit can store the tally data in the database 24 of the data processing device 12. The analysis unit can analyze sales trends and inventory trends based on the data stored by the specific processing unit 290 of the data processing device 12. The calculation unit can calculate the order quantity based on the analysis results using the specific processing unit 290 of the data processing device 12. The suggestion unit can suggest the latest products and popular products based on the latest trends using the specific processing unit 290 of the data processing device 12.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The data acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data acquisition can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of data acquisition can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data acquisition can be performed quickly, with the minimum necessary data being acquired as a priority. In this way, the burden on the user can be reduced by adjusting the timing of data acquisition according to the user's emotions.

[0114] The data import unit can analyze past data import history and select an import method. For example, it can identify the most efficient import time period from past data import history and import data during that time period. It can also select the optimal import method for a specific day of the week or time period. It can also analyze past data import history to find areas for improvement in the import method and optimize it. This makes it possible to select the optimal import method by analyzing past data import history.

[0115] The tallying unit can estimate the user's emotions and adjust the way the tally is presented based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible tally table can be provided. If the user is relaxed, a tally table containing detailed information can be provided. Furthermore, if the user is in a hurry, a tally table that focuses on the main points can be provided. In this way, by adjusting the way the tally is presented according to the user's emotions, it is possible to provide a tally table that is easy for the user to read.

[0116] The counting unit can adjust the level of detail of the counting based on the importance of the products when counting. For example, detailed counting can be performed for products with high importance. Also, simplified counting can be performed for products with low importance. Furthermore, the level of detail of the counting can be adjusted according to the importance of the products. In this way, by adjusting the level of detail of the counting based on the importance of the products, it is possible to perform detailed counting of important products.

[0117] The storage unit can estimate the user's emotions and select data to be stored based on the estimated user emotions. For example, if the user is feeling stressed, data of high importance can be stored with priority. Also, if the user is relaxed, detailed data can be stored with priority. Furthermore, if the user is in a hurry, data that can be obtained quickly can be stored with priority. In this way, by selecting data to be stored according to the user's emotions, important data can be stored with priority.

[0118] When storing data, the storage unit can select a storage method by referring to past storage data. For example, it can select the optimal storage method based on past storage data. It can also refer to past storage data to find improvements to the storage method and optimize it. It can also analyze past storage data and select the optimal storage method. This makes it possible to select the optimal storage method by referring to past storage data.

[0119] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can also provide analysis results that include detailed information. Furthermore, if the user is in a hurry, it can also provide analysis results that focus on the main points. In this way, by adjusting the analysis criteria according to the user's emotions, it is possible to provide analysis results that are easy for the user to see.

[0120] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships of data. For example, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of data. The analysis results can also be corrected based on the interrelationships of data. Furthermore, the accuracy of the analysis can be improved by referring to the interrelationships of data. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships of data.

[0121] The calculation unit can estimate the user's emotions and adjust the calculation method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible calculation method can be provided. If the user is relaxed, a calculation method including detailed information can be provided. Furthermore, if the user is in a hurry, a calculation method that focuses on the main points can be provided. In this way, by adjusting the calculation method according to the user's emotions, it is possible to provide calculation results that are easy for the user to see.

[0122] The calculation unit can select a calculation method by referring to past sales data when performing calculations. For example, the calculation unit can select the optimal calculation method based on past sales data. It can also refer to past sales data to find areas for improvement in the calculation method and optimize it. It can also analyze past sales data and select the optimal calculation method. In this way, it is possible to select the optimal calculation method by referring to past sales data.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The data acquisition unit acquires POS data and inventory data. POS data includes sales data, customer data, product data, etc., while inventory data includes inventory data, shipping data, and receiving data. The data acquisition unit can acquire data directly from the POS system and inventory management system, and can also acquire data from external systems via API. Step 2: The aggregation unit aggregates the sales items and quantities, as well as the items and quantities received and shipped by day and day of the week. For example, by aggregating daily sales data, it is possible to grasp sales trends by day of the week. It is also possible to aggregate incoming and outgoing data to grasp inventory trends. Step 3: The storage unit stores the aggregated data as past data. The storage unit can also save the data in a database or cloud storage, and back up the data. Step 4: The analysis unit analyzes sales trends and inventory trends based on the accumulated data. For example, it can analyze the tendency for certain products to be sold in large quantities on certain days of the week, or the tendency for certain products to be stocked in large quantities during certain periods. Step 5: The calculation unit calculates the order quantity based on the analysis results. For example, it can calculate the order quantity of product A needed for the following Monday based on past sales data. It can also suggest the latest products or popular products by taking into account the latest trends. Step 6: The proposal department proposes the latest and most popular products based on the latest trends. For example, they can identify the latest trends based on social media data and industry reports, and propose popular products based on the results of consumer surveys.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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 data import unit that imports POS data and inventory data; a counting unit that counts the data acquired by the data acquisition unit; a storage unit that stores the data collected by the collection unit; an analysis unit that analyzes the data stored by the storage unit; a calculation unit that calculates the order quantity based on the data analyzed by the analysis unit; a proposal unit that proposes an order quantity and an item based on the order quantity calculated by the calculation unit and the latest trend. A system characterized by:

2. The data acquisition unit Import POS data and inventory data The system of claim 1 .

3. The aggregation unit Summarize sales items and quantities, and inbound and outbound items and quantities by day and day of the week The system of claim 1 .

4. The storage unit is Accumulate the aggregated data as historical data The system of claim 1 .

5. The analysis unit Analyze sales trends and inventory trends based on accumulated data The system of claim 1 .

6. The calculation unit Calculate the order quantity based on the analysis results The system of claim 1 .

7. The proposal unit Propose the latest and most popular products based on the latest trends The system of claim 1 .

8. The data acquisition unit Infer user emotions and adjust the timing of data capture based on the estimated user emotions The system of claim 1 .

9. The data acquisition unit Analyze past data import history and select the import method The system of claim 1 .

10. The data acquisition unit When importing data, import it based on specific events and campaign periods. The system of claim 1 .

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