system

Abstract

The system according to this embodiment aims to accurately predict sales and appropriately adjust incoming stock quantities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a prediction unit, an adjustment unit, and a linkage unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The prediction unit predicts sales based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the incoming quantity based on the sales predicted by the prediction unit. The linkage unit links the incoming quantity adjusted by the adjustment unit with the inventory management system.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that sales forecasting and adjustment of the incoming quantity are complicated and accurate prediction is difficult.

[0005] The system according to the embodiment aims to accurately predict sales and appropriately adjust the incoming quantity.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a forecasting unit, an adjustment unit, and a linking unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The forecasting unit forecasts sales based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the incoming quantity based on the sales forecasted by the forecasting unit. The linking unit links the incoming quantity adjusted by the adjustment unit with the inventory management system. [Effects of the Invention]

[0007] The system according to this embodiment can accurately predict sales and appropriately adjust incoming stock quantities. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network). [[ID=二十]]

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The sales forecasting system according to an embodiment of the present invention is a system that minimizes unsold inventory by using an AI agent to predict sales from a combination of factors and adjusting the amount of goods received. The sales forecasting system uses an AI agent to collect and analyze a combination of information in real time. Next, it predicts sales based on the collected information and adjusts the amount of goods received. This minimizes unsold inventory. For example, the sales forecasting system collects information that includes external factors such as weather and surrounding events. For example, this includes weather forecasts and local event information. This makes it possible to understand the factors that affect sales. Next, the sales forecasting system uses an AI agent to analyze the collected information. The AI ​​agent predicts sales based on past sales data and real-time information. For example, it can predict sales for a specific day by combining past sales data and weather forecasts. This enables more accurate sales predictions. Furthermore, the sales forecasting system adjusts the amount of goods received based on the sales forecast. The AI ​​agent determines the appropriate amount of goods received based on the predicted sales. For example, if sales are predicted to increase, the amount of goods received is increased, and if sales are predicted to decrease, the amount of goods received is decreased. In this way, unsold inventory can be minimized. This system minimizes unsold inventory in the retail industry in general, especially in the sale of perishable foods. By having an AI agent analyze complex information in real time and predict sales, it contributes to a more environmentally friendly society. As a result, the sales forecasting system minimizes unsold inventory.

[0029] The sales forecasting system according to this embodiment comprises a collection unit, an analysis unit, a forecasting unit, an adjustment unit, and a linking unit. The collection unit collects information. The collection unit collects, for example, weather, local events, competitor pricing information, and promotional information. For example, the collection unit collects weather forecast data to understand factors that affect sales. The collection unit can also collect local event information to understand factors that affect sales. Furthermore, the collection unit can collect competitor pricing information to understand factors that affect sales. For example, the collection unit collects weather forecast data in real time to understand factors that affect sales. It can also collect local event information to understand factors that affect sales. It can also collect competitor pricing information to understand factors that affect sales. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes complex information, including past sales data, and considers seasonality and trends. For example, the analysis unit can analyze past sales data and consider seasonality and trends. Furthermore, the analysis unit can analyze real-time information and consider seasonality and trends. Furthermore, the analysis unit can analyze complex information and consider seasonality and trends. For example, the analysis unit can analyze historical sales data and consider seasonality and trends. It can also analyze real-time information and consider seasonality and trends. It can also analyze complex information and consider seasonality and trends. The forecasting unit forecasts sales based on the analysis results obtained by the analysis unit. The forecasting unit forecasts sales based on the analysis results, for example. The forecasting unit can forecast sales using time series analysis, for example. The forecasting unit can also forecast sales using regression analysis. Furthermore, the forecasting unit can forecast sales using a forecasting model. For example, the forecasting unit forecasts sales using time series analysis. It can also forecast sales using regression analysis. It can also forecast sales using a forecasting model. The adjustment unit adjusts incoming quantities based on the sales forecasted by the forecasting unit. The adjustment unit adjusts incoming quantities based on the forecasted sales, for example. The adjustment unit can adjust incoming quantities considering inventory levels, for example.Furthermore, the adjustment unit can also adjust the incoming quantity considering demand forecasts. In addition, the adjustment unit can also adjust the incoming quantity considering supply chain constraints. For example, the adjustment unit adjusts the incoming quantity considering inventory levels. It can also adjust the incoming quantity considering demand forecasts. It can also adjust the incoming quantity considering supply chain constraints. The integration unit integrates the incoming quantity adjusted by the adjustment unit with the inventory management system. The integration unit performs inventory management in conjunction with the inventory management system, for example. The integration unit can integrate with the inventory management system using API integration, for example. It can also integrate with the inventory management system using data synchronization. Furthermore, the integration unit can also integrate with the inventory management system using real-time updates. For example, the integration unit integrates with the inventory management system using API integration. It can also integrate with the inventory management system using data synchronization. It can also integrate with the inventory management system using real-time updates. As a result, the sales forecasting system according to the embodiment can minimize unsold inventory.

[0030] The data collection department collects information. For example, it collects weather data, local events, competitor pricing information, and promotional information. Specifically, it collects weather forecast data to understand factors influencing sales. Weather forecast data includes detailed meteorological information such as temperature, precipitation, and wind speed, and this data can directly impact sales. For example, indoor consumption tends to increase on rainy days, while outdoor consumption tends to increase on sunny days, making the collection of this data important. The data collection department can also collect local event information to understand factors influencing sales. Local event information includes festivals, concerts, and sporting events, and these events can significantly impact sales of specific products. Furthermore, the data collection department can collect competitor pricing information to understand factors influencing sales. Competitor pricing information is important for understanding price trends for identical and similar products, allowing for appropriate adjustments to the company's pricing strategy. For example, the data collection department collects weather forecast data in real time to understand factors influencing sales. It can also collect local event information to understand factors influencing sales. It can also collect competitor pricing information to understand factors influencing sales. This allows the data collection unit to gather data from diverse sources and comprehensively understand the factors that influence sales.

[0031] The analysis department analyzes the information collected by the data collection department. For example, the analysis department analyzes complex information, including historical sales data, and considers seasonality and trends. Specifically, it can analyze historical sales data and consider seasonality and trends. Historical sales data includes monthly, weekly, and daily sales data, and by analyzing this data, it is possible to understand sales fluctuation patterns related to specific seasons or events. The analysis department can also analyze real-time information and consider seasonality and trends. Real-time information includes current weather, competitor price fluctuations, and the effectiveness of promotions, and by analyzing this information, it is possible to make sales forecasts that are in line with current market conditions. Furthermore, the analysis department can analyze complex information and consider seasonality and trends. For example, it can analyze historical sales data and consider seasonality and trends. It can also analyze real-time information and consider seasonality and trends. It can also analyze complex information and consider seasonality and trends. This allows the analysis department to analyze the collected information from multiple perspectives and comprehensively evaluate the factors influencing sales. Furthermore, the analysis department can use AI to analyze data and perform more advanced analysis. For example, machine learning algorithms can be used to learn patterns from past data and predict future sales. Furthermore, natural language processing technology can be used to analyze collected text data (e.g., social media posts and news articles) to understand consumer opinions and market trends. This allows the analytics department to make more accurate sales forecasts and contribute to the development of business strategies.

[0032] The forecasting unit predicts sales based on the analysis results obtained by the analysis unit. Specifically, it can predict sales using time series analysis. Time series analysis is a method for predicting future sales based on past sales data, and it allows for predictions that take seasonality and trends into account. The forecasting unit can also predict sales using regression analysis. Regression analysis is a method for modeling the relationship between sales and multiple factors that influence sales (e.g., weather, events, competitor prices, etc.). Furthermore, the forecasting unit can predict sales using prediction models. Prediction models include models using machine learning algorithms and models using statistical methods, and using these models enables more accurate sales forecasts. For example, the forecasting unit can predict sales using time series analysis, regression analysis, and prediction models. This allows the forecasting unit to predict sales with high accuracy based on analysis results, which can be used to formulate business strategies. Furthermore, the forecasting unit can improve prediction accuracy using AI. For example, by building a predictive model using deep learning and learning complex patterns from past data, more accurate sales forecasts can be made. Furthermore, the forecasting unit can continuously revise its prediction results based on real-time updated data, adapting to the latest situations. This allows the forecasting unit to always provide highly accurate sales forecasts based on the latest information, supporting the rapid and appropriate development of business strategies.

[0033] The adjustment unit adjusts incoming quantities based on sales forecasted by the forecasting unit. Specifically, it can adjust incoming quantities while considering inventory levels. Inventory levels are calculated based on current inventory status and past inventory consumption patterns, which allows for the determination of appropriate incoming quantities. The adjustment unit can also adjust incoming quantities while considering demand forecasts. Demand forecasts are made based on sales forecast data provided by the forecasting unit, which ensures appropriate incoming quantities in line with demand. Furthermore, the adjustment unit can adjust incoming quantities while considering supply chain constraints. Supply chain constraints include the production capacity of suppliers, transportation constraints, and delivery deadlines. By considering these constraints, a realistic and efficient incoming quantity plan can be created. For example, the adjustment unit can adjust incoming quantities while considering inventory levels. It can also adjust incoming quantities while considering demand forecasts. It can also adjust incoming quantities while considering supply chain constraints. This allows the adjustment unit to ensure appropriate incoming quantities based on forecasted sales, minimizing unsold inventory and stockouts. Furthermore, the adjustment unit can improve the accuracy of incoming quantity adjustments using AI. For example, machine learning algorithms can be used to learn from past incoming and demand data and predict optimal incoming quantities. Furthermore, the adjustment unit can continuously revise the receiving plan based on real-time updated data, enabling it to respond to the latest situation. This allows the adjustment unit to always perform highly accurate receiving adjustments based on the latest information, achieving efficient inventory management.

[0034] The integration unit links the incoming quantity adjusted by the adjustment unit with the inventory management system. For example, the integration unit manages inventory in conjunction with the inventory management system. Specifically, it can integrate with the inventory management system using API integration. API integration provides an interface for exchanging data between different systems, enabling real-time data synchronization. The integration unit can also integrate with the inventory management system using data synchronization. Data synchronization is a method of periodically updating data to ensure consistency between the inventory management system and the adjustment unit's data. Furthermore, the integration unit can also integrate with the inventory management system using real-time updates. Real-time updates reflect data changes immediately, allowing for constant monitoring of the latest inventory status. For example, the integration unit can integrate with the inventory management system using API integration. It can also integrate with the inventory management system using data synchronization. It can also integrate with the inventory management system using real-time updates. This allows the integration unit to accurately reflect the incoming quantity adjusted by the adjustment unit in the inventory management system, achieving efficient inventory management. Furthermore, the integration unit can improve the efficiency of integration using AI. For example, machine learning algorithms can be used to optimize data synchronization timing and update frequency. Furthermore, the integration unit can use anomaly detection algorithms to detect data inconsistencies and abnormal patterns, enabling early intervention. This allows the integration unit to perform highly accurate inventory management based on the latest information at all times, supporting efficient business operations.

[0035] The data collection unit can collect information such as weather, local events, competitor pricing, and promotional information. For example, the data collection unit can collect weather forecast data to understand factors that affect sales. The data collection unit can also collect local event information to understand factors that affect sales. The data collection unit can also collect competitor pricing information to understand factors that affect sales. The data collection unit can also collect promotional information to understand factors that affect sales. In this way, by collecting information such as weather, local events, competitor pricing, and promotional information, it is possible to understand factors that affect sales. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather forecast data into a generating AI and have the generating AI perform analysis of the weather forecast data.

[0036] The analysis department can analyze complex information, including historical sales data, and consider seasonality and trends. For example, the analysis department can analyze historical sales data and consider seasonality and trends. The analysis department can also analyze real-time information and consider seasonality and trends. The analysis department can also analyze complex information and consider seasonality and trends. This allows for more accurate sales forecasts by analyzing complex information, including historical sales data, and considering seasonality and trends. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input historical sales data into a generating AI and have the generating AI perform seasonality and trend analysis.

[0037] The forecasting unit can forecast sales based on the analysis results obtained by the analysis unit. For example, the forecasting unit can forecast sales based on the analysis results. For example, the forecasting unit can forecast sales using time series analysis. For example, the forecasting unit can forecast sales using regression analysis. For example, the forecasting unit can forecast sales using a forecasting model. This makes it possible to forecast sales more accurately by forecasting sales based on the analysis results. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input the analysis results into a generating AI and have the generating AI perform the sales forecast.

[0038] The adjustment unit can adjust incoming quantities based on predicted sales. The adjustment unit can adjust incoming quantities based on predicted sales, for example. The adjustment unit can adjust incoming quantities considering inventory levels, for example. The adjustment unit can also adjust incoming quantities considering demand forecasts, for example. The adjustment unit can also adjust incoming quantities considering supply chain constraints, for example. This minimizes unsold inventory by adjusting incoming quantities based on predicted sales. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input predicted sales into a generating AI and have the generating AI perform the adjustment of incoming quantities.

[0039] The integration unit can perform inventory management in conjunction with the inventory management system. For example, the integration unit can perform inventory management in conjunction with the inventory management system. For example, the integration unit can integrate with the inventory management system using API integration. For example, the integration unit can also integrate with the inventory management system using data synchronization. For example, the integration unit can also integrate with the inventory management system using real-time updates. This enables proper inventory management by performing inventory management in conjunction with the inventory management system. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data from the inventory management system into a generating AI and have the generating AI perform the inventory management integration.

[0040] The data collection unit can analyze past collected data and select the optimal information collection method. For example, the data collection unit can identify information from past collected data that can be collected at specific times to improve accuracy. For example, the data collection unit can also confirm that data from specific information sources is useful based on past collected data and prioritize those information sources. For example, the data collection unit can analyze past collected data, identify areas for improvement in the collection method, and select the optimal collection method. As a result, the accuracy of information collection is improved by analyzing past collected data and selecting the optimal information collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal information collection method.

[0041] The data collection unit can filter the data to be collected based on specific events or seasons. For example, the data collection unit can prioritize the collection of relevant information based on seasonal event information. For example, the data collection unit can also collect information related to a specific event during the period in which that event is held. For example, the data collection unit can filter and collect relevant information by considering seasonal sales trends. This allows for the efficient collection of highly relevant information by filtering the data to be collected based on specific events or seasons. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input event information into a generating AI and have the generating AI perform the filtering of the data to be collected.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of nearby event information based on the user's current location. For example, the data collection unit can also prioritize the collection of weather information for a specific region based on geographical location information. For example, the data collection unit can also prioritize the collection of price information from competing stores by considering geographical location information. This enables region-specific information collection by prioritizing the collection of highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0043] The data collection unit can analyze social media activity and collect relevant information during data collection. For example, the data collection unit can collect event information that is trending on social media. For example, the data collection unit can analyze social media posts and collect information that affects sales. For example, the data collection unit can collect relevant promotional information based on social media trends. This enables trend-based information collection by analyzing social media activity and collecting relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI collect relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data. For example, the analysis unit can also perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis based on the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a sales forecasting algorithm to sales data. For example, the analysis unit can apply a weather forecasting algorithm to weather data. For example, the analysis unit can apply a trend analysis algorithm to social media data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. The analysis unit may also adjust the analysis schedule based on the submission date. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The prediction unit can improve the accuracy of its predictions by considering the interrelationships between data. For example, the prediction unit can make predictions by considering the interrelationships between sales data and weather data. The prediction unit can also make predictions by considering the interrelationships between social media data and sales data. The prediction unit can also make predictions by considering the interrelationships between past sales data and current promotional information. By improving the accuracy of predictions by considering the interrelationships between data, more accurate predictions become possible. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0049] The prediction unit can make predictions while considering the attribute information of the data submitter. For example, the prediction unit can make predictions while considering the age group of the submitter. The prediction unit can also make predictions while considering the region information of the submitter. The prediction unit can also make predictions while considering the purchase history of the submitter. This makes it possible to make more personalized predictions by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the submitter's attribute information into a generating AI and have the generating AI execute the prediction.

[0050] The forecasting unit can perform forecasts while considering the geographical distribution of the data. For example, the forecasting unit can perform sales forecasts for each region based on the geographical distribution. The forecasting unit can also, for example, forecast demand for a specific region while considering the geographical distribution. The forecasting unit can also, for example, forecast the promotional effect for each region based on the geographical distribution. This makes it possible to forecast demand for each region by performing forecasts while considering the geographical distribution of the data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input geographical distribution data into a generating AI and have the generating AI perform the forecast.

[0051] The prediction unit can improve the accuracy of its predictions by referring to relevant literature during the prediction process. For example, the prediction unit can improve the sales forecasting algorithm based on relevant literature. The prediction unit can also improve the accuracy of its predictions by referring to relevant literature. For example, the prediction unit can introduce new prediction methods based on relevant literature. This makes it possible to make more reliable predictions by improving the accuracy of predictions by referring to relevant literature. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0052] The adjustment unit can analyze past adjustment data to select the optimal adjustment method during adjustment. For example, the adjustment unit selects the optimal adjustment method based on past adjustment data. The adjustment unit can also analyze past adjustment data to identify areas for improvement in the adjustment method. For example, the adjustment unit can determine the priority of adjustment methods based on past adjustment data. This improves the accuracy of the adjustment by analyzing past adjustment data and selecting the optimal adjustment method. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input past adjustment data into a generating AI and have the generating AI select the optimal adjustment method.

[0053] The adjustment unit can customize the means of adjustment based on the current situation during the adjustment process. For example, the adjustment unit can customize the means of adjustment based on the current inventory status. The adjustment unit can also customize the means of adjustment based on current market trends. The adjustment unit can also customize the means of adjustment based on current demand forecasts. By customizing the means of adjustment based on the current situation, more appropriate adjustments become possible. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input current situation data into a generating AI and have the generating AI perform the customization of the means of adjustment.

[0054] The adjustment unit can select the optimal adjustment method while considering geographical location information. For example, the adjustment unit can select an adjustment method that corresponds to the demand of a specific region based on geographical location information. The adjustment unit can also select the optimal delivery route while considering geographical location information. For example, the adjustment unit can select an adjustment method that corresponds to the inventory status of a specific region based on geographical location information. This makes it possible to perform region-specific adjustments by selecting the optimal adjustment method while considering geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal adjustment method.

[0055] The adjustment unit can analyze social media activity and propose adjustment methods during the adjustment process. For example, the adjustment unit can propose adjustment methods for products that are trending on social media. The adjustment unit can also analyze social media posts and propose adjustment methods that meet demand. The adjustment unit can also propose adjustment methods based on social media trends. This enables trend-based adjustments by analyzing social media activity and proposing adjustment methods. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input social media data into a generating AI and have the generating AI execute the proposal of adjustment methods.

[0056] The integration unit can analyze past integration data to select the optimal integration method during integration. For example, the integration unit can select the optimal integration method based on past integration data. The integration unit can also analyze past integration data to identify areas for improvement in the integration method. For example, the integration unit can determine the priority of integration methods based on past integration data. This improves the accuracy of integration by analyzing past integration data and selecting the optimal integration method. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input past integration data into a generating AI and have the generating AI select the optimal integration method.

[0057] The integration unit can customize the means of integration based on the current situation during integration. For example, the integration unit can customize the means of integration based on the current inventory status. For example, the integration unit can also customize the means of integration based on current market trends. For example, the integration unit can also customize the means of integration based on current demand forecasts. By customizing the means of integration based on the current situation, more appropriate integration becomes possible. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input current situation data into a generating AI and have the generating AI perform the customization of the means of integration.

[0058] The collaboration unit can select the optimal collaboration method by considering geographical location information during collaboration. For example, the collaboration unit can select a collaboration method that meets the demand of a specific region based on geographical location information. The collaboration unit can also select the optimal delivery route by considering geographical location information. The collaboration unit can also select a collaboration method that meets the inventory status of a specific region based on geographical location information. This enables region-specific collaboration by selecting the optimal collaboration method by considering geographical location information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input geographical location information into a generating AI and have the generating AI select the optimal collaboration method.

[0059] The collaboration unit can analyze social media activity and propose collaboration methods during the collaboration process. For example, the collaboration unit can propose collaboration methods for products that are trending on social media. The collaboration unit can also analyze social media posts and propose collaboration methods that meet the demand. The collaboration unit can also propose collaboration methods based on social media trends. This enables trend-based collaboration by analyzing social media activity and proposing collaboration methods. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input social media data into a generating AI and have the generating AI propose collaboration methods.

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

[0061] The sales forecasting system can further analyze users' purchase history and provide personalized promotions to individual users. For example, the data collection unit collects users' past purchase history, and the analysis unit analyzes users' purchasing patterns based on that data. Next, the forecasting unit predicts future purchasing behavior based on the user's purchasing patterns. The adjustment unit then provides optimal promotions to individual users based on the predicted purchasing behavior. This can increase users' purchasing intent and boost sales.

[0062] The sales forecasting system can further develop competitive promotional strategies by collecting and analyzing promotional information from rival stores. For example, the data collection unit collects promotional information from rival stores, and the analysis unit analyzes the rival stores' promotional strategies based on that data. Next, the forecasting unit predicts the company's own promotional strategy based on the rival stores' promotional strategies. The adjustment unit then implements promotions to compete with rival stores based on the predicted promotional strategy. This allows the company to compete with rival stores and increase sales.

[0063] The sales forecasting system can further analyze trends on social media and conduct trend-based promotions. For example, the data collection unit gathers trend information from social media, and the analysis unit analyzes the trends based on that data. Next, the forecasting unit predicts the optimal promotion content based on the trends. The adjustment unit then implements the promotion in accordance with the trends based on the predicted promotion content. In this way, by conducting trend-based promotions, it is possible to attract user interest and increase sales.

[0064] The sales forecasting system can further analyze seasonal sales data and implement seasonally tailored promotions. For example, the data collection unit collects historical seasonal sales data, and the analysis unit analyzes seasonal sales trends based on that data. Next, the forecasting unit predicts the optimal promotion content based on the seasonal sales trends. The adjustment unit then implements seasonally tailored promotions based on the predicted promotion content. This allows for increased sales through seasonally tailored promotions.

[0065] The sales forecasting system can also take geographical location information into account to conduct region-specific promotions. For example, the data collection unit collects users' geographical location information, and the analysis unit analyzes sales trends for each region based on that data. Next, the forecasting unit predicts the optimal promotion content based on the sales trends for each region. The adjustment unit then implements region-specific promotions based on the predicted promotion content. This allows for increased sales through region-specific promotions.

[0066] The sales forecasting system can further analyze users' purchase history and predict demand for specific products. For example, the data collection unit collects users' past purchase history, and the analysis unit analyzes demand for specific products based on that data. Next, the forecasting unit predicts demand for specific products, and the adjustment unit adjusts product inventory based on the predicted demand. This allows for increased sales by predicting demand for specific products and appropriately managing inventory.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection department gathers information. For example, they collect weather forecast data, local event information, competitor pricing information, and promotional information to understand the factors that influence sales. Step 2: The analysis department analyzes the information collected by the data collection department. For example, it analyzes complex information including historical sales data, taking seasonality and trends into consideration. Step 3: The forecasting unit forecasts sales based on the analysis results obtained by the analysis unit. For example, it may use time series analysis, regression analysis, or forecasting models to forecast sales. Step 4: The adjustment unit adjusts the incoming quantity based on the sales forecasted by the forecasting unit. For example, it adjusts the incoming quantity considering inventory levels, demand forecasts, and supply chain constraints. Step 5: The integration unit connects the incoming quantity adjusted by the adjustment unit to the inventory management system. For example, it connects to the inventory management system using API integration, data synchronization, and real-time updates.

[0069] (Example of form 2) The sales forecasting system according to an embodiment of the present invention is a system that minimizes unsold inventory by using an AI agent to predict sales from a combination of factors and adjusting the amount of goods received. The sales forecasting system uses an AI agent to collect and analyze a combination of information in real time. Next, it predicts sales based on the collected information and adjusts the amount of goods received. This minimizes unsold inventory. For example, the sales forecasting system collects information that includes external factors such as weather and surrounding events. For example, this includes weather forecasts and local event information. This makes it possible to understand the factors that affect sales. Next, the sales forecasting system uses an AI agent to analyze the collected information. The AI ​​agent predicts sales based on past sales data and real-time information. For example, it can predict sales for a specific day by combining past sales data and weather forecasts. This enables more accurate sales predictions. Furthermore, the sales forecasting system adjusts the amount of goods received based on the sales forecast. The AI ​​agent determines the appropriate amount of goods received based on the predicted sales. For example, if sales are predicted to increase, the amount of goods received is increased, and if sales are predicted to decrease, the amount of goods received is decreased. In this way, unsold inventory can be minimized. This system minimizes unsold inventory in the retail industry in general, especially in the sale of perishable foods. By having an AI agent analyze complex information in real time and predict sales, it contributes to a more environmentally friendly society. As a result, the sales forecasting system minimizes unsold inventory.

[0070] The sales forecasting system according to this embodiment comprises a collection unit, an analysis unit, a forecasting unit, an adjustment unit, and a linking unit. The collection unit collects information. The collection unit collects, for example, weather, local events, competitor pricing information, and promotional information. For example, the collection unit collects weather forecast data to understand factors that affect sales. The collection unit can also collect local event information to understand factors that affect sales. Furthermore, the collection unit can collect competitor pricing information to understand factors that affect sales. For example, the collection unit collects weather forecast data in real time to understand factors that affect sales. It can also collect local event information to understand factors that affect sales. It can also collect competitor pricing information to understand factors that affect sales. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes complex information, including past sales data, and considers seasonality and trends. For example, the analysis unit can analyze past sales data and consider seasonality and trends. Furthermore, the analysis unit can analyze real-time information and consider seasonality and trends. Furthermore, the analysis unit can analyze complex information and consider seasonality and trends. For example, the analysis unit can analyze historical sales data and consider seasonality and trends. It can also analyze real-time information and consider seasonality and trends. It can also analyze complex information and consider seasonality and trends. The forecasting unit forecasts sales based on the analysis results obtained by the analysis unit. The forecasting unit forecasts sales based on the analysis results, for example. The forecasting unit can forecast sales using time series analysis, for example. The forecasting unit can also forecast sales using regression analysis. Furthermore, the forecasting unit can forecast sales using a forecasting model. For example, the forecasting unit forecasts sales using time series analysis. It can also forecast sales using regression analysis. It can also forecast sales using a forecasting model. The adjustment unit adjusts incoming quantities based on the sales forecasted by the forecasting unit. The adjustment unit adjusts incoming quantities based on the forecasted sales, for example. The adjustment unit can adjust incoming quantities considering inventory levels, for example.Furthermore, the adjustment unit can also adjust the incoming quantity considering demand forecasts. In addition, the adjustment unit can also adjust the incoming quantity considering supply chain constraints. For example, the adjustment unit adjusts the incoming quantity considering inventory levels. It can also adjust the incoming quantity considering demand forecasts. It can also adjust the incoming quantity considering supply chain constraints. The integration unit integrates the incoming quantity adjusted by the adjustment unit with the inventory management system. The integration unit performs inventory management in conjunction with the inventory management system, for example. The integration unit can integrate with the inventory management system using API integration, for example. It can also integrate with the inventory management system using data synchronization. Furthermore, the integration unit can also integrate with the inventory management system using real-time updates. For example, the integration unit integrates with the inventory management system using API integration. It can also integrate with the inventory management system using data synchronization. It can also integrate with the inventory management system using real-time updates. As a result, the sales forecasting system according to the embodiment can minimize unsold inventory.

[0071] The data collection department collects information. For example, it collects weather data, local events, competitor pricing information, and promotional information. Specifically, it collects weather forecast data to understand factors influencing sales. Weather forecast data includes detailed meteorological information such as temperature, precipitation, and wind speed, and this data can directly impact sales. For example, indoor consumption tends to increase on rainy days, while outdoor consumption tends to increase on sunny days, making the collection of this data important. The data collection department can also collect local event information to understand factors influencing sales. Local event information includes festivals, concerts, and sporting events, and these events can significantly impact sales of specific products. Furthermore, the data collection department can collect competitor pricing information to understand factors influencing sales. Competitor pricing information is important for understanding price trends for identical and similar products, allowing for appropriate adjustments to the company's pricing strategy. For example, the data collection department collects weather forecast data in real time to understand factors influencing sales. It can also collect local event information to understand factors influencing sales. It can also collect competitor pricing information to understand factors influencing sales. This allows the data collection unit to gather data from diverse sources and comprehensively understand the factors that influence sales.

[0072] The analysis department analyzes the information collected by the data collection department. For example, the analysis department analyzes complex information, including historical sales data, and considers seasonality and trends. Specifically, it can analyze historical sales data and consider seasonality and trends. Historical sales data includes monthly, weekly, and daily sales data, and by analyzing this data, it is possible to understand sales fluctuation patterns related to specific seasons or events. The analysis department can also analyze real-time information and consider seasonality and trends. Real-time information includes current weather, competitor price fluctuations, and the effectiveness of promotions, and by analyzing this information, it is possible to make sales forecasts that are in line with current market conditions. Furthermore, the analysis department can analyze complex information and consider seasonality and trends. For example, it can analyze historical sales data and consider seasonality and trends. It can also analyze real-time information and consider seasonality and trends. It can also analyze complex information and consider seasonality and trends. This allows the analysis department to analyze the collected information from multiple perspectives and comprehensively evaluate the factors influencing sales. Furthermore, the analysis department can use AI to analyze data and perform more advanced analysis. For example, machine learning algorithms can be used to learn patterns from past data and predict future sales. Furthermore, natural language processing technology can be used to analyze collected text data (e.g., social media posts and news articles) to understand consumer opinions and market trends. This allows the analytics department to make more accurate sales forecasts and contribute to the development of business strategies.

[0073] The forecasting unit predicts sales based on the analysis results obtained by the analysis unit. Specifically, it can predict sales using time series analysis. Time series analysis is a method for predicting future sales based on past sales data, and it allows for predictions that take seasonality and trends into account. The forecasting unit can also predict sales using regression analysis. Regression analysis is a method for modeling the relationship between sales and multiple factors that influence sales (e.g., weather, events, competitor prices, etc.). Furthermore, the forecasting unit can predict sales using prediction models. Prediction models include models using machine learning algorithms and models using statistical methods, and using these models enables more accurate sales forecasts. For example, the forecasting unit can predict sales using time series analysis, regression analysis, and prediction models. This allows the forecasting unit to predict sales with high accuracy based on analysis results, which can be used to formulate business strategies. Furthermore, the forecasting unit can improve prediction accuracy using AI. For example, by building a predictive model using deep learning and learning complex patterns from past data, more accurate sales forecasts can be made. Furthermore, the forecasting unit can continuously revise its prediction results based on real-time updated data, adapting to the latest situations. This allows the forecasting unit to always provide highly accurate sales forecasts based on the latest information, supporting the rapid and appropriate development of business strategies.

[0074] The adjustment unit adjusts incoming quantities based on sales forecasted by the forecasting unit. Specifically, it can adjust incoming quantities while considering inventory levels. Inventory levels are calculated based on current inventory status and past inventory consumption patterns, which allows for the determination of appropriate incoming quantities. The adjustment unit can also adjust incoming quantities while considering demand forecasts. Demand forecasts are made based on sales forecast data provided by the forecasting unit, which ensures appropriate incoming quantities in line with demand. Furthermore, the adjustment unit can adjust incoming quantities while considering supply chain constraints. Supply chain constraints include the production capacity of suppliers, transportation constraints, and delivery deadlines. By considering these constraints, a realistic and efficient incoming quantity plan can be created. For example, the adjustment unit can adjust incoming quantities while considering inventory levels. It can also adjust incoming quantities while considering demand forecasts. It can also adjust incoming quantities while considering supply chain constraints. This allows the adjustment unit to ensure appropriate incoming quantities based on forecasted sales, minimizing unsold inventory and stockouts. Furthermore, the adjustment unit can improve the accuracy of incoming quantity adjustments using AI. For example, machine learning algorithms can be used to learn from past incoming and demand data and predict optimal incoming quantities. Furthermore, the adjustment unit can continuously revise the receiving plan based on real-time updated data, enabling it to respond to the latest situation. This allows the adjustment unit to always perform highly accurate receiving adjustments based on the latest information, achieving efficient inventory management.

[0075] The integration unit links the incoming quantity adjusted by the adjustment unit with the inventory management system. For example, the integration unit manages inventory in conjunction with the inventory management system. Specifically, it can integrate with the inventory management system using API integration. API integration provides an interface for exchanging data between different systems, enabling real-time data synchronization. The integration unit can also integrate with the inventory management system using data synchronization. Data synchronization is a method of periodically updating data to ensure consistency between the inventory management system and the adjustment unit's data. Furthermore, the integration unit can also integrate with the inventory management system using real-time updates. Real-time updates reflect data changes immediately, allowing for constant monitoring of the latest inventory status. For example, the integration unit can integrate with the inventory management system using API integration. It can also integrate with the inventory management system using data synchronization. It can also integrate with the inventory management system using real-time updates. This allows the integration unit to accurately reflect the incoming quantity adjusted by the adjustment unit in the inventory management system, achieving efficient inventory management. Furthermore, the integration unit can improve the efficiency of integration using AI. For example, machine learning algorithms can be used to optimize data synchronization timing and update frequency. Furthermore, the integration unit can use anomaly detection algorithms to detect data inconsistencies and abnormal patterns, enabling early intervention. This allows the integration unit to perform highly accurate inventory management based on the latest information at all times, supporting efficient business operations.

[0076] The data collection unit can collect information such as weather, local events, competitor pricing, and promotional information. For example, the data collection unit can collect weather forecast data to understand factors that affect sales. The data collection unit can also collect local event information to understand factors that affect sales. The data collection unit can also collect competitor pricing information to understand factors that affect sales. The data collection unit can also collect promotional information to understand factors that affect sales. In this way, by collecting information such as weather, local events, competitor pricing, and promotional information, it is possible to understand factors that affect sales. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather forecast data into a generating AI and have the generating AI perform analysis of the weather forecast data.

[0077] The analysis department can analyze complex information, including historical sales data, and consider seasonality and trends. For example, the analysis department can analyze historical sales data and consider seasonality and trends. The analysis department can also analyze real-time information and consider seasonality and trends. The analysis department can also analyze complex information and consider seasonality and trends. This allows for more accurate sales forecasts by analyzing complex information, including historical sales data, and considering seasonality and trends. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input historical sales data into a generating AI and have the generating AI perform seasonality and trend analysis.

[0078] The forecasting unit can forecast sales based on the analysis results obtained by the analysis unit. For example, the forecasting unit can forecast sales based on the analysis results. For example, the forecasting unit can forecast sales using time series analysis. For example, the forecasting unit can forecast sales using regression analysis. For example, the forecasting unit can forecast sales using a forecasting model. This makes it possible to forecast sales more accurately by forecasting sales based on the analysis results. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input the analysis results into a generating AI and have the generating AI perform the sales forecast.

[0079] The adjustment unit can adjust incoming quantities based on predicted sales. The adjustment unit can adjust incoming quantities based on predicted sales, for example. The adjustment unit can adjust incoming quantities considering inventory levels, for example. The adjustment unit can also adjust incoming quantities considering demand forecasts, for example. The adjustment unit can also adjust incoming quantities considering supply chain constraints, for example. This minimizes unsold inventory by adjusting incoming quantities based on predicted sales. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input predicted sales into a generating AI and have the generating AI perform the adjustment of incoming quantities.

[0080] The integration unit can perform inventory management in conjunction with the inventory management system. For example, the integration unit can perform inventory management in conjunction with the inventory management system. For example, the integration unit can integrate with the inventory management system using API integration. For example, the integration unit can also integrate with the inventory management system using data synchronization. For example, the integration unit can also integrate with the inventory management system using real-time updates. This enables proper inventory management by performing inventory management in conjunction with the inventory management system. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data from the inventory management system into a generating AI and have the generating AI perform the inventory management integration.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of information collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can speed up the timing of information collection to provide the necessary information immediately. By adjusting the timing of information collection based on the user's emotions, the user's burden is reduced and efficient information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of information collection.

[0082] The data collection unit can analyze past collected data and select the optimal information collection method. For example, the data collection unit can identify information from past collected data that can be collected at specific times to improve accuracy. For example, the data collection unit can also confirm that data from specific information sources is useful based on past collected data and prioritize those information sources. For example, the data collection unit can analyze past collected data, identify areas for improvement in the collection method, and select the optimal collection method. As a result, the accuracy of information collection is improved by analyzing past collected data and selecting the optimal information collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal information collection method.

[0083] The data collection unit can filter the data to be collected based on specific events or seasons. For example, the data collection unit can prioritize the collection of relevant information based on seasonal event information. For example, the data collection unit can also collect information related to a specific event during the period in which that event is held. For example, the data collection unit can filter and collect relevant information by considering seasonal sales trends. This allows for the efficient collection of highly relevant information by filtering the data to be collected based on specific events or seasons. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input event information into a generating AI and have the generating AI perform the filtering of the data to be collected.

[0084] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information of high importance. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed information. For example, if the user is in a hurry, the data collection unit may prioritize collecting information that can be collected quickly. This allows for information collection that meets the user's needs by prioritizing information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information collection.

[0085] The data collection unit can prioritize the collection of highly relevant information by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of nearby event information based on the user's current location. For example, the data collection unit can also prioritize the collection of weather information for a specific region based on geographical location information. For example, the data collection unit can also prioritize the collection of price information from competing stores by considering geographical location information. This enables region-specific information collection by prioritizing the collection of highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0086] The data collection unit can analyze social media activity and collect relevant information during data collection. For example, the data collection unit can collect event information that is trending on social media. For example, the data collection unit can analyze social media posts and collect information that affects sales. For example, the data collection unit can collect relevant promotional information based on social media trends. This enables trend-based information collection by analyzing social media activity and collecting relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI collect relevant information.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data. For example, the analysis unit can also perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis based on the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a sales forecasting algorithm to sales data. For example, the analysis unit can apply a weather forecasting algorithm to weather data. For example, the analysis unit can apply a trend analysis algorithm to social media data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. For example, if the user is excited, the analysis unit can also provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide analysis results that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0091] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. The analysis unit may also adjust the analysis schedule based on the submission date. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0093] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated emotions. For example, if the user is relaxed, the prediction unit may use detailed prediction criteria. For example, if the user is in a hurry, the prediction unit may use simplified prediction criteria. For example, if the user is excited, the prediction unit may use prediction criteria with visually stimulating effects. By adjusting the prediction criteria based on the user's emotions, prediction results that meet the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the prediction criteria.

[0094] The prediction unit can improve the accuracy of its predictions by considering the interrelationships between data. For example, the prediction unit can make predictions by considering the interrelationships between sales data and weather data. The prediction unit can also make predictions by considering the interrelationships between social media data and sales data. The prediction unit can also make predictions by considering the interrelationships between past sales data and current promotional information. By improving the accuracy of predictions by considering the interrelationships between data, more accurate predictions become possible. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0095] The prediction unit can make predictions while considering the attribute information of the data submitter. For example, the prediction unit can make predictions while considering the age group of the submitter. The prediction unit can also make predictions while considering the region information of the submitter. The prediction unit can also make predictions while considering the purchase history of the submitter. This makes it possible to make more personalized predictions by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the submitter's attribute information into a generating AI and have the generating AI execute the prediction.

[0096] The prediction unit can estimate the user's emotions and adjust the order in which prediction results are displayed based on the estimated emotions. For example, if the user is nervous, the prediction unit can display important prediction results first. If the user is relaxed, the prediction unit can also display detailed prediction results in a sequential manner. If the user is in a hurry, the prediction unit can also display concise prediction results first. By adjusting the order in which prediction results are displayed based on the user's emotions, prediction results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the display order of prediction results.

[0097] The forecasting unit can perform forecasts while considering the geographical distribution of the data. For example, the forecasting unit can perform sales forecasts for each region based on the geographical distribution. The forecasting unit can also, for example, forecast demand for a specific region while considering the geographical distribution. The forecasting unit can also, for example, forecast the promotional effect for each region based on the geographical distribution. This makes it possible to forecast demand for each region by performing forecasts while considering the geographical distribution of the data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input geographical distribution data into a generating AI and have the generating AI perform the forecast.

[0098] The prediction unit can improve the accuracy of its predictions by referring to relevant literature during the prediction process. For example, the prediction unit can improve the sales forecasting algorithm based on relevant literature. The prediction unit can also improve the accuracy of its predictions by referring to relevant literature. For example, the prediction unit can introduce new prediction methods based on relevant literature. This makes it possible to make more reliable predictions by improving the accuracy of predictions by referring to relevant literature. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0099] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated user emotions. For example, if the user is tense, the adjustment unit can provide a simple and easily understandable adjustment method. For example, if the user is relaxed, the adjustment unit can also provide a detailed adjustment method. For example, if the user is in a hurry, the adjustment unit can also provide a concise adjustment method. In this way, by adjusting the adjustment method based on the user's emotions, an adjustment method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the adjustment method.

[0100] The adjustment unit can analyze past adjustment data to select the optimal adjustment method during adjustment. For example, the adjustment unit selects the optimal adjustment method based on past adjustment data. The adjustment unit can also analyze past adjustment data to identify areas for improvement in the adjustment method. For example, the adjustment unit can determine the priority of adjustment methods based on past adjustment data. This improves the accuracy of the adjustment by analyzing past adjustment data and selecting the optimal adjustment method. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input past adjustment data into a generating AI and have the generating AI select the optimal adjustment method.

[0101] The adjustment unit can customize the means of adjustment based on the current situation during the adjustment process. For example, the adjustment unit can customize the means of adjustment based on the current inventory status. The adjustment unit can also customize the means of adjustment based on current market trends. The adjustment unit can also customize the means of adjustment based on current demand forecasts. By customizing the means of adjustment based on the current situation, more appropriate adjustments become possible. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input current situation data into a generating AI and have the generating AI perform the customization of the means of adjustment.

[0102] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the user is tense, the adjustment unit will prioritize important adjustments. For example, if the user is relaxed, the adjustment unit may also prioritize detailed adjustments. For example, if the user is in a hurry, the adjustment unit may also perform quick adjustments. This allows for adjustments tailored to the user's needs by determining the priority of adjustments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine the priority of adjustments.

[0103] The adjustment unit can select the optimal adjustment method while considering geographical location information. For example, the adjustment unit can select an adjustment method that corresponds to the demand of a specific region based on geographical location information. The adjustment unit can also select the optimal delivery route while considering geographical location information. For example, the adjustment unit can select an adjustment method that corresponds to the inventory status of a specific region based on geographical location information. This makes it possible to perform region-specific adjustments by selecting the optimal adjustment method while considering geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal adjustment method.

[0104] The adjustment unit can analyze social media activity and propose adjustment methods during the adjustment process. For example, the adjustment unit can propose adjustment methods for products that are trending on social media. The adjustment unit can also analyze social media posts and propose adjustment methods that meet demand. The adjustment unit can also propose adjustment methods based on social media trends. This enables trend-based adjustments by analyzing social media activity and proposing adjustment methods. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input social media data into a generating AI and have the generating AI execute the proposal of adjustment methods.

[0105] The interaction unit can estimate the user's emotions and adjust the interaction method based on the estimated emotions. For example, if the user is nervous, the interaction unit can provide a simple and easily understandable interaction method. For example, if the user is relaxed, the interaction unit can also provide a detailed interaction method. For example, if the user is in a hurry, the interaction unit can provide a concise interaction method. In this way, by adjusting the interaction method based on the user's emotions, an easy-to-understand interaction method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI, for example, or not using AI. For example, the interaction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the interaction method.

[0106] The integration unit can analyze past integration data to select the optimal integration method during integration. For example, the integration unit can select the optimal integration method based on past integration data. The integration unit can also analyze past integration data to identify areas for improvement in the integration method. For example, the integration unit can determine the priority of integration methods based on past integration data. This improves the accuracy of integration by analyzing past integration data and selecting the optimal integration method. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input past integration data into a generating AI and have the generating AI select the optimal integration method.

[0107] The integration unit can customize the means of integration based on the current situation during integration. For example, the integration unit can customize the means of integration based on the current inventory status. For example, the integration unit can also customize the means of integration based on current market trends. For example, the integration unit can also customize the means of integration based on current demand forecasts. By customizing the means of integration based on the current situation, more appropriate integration becomes possible. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input current situation data into a generating AI and have the generating AI perform the customization of the means of integration.

[0108] The collaboration unit can estimate the user's emotions and determine the priority of collaborations based on the estimated emotions. For example, if the user is tense, the collaboration unit will prioritize important collaborations. For example, if the user is relaxed, the collaboration unit may also prioritize detailed collaborations. For example, if the user is in a hurry, the collaboration unit may also perform collaborations quickly. This allows for collaborations tailored to the user's needs by determining the priority of collaborations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not using AI. For example, the collaboration unit can input user emotion data into a generative AI and have the generative AI determine the priority of collaborations.

[0109] The collaboration unit can select the optimal collaboration method by considering geographical location information during collaboration. For example, the collaboration unit can select a collaboration method that meets the demand of a specific region based on geographical location information. The collaboration unit can also select the optimal delivery route by considering geographical location information. The collaboration unit can also select a collaboration method that meets the inventory status of a specific region based on geographical location information. This enables region-specific collaboration by selecting the optimal collaboration method by considering geographical location information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input geographical location information into a generating AI and have the generating AI select the optimal collaboration method.

[0110] The collaboration unit can analyze social media activity and propose collaboration methods during the collaboration process. For example, the collaboration unit can propose collaboration methods for products that are trending on social media. The collaboration unit can also analyze social media posts and propose collaboration methods that meet the demand. The collaboration unit can also propose collaboration methods based on social media trends. This enables trend-based collaboration by analyzing social media activity and proposing collaboration methods. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input social media data into a generating AI and have the generating AI propose collaboration methods.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The sales forecasting system can further analyze users' purchase history and provide personalized promotions to individual users. For example, the data collection unit collects users' past purchase history, and the analysis unit analyzes users' purchasing patterns based on that data. Next, the forecasting unit predicts future purchasing behavior based on the user's purchasing patterns. The adjustment unit then provides optimal promotions to individual users based on the predicted purchasing behavior. This can increase users' purchasing intent and boost sales.

[0113] The sales forecasting system can further estimate user emotions and adjust promotional content based on those emotions. For example, the data collection unit collects user emotion data, and the analysis unit analyzes user emotions based on that data. Next, the forecasting unit predicts the optimal promotional content based on user emotions. The adjustment unit then provides appropriate promotions to users based on the predicted promotional content. This allows for increased user purchasing intent by providing promotions that align with user emotions.

[0114] The sales forecasting system can further develop competitive promotional strategies by collecting and analyzing promotional information from rival stores. For example, the data collection unit collects promotional information from rival stores, and the analysis unit analyzes the rival stores' promotional strategies based on that data. Next, the forecasting unit predicts the company's own promotional strategy based on the rival stores' promotional strategies. The adjustment unit then implements promotions to compete with rival stores based on the predicted promotional strategy. This allows the company to compete with rival stores and increase sales.

[0115] The sales forecasting system can further analyze trends on social media and conduct trend-based promotions. For example, the data collection unit gathers trend information from social media, and the analysis unit analyzes the trends based on that data. Next, the forecasting unit predicts the optimal promotion content based on the trends. The adjustment unit then implements the promotion in accordance with the trends based on the predicted promotion content. In this way, by conducting trend-based promotions, it is possible to attract user interest and increase sales.

[0116] The sales forecasting system can further estimate user emotions and adjust product display methods based on those emotions. For example, the data collection unit collects user emotion data, and the analysis unit analyzes user emotions based on that data. Next, the forecasting unit predicts the optimal product display method based on user emotions. The adjustment unit adjusts the product display based on the predicted display method. This allows for increased purchasing intent by displaying products in a way that aligns with user emotions.

[0117] The sales forecasting system can further analyze seasonal sales data and implement seasonally tailored promotions. For example, the data collection unit collects historical seasonal sales data, and the analysis unit analyzes seasonal sales trends based on that data. Next, the forecasting unit predicts the optimal promotion content based on the seasonal sales trends. The adjustment unit then implements seasonally tailored promotions based on the predicted promotion content. This allows for increased sales through seasonally tailored promotions.

[0118] The sales forecasting system can further estimate user emotions and adjust pricing based on those emotions. For example, the data collection unit collects user emotion data, and the analysis unit analyzes user emotions based on that data. Next, the forecasting unit predicts the optimal pricing based on user emotions. The adjustment unit adjusts the product price based on the predicted pricing. This allows for pricing that aligns with user emotions, thereby increasing purchasing intent.

[0119] The sales forecasting system can also take geographical location information into account to conduct region-specific promotions. For example, the data collection unit collects users' geographical location information, and the analysis unit analyzes sales trends for each region based on that data. Next, the forecasting unit predicts the optimal promotion content based on the sales trends for each region. The adjustment unit then implements region-specific promotions based on the predicted promotion content. This allows for increased sales through region-specific promotions.

[0120] The sales forecasting system can further estimate user emotions and adjust how ads are displayed based on those emotions. For example, the data collection unit collects user emotion data, and the analysis unit analyzes user emotions based on that data. Next, the forecasting unit predicts the optimal ad display method based on user emotions. The adjustment unit adjusts the ad display based on the predicted ad display method. This allows for improved advertising effectiveness by displaying ads in accordance with user emotions.

[0121] The sales forecasting system can further analyze users' purchase history and predict demand for specific products. For example, the data collection unit collects users' past purchase history, and the analysis unit analyzes demand for specific products based on that data. Next, the forecasting unit predicts demand for specific products, and the adjustment unit adjusts product inventory based on the predicted demand. This allows for increased sales by predicting demand for specific products and appropriately managing inventory.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection department gathers information. For example, they collect weather forecast data, local event information, competitor pricing information, and promotional information to understand the factors that influence sales. Step 2: The analysis department analyzes the information collected by the data collection department. For example, it analyzes complex information including historical sales data, taking seasonality and trends into consideration. Step 3: The forecasting unit forecasts sales based on the analysis results obtained by the analysis unit. For example, it may use time series analysis, regression analysis, or forecasting models to forecast sales. Step 4: The adjustment unit adjusts the incoming quantity based on the sales forecasted by the forecasting unit. For example, it adjusts the incoming quantity considering inventory levels, demand forecasts, and supply chain constraints. Step 5: The integration unit connects the incoming quantity adjusted by the adjustment unit to the inventory management system. For example, it connects to the inventory management system using API integration, data synchronization, and real-time updates.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, forecasting unit, adjustment unit, and linkage unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information such as weather, surrounding events, competitor pricing information, and promotional information. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The forecasting unit is implemented by the specific processing unit 290 of the data processing device 12 and forecasts sales based on the analysis results. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the incoming quantity based on the predicted sales. The linkage unit is implemented by the control unit 46A of the smart device 14 and links the adjusted incoming quantity with the inventory management system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, forecasting unit, adjustment unit, and linkage unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects weather, surrounding events, competitor price information, promotion information, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and forecasts sales based on the analysis results. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the incoming quantity based on the predicted sales. The linkage unit is implemented by the control unit 46A of the smart glasses 214 and links the adjusted incoming quantity with the inventory management system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, forecasting unit, adjustment unit, and linkage unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information such as weather, surrounding events, competitor pricing information, and promotional information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and forecasts sales based on the analysis results. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the incoming quantity based on the predicted sales. The linkage unit is implemented by the control unit 46A of the headset terminal 314 and links the adjusted incoming quantity with the inventory management system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, forecasting unit, adjustment unit, and linkage unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects weather, surrounding events, competitor price information, promotion information, etc. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The forecasting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and forecasts sales based on the analysis results. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts the incoming quantity based on the predicted sales. The linkage unit is implemented by, for example, the control unit 46A of the robot 414 and links the adjusted incoming quantity with the inventory management system. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A forecasting unit that predicts sales based on the analysis results obtained by the aforementioned analysis unit, An adjustment unit adjusts the quantity of goods received based on the sales predicted by the forecasting unit, The system includes a linking unit that connects the amount of incoming goods adjusted by the adjustment unit with the inventory management system. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect information such as weather, local events, competitor pricing, and promotional information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze a combination of information, including past sales data, and consider seasonality and trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Based on the analysis results obtained by the aforementioned analysis unit, sales are predicted. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, Adjust inventory levels based on projected sales. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, Inventory management is performed in conjunction with an inventory management system. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past collected data to select the optimal information collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, filter the data to be collected based on specific events or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, analyze social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, It estimates the user's emotions and adjusts the prediction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When making predictions, consider the interrelationships between data to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, the attribute information of the data submitters is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, It estimates the user's sentiment and adjusts the order in which the prediction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making predictions, refer to relevant literature to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, It estimates the user's emotions and adjusts the adjustment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, During adjustment, past adjustment data is analyzed to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, During adjustment, customize the adjustment method based on the current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, During the adjustment process, the optimal adjustment method is selected by considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, During the adjustment process, we analyze social media activity and propose adjustment methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, During integration, past integration data is analyzed to select the optimal integration method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, When integrating, customize the integration method based on the current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, When integrating, the optimal integration method will be selected considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, During the collaboration process, we analyze social media activity and propose methods for collaboration. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A forecasting unit that predicts sales based on the analysis results obtained by the aforementioned analysis unit, An adjustment unit adjusts the quantity of goods received based on the sales predicted by the forecasting unit, The system includes a linking unit that connects the amount of incoming goods adjusted by the adjustment unit with the inventory management system. A system characterized by the following features.

2. The aforementioned collection unit is We collect information such as weather, local events, competitor pricing, and promotional information. The system according to feature 1.

3. The aforementioned analysis unit is Analyze a combination of information, including past sales data, and consider seasonality and trends. The system according to feature 1.

4. The prediction unit, Based on the analysis results obtained by the aforementioned analysis unit, sales are predicted. The system according to feature 1.

5. The adjustment unit is, Adjust inventory levels based on projected sales. The system according to feature 1.

6. The aforementioned linkage unit is, Inventory management is performed in conjunction with an inventory management system. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past collected data to select the optimal information collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, filter the data to be collected based on specific events or seasons. The system according to feature 1.

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