System

Abstract

An object of a system according to an embodiment is to solve a food loss problem by appropriately performing demand prediction and inventory management.SOLUTION: A system includes a demand prediction unit, an inventory management unit, and an information sharing unit. The demand prediction unit predicts demand in consideration of past sales data or external factors. The inventory management unit manages inventory based on the demand predicted by the demand prediction unit. The information sharing unit provides the inventory information managed by the inventory management unit to a person concerned.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies lack sufficient demand forecasting and inventory management, which can lead to food waste problems.

[0005] The system according to the embodiment aims to solve the food waste problem by appropriately carrying out demand forecasting and inventory management. [Means for solving the problem]

[0006] The system according to the embodiment includes a demand forecasting unit, an inventory management unit, and an information sharing unit. The demand forecasting unit forecasts demand taking into account past sales data or external factors. The inventory management unit manages inventory based on the demand forecasted by the demand forecasting unit. The information sharing unit provides inventory information managed by the inventory management unit to relevant parties. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately perform demand forecasting and inventory management, thereby solving the food waste problem. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is an AI-based demand forecasting, inventory management, and information sharing platform for stakeholders to solve the food waste problem in the restaurant industry. This system predicts demand, manages inventory, and shares information with stakeholders, taking into account factors such as past sales data, seasons, weather, and events. For example, AI learns sales trends under specific seasons and weather conditions from past sales data and predicts future demand. Next, the AI ​​automates ordering to maintain appropriate inventory levels based on the demand forecast. Furthermore, an information sharing platform is provided for stakeholders, sharing demand forecasts and inventory status with them in real time. This effectively solves the food waste problem in the restaurant industry and contributes to the realization of a sustainable society. For example, the system can prevent excess inventory and reduce waste. It also enables efficient inventory management and supports rapid decision-making.

[0029] The food waste solution system according to the embodiment includes a demand forecasting unit, an inventory management unit, and an information sharing unit. The demand forecasting unit forecasts demand taking into account past sales data or external factors. For example, the demand forecasting unit forecasts demand taking into account factors such as past sales data, seasons, weather, and events. The demand forecasting unit can also learn sales trends under specific seasons or weather conditions and predict future demand. The demand forecasting unit can also analyze social media trends and reflect them in the demand forecast. The inventory management unit manages inventory based on the demand forecast. For example, the inventory management unit automates ordering based on the demand forecast to prevent inventory surpluses and shortages. The inventory management unit can also adjust inventory to increase inventory during periods when demand is predicted to increase and decrease inventory during periods when demand is predicted to decrease. The inventory management unit can also optimize inventory turnover by taking product expiration dates into account. The information sharing unit provides inventory information to relevant parties. For example, the information sharing unit provides inventory status in real time to relevant parties. The information sharing unit can also provide different information display formats for each relevant party to facilitate understanding of the information. Furthermore, the information sharing unit can collect feedback from stakeholders in real time to improve the accuracy of the information. As a result, the food waste problem solving system according to the embodiment can effectively solve the food waste problem through demand forecasting, inventory management, and information sharing.

[0030] The demand forecasting unit can predict demand taking into account factors such as past sales data, seasons, weather, and events. The demand forecasting unit, for example, predicts demand based on past sales data. For example, the demand forecasting unit predicts demand by analyzing data such as past sales quantities, sales amounts, and sales periods. The demand forecasting unit can also predict demand taking into account seasonal factors. For example, the demand forecasting unit predicts demand taking into account the four seasons, specific months, temperature fluctuations, and the like. The demand forecasting unit can also predict demand taking into account weather factors. For example, the demand forecasting unit predicts demand based on weather data such as temperature, precipitation, and wind speed. The demand forecasting unit can also predict demand taking into account event factors. For example, the demand forecasting unit predicts demand based on event data such as sporting events, festivals, and sales. This improves the accuracy of demand forecasting by taking into account a variety of factors.

[0031] The inventory management unit automates ordering based on demand forecasts, thereby preventing inventory surpluses and shortages. The inventory management unit automates ordering based on demand forecasts, for example. For example, the inventory management unit determines the appropriate timing for ordering based on demand forecasts and automates ordering. The inventory management unit can also determine the order quantity based on demand forecasts, thereby preventing inventory surpluses and shortages. Furthermore, the inventory management unit can optimize inventory by automating ordering. For example, the inventory management unit adjusts inventory to increase when demand is expected to increase and decrease when demand is expected to decrease. In this way, automating ordering prevents inventory surpluses and shortages and enables efficient inventory management.

[0032] The information sharing unit can provide inventory status to relevant parties in real time. The information sharing unit, for example, provides inventory status to relevant parties in real time. For example, the information sharing unit updates inventory information in real time and provides it to relevant parties. The information sharing unit can also provide different information display formats for each relevant party to facilitate understanding of the information. For example, the information sharing unit provides detailed inventory information to managers and concise inventory information to purchasing personnel. Furthermore, the information sharing unit can also collect feedback from relevant parties in real time to improve the accuracy of the information. For example, the information sharing unit collects feedback from relevant parties in real time to improve the accuracy of the information. This enables quick decision-making by sharing information in real time.

[0033] The demand forecasting unit can learn sales trends under specific seasons or weather conditions and predict future demand. The demand forecasting unit, for example, learns sales trends under specific seasons or weather conditions. For example, the demand forecasting unit learns sales trends under specific seasons or weather conditions based on past sales data. The demand forecasting unit can also analyze seasonal sales fluctuation patterns and predict future demand. Furthermore, the demand forecasting unit can learn sales trends under specific weather conditions based on weather data and predict future demand. For example, the demand forecasting unit learns sales trends under specific weather conditions based on weather data such as temperature, precipitation, and wind speed. This improves the accuracy of demand forecasting by taking seasons and weather conditions into consideration.

[0034] The inventory management unit can adjust inventory to increase when demand is predicted to increase and to decrease when demand is predicted to decrease. The inventory management unit, for example, increases inventory when demand is predicted to increase. For example, the inventory management unit adjusts inventory based on a demand forecast to increase when demand is predicted to increase. The inventory management unit can also adjust inventory to decrease when demand is predicted to decrease. For example, the inventory management unit adjusts inventory based on a demand forecast to decrease when demand is predicted to decrease. In this way, inventory adjustments in response to fluctuations in demand can prevent inventory surpluses and shortages.

[0035] The demand forecasting unit can analyze social media trends in addition to past sales data and reflect the results in the demand forecast. The demand forecasting unit, for example, analyzes social media trends. For example, the demand forecasting unit analyzes the popularity of a specific product on social media and reflects the results in the demand forecast. The demand forecasting unit can also analyze social media hashtags, understand trends, and use them in the demand forecast. Furthermore, the demand forecasting unit can analyze the content of social media posts using natural language processing and use the results in the demand forecast. For example, the demand forecasting unit analyzes the content of social media posts and reflects the results in the demand forecast. In this way, the accuracy of the demand forecast is improved by taking social media trends into consideration.

[0036] The demand forecasting unit can improve the accuracy of the forecast by taking into account the impact of specific events or campaigns. The demand forecasting unit, for example, takes into account the impact of specific events or campaigns. For example, the demand forecasting unit makes a demand forecast by taking into account the schedule of events to be held in the neighborhood. The demand forecasting unit can also adjust the demand forecast based on sales data from past campaigns. Furthermore, the demand forecasting unit can make a demand forecast by taking into account the scale and number of participants of the event. For example, the demand forecasting unit makes a demand forecast based on the scale and number of participants of the event. In this way, the accuracy of the demand forecast is improved by taking into account the impact of events and campaigns.

[0037] The demand forecasting unit can analyze consumption trends for each region and perform demand forecasts for each region. The demand forecasting unit, for example, analyzes consumption trends for each region. For example, the demand forecasting unit analyzes past sales data for each region and reflects this in the demand forecast. The demand forecasting unit can also perform demand forecasts taking into account consumer preferences for each region. Furthermore, the demand forecasting unit can also perform demand forecasts taking into account the influence of seasons and events for each region. For example, the demand forecasting unit performs demand forecasts based on the influence of seasons and events for each region. In this way, the accuracy of the demand forecast is improved by taking into account consumption trends for each region.

[0038] The demand forecasting unit can take into account traffic conditions and information on public events in addition to weather data when forecasting demand. The demand forecasting unit, for example, takes into account traffic conditions and information on public events in addition to weather data. For example, the demand forecasting unit makes a demand forecast taking into account traffic congestion information. The demand forecasting unit can also adjust the demand forecast based on information on public events. Furthermore, the demand forecasting unit can make a demand forecast by combining weather data and traffic conditions. For example, the demand forecasting unit makes a demand forecast based on weather data and traffic conditions. In this way, by taking into account weather data, traffic conditions, and information on public events, the accuracy of the demand forecast is improved.

[0039] When making a demand forecast, the demand forecasting unit can refer to the sales data of competitors and reflect the data in the demand forecast. The demand forecasting unit, for example, refers to the sales data of competitors. For example, the demand forecasting unit collects the sales data of competitors and uses it in the demand forecast. The demand forecasting unit can also make a demand forecast taking into account campaign information of competitors. Furthermore, the demand forecasting unit can analyze the sales trends of competitors and reflect the results in the demand forecast. For example, the demand forecasting unit makes a demand forecast based on the sales trends of competitors. In this way, by referring to the sales data of competitors, the accuracy of the demand forecast is improved.

[0040] When making a demand forecast, the demand forecasting unit can analyze the user's purchase history and make an individual demand forecast. The demand forecasting unit, for example, analyzes the user's purchase history. For example, the demand forecasting unit makes an individual demand forecast based on the user's past purchase history. The demand forecasting unit can also analyze the user's purchasing patterns and reflect them in the demand forecast. Furthermore, the demand forecasting unit can predict demand for a specific product from the user's purchase history. For example, the demand forecasting unit predicts demand for a specific product based on the user's purchase history. This makes it possible to make an individual demand forecast by analyzing the user's purchase history.

[0041] The inventory management unit can optimize inventory turnover by taking into account product expiration dates when managing inventory. The inventory management unit, for example, takes into account product expiration dates. For example, the inventory management unit performs inventory management so as to prioritize the sale of products with upcoming expiration dates. The inventory management unit can also perform inventory management by putting products with longer expiration dates on hold. Furthermore, the inventory management unit can adjust inventory turnover based on expiration dates. For example, the inventory management unit performs inventory management so as to prioritize the sale of products with upcoming expiration dates. In this way, inventory turnover is optimized by taking into account product expiration dates.

[0042] During inventory management, the inventory management unit can monitor product storage conditions (temperature, humidity, light, etc.) and maintain an optimal storage environment. The inventory management unit, for example, monitors product storage conditions. For example, the inventory management unit uses a temperature sensor to monitor the product storage temperature. The inventory management unit can also use a humidity sensor to monitor the product storage humidity. Furthermore, the inventory management unit can maintain an optimal storage environment based on temperature and humidity data. For example, the inventory management unit uses a temperature sensor to monitor the product storage temperature. In this way, by monitoring the product storage conditions, an optimal storage environment is maintained.

[0043] During inventory management, the inventory management department can analyze past inventory data and propose optimal inventory allocation. The inventory management department, for example, analyzes past inventory data. For example, the inventory management department proposes optimal inventory allocation based on the past inventory data. The inventory management department can also analyze past data to optimize inventory allocation. Furthermore, the inventory management department can utilize past inventory data to make inventory allocation more efficient. For example, the inventory management department proposes optimal inventory allocation based on the past inventory data. In this way, optimal inventory allocation is proposed by analyzing past inventory data.

[0044] During inventory management, the inventory management unit can monitor the transportation status of products in real time and adjust inventory. The inventory management unit, for example, monitors the transportation status of products in real time. For example, the inventory management unit monitors the transportation status of products in real time and adjusts inventory. The inventory management unit can also adjust inventory taking into account the estimated arrival time of products in transit. Furthermore, the inventory management unit can also adjust inventory based on delay information for products in transit. For example, the inventory management unit adjusts inventory taking into account the estimated arrival time of products in transit. In this way, by monitoring the transportation status of products in real time, inventory can be adjusted quickly.

[0045] During inventory management, the inventory management unit can automatically place orders based on a demand forecast for a product. The inventory management unit, for example, automatically places orders based on a demand forecast for a product. For example, the inventory management unit automatically places orders based on the demand forecast before inventory runs out. The inventory management unit can also adjust orders based on the demand forecast to prevent excess inventory. Furthermore, the inventory management unit can also place orders at the optimal timing based on the demand forecast. For example, the inventory management unit automatically places orders based on the demand forecast before inventory runs out. In this way, by automatically placing orders based on the demand forecast, it is possible to prevent excess or shortage of inventory.

[0046] The inventory management department can analyze the product return rate during inventory management and optimize inventory. The inventory management department, for example, analyzes the product return rate. For example, the inventory management department optimizes inventory based on the product return rate. The inventory management department can also prioritize managing products with high return rates and adjust inventory. Furthermore, the inventory management department can also optimize inventory placement based on the return rate. For example, the inventory management department optimizes inventory based on the product return rate. In this way, inventory optimization becomes possible by analyzing the product return rate.

[0047] The information sharing unit can provide different information display formats for each party involved when sharing information, thereby facilitating understanding of the information. The information sharing unit, for example, provides different information display formats for each party involved. For example, the information sharing unit can provide detailed inventory information to managers to promote understanding. The information sharing unit can also provide concise inventory information to purchasing staff to support quick decision-making. Furthermore, the information sharing unit can provide visually easy-to-understand inventory information to store staff to improve work efficiency. For example, the information sharing unit can provide detailed inventory information to managers and concise inventory information to purchasing staff. In this way, by providing different information display formats for each party involved, understanding of the information can be promoted.

[0048] The information sharing unit can collect feedback from stakeholders in real time when sharing information, thereby improving the accuracy of the information. The information sharing unit, for example, collects feedback from stakeholders in real time. For example, the information sharing unit collects feedback from stakeholders in real time to improve the accuracy of the information. The information sharing unit can also improve the method of information sharing based on the feedback. Furthermore, the information sharing unit can analyze the feedback and improve the accuracy of the information. For example, the information sharing unit collects feedback from stakeholders in real time to improve the accuracy of the information. In this way, by collecting feedback from stakeholders in real time, the accuracy of the information is improved.

[0049] When sharing information, the information sharing unit can analyze past information sharing history and propose the optimal timing for providing information. The information sharing unit, for example, analyzes past information sharing history. For example, the information sharing unit proposes the optimal timing for providing information based on the past information sharing history. The information sharing unit can also utilize past history to optimize the timing for providing information. Furthermore, the information sharing unit can analyze past history to streamline the timing for providing information. For example, the information sharing unit proposes the optimal timing for providing information based on the past information sharing history. In this way, the optimal timing for providing information is proposed by analyzing the past information sharing history.

[0050] The information sharing unit can provide information customized according to the roles of the parties involved when sharing information. The information sharing unit provides information customized according to, for example, the roles of the parties involved. For example, the information sharing unit provides detailed inventory information and demand forecast data to a manager. The information sharing unit can also provide information necessary for ordering to a purchasing officer. Furthermore, the information sharing unit can provide information necessary for daily work to store staff. For example, the information sharing unit provides detailed inventory information and demand forecast data to a manager, and information necessary for ordering to a purchasing officer. In this way, providing customized information according to the roles of the parties involved promotes understanding of the information.

[0051] When sharing information, the information sharing unit can provide optimal information by taking into account the geographical location information of the parties involved. The information sharing unit, for example, takes into account the geographical location information of the parties involved. For example, the information sharing unit provides optimal inventory information based on store location information. The information sharing unit can also provide order information by taking into account the location information of suppliers. Furthermore, the information sharing unit can also provide delivery status in real time based on the location information of delivery companies. For example, the information sharing unit provides optimal inventory information based on store location information. This makes it possible to provide optimal information by taking into account the geographical location information of the parties involved.

[0052] When sharing information, the information sharing unit can analyze the past behavioral history of the relevant person and propose the optimal information provision method. The information sharing unit, for example, analyzes the past behavioral history of the relevant person. For example, the information sharing unit proposes the optimal information provision method based on the past behavioral history of the relevant person. The information sharing unit can also analyze the past behavioral history to improve the efficiency of information provision. Furthermore, the information sharing unit can utilize the behavioral history to provide the optimal information to the relevant person. For example, the information sharing unit proposes the optimal information provision method based on the past behavioral history of the relevant person. In this way, the optimal information provision method is proposed by analyzing the past behavioral history of the relevant person.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The demand forecasting unit can analyze the user's purchasing history and perform individual demand forecasting. For example, the demand forecasting unit performs individual demand forecasting based on the user's past purchasing history. The demand forecasting unit can also analyze the user's purchasing patterns and reflect this in the demand forecast. Furthermore, the demand forecasting unit can predict demand for specific products based on the user's purchasing history. For example, the demand forecasting unit predicts demand for specific products based on the user's purchasing history. This makes it possible to perform individual demand forecasting by analyzing the user's purchasing history.

[0055] The inventory management department can monitor the storage conditions (temperature, humidity, light, etc.) of products to maintain an optimal storage environment. For example, the inventory management department can use a temperature sensor to monitor the storage temperature of products. The inventory management department can also use a humidity sensor to monitor the storage humidity of products. Furthermore, the inventory management department can maintain an optimal storage environment based on the temperature and humidity data. For example, the inventory management department can use a temperature sensor to monitor the storage temperature of products. In this way, by monitoring the storage conditions of products, an optimal storage environment can be maintained.

[0056] The information sharing unit can provide optimal information by taking into account the geographical location information of the parties involved. For example, the information sharing unit can provide optimal inventory information based on store location information. The information sharing unit can also provide order information by taking into account the location information of suppliers. Furthermore, the information sharing unit can also provide delivery status in real time based on the location information of delivery companies. For example, the information sharing unit can provide optimal inventory information based on store location information. This makes it possible to provide optimal information by taking into account the geographical location information of the parties involved.

[0057] The demand forecasting unit can improve the accuracy of the forecast by taking into account the impact of specific events or campaigns. For example, the demand forecasting unit makes a demand forecast by taking into account the schedule of events to be held in the neighborhood. The demand forecasting unit can also adjust the demand forecast based on sales data from past campaigns. Furthermore, the demand forecasting unit can make a demand forecast by taking into account the scale of the event and the number of participants. For example, the demand forecasting unit makes a demand forecast based on the scale of the event and the number of participants. In this way, the accuracy of the demand forecast is improved by taking into account the impact of events and campaigns.

[0058] The inventory management department can monitor the transportation status of products in real time and adjust inventory. For example, the inventory management department can monitor the transportation status of products in real time and adjust inventory. The inventory management department can also adjust inventory taking into account the estimated arrival time of products in transit. Furthermore, the inventory management department can also adjust inventory based on delay information for products in transit. For example, the inventory management department can adjust inventory taking into account the estimated arrival time of products in transit. In this way, inventory can be adjusted quickly by monitoring the transportation status of products in real time.

[0059] The demand forecasting unit can take into account traffic conditions and information on public events in addition to weather data when forecasting demand. For example, the demand forecasting unit makes a demand forecast taking into account traffic congestion information. The demand forecasting unit can also adjust the demand forecast based on information on public events. Furthermore, the demand forecasting unit can make a demand forecast by combining weather data and traffic conditions. For example, the demand forecasting unit makes a demand forecast based on weather data and traffic conditions. In this way, the accuracy of the demand forecast is improved by taking into account weather data, traffic conditions, and information on public events.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The demand forecasting unit predicts demand taking into account past sales data or external factors. For example, the demand forecasting unit predicts demand taking into account factors such as past sales data, seasons, weather, and events. The demand forecasting unit can also learn sales trends under specific seasons or weather conditions and predict future demand. Furthermore, the demand forecasting unit can analyze social media trends and reflect them in the demand forecast. Step 2: The inventory management department manages inventory based on demand forecasts. For example, the inventory management department automates ordering based on demand forecasts to prevent inventory surpluses and shortages. The inventory management department can also adjust inventory to increase inventory when demand is expected to increase and decrease inventory when demand decreases. Furthermore, the inventory management department can optimize inventory turnover by taking into account product expiration dates. Step 3: The information sharing unit provides inventory information to the relevant parties. For example, the information sharing unit provides inventory status to the relevant parties in real time. The information sharing unit can also provide different information display formats for each relevant party to facilitate understanding of the information. Furthermore, the information sharing unit can collect feedback from the relevant parties in real time to improve the accuracy of the information.

[0062] (Example 2) A system according to an embodiment of the present invention is an AI-based demand forecasting, inventory management, and information sharing platform for stakeholders to solve the food waste problem in the restaurant industry. This system predicts demand, manages inventory, and shares information with stakeholders, taking into account factors such as past sales data, seasons, weather, and events. For example, AI learns sales trends under specific seasons and weather conditions from past sales data and predicts future demand. Next, the AI ​​automates ordering to maintain appropriate inventory levels based on the demand forecast. Furthermore, an information sharing platform is provided for stakeholders, sharing demand forecasts and inventory status with them in real time. This effectively solves the food waste problem in the restaurant industry and contributes to the realization of a sustainable society. For example, the system can prevent excess inventory and reduce waste. It also enables efficient inventory management and supports rapid decision-making.

[0063] The food waste solution system according to the embodiment includes a demand forecasting unit, an inventory management unit, and an information sharing unit. The demand forecasting unit forecasts demand taking into account past sales data or external factors. For example, the demand forecasting unit forecasts demand taking into account factors such as past sales data, seasons, weather, and events. The demand forecasting unit can also learn sales trends under specific seasons or weather conditions and predict future demand. The demand forecasting unit can also analyze social media trends and reflect them in the demand forecast. The inventory management unit manages inventory based on the demand forecast. For example, the inventory management unit automates ordering based on the demand forecast to prevent inventory surpluses and shortages. The inventory management unit can also adjust inventory to increase inventory during periods when demand is predicted to increase and decrease inventory during periods when demand is predicted to decrease. The inventory management unit can also optimize inventory turnover by taking product expiration dates into account. The information sharing unit provides inventory information to relevant parties. For example, the information sharing unit provides inventory status in real time to relevant parties. The information sharing unit can also provide different information display formats for each relevant party to facilitate understanding of the information. Furthermore, the information sharing unit can collect feedback from stakeholders in real time to improve the accuracy of the information. As a result, the food waste problem solving system according to the embodiment can effectively solve the food waste problem through demand forecasting, inventory management, and information sharing.

[0064] The demand forecasting unit can predict demand taking into account factors such as past sales data, seasons, weather, and events. The demand forecasting unit, for example, predicts demand based on past sales data. For example, the demand forecasting unit predicts demand by analyzing data such as past sales quantities, sales amounts, and sales periods. The demand forecasting unit can also predict demand taking into account seasonal factors. For example, the demand forecasting unit predicts demand taking into account the four seasons, specific months, temperature fluctuations, and the like. The demand forecasting unit can also predict demand taking into account weather factors. For example, the demand forecasting unit predicts demand based on weather data such as temperature, precipitation, and wind speed. The demand forecasting unit can also predict demand taking into account event factors. For example, the demand forecasting unit predicts demand based on event data such as sporting events, festivals, and sales. This improves the accuracy of demand forecasting by taking into account a variety of factors.

[0065] The inventory management unit automates ordering based on demand forecasts, thereby preventing inventory surpluses and shortages. The inventory management unit automates ordering based on demand forecasts, for example. For example, the inventory management unit determines the appropriate timing for ordering based on demand forecasts and automates ordering. The inventory management unit can also determine the order quantity based on demand forecasts, thereby preventing inventory surpluses and shortages. Furthermore, the inventory management unit can optimize inventory by automating ordering. For example, the inventory management unit adjusts inventory to increase when demand is expected to increase and decrease when demand is expected to decrease. In this way, automating ordering prevents inventory surpluses and shortages and enables efficient inventory management.

[0066] The information sharing unit can provide inventory status to relevant parties in real time. The information sharing unit, for example, provides inventory status to relevant parties in real time. For example, the information sharing unit updates inventory information in real time and provides it to relevant parties. The information sharing unit can also provide different information display formats for each relevant party to facilitate understanding of the information. For example, the information sharing unit provides detailed inventory information to managers and concise inventory information to purchasing personnel. Furthermore, the information sharing unit can also collect feedback from relevant parties in real time to improve the accuracy of the information. For example, the information sharing unit collects feedback from relevant parties in real time to improve the accuracy of the information. This enables quick decision-making by sharing information in real time.

[0067] The demand forecasting unit can learn sales trends under specific seasons or weather conditions and predict future demand. The demand forecasting unit, for example, learns sales trends under specific seasons or weather conditions. For example, the demand forecasting unit learns sales trends under specific seasons or weather conditions based on past sales data. The demand forecasting unit can also analyze seasonal sales fluctuation patterns and predict future demand. Furthermore, the demand forecasting unit can learn sales trends under specific weather conditions based on weather data and predict future demand. For example, the demand forecasting unit learns sales trends under specific weather conditions based on weather data such as temperature, precipitation, and wind speed. This improves the accuracy of demand forecasting by taking seasons and weather conditions into consideration.

[0068] The inventory management unit can adjust inventory to increase when demand is predicted to increase and to decrease when demand is predicted to decrease. The inventory management unit, for example, increases inventory when demand is predicted to increase. For example, the inventory management unit adjusts inventory based on a demand forecast to increase when demand is predicted to increase. The inventory management unit can also adjust inventory to decrease when demand is predicted to decrease. For example, the inventory management unit adjusts inventory based on a demand forecast to decrease when demand is predicted to decrease. In this way, inventory adjustments in response to fluctuations in demand can prevent inventory surpluses and shortages.

[0069] The demand forecasting unit can estimate a user's emotions and adjust the accuracy of the demand forecast based on the estimated user emotions. The demand forecasting unit, for example, estimates the user's emotions. For example, the demand forecasting unit estimates the user's emotions using an emotion analysis algorithm. The demand forecasting unit can also adjust the accuracy of the demand forecast based on the estimated user emotions. For example, if the user is stressed, the demand forecasting unit uses more detailed data to improve the accuracy of the demand forecast. If the user is relaxed, the demand forecasting unit can adjust the accuracy of the demand forecast to avoid excessive data processing. Furthermore, if the user is in a hurry, the demand forecasting unit can use only key data to quickly perform a demand forecast. This enables more accurate predictions by adjusting the accuracy of the demand forecast according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The demand forecasting unit can analyze social media trends in addition to past sales data and reflect the results in the demand forecast. The demand forecasting unit, for example, analyzes social media trends. For example, the demand forecasting unit analyzes the popularity of a specific product on social media and reflects the results in the demand forecast. The demand forecasting unit can also analyze social media hashtags, understand trends, and use them in the demand forecast. Furthermore, the demand forecasting unit can analyze the content of social media posts using natural language processing and use the results in the demand forecast. For example, the demand forecasting unit analyzes the content of social media posts and reflects the results in the demand forecast. In this way, the accuracy of the demand forecast is improved by taking social media trends into consideration.

[0071] The demand forecasting unit can improve the accuracy of the forecast by taking into account the impact of specific events or campaigns. The demand forecasting unit, for example, takes into account the impact of specific events or campaigns. For example, the demand forecasting unit makes a demand forecast by taking into account the schedule of events to be held in the neighborhood. The demand forecasting unit can also adjust the demand forecast based on sales data from past campaigns. Furthermore, the demand forecasting unit can make a demand forecast by taking into account the scale and number of participants of the event. For example, the demand forecasting unit makes a demand forecast based on the scale and number of participants of the event. In this way, the accuracy of the demand forecast is improved by taking into account the impact of events and campaigns.

[0072] The demand forecasting unit can analyze consumption trends for each region and perform demand forecasts for each region. The demand forecasting unit, for example, analyzes consumption trends for each region. For example, the demand forecasting unit analyzes past sales data for each region and reflects this in the demand forecast. The demand forecasting unit can also perform demand forecasts taking into account consumer preferences for each region. Furthermore, the demand forecasting unit can also perform demand forecasts taking into account the influence of seasons and events for each region. For example, the demand forecasting unit performs demand forecasts based on the influence of seasons and events for each region. In this way, the accuracy of the demand forecast is improved by taking into account consumption trends for each region.

[0073] The demand prediction unit can estimate a user's emotion and adjust the timing of demand prediction based on the estimated user's emotion. The demand prediction unit, for example, estimates the user's emotion. For example, the demand prediction unit estimates the user's emotion using an emotion analysis algorithm. The demand prediction unit can also adjust the timing of demand prediction based on the estimated user's emotion. For example, the demand prediction unit can advance the timing of demand prediction when the user is stressed. The demand prediction unit can also delay the timing of demand prediction when the user is relaxed. Furthermore, the demand prediction unit can immediately perform demand prediction when the user is in a hurry. This enables more accurate prediction by adjusting the timing of demand prediction according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The demand forecasting unit can take into account traffic conditions and information on public events in addition to weather data when forecasting demand. The demand forecasting unit, for example, takes into account traffic conditions and information on public events in addition to weather data. For example, the demand forecasting unit makes a demand forecast taking into account traffic congestion information. The demand forecasting unit can also adjust the demand forecast based on information on public events. Furthermore, the demand forecasting unit can make a demand forecast by combining weather data and traffic conditions. For example, the demand forecasting unit makes a demand forecast based on weather data and traffic conditions. In this way, by taking into account weather data, traffic conditions, and information on public events, the accuracy of the demand forecast is improved.

[0075] When making a demand forecast, the demand forecasting unit can refer to the sales data of competitors and reflect the data in the demand forecast. The demand forecasting unit, for example, refers to the sales data of competitors. For example, the demand forecasting unit collects the sales data of competitors and uses it in the demand forecast. The demand forecasting unit can also make a demand forecast taking into account campaign information of competitors. Furthermore, the demand forecasting unit can analyze the sales trends of competitors and reflect the results in the demand forecast. For example, the demand forecasting unit makes a demand forecast based on the sales trends of competitors. In this way, by referring to the sales data of competitors, the accuracy of the demand forecast is improved.

[0076] When making a demand forecast, the demand forecasting unit can analyze the user's purchase history and make an individual demand forecast. The demand forecasting unit, for example, analyzes the user's purchase history. For example, the demand forecasting unit makes an individual demand forecast based on the user's past purchase history. The demand forecasting unit can also analyze the user's purchasing patterns and reflect them in the demand forecast. Furthermore, the demand forecasting unit can predict demand for a specific product from the user's purchase history. For example, the demand forecasting unit predicts demand for a specific product based on the user's purchase history. This makes it possible to make an individual demand forecast by analyzing the user's purchase history.

[0077] The inventory management unit can estimate a user's emotions and adjust the inventory management method based on the estimated user emotions. The inventory management unit, for example, estimates the user's emotions. For example, the inventory management unit estimates the user's emotions using an emotion analysis algorithm. The inventory management unit can also adjust the inventory management method based on the estimated user emotions. For example, the inventory management unit can simplify the inventory management procedure when the user is stressed. The inventory management unit can also provide detailed inventory management procedures when the user is relaxed. Furthermore, the inventory management unit can provide a method for quickly managing inventory when the user is in a hurry. This enables more efficient inventory management by adjusting the inventory management method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The inventory management unit can optimize inventory turnover by taking into account product expiration dates when managing inventory. The inventory management unit, for example, takes into account product expiration dates. For example, the inventory management unit performs inventory management so as to prioritize the sale of products with upcoming expiration dates. The inventory management unit can also perform inventory management by putting products with longer expiration dates on hold. Furthermore, the inventory management unit can adjust inventory turnover based on expiration dates. For example, the inventory management unit performs inventory management so as to prioritize the sale of products with upcoming expiration dates. In this way, inventory turnover is optimized by taking into account product expiration dates.

[0079] During inventory management, the inventory management unit can monitor product storage conditions (temperature, humidity, light, etc.) and maintain an optimal storage environment. The inventory management unit, for example, monitors product storage conditions. For example, the inventory management unit uses a temperature sensor to monitor the product storage temperature. The inventory management unit can also use a humidity sensor to monitor the product storage humidity. Furthermore, the inventory management unit can maintain an optimal storage environment based on temperature and humidity data. For example, the inventory management unit uses a temperature sensor to monitor the product storage temperature. In this way, by monitoring the product storage conditions, an optimal storage environment is maintained.

[0080] During inventory management, the inventory management department can analyze past inventory data and propose optimal inventory allocation. The inventory management department, for example, analyzes past inventory data. For example, the inventory management department proposes optimal inventory allocation based on the past inventory data. The inventory management department can also analyze past data to optimize inventory allocation. Furthermore, the inventory management department can utilize past inventory data to make inventory allocation more efficient. For example, the inventory management department proposes optimal inventory allocation based on the past inventory data. In this way, optimal inventory allocation is proposed by analyzing past inventory data.

[0081] The inventory management unit can estimate a user's emotions and prioritize inventory based on the estimated user emotions. The inventory management unit, for example, estimates the user's emotions. For example, the inventory management unit estimates the user's emotions using an emotion analysis algorithm. The inventory management unit can also prioritize inventory based on the estimated user emotions. For example, the inventory management unit prioritizes important inventory when the user is stressed. The inventory management unit can also perform detailed inventory management when the user is relaxed. Furthermore, the inventory management unit can quickly prioritize inventory when the user is in a hurry. This enables more efficient inventory management by prioritizing inventory based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] During inventory management, the inventory management unit can monitor the transportation status of products in real time and adjust inventory. The inventory management unit, for example, monitors the transportation status of products in real time. For example, the inventory management unit monitors the transportation status of products in real time and adjusts inventory. The inventory management unit can also adjust inventory taking into account the estimated arrival time of products in transit. Furthermore, the inventory management unit can also adjust inventory based on delay information for products in transit. For example, the inventory management unit adjusts inventory taking into account the estimated arrival time of products in transit. In this way, by monitoring the transportation status of products in real time, inventory can be adjusted quickly.

[0083] During inventory management, the inventory management unit can automatically place orders based on a demand forecast for a product. The inventory management unit, for example, automatically places orders based on a demand forecast for a product. For example, the inventory management unit automatically places orders based on the demand forecast before inventory runs out. The inventory management unit can also adjust orders based on the demand forecast to prevent excess inventory. Furthermore, the inventory management unit can also place orders at the optimal timing based on the demand forecast. For example, the inventory management unit automatically places orders based on the demand forecast before inventory runs out. In this way, by automatically placing orders based on the demand forecast, it is possible to prevent excess or shortage of inventory.

[0084] The inventory management department can analyze the product return rate during inventory management and optimize inventory. The inventory management department, for example, analyzes the product return rate. For example, the inventory management department optimizes inventory based on the product return rate. The inventory management department can also prioritize managing products with high return rates and adjust inventory. Furthermore, the inventory management department can also optimize inventory placement based on the return rate. For example, the inventory management department optimizes inventory based on the product return rate. In this way, inventory optimization becomes possible by analyzing the product return rate.

[0085] The information sharing unit can estimate a user's emotion and adjust the information sharing method based on the estimated user's emotion. The information sharing unit, for example, estimates the user's emotion. For example, the information sharing unit can estimate the user's emotion using an emotion analysis algorithm. The information sharing unit can also adjust the information sharing method based on the estimated user's emotion. For example, the information sharing unit can provide a simple information sharing method when the user is stressed. The information sharing unit can also provide a detailed information sharing method when the user is relaxed. Furthermore, the information sharing unit can also provide a method for quickly sharing information when the user is in a hurry. This enables more appropriate information sharing by adjusting the information sharing method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The information sharing unit can provide different information display formats for each party involved when sharing information, thereby facilitating understanding of the information. The information sharing unit, for example, provides different information display formats for each party involved. For example, the information sharing unit can provide detailed inventory information to managers to promote understanding. The information sharing unit can also provide concise inventory information to purchasing staff to support quick decision-making. Furthermore, the information sharing unit can provide visually easy-to-understand inventory information to store staff to improve work efficiency. For example, the information sharing unit can provide detailed inventory information to managers and concise inventory information to purchasing staff. In this way, by providing different information display formats for each party involved, understanding of the information can be promoted.

[0087] The information sharing unit can collect feedback from stakeholders in real time when sharing information, thereby improving the accuracy of the information. The information sharing unit, for example, collects feedback from stakeholders in real time. For example, the information sharing unit collects feedback from stakeholders in real time to improve the accuracy of the information. The information sharing unit can also improve the method of information sharing based on the feedback. Furthermore, the information sharing unit can analyze the feedback and improve the accuracy of the information. For example, the information sharing unit collects feedback from stakeholders in real time to improve the accuracy of the information. In this way, by collecting feedback from stakeholders in real time, the accuracy of the information is improved.

[0088] When sharing information, the information sharing unit can analyze past information sharing history and propose the optimal timing for providing information. The information sharing unit, for example, analyzes past information sharing history. For example, the information sharing unit proposes the optimal timing for providing information based on the past information sharing history. The information sharing unit can also utilize past history to optimize the timing for providing information. Furthermore, the information sharing unit can analyze past history to streamline the timing for providing information. For example, the information sharing unit proposes the optimal timing for providing information based on the past information sharing history. In this way, the optimal timing for providing information is proposed by analyzing the past information sharing history.

[0089] The information sharing unit can estimate a user's emotion and determine the priority of information based on the estimated user's emotion. The information sharing unit, for example, estimates the user's emotion. For example, the information sharing unit estimates the user's emotion using an emotion analysis algorithm. The information sharing unit can also determine the priority of information based on the estimated user's emotion. For example, the information sharing unit can provide important information preferentially when the user is stressed. The information sharing unit can also provide detailed information when the user is relaxed. Furthermore, the information sharing unit can quickly provide important information when the user is in a hurry. This enables more appropriate information to be provided by determining the priority of information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The information sharing unit can provide information customized according to the roles of the parties involved when sharing information. The information sharing unit provides information customized according to, for example, the roles of the parties involved. For example, the information sharing unit provides detailed inventory information and demand forecast data to a manager. The information sharing unit can also provide information necessary for ordering to a purchasing officer. Furthermore, the information sharing unit can provide information necessary for daily work to store staff. For example, the information sharing unit provides detailed inventory information and demand forecast data to a manager, and information necessary for ordering to a purchasing officer. In this way, providing customized information according to the roles of the parties involved promotes understanding of the information.

[0091] When sharing information, the information sharing unit can provide optimal information by taking into account the geographical location information of the parties involved. The information sharing unit, for example, takes into account the geographical location information of the parties involved. For example, the information sharing unit provides optimal inventory information based on store location information. The information sharing unit can also provide order information by taking into account the location information of suppliers. Furthermore, the information sharing unit can also provide delivery status in real time based on the location information of delivery companies. For example, the information sharing unit provides optimal inventory information based on store location information. This makes it possible to provide optimal information by taking into account the geographical location information of the parties involved.

[0092] When sharing information, the information sharing unit can analyze the past behavioral history of the relevant person and propose the optimal information provision method. The information sharing unit, for example, analyzes the past behavioral history of the relevant person. For example, the information sharing unit proposes the optimal information provision method based on the past behavioral history of the relevant person. The information sharing unit can also analyze the past behavioral history to improve the efficiency of information provision. Furthermore, the information sharing unit can utilize the behavioral history to provide the optimal information to the relevant person. For example, the information sharing unit proposes the optimal information provision method based on the past behavioral history of the relevant person. In this way, the optimal information provision method is proposed by analyzing the past behavioral history of the relevant person. === Hard Collateral 1-1 === Each of the multiple elements, including the demand forecasting unit, inventory management unit, and information sharing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the demand forecasting unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, external factors can be detected using the camera 42 or microphone 38B of the smart device 14, and demand can be predicted by the control unit 46A. The inventory management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages inventory based on the demand forecast and automates ordering. The information sharing unit is realized, for example, by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides inventory information to relevant parties in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the demand forecasting unit, inventory management unit, and information sharing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the demand forecasting unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, external factors can be detected using the camera 42 or microphone 238 of the smart glasses 214, and demand can be predicted by the control unit 46A. The inventory management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages inventory and automates ordering based on the demand forecast. The information sharing unit is realized, for example, by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides inventory information to relevant parties in real time. === Hard Collateral 1-3 === Each of the multiple elements including the demand forecasting unit, inventory management unit, and information sharing unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the demand forecasting unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, external factors can be detected using the camera 42 and microphone 238 of the headset terminal 314, and demand can be predicted by the control unit 46A. The inventory management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages inventory based on the demand forecast and automates ordering. The information sharing unit is realized, for example, by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and provides inventory information to relevant parties in real time. === Hard Collateral 1-4 === Each of the multiple elements including the demand forecasting unit, inventory management unit, and information sharing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the demand forecasting unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, external factors can be detected using the camera 42 or microphone 238 of the robot 414, and demand can be predicted by the control unit 46A. The inventory management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages inventory based on the demand forecast and automates ordering. The information sharing unit is realized, for example, by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides inventory information to relevant parties in real time.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The demand forecasting unit can analyze the user's purchasing history and perform individual demand forecasting. For example, the demand forecasting unit performs individual demand forecasting based on the user's past purchasing history. The demand forecasting unit can also analyze the user's purchasing patterns and reflect this in the demand forecast. Furthermore, the demand forecasting unit can predict demand for specific products based on the user's purchasing history. For example, the demand forecasting unit predicts demand for specific products based on the user's purchasing history. This makes it possible to perform individual demand forecasting by analyzing the user's purchasing history.

[0095] The inventory management department can monitor the storage conditions (temperature, humidity, light, etc.) of products to maintain an optimal storage environment. For example, the inventory management department can use a temperature sensor to monitor the storage temperature of products. The inventory management department can also use a humidity sensor to monitor the storage humidity of products. Furthermore, the inventory management department can maintain an optimal storage environment based on the temperature and humidity data. For example, the inventory management department can use a temperature sensor to monitor the storage temperature of products. In this way, by monitoring the storage conditions of products, an optimal storage environment can be maintained.

[0096] The information sharing unit can provide optimal information by taking into account the geographical location information of the parties involved. For example, the information sharing unit can provide optimal inventory information based on store location information. The information sharing unit can also provide order information by taking into account the location information of suppliers. Furthermore, the information sharing unit can also provide delivery status in real time based on the location information of delivery companies. For example, the information sharing unit can provide optimal inventory information based on store location information. This makes it possible to provide optimal information by taking into account the geographical location information of the parties involved.

[0097] The demand forecasting unit can improve the accuracy of the forecast by taking into account the impact of specific events or campaigns. For example, the demand forecasting unit makes a demand forecast by taking into account the schedule of events to be held in the neighborhood. The demand forecasting unit can also adjust the demand forecast based on sales data from past campaigns. Furthermore, the demand forecasting unit can make a demand forecast by taking into account the scale of the event and the number of participants. For example, the demand forecasting unit makes a demand forecast based on the scale of the event and the number of participants. In this way, the accuracy of the demand forecast is improved by taking into account the impact of events and campaigns.

[0098] The inventory management department can monitor the transportation status of products in real time and adjust inventory. For example, the inventory management department can monitor the transportation status of products in real time and adjust inventory. The inventory management department can also adjust inventory taking into account the estimated arrival time of products in transit. Furthermore, the inventory management department can also adjust inventory based on delay information for products in transit. For example, the inventory management department can adjust inventory taking into account the estimated arrival time of products in transit. In this way, inventory can be adjusted quickly by monitoring the transportation status of products in real time.

[0099] The demand forecasting unit can estimate a user's emotions and adjust the accuracy of the demand forecast based on the estimated user emotions. For example, the demand forecasting unit estimates a user's emotions using an emotion analysis algorithm. The demand forecasting unit can also adjust the accuracy of the demand forecast based on the estimated user emotions. For example, if the user is stressed, the demand forecasting unit uses more detailed data to improve the accuracy of the demand forecast. If the user is relaxed, the demand forecasting unit can adjust the accuracy of the demand forecast to avoid excessive data processing. Furthermore, if the user is in a hurry, the demand forecasting unit can use only key data to quickly perform a demand forecast. This enables more accurate forecasts by adjusting the accuracy of the demand forecast according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The inventory management unit can estimate a user's emotions and adjust the inventory management method based on the estimated user emotions. For example, the inventory management unit estimates the user's emotions using an emotion analysis algorithm. The inventory management unit can also adjust the inventory management method based on the estimated user emotions. For example, the inventory management unit can simplify the inventory management procedure if the user is stressed. The inventory management unit can also provide detailed inventory management procedures if the user is relaxed. Furthermore, the inventory management unit can also provide a method for quickly managing inventory if the user is in a hurry. This enables more efficient inventory management by adjusting the inventory management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The information sharing unit can estimate a user's emotions and adjust the information sharing method based on the estimated user emotions. For example, the information sharing unit estimates the user's emotions using an emotion analysis algorithm. The information sharing unit can also adjust the information sharing method based on the estimated user emotions. For example, the information sharing unit can provide a simple information sharing method when the user is stressed. The information sharing unit can also provide a detailed information sharing method when the user is relaxed. Furthermore, the information sharing unit can also provide a quick information sharing method when the user is in a hurry. This enables more appropriate information sharing by adjusting the information sharing method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The information sharing unit can estimate a user's emotions and prioritize information based on the estimated user emotions. For example, the information sharing unit estimates a user's emotions using an emotion analysis algorithm. The information sharing unit can also prioritize information based on the estimated user emotions. For example, the information sharing unit can prioritize important information when the user is stressed. The information sharing unit can also provide detailed information when the user is relaxed. Furthermore, the information sharing unit can quickly provide important information when the user is in a hurry. This enables more appropriate information to be provided by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The demand forecasting unit can take into account traffic conditions and information on public events in addition to weather data when forecasting demand. For example, the demand forecasting unit makes a demand forecast taking into account traffic congestion information. The demand forecasting unit can also adjust the demand forecast based on information on public events. Furthermore, the demand forecasting unit can make a demand forecast by combining weather data and traffic conditions. For example, the demand forecasting unit makes a demand forecast based on weather data and traffic conditions. In this way, the accuracy of the demand forecast is improved by taking into account weather data, traffic conditions, and information on public events.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The demand forecasting unit predicts demand taking into account past sales data or external factors. For example, the demand forecasting unit predicts demand taking into account factors such as past sales data, seasons, weather, and events. The demand forecasting unit can also learn sales trends under specific seasons or weather conditions and predict future demand. Furthermore, the demand forecasting unit can analyze social media trends and reflect them in the demand forecast. Step 2: The inventory management department manages inventory based on demand forecasts. For example, the inventory management department automates ordering based on demand forecasts to prevent inventory surpluses and shortages. The inventory management department can also adjust inventory to increase inventory when demand is expected to increase and decrease inventory when demand decreases. Furthermore, the inventory management department can optimize inventory turnover by taking into account product expiration dates. Step 3: The information sharing unit provides inventory information to the relevant parties. For example, the information sharing unit provides inventory status to the relevant parties in real time. The information sharing unit can also provide different information display formats for each relevant party to facilitate understanding of the information. Furthermore, the information sharing unit can collect feedback from the relevant parties in real time to improve the accuracy of the information.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a demand forecasting unit that forecasts demand taking into account past sales data or external factors; an inventory management unit that manages inventory based on the demand predicted by the demand forecasting unit; an information sharing unit that provides stock information managed by the stock management unit to related parties; A system characterized by:

2. The demand forecasting unit Forecast demand by taking into account historical sales data, seasonal, weather, and event factors 2. The system of claim 1.

3. The inventory management unit Automate ordering based on demand forecasts to prevent inventory surpluses and shortages 2. The system of claim 1.

4. The information sharing unit Provide real-time inventory status to stakeholders 2. The system of claim 1.

5. The demand forecasting unit Learn sales trends during specific seasons or weather conditions to forecast future demand 2. The system of claim 1.

6. The inventory management unit Increase inventory when demand is expected to increase and decrease inventory when demand is expected to decrease 2. The system of claim 1.

7. The demand forecasting unit Estimate user emotions and adjust the accuracy of demand forecasts based on the estimated user emotions 2. The system of claim 1.

8. The demand forecasting unit In addition to past sales data, analyze social media trends and incorporate them into demand forecasts.

2. The system of claim 1.

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