System

Abstract

An object of a system according to an embodiment is to predict a demand based on sales data and propose an optimal delivery route.SOLUTION: A system according to an embodiment includes a sales data acquisition unit, a demand prediction unit, and a delivery route proposal unit. The sales data acquisition unit acquires sales data. The demand prediction unit predicts a demand based on the sales data acquired by the sales data acquisition unit. The delivery route proposal unit proposes an optimal delivery route based on the demand predicted by the demand prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to analyze sales data and establish optimal delivery routes, which meant that efficient logistics management was not possible.

[0005] The system according to the embodiment aims to predict demand based on sales data and propose optimal delivery routes. [Means for solving the problem]

[0006] The system according to the embodiment includes a sales data acquisition unit, a demand forecasting unit, and a delivery route proposal unit. The sales data acquisition unit acquires sales data. The demand forecasting unit predicts demand based on the sales data acquired by the sales data acquisition unit. The delivery route proposal unit proposes an optimal delivery route based on the demand predicted by the demand forecasting unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict demand based on sales data and propose optimal delivery routes. [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 logistics support system according to an embodiment of the present invention is a system that automatically analyzes sales data, uses a generation AI to predict demand, and proposes optimal delivery routes. This allows the logistics support system to efficiently analyze sales data and propose optimal delivery routes.

[0029] A logistics support system according to an embodiment includes a sales data acquisition unit, a demand forecasting unit, and a delivery route proposal unit. The sales data acquisition unit acquires sales data. For example, the sales data acquisition unit collects sales data from a POS system. The sales data acquisition unit can also acquire sales data from an online sales platform. The sales data acquisition unit can also collect manually entered sales data. For example, the sales data acquisition unit automatically collects daily sales data from a POS system. The sales data is acquired in real time from the online sales platform via an API. The manually entered sales data is collected through a dedicated input form. The demand forecasting unit predicts demand based on the sales data acquired by the sales data acquisition unit. For example, the demand forecasting unit analyzes sales data using a generation AI to create a demand forecasting model. The demand forecasting unit can also predict demand taking into account related factors such as season and weather. The demand forecasting unit can also predict future demand based on past sales data. For example, the demand forecasting unit uses a generation AI to learn sales data and analyze demand fluctuation patterns. Using seasonal and weather data as input, the demand forecasting unit performs demand forecasting taking these factors into consideration. Based on past sales data, the demand forecasting unit predicts demand for the next month or the next season. The delivery route proposal unit proposes an optimal delivery route based on the demand predicted by the demand forecasting unit. For example, the delivery route proposal unit calculates an optimal route taking real-time traffic information into consideration. The delivery route proposal unit can also optimize a route by reflecting the latest data. The delivery route proposal unit can also propose an efficient delivery route based on the demand forecast results. For example, the delivery route proposal unit optimizes a route taking real-time information such as traffic congestion and road construction into consideration. Based on the latest data, the delivery route proposal unit calculates the most efficient delivery route. Based on the demand forecast results, the delivery route proposal unit proposes an efficient delivery route. This allows the logistics support system according to the embodiment to efficiently analyze sales data and propose an optimal delivery route. For example, the logistics support system can quickly analyze sales data, perform demand forecasting, and propose an optimal delivery route.This will improve inventory management and increase delivery efficiency, and by taking real-time traffic information into account, it is expected to shorten delivery times and reduce costs.

[0030] The demand forecasting unit can learn customer purchasing history and social media trends in addition to sales data to refine the demand forecasting model. For example, the generation AI in the demand forecasting unit learns customer purchasing history in addition to sales data and analyzes individual customer purchasing patterns. For example, it identifies the tendency for specific customers to purchase specific products at specific times and reflects this in the demand forecast. The demand forecasting unit also collects social media trend data and the generation AI studies it to understand consumer interests and trends. For example, if a specific product is a hot topic on social media, it predicts the possibility of increased demand for that product. The demand forecasting unit also combines customer purchasing history and social media trends to create a demand forecasting model. For example, it predicts the amount of demand for the next month based on past purchasing history and current trends. This improves the accuracy of demand forecasts.

[0031] The demand forecasting unit can compare sales data from different regions and create a forecasting model that takes into account regional differences in demand. In the demand forecasting unit, for example, the generation AI collects sales data from different regions and analyzes the demand patterns for each region. For example, it understands the differences in demand between urban and rural areas and creates a forecasting model that is appropriate for each. The demand forecasting unit also compares sales data from each region, and the generation AI learns demand fluctuations specific to that region. For example, if a particular product is popular in a particular region, this is reflected in the demand forecast for that region. The demand forecasting unit also integrates sales data from different regions, and the generation AI creates a forecasting model that takes into account regional differences in demand. For example, it predicts demand fluctuations according to seasons and events in each region. This makes it possible to make forecasts that take into account regional differences in demand.

[0032] The demand forecasting unit can learn the sales data of competitors in addition to sales data, and perform demand forecasts that take the competitive environment into account. In the demand forecasting unit, for example, the generation AI collects the sales data of competitors and analyzes the competitive environment. For example, if a competitor's product is selling well, it predicts the possibility that demand for the company's product will decrease. The demand forecasting unit also learns the sales data of competitors, and the generation AI creates a demand forecasting model that takes the competitive environment into account. For example, it predicts how demand for the company's product will fluctuate when a competitor releases a new product. The demand forecasting unit also integrates the sales data of competitors, and performs demand forecasts that take the competitive environment into account. For example, it predicts the impact that a competitor's promotional activities will have on demand for the company's product. This makes it possible to perform demand forecasts that take the competitive environment into account.

[0033] In addition to learning sales data, the demand forecasting unit can predict demand peaks and declines by taking product lifecycle data into consideration. In the demand forecasting unit, for example, the generation AI learns product lifecycle data and predicts demand peaks and declines. For example, it predicts a pattern in which demand peaks immediately after the release of a new product and then gradually declines. The demand forecasting unit also takes product lifecycle data into consideration and the generation AI creates a demand forecasting model. For example, it predicts that demand will stabilize during the product's maturity period and then decline during the subsequent decline period. In the demand forecasting unit, the generation AI integrates sales data and product lifecycle data to predict demand peaks and declines. For example, it predicts demand fluctuations according to the product's lifecycle. This makes it possible to make demand forecasts that take the product's lifecycle into consideration.

[0034] The demand forecasting unit takes into account local events and holidays in addition to the season and weather, thereby improving the accuracy of demand forecasts. In the demand forecasting unit, for example, the generation AI collects local event data and reflects it in the demand forecast. For example, it predicts that demand will increase during periods when local festivals and sporting events are held. The demand forecasting unit also learns holiday data, and the generation AI reflects it in the demand forecast. For example, it predicts that demand will increase during holidays such as Christmas and New Year's. The demand forecasting unit also integrates local event and holiday data in addition to the season and weather, and the generation AI creates a demand forecasting model. For example, it predicts that demand will peak during periods when specific events are held. This makes it possible to make demand forecasts that take local events and holidays into account.

[0035] The demand forecasting unit can learn from past abnormal weather data and predict the impact of abnormal weather on demand. In the demand forecasting unit, for example, the generation AI collects past abnormal weather data and reflects it in the demand forecast. For example, it analyzes demand fluctuations when abnormal cold waves or heat waves occur and predicts demand during future abnormal weather. The demand forecasting unit also learns abnormal weather data and the generation AI reflects it in the demand forecast. For example, it predicts increases or decreases in demand due to abnormal rainfall or the effects of typhoons. The demand forecasting unit also integrates past abnormal weather data and the generation AI predicts the impact of abnormal weather on demand. For example, it predicts peaks and decreases in demand when abnormal weather occurs. This makes it possible to make demand forecasts that take abnormal weather into account.

[0036] The demand forecasting unit can take into account macroeconomic factors such as regional economic indicators and consumer confidence indexes in demand forecasts. In the demand forecasting unit, for example, the generation AI collects regional economic indicator data and reflects it in the demand forecast. For example, it predicts the impact of regional GDP and unemployment rate on demand. The demand forecasting unit also learns the consumer confidence index, and the generation AI reflects it in the demand forecast. For example, it predicts that demand will increase during periods of high consumer confidence. The demand forecasting unit also integrates macroeconomic factors, and the generation AI creates a demand forecasting model. For example, it predicts demand fluctuations depending on regional economic conditions and the consumer confidence index. This makes it possible to perform demand forecasting that takes macroeconomic factors into account.

[0037] The demand forecasting unit can predict fluctuations in demand by taking into account regional demographic trends and migration patterns in its demand forecasts. In the demand forecasting unit, for example, the generation AI collects regional demographic data and reflects it in the demand forecast. For example, it predicts that demand will increase in areas with growing populations. The demand forecasting unit also learns migration patterns, and the generation AI reflects this in the demand forecast. For example, it predicts that demand will increase during periods of increased migration to urban areas. The demand forecasting unit also integrates regional demographic trends and migration patterns, and the generation AI creates a demand forecasting model. For example, it predicts fluctuations in demand in response to changes in demographic trends. This makes it possible to make demand forecasts that take demographic trends and migration patterns into account.

[0038] The delivery route proposal unit can propose cost-effective routes by taking into account fuel efficiency and maintenance data of delivery vehicles. For example, the generation AI collects fuel efficiency data of delivery vehicles and proposes cost-effective delivery routes. For example, it prioritizes the selection of routes with good fuel efficiency. The delivery route proposal unit also learns maintenance data, and the generation AI reflects this in the proposed delivery routes. For example, it proposes routes that avoid vehicles that require maintenance. The delivery route proposal unit also integrates fuel efficiency and maintenance data, and the generation AI proposes cost-effective delivery routes. For example, it selects routes that are fuel efficient and do not require maintenance. This makes it possible to propose cost-effective delivery routes.

[0039] The delivery route proposal unit can take into account the available receiving times at the delivery destination and specific delivery conditions. For example, the generation AI collects available receiving times at the delivery destination and proposes a delivery route that takes this into account. For example, it selects a route that matches the available receiving times. The delivery route proposal unit also learns specific delivery conditions, and the generation AI reflects them in the proposed delivery route. For example, it proposes a route that takes into account the delivery conditions for refrigerated goods and hazardous materials. The delivery route proposal unit also integrates the available receiving times and specific delivery conditions, and the generation AI proposes the optimal delivery route. For example, it selects a route that meets the available receiving times and delivery conditions. This makes it possible to propose a delivery route that takes into account the available receiving times and specific delivery conditions.

[0040] The delivery route proposal unit can integrate data from multiple delivery companies and select the optimal company. For example, the generation AI collects data from multiple delivery companies and selects the optimal company based on that data. For example, the optimal company is selected by comparing delivery time and cost. The delivery route proposal unit also learns data from multiple delivery companies and the generation AI reflects that data in proposing delivery routes. For example, the optimal company is selected taking into account reliability and service quality. The delivery route proposal unit also integrates data from delivery companies and the generation AI selects the optimal company. For example, the optimal company is selected based on the delivery company's past performance data. This makes it possible to select the optimal company taking into account data from multiple delivery companies.

[0041] The delivery route proposal unit can take into account the geographical characteristics and infrastructure conditions of the delivery destination. For example, the generation AI collects the geographical characteristics of the delivery destination and proposes a delivery route that takes them into account. For example, it selects a route that suits geographical characteristics such as mountainous areas or urban areas. The delivery route proposal unit also learns the infrastructure conditions, and the generation AI reflects them in the proposed delivery route. For example, it proposes a route that takes road conditions and traffic conditions into account. The delivery route proposal unit also integrates the geographical characteristics and infrastructure conditions, and the generation AI proposes the optimal delivery route. For example, it selects a route that satisfies the geographical characteristics and infrastructure conditions. This makes it possible to propose a delivery route that takes geographical characteristics and infrastructure conditions into account.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The logistics support system can further include an energy consumption optimization unit. The energy consumption optimization unit collects energy consumption data of delivery vehicles and proposes optimal energy consumption routes. For example, the energy consumption optimization unit can optimize routes taking into account the locations of electric vehicle charging stations. The energy consumption optimization unit can also monitor the remaining battery power of vehicles in real time and propose efficient charging schedules. Furthermore, the energy consumption optimization unit can calculate the most energy-efficient delivery route based on the energy consumption data. This enables deliveries with minimal energy consumption.

[0044] The logistics support system can further include an environmental impact assessment unit. The environmental impact assessment unit assesses the environmental impact of delivery routes and proposes environmentally friendly routes. For example, the environmental impact assessment unit can calculate CO2 emissions and select the route with the lowest emissions. The environmental impact assessment unit can also collect emission data from delivery vehicles and propose routes with the lowest environmental impact. Furthermore, the environmental impact assessment unit can take into account local environmental protection regulations and propose routes that comply with the regulations. This enables environmentally friendly deliveries.

[0045] The logistics support system can further include a preventive maintenance unit. The preventive maintenance unit collects maintenance data for delivery vehicles and proposes preventive maintenance schedules. For example, the preventive maintenance unit can propose the optimal maintenance period based on the vehicle's mileage and usage status. The preventive maintenance unit can also predict the risk of vehicle breakdown and prevent breakdowns by performing maintenance in advance. Furthermore, the preventive maintenance unit can propose the optimal operating method to extend the vehicle's lifespan based on the maintenance data. This is expected to increase vehicle utilization rates and reduce costs.

[0046] The logistics support system can further include a real-time inventory management unit. The real-time inventory management unit monitors inventory data in the warehouse in real time and optimizes inventory. For example, the real-time inventory management unit can collect product inbound and outbound data in real time and keep the inventory status up to date at all times. The real-time inventory management unit can also predict inventory surpluses and shortages and propose appropriate replenishment or reduction. Furthermore, the real-time inventory management unit can propose optimal warehouse layouts and picking routes based on the inventory data. This can improve the efficiency of inventory management and is expected to reduce costs.

[0047] The logistics support system can further include a unit that analyzes the purchase history of the customer at the delivery destination. The unit that analyzes the purchase history of the customer at the delivery destination collects past purchase data of the customer and analyzes their purchasing patterns. For example, it can identify the tendency of a particular customer to regularly purchase a particular product. The unit that analyzes the purchase history of the customer at the delivery destination can also suggest a particular product to the customer based on the analysis results. Furthermore, the unit that analyzes the purchase history of the customer at the delivery destination can perform demand forecasting based on the purchasing patterns. This makes it possible to make demand forecasts and suggest products that take into account the customer's purchase history.

[0048] The logistics support system can further include a unit that estimates the preferences of the customer at the delivery destination. The unit that estimates the preferences of the customer at the delivery destination analyzes the customer's past purchase history and behavioral data to estimate the preferences. For example, it can grasp the customer's tendency to prefer products of a particular brand or category. The unit that estimates the preferences of the customer at the delivery destination can also suggest specific products based on the estimated preferences. Furthermore, the unit that estimates the preferences of the customer at the delivery destination can optimize marketing strategies based on the preference data. This makes it possible to suggest products and conduct marketing that take customer preferences into consideration.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The sales data acquisition unit acquires sales data. For example, the sales data acquisition unit collects sales data from a POS system. It can also acquire sales data from an online sales platform. It can also collect manually entered sales data. Specifically, it automatically collects daily sales data from the POS system and acquires sales data in real time from the online sales platform via an API. Manually entered sales data is collected through a dedicated input form. Step 2: The demand forecasting unit predicts demand based on the sales data acquired by the sales data acquisition unit. For example, the demand forecasting unit uses a generation AI to analyze sales data and create a demand forecasting model. The demand forecasting unit can also predict demand taking into account related factors such as season and weather. It can also predict future demand based on past sales data. Specifically, the generation AI learns from the sales data and analyzes fluctuation patterns in demand. Using seasonal and weather data as input, the demand forecasting unit makes a demand forecast taking these factors into account. Based on past sales data, the demand forecasting unit predicts demand for the next month or season. Step 3: The delivery route proposal unit proposes an optimal delivery route based on the demand predicted by the demand prediction unit. For example, the delivery route proposal unit calculates the optimal route taking into account real-time traffic information. It can also optimize the route by reflecting the latest data. It can also propose an efficient delivery route based on the demand prediction results. Specifically, it optimizes the route by taking into account real-time information such as traffic congestion and road construction. Based on the latest data, the delivery route proposal unit calculates the most efficient delivery route. Based on the demand prediction results, the delivery route proposal unit proposes an efficient delivery route.

[0051] (Example 2) A logistics support system according to an embodiment of the present invention is a system that automatically analyzes sales data, uses a generation AI to predict demand, and proposes optimal delivery routes. This allows the logistics support system to efficiently analyze sales data and propose optimal delivery routes.

[0052] A logistics support system according to an embodiment includes a sales data acquisition unit, a demand forecasting unit, and a delivery route proposal unit. The sales data acquisition unit acquires sales data. For example, the sales data acquisition unit collects sales data from a POS system. The sales data acquisition unit can also acquire sales data from an online sales platform. The sales data acquisition unit can also collect manually entered sales data. For example, the sales data acquisition unit automatically collects daily sales data from a POS system. The sales data is acquired in real time from the online sales platform via an API. The manually entered sales data is collected through a dedicated input form. The demand forecasting unit predicts demand based on the sales data acquired by the sales data acquisition unit. For example, the demand forecasting unit analyzes sales data using a generation AI to create a demand forecasting model. The demand forecasting unit can also predict demand taking into account related factors such as season and weather. The demand forecasting unit can also predict future demand based on past sales data. For example, the demand forecasting unit uses a generation AI to learn sales data and analyze demand fluctuation patterns. Using seasonal and weather data as input, the demand forecasting unit performs demand forecasting taking these factors into consideration. Based on past sales data, the demand forecasting unit predicts demand for the next month or the next season. The delivery route proposal unit proposes an optimal delivery route based on the demand predicted by the demand forecasting unit. For example, the delivery route proposal unit calculates an optimal route taking real-time traffic information into consideration. The delivery route proposal unit can also optimize a route by reflecting the latest data. The delivery route proposal unit can also propose an efficient delivery route based on the demand forecast results. For example, the delivery route proposal unit optimizes a route taking real-time information such as traffic congestion and road construction into consideration. Based on the latest data, the delivery route proposal unit calculates the most efficient delivery route. Based on the demand forecast results, the delivery route proposal unit proposes an efficient delivery route. This allows the logistics support system according to the embodiment to efficiently analyze sales data and propose an optimal delivery route. For example, the logistics support system can quickly analyze sales data, perform demand forecasting, and propose an optimal delivery route.This will improve inventory management and increase delivery efficiency, and by taking real-time traffic information into account, it is expected to shorten delivery times and reduce costs.

[0053] The demand forecasting unit can learn customer purchasing history and social media trends in addition to sales data to refine the demand forecasting model. For example, the generation AI in the demand forecasting unit learns customer purchasing history in addition to sales data and analyzes individual customer purchasing patterns. For example, it identifies the tendency for specific customers to purchase specific products at specific times and reflects this in the demand forecast. The demand forecasting unit also collects social media trend data and the generation AI studies it to understand consumer interests and trends. For example, if a specific product is a hot topic on social media, it predicts the possibility of increased demand for that product. The demand forecasting unit also combines customer purchasing history and social media trends to create a demand forecasting model. For example, it predicts the amount of demand for the next month based on past purchasing history and current trends. This improves the accuracy of demand forecasts.

[0054] The demand forecasting unit can compare sales data from different regions and create a forecasting model that takes into account regional differences in demand. In the demand forecasting unit, for example, the generation AI collects sales data from different regions and analyzes the demand patterns for each region. For example, it understands the differences in demand between urban and rural areas and creates a forecasting model that is appropriate for each. The demand forecasting unit also compares sales data from each region, and the generation AI learns demand fluctuations specific to that region. For example, if a particular product is popular in a particular region, this is reflected in the demand forecast for that region. The demand forecasting unit also integrates sales data from different regions, and the generation AI creates a forecasting model that takes into account regional differences in demand. For example, it predicts demand fluctuations according to seasons and events in each region. This makes it possible to make forecasts that take into account regional differences in demand.

[0055] The demand forecasting unit can analyze emotions from customer reviews and feedback and reflect that emotional data in demand forecasts. In the demand forecasting unit, for example, the generation AI analyzes customer reviews and feedback and calculates an emotional score. For example, it predicts that demand is likely to increase for products with many positive reviews. The demand forecasting unit also uses an emotion estimation function to analyze emotions from customer feedback and reflects that data in demand forecasts. For example, it predicts that demand is likely to decrease for products with many negative feedback. The demand forecasting unit also analyzes customer reviews and feedback with the emotion estimation function, and the generation AI creates a demand forecasting model based on that emotional data. For example, it predicts that demand will increase for products with a high emotional score. This makes it possible to make demand forecasts that take customer emotions into account.

[0056] The demand forecasting unit can learn the sales data of competitors in addition to sales data, and perform demand forecasts that take the competitive environment into account. In the demand forecasting unit, for example, the generation AI collects the sales data of competitors and analyzes the competitive environment. For example, if a competitor's product is selling well, it predicts the possibility that demand for the company's product will decrease. The demand forecasting unit also learns the sales data of competitors, and the generation AI creates a demand forecasting model that takes the competitive environment into account. For example, it predicts how demand for the company's product will fluctuate when a competitor releases a new product. The demand forecasting unit also integrates the sales data of competitors, and performs demand forecasts that take the competitive environment into account. For example, it predicts the impact that a competitor's promotional activities will have on demand for the company's product. This makes it possible to perform demand forecasts that take the competitive environment into account.

[0057] In addition to learning sales data, the demand forecasting unit can predict demand peaks and declines by taking product lifecycle data into consideration. In the demand forecasting unit, for example, the generation AI learns product lifecycle data and predicts demand peaks and declines. For example, it predicts a pattern in which demand peaks immediately after the release of a new product and then gradually declines. The demand forecasting unit also takes product lifecycle data into consideration and the generation AI creates a demand forecasting model. For example, it predicts that demand will stabilize during the product's maturity period and then decline during the subsequent decline period. In the demand forecasting unit, the generation AI integrates sales data and product lifecycle data to predict demand peaks and declines. For example, it predicts demand fluctuations according to the product's lifecycle. This makes it possible to make demand forecasts that take the product's lifecycle into consideration.

[0058] The demand forecasting unit can analyze customers' emotional reactions to fluctuations in sales data and reflect those emotional reactions in the demand forecast. In the demand forecasting unit, for example, the generation AI analyzes customers' emotional reactions to fluctuations in sales data and reflects that data in the demand forecast. For example, the emotional score of a product whose sales have increased sharply is analyzed and used in the demand forecast. The demand forecasting unit also uses an emotion estimation function to analyze customers' emotional reactions to fluctuations in sales data, and the generation AI creates a demand forecasting model based on that data. For example, it predicts that demand for products with a high number of positive emotional reactions will increase. In addition, the demand forecasting unit uses the generation AI to analyze fluctuations in sales data based on customer emotional reaction data and reflects that in the demand forecast. For example, it predicts that demand for products with a high number of negative emotional reactions will decrease. This makes it possible to make demand forecasts that take customers' emotional reactions into account.

[0059] The demand forecasting unit takes into account local events and holidays in addition to the season and weather, thereby improving the accuracy of demand forecasts. In the demand forecasting unit, for example, the generation AI collects local event data and reflects it in the demand forecast. For example, it predicts that demand will increase during periods when local festivals and sporting events are held. The demand forecasting unit also learns holiday data, and the generation AI reflects it in the demand forecast. For example, it predicts that demand will increase during holidays such as Christmas and New Year's. The demand forecasting unit also integrates local event and holiday data in addition to the season and weather, and the generation AI creates a demand forecasting model. For example, it predicts that demand will peak during periods when specific events are held. This makes it possible to make demand forecasts that take local events and holidays into account.

[0060] The demand forecasting unit can learn from past abnormal weather data and predict the impact of abnormal weather on demand. In the demand forecasting unit, for example, the generation AI collects past abnormal weather data and reflects it in the demand forecast. For example, it analyzes demand fluctuations when abnormal cold waves or heat waves occur and predicts demand during future abnormal weather. The demand forecasting unit also learns abnormal weather data and the generation AI reflects it in the demand forecast. For example, it predicts increases or decreases in demand due to abnormal rainfall or the effects of typhoons. The demand forecasting unit also integrates past abnormal weather data and the generation AI predicts the impact of abnormal weather on demand. For example, it predicts peaks and decreases in demand when abnormal weather occurs. This makes it possible to make demand forecasts that take abnormal weather into account.

[0061] The demand forecasting unit can analyze customer emotions regarding weather and seasonal changes and reflect that emotion data in the demand forecast. For example, the demand forecasting unit uses a generation AI to analyze customer emotions regarding weather and seasonal changes and reflect that data in the demand forecast. For example, it predicts that demand for products associated with a high level of positive emotion will increase during cold seasons. The demand forecasting unit also uses an emotion estimation function to analyze customer emotions regarding weather and seasonal changes, and the generation AI creates a demand forecasting model based on that data. For example, it predicts that demand for products associated with a high level of negative emotion will decrease on rainy days. The demand forecasting unit also uses the generation AI to perform demand forecasts that take into account weather and seasonal changes based on customer emotion data. For example, it predicts that demand for products associated with a high level of positive emotion will increase during a particular season. This makes it possible to perform demand forecasts that take into account customer emotions regarding weather and seasonal changes.

[0062] The demand forecasting unit can take into account macroeconomic factors such as regional economic indicators and consumer confidence indexes in demand forecasts. In the demand forecasting unit, for example, the generation AI collects regional economic indicator data and reflects it in the demand forecast. For example, it predicts the impact of regional GDP and unemployment rate on demand. The demand forecasting unit also learns the consumer confidence index, and the generation AI reflects it in the demand forecast. For example, it predicts that demand will increase during periods of high consumer confidence. The demand forecasting unit also integrates macroeconomic factors, and the generation AI creates a demand forecasting model. For example, it predicts demand fluctuations depending on regional economic conditions and the consumer confidence index. This makes it possible to perform demand forecasting that takes macroeconomic factors into account.

[0063] The demand forecasting unit can predict fluctuations in demand by taking into account regional demographic trends and migration patterns in its demand forecasts. In the demand forecasting unit, for example, the generation AI collects regional demographic data and reflects it in the demand forecast. For example, it predicts that demand will increase in areas with growing populations. The demand forecasting unit also learns migration patterns, and the generation AI reflects this in the demand forecast. For example, it predicts that demand will increase during periods of increased migration to urban areas. The demand forecasting unit also integrates regional demographic trends and migration patterns, and the generation AI creates a demand forecasting model. For example, it predicts fluctuations in demand in response to changes in demographic trends. This makes it possible to make demand forecasts that take demographic trends and migration patterns into account.

[0064] The demand forecasting unit can analyze customers' emotional responses to seasonal and weather changes and reflect that emotional data in the demand forecast. For example, the demand forecasting unit uses a generation AI to analyze customers' emotional responses to seasonal and weather changes and reflects that data in the demand forecast. For example, it predicts that demand for products with a high level of positive emotions will increase in the summer. The demand forecasting unit also uses an emotion estimation function to analyze customers' emotional responses to seasonal and weather changes, and the generation AI creates a demand forecasting model based on that data. For example, it predicts that demand for products with a high level of negative emotions will decrease on rainy days. The demand forecasting unit also uses the generation AI to perform demand forecasts that take into account seasonal and weather changes based on customer emotional response data. For example, it predicts that demand for products with a high level of positive emotions will increase in a particular season. This makes it possible to make demand forecasts that take into account customers' emotional responses to seasonal and weather changes.

[0065] The delivery route proposal unit can propose cost-effective routes by taking into account fuel efficiency and maintenance data of delivery vehicles. For example, the generation AI collects fuel efficiency data of delivery vehicles and proposes cost-effective delivery routes. For example, it prioritizes the selection of routes with good fuel efficiency. The delivery route proposal unit also learns maintenance data, and the generation AI reflects this in the proposed delivery routes. For example, it proposes routes that avoid vehicles that require maintenance. The delivery route proposal unit also integrates fuel efficiency and maintenance data, and the generation AI proposes cost-effective delivery routes. For example, it selects routes that are fuel efficient and do not require maintenance. This makes it possible to propose cost-effective delivery routes.

[0066] The delivery route proposal unit can take into account the available receiving times at the delivery destination and specific delivery conditions. For example, the generation AI collects available receiving times at the delivery destination and proposes a delivery route that takes this into account. For example, it selects a route that matches the available receiving times. The delivery route proposal unit also learns specific delivery conditions, and the generation AI reflects them in the proposed delivery route. For example, it proposes a route that takes into account the delivery conditions for refrigerated goods and hazardous materials. The delivery route proposal unit also integrates the available receiving times and specific delivery conditions, and the generation AI proposes the optimal delivery route. For example, it selects a route that meets the available receiving times and delivery conditions. This makes it possible to propose a delivery route that takes into account the available receiving times and specific delivery conditions.

[0067] The delivery route suggestion unit can analyze customers' emotional reactions to changes in delivery routes and propose routes that will increase customer satisfaction. For example, the delivery route suggestion unit uses a generation AI to analyze customers' emotional reactions to changes in delivery routes and proposes routes that will increase customer satisfaction based on that data. For example, it selects a route that has a high number of positive emotional reactions. The delivery route suggestion unit also uses an emotion estimation function to analyze customers' emotional reactions to changes in delivery routes, and the generation AI proposes the optimal route based on that data. For example, it selects a route that has a low number of negative emotional reactions. The delivery route suggestion unit also uses a generation AI to propose delivery routes that will increase customer satisfaction based on customer emotional reaction data. For example, it selects a route that results in a short delivery time and high customer satisfaction. This makes it possible to propose delivery routes that take customers' emotional reactions into consideration.

[0068] The delivery route proposal unit can integrate data from multiple delivery companies and select the optimal company. For example, the generation AI collects data from multiple delivery companies and selects the optimal company based on that data. For example, the optimal company is selected by comparing delivery time and cost. The delivery route proposal unit also learns data from multiple delivery companies and the generation AI reflects that data in proposing delivery routes. For example, the optimal company is selected taking into account reliability and service quality. The delivery route proposal unit also integrates data from delivery companies and the generation AI selects the optimal company. For example, the optimal company is selected based on the delivery company's past performance data. This makes it possible to select the optimal company taking into account data from multiple delivery companies.

[0069] The delivery route proposal unit can take into account the geographical characteristics and infrastructure conditions of the delivery destination. For example, the generation AI collects the geographical characteristics of the delivery destination and proposes a delivery route that takes them into account. For example, it selects a route that suits geographical characteristics such as mountainous areas or urban areas. The delivery route proposal unit also learns the infrastructure conditions, and the generation AI reflects them in the proposed delivery route. For example, it proposes a route that takes road conditions and traffic conditions into account. The delivery route proposal unit also integrates the geographical characteristics and infrastructure conditions, and the generation AI proposes the optimal delivery route. For example, it selects a route that satisfies the geographical characteristics and infrastructure conditions. This makes it possible to propose a delivery route that takes geographical characteristics and infrastructure conditions into account.

[0070] The delivery route suggestion unit monitors customers' emotional reactions to proposed delivery routes in real time, and is able to continuously search for the optimal route. For example, the generation AI in the delivery route suggestion unit monitors customers' emotional reactions to proposed delivery routes in real time, and continuously searches for the optimal route based on that data. For example, it prioritizes selecting routes with a high number of positive emotional reactions. The delivery route suggestion unit also uses an emotion estimation function to analyze customers' emotional reactions to proposed delivery routes in real time, and the generation AI continuously searches for the optimal route based on that data. For example, it selects a route with a low number of negative emotional reactions. The delivery route suggestion unit also uses the generation AI to continuously search for the optimal delivery route based on customer emotional reaction data. For example, it selects a route with short delivery time and high customer satisfaction. This makes it possible to monitor customers' emotional reactions in real time and continuously search for the optimal delivery route.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The logistics support system can further include an energy consumption optimization unit. The energy consumption optimization unit collects energy consumption data of delivery vehicles and proposes optimal energy consumption routes. For example, the energy consumption optimization unit can optimize routes taking into account the locations of electric vehicle charging stations. The energy consumption optimization unit can also monitor the remaining battery power of vehicles in real time and propose efficient charging schedules. Furthermore, the energy consumption optimization unit can calculate the most energy-efficient delivery route based on the energy consumption data. This enables deliveries with minimal energy consumption.

[0073] The logistics support system can further include an environmental impact assessment unit. The environmental impact assessment unit assesses the environmental impact of delivery routes and proposes environmentally friendly routes. For example, the environmental impact assessment unit can calculate CO2 emissions and select the route with the lowest emissions. The environmental impact assessment unit can also collect emission data from delivery vehicles and propose routes with the lowest environmental impact. Furthermore, the environmental impact assessment unit can take into account local environmental protection regulations and propose routes that comply with the regulations. This enables environmentally friendly deliveries.

[0074] The logistics support system can further include a customer satisfaction evaluation unit. The customer satisfaction evaluation unit evaluates customer satisfaction with the delivery service and improves the service based on that data. For example, the customer satisfaction evaluation unit can collect feedback on delivery time and service quality and calculate a satisfaction score. The customer satisfaction evaluation unit can also analyze customer feedback and identify areas for improvement. Furthermore, the customer satisfaction evaluation unit can optimize delivery routes and service content based on the satisfaction score. This is expected to improve customer satisfaction.

[0075] The logistics support system can further include a preventive maintenance unit. The preventive maintenance unit collects maintenance data for delivery vehicles and proposes preventive maintenance schedules. For example, the preventive maintenance unit can propose the optimal maintenance period based on the vehicle's mileage and usage status. The preventive maintenance unit can also predict the risk of vehicle breakdown and prevent breakdowns by performing maintenance in advance. Furthermore, the preventive maintenance unit can propose the optimal operating method to extend the vehicle's lifespan based on the maintenance data. This is expected to increase vehicle utilization rates and reduce costs.

[0076] The logistics support system can further include a real-time inventory management unit. The real-time inventory management unit monitors inventory data in the warehouse in real time and optimizes inventory. For example, the real-time inventory management unit can collect product inbound and outbound data in real time and keep the inventory status up to date at all times. The real-time inventory management unit can also predict inventory surpluses and shortages and propose appropriate replenishment or reduction. Furthermore, the real-time inventory management unit can propose optimal warehouse layouts and picking routes based on the inventory data. This can improve the efficiency of inventory management and is expected to reduce costs.

[0077] The logistics support system can further include a unit that estimates a customer's purchasing intent. The unit that estimates a customer's purchasing intent analyzes the customer's past purchasing history and behavioral data to estimate the customer's purchasing intent. For example, the unit can estimate the customer's purchasing intent based on the customer's website browsing history and data on products added to the cart. The unit that estimates a customer's purchasing intent can also suggest specific products based on the estimated purchasing intent. Furthermore, the unit that estimates a customer's purchasing intent can optimize marketing strategies based on the estimation results. This can increase the customer's purchasing intent and is expected to increase sales.

[0078] The logistics support system can further include a unit for estimating the emotions of customers at delivery destinations. The unit for estimating the emotions of customers at delivery destinations analyzes past feedback and reviews of customers and estimates emotions. For example, emotions can be estimated based on the content of customer reviews and evaluation scores. The unit for estimating the emotions of customers at delivery destinations can also optimize delivery routes and service contents based on the estimated emotions. Furthermore, the unit for estimating the emotions of customers at delivery destinations can propose measures to improve customer satisfaction based on customer emotion data. This makes it possible to provide services that take customer emotions into consideration.

[0079] The logistics support system can further include a unit that estimates the emotions of delivery drivers. The unit that estimates the emotions of delivery drivers analyzes the driver's behavioral data and feedback to estimate emotions. For example, emotions can be estimated based on data on the driver's driving patterns and rest periods. The unit that estimates the emotions of delivery drivers can also optimize the driver's work schedule and route based on the estimated emotions. Furthermore, the unit that estimates the emotions of delivery drivers can propose measures to reduce stress and improve motivation based on the driver's emotional data. This makes it possible to improve the working environment by taking the driver's emotions into consideration.

[0080] The logistics support system can further include a unit that analyzes the purchase history of the customer at the delivery destination. The unit that analyzes the purchase history of the customer at the delivery destination collects past purchase data of the customer and analyzes their purchasing patterns. For example, it can identify the tendency of a particular customer to regularly purchase a particular product. The unit that analyzes the purchase history of the customer at the delivery destination can also suggest a particular product to the customer based on the analysis results. Furthermore, the unit that analyzes the purchase history of the customer at the delivery destination can perform demand forecasting based on the purchasing patterns. This makes it possible to make demand forecasts and suggest products that take into account the customer's purchase history.

[0081] The logistics support system can further include a unit that estimates the preferences of the customer at the delivery destination. The unit that estimates the preferences of the customer at the delivery destination analyzes the customer's past purchase history and behavioral data to estimate the preferences. For example, it can grasp the customer's tendency to prefer products of a particular brand or category. The unit that estimates the preferences of the customer at the delivery destination can also suggest specific products based on the estimated preferences. Furthermore, the unit that estimates the preferences of the customer at the delivery destination can optimize marketing strategies based on the preference data. This makes it possible to suggest products and conduct marketing that take customer preferences into consideration.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The sales data acquisition unit acquires sales data. For example, the sales data acquisition unit collects sales data from a POS system. It can also acquire sales data from an online sales platform. It can also collect manually entered sales data. Specifically, it automatically collects daily sales data from the POS system and acquires sales data in real time from the online sales platform via an API. Manually entered sales data is collected through a dedicated input form. Step 2: The demand forecasting unit predicts demand based on the sales data acquired by the sales data acquisition unit. For example, the demand forecasting unit uses a generation AI to analyze sales data and create a demand forecasting model. The demand forecasting unit can also predict demand taking into account related factors such as season and weather. It can also predict future demand based on past sales data. Specifically, the generation AI learns from the sales data and analyzes fluctuation patterns in demand. Using seasonal and weather data as input, the demand forecasting unit makes a demand forecast taking these factors into account. Based on past sales data, the demand forecasting unit predicts demand for the next month or season. Step 3: The delivery route proposal unit proposes an optimal delivery route based on the demand predicted by the demand prediction unit. For example, the delivery route proposal unit calculates the optimal route taking into account real-time traffic information. It can also optimize the route by reflecting the latest data. It can also propose an efficient delivery route based on the demand prediction results. Specifically, it optimizes the route by taking into account real-time information such as traffic congestion and road construction. Based on the latest data, the delivery route proposal unit calculates the most efficient delivery route. Based on the demand prediction results, the delivery route proposal unit proposes an efficient delivery route.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 AI 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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 AI 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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 AI 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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 sales data acquisition unit that acquires sales data; a demand forecasting unit that forecasts demand based on the sales data acquired by the sales data acquisition unit; a delivery route proposal unit that proposes an optimal delivery route based on the demand predicted by the demand prediction unit. A system characterized by:

2. The demand forecasting unit In addition to the sales data, the company will learn about customer purchasing history and social media trends to refine its demand forecasting model.

2. The system of claim 1.

3. The demand forecasting unit Compare the sales data from different regions and create a forecasting model that takes into account regional demand differences 2. The system of claim 1.

4. The demand forecasting unit Analyze sentiment from customer reviews and feedback and incorporate that sentiment data into demand forecasting 2. The system of claim 1.

5. The demand forecasting unit In addition to the sales data mentioned above, the system learns the sales data of competitors and makes demand forecasts that take the competitive environment into account.

2. The system of claim 1.

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