system

Abstract

The system according to the embodiment aims to improve the efficiency of inventory management and ordering operations, and reduce waste and unnecessary inventory. [Solution] A system according to an embodiment includes an information collection unit, an analysis unit, and an ordering unit. The information collection unit collects information. The analysis unit analyzes the information collected by the information collection unit and derives the optimum number of purchased products for each day. The ordering unit automatically places an order based on the optimum number of purchased products derived by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not sufficiently streamline inventory management and ordering processes, which can lead to waste and unnecessary inventory.

[0005] The system according to the embodiment aims to improve the efficiency of inventory management and ordering operations, and reduce waste and unnecessary inventory. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and an ordering unit. The information collection unit collects information. The analysis unit analyzes the information collected by the information collection unit and derives the optimum number of purchased commodities for each day. The ordering unit automatically places an order based on the optimum number of purchased commodities derived by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of inventory management and ordering operations, and reduce waste and unnecessary inventory. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An inventory management and ordering system according to an embodiment of the present invention collects information on the area where a store is located, the weather in that area, the day of the week, general product purchasing trends, the lowest-priced seller of stock, events, and so on, and uses a generation AI to determine the optimal number of merchandise to be purchased each day. This system builds a mechanism for reducing waste and unnecessary inventory and appropriately automating ordering operations, and provides this system to various industries. For example, information on the area where a store is located is collected. Specifically, data on the store's location, the local population, and traffic conditions are collected. Next, weather information for the area is collected. For example, data on temperature, precipitation, wind speed, etc. are collected. Furthermore, information on the day of the week and general product purchasing trends is collected. For example, data on the tendency for certain products to sell well on certain days of the week and seasonal product purchasing trends are collected. Next, information on the lowest-priced seller of stock is collected. For example, data on price information and inventory status from multiple suppliers is collected. Furthermore, event information is collected. For example, data on local events and festivals is collected. This information is input into the generation AI to determine the optimal number of merchandise to be purchased each day. The generation AI analyzes the collected data and calculates the optimal purchase quantity. For example, if a particular product tends to sell well on a particular day of the week, it adjusts the purchase quantity to suit that day. It also predicts demand fluctuations based on weather and event information and optimizes the purchase quantity. Furthermore, it automates ordering operations as appropriate based on the inventory management described above. For example, a system can be built to automatically place orders based on the purchase quantity determined by the generation AI. This improves the efficiency of ordering operations and prevents human error. This system can be applied to a variety of industries, including retail, food service, and manufacturing, which require inventory management and ordering operations. This reduces waste and unnecessary inventory and improves operational efficiency. This allows inventory management and ordering systems to reduce waste and unnecessary inventory and automate ordering operations as appropriate.

[0029] The inventory management and ordering system according to the embodiment includes an information collection unit, an analysis unit, and an ordering unit. The information collection unit collects information. Examples of the information include, but are not limited to, information about the area where the store is located, weather information, information about the day of the week and general product purchasing trends, information about the lowest-priced seller of stocked inventory, and event information. The information collection unit, for example, collects information about the area where the store is located. Specifically, the information collection unit collects data about the location of the store, the population of the area, traffic conditions, and the like. The information collection unit also collects weather information about the area. For example, data such as temperature, precipitation, and wind speed can be collected. The information collection unit also collects information about the day of the week and general product purchasing trends. For example, data such as the tendency for certain products to sell well on certain days of the week and seasonal product purchasing trends can be collected. Next, the information collection unit collects information about the lowest-priced seller of stocked inventory. For example, data such as price information and inventory status can be collected from multiple suppliers. The information collection unit also collects event information. For example, data about events and festivals held in the area can be collected. The analysis unit uses a generation AI to analyze the information collected by the information collection unit and derive the optimal number of merchandise to purchase each day. The analysis unit, for example, calculates the optimal purchase quantity based on the collected data. For example, if a particular product tends to sell well on a particular day of the week, the analysis unit adjusts the purchase quantity to suit that day. The analysis unit can also predict demand fluctuations based on weather information and event information and optimize the purchase quantity. Some or all of the above-described processing in the analysis unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). The ordering unit automatically places an order based on the optimal number of merchandise derived by the analysis unit. The ordering unit, for example, establishes a system for automatically placing an order based on the purchase quantity derived by the generation AI. This improves the efficiency of ordering operations and prevents human error. Some or all of the above-described processing in the ordering unit may be performed using AI or without AI. As a result, the inventory management and ordering system according to the embodiment automates the information collection, analysis, and ordering processes, enabling efficient inventory management and ordering.

[0030] The information collection unit can collect information about the area where the store is located. Information about the area where the store is located includes, but is not limited to, the location of the store, the population of the area, and traffic conditions. For example, the information collection unit collects information about the location of the store. Specifically, the information collection unit collects information about the store's address and geographical location. The information collection unit can also collect information about the population of the area. For example, the information collection unit collects information such as demographic data and population density of the area. The information collection unit can also collect information about traffic conditions. For example, the information collection unit collects data such as traffic volume and public transportation operation status of the area. By collecting information about the area where the store is located, inventory management according to the characteristics of the area becomes possible. Some or all of the above-mentioned processing by the information collection unit may be performed using AI, or may be performed without using AI. For example, the information collection unit can input demographic data about the area into the generation AI and cause the generation AI to analyze the characteristics of the area.

[0031] The information collection unit can collect weather information for the region. Weather information includes, but is not limited to, temperature, precipitation, wind speed, etc. For example, the information collection unit collects temperature information. Specifically, it collects temperature data and temperature fluctuation patterns for the region. The information collection unit can also collect precipitation information. For example, it collects information such as precipitation data and precipitation frequency for the region. The information collection unit can also collect wind speed information. For example, it collects information such as wind speed data and wind strength fluctuation patterns for the region. By collecting weather information, inventory management according to the weather becomes possible. Some or all of the above-described processing by the information collection unit may be performed using AI, or may be performed without using AI. For example, the information collection unit can input temperature data for the region into the generation AI and cause the generation AI to analyze the temperature fluctuation patterns.

[0032] The information collecting unit can collect information on days of the week or general product purchasing trends. Information based on days of the week includes, but is not limited to, sales data for specific days of the week and demand patterns for each day of the week. For example, the information collecting unit collects sales data for specific days of the week. Specifically, it collects product sales quantities and sales data for specific days of the week. The information collecting unit can also collect demand patterns for each day of the week. For example, it collects data on the tendency for specific products to sell well on specific days of the week and patterns of sales fluctuations for each day of the week. Information based on product purchasing trends includes, but is not limited to, past purchase history and trend analysis. For example, the information collecting unit collects past purchase history. Specifically, it collects information such as customer purchase history data and product sales history. The information collecting unit can also perform trend analysis. For example, it collects and analyzes data on general trends and popular products. By collecting information on days of the week and product purchasing trends, demand forecasting becomes possible. Some or all of the above-described processing by the information collecting unit may be performed using AI or without AI. For example, the information collection unit can input sales data for a specific day of the week into the generation AI and have the generation AI analyze demand patterns for each day of the week.

[0033] The information collection unit can collect information on the lowest-priced supplier of inventory. Information on the lowest-priced supplier of inventory includes, but is not limited to, price information and inventory status from multiple suppliers. For example, the information collection unit collects price information from multiple suppliers. Specifically, it collects price lists from each supplier and data from price comparison sites. The information collection unit can also collect inventory status from suppliers. For example, it collects information such as inventory levels and inventory turnover rates from each supplier. This allows for cost reduction by collecting information on the supplier with the lowest price. Some or all of the above-described processing by the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input price information from multiple suppliers into the generation AI and have the generation AI perform an analysis to identify the supplier with the lowest price.

[0034] The information collection unit can collect event information. Event information includes, but is not limited to, events and festivals held in the local area. For example, the information collection unit collects information on events held in the local area. Specifically, the information collection unit collects information on local event calendars and event announcements. The information collection unit can also collect information on seasonal events. For example, the information collection unit collects data on seasonal events and promotional events. By collecting event information, inventory management according to events becomes possible. Some or all of the above-described processing in the information collection unit may be performed using AI, or may be performed without using AI. For example, the information collection unit can input a local event calendar into the generation AI and cause the generation AI to execute a process to analyze the impact of the event.

[0035] The analysis unit can analyze the collected information and calculate an appropriate purchase quantity. Calculation of an appropriate purchase quantity includes, but is not limited to, a demand forecasting model and inventory turnover. The analysis unit calculates the purchase quantity using, for example, a demand forecasting model. Specifically, the analysis unit predicts future demand based on past sales data and demand fluctuation patterns and derives an optimal purchase quantity. The analysis unit can also adjust the purchase quantity taking inventory turnover into account. For example, a larger amount of stock is purchased for products with a faster inventory turnover, and a smaller amount is purchased for products with a slower inventory turnover. This allows the optimal purchase quantity to be derived by analyzing the collected information. Some or all of the above-described processing in the analysis unit can be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the analysis unit can input the collected information into a generation AI and have the generation AI calculate the optimal purchase quantity.

[0036] The ordering unit can automatically place an order based on the purchase quantity derived by the generation AI. The ordering unit, for example, constructs a system for automatically placing an order based on the purchase quantity derived by the generation AI. Specifically, the ordering system receives the calculation results of the generation AI and automatically orders the required products. The ordering unit can also adjust the timing of the order. For example, it can be set to automatically place an order when inventory is low. This improves the efficiency of ordering operations by automatically placing an order based on the purchase quantity derived by the generation AI. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, or may be performed without using AI. For example, the ordering unit can place an order using an AI model that inputs the calculation results of the generation AI and outputs order instructions.

[0037] The information collection unit can analyze past information collection history and select the optimal information collection method. The information collection unit, for example, uses an analysis tool to analyze the past information collection history. Specifically, the information collection unit analyzes the past information collection history using a log of collected data and an analysis tool. The information collection unit can also select the most efficient collection method from the past information collection history. For example, the information collection unit can concentrate information collection in a specific time period based on the past information collection history. The information collection unit can also analyze the past information collection history and eliminate unnecessary information collection. This enables efficient information collection by analyzing the past information collection history. Some or all of the above-mentioned processing in the information collection unit may be performed using AI, or may be performed without using AI. For example, the information collection unit can input the past information collection history into a generation AI and have the generation AI select the optimal information collection method.

[0038] The information collection unit can adjust the type of information to be collected based on specific events or seasons when collecting information. For example, the information collection unit refers to an event calendar or seasonal sales data to adjust the type of information to be collected based on specific events or seasons. Specifically, the type of information to be collected is adjusted according to seasonal demand. For example, information on cooling products and beverages is prioritized in summer, and information on heating products and cold weather gear is prioritized in winter. The information collection unit can also prioritize collecting information related to specific events. For example, information on products related to local festivals and promotional events is collected. Furthermore, the information collection unit can expand the range of information to be collected based on seasons and events. This allows for more appropriate information collection by adjusting the type of information to be collected depending on events and seasons. Some or all of the above-described processing by the information collection unit may be performed using AI or without AI. For example, the information collection unit can input an event calendar into the generation AI and cause the generation AI to perform an analysis to adjust the type of information to be collected.

[0039] When collecting information, the information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The information collection unit, for example, uses GPS data or location information services to collect the user's geographical location information. Specifically, the information collection unit prioritizes collecting information on related areas based on the user's current location. For example, if the user is in a specific area, sales data and demand information for that area are collected. The information collection unit can also collect highly relevant information by taking into account the user's movement history. For example, relevant information is collected based on information on areas the user has previously visited. Furthermore, the information collection unit can select the optimal information collection point based on the user's geographical location information. This allows highly relevant information to be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the information collection unit may be performed using AI or without AI. For example, the information collection unit can input the user's GPS data into the generation AI and cause the generation AI to collect highly relevant information.

[0040] The information collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the information collection unit uses analysis of post content and engagement data to analyze the user's social media activities. Specifically, the information collection unit analyzes the user's social media posts and collects related information. For example, if the user posts about a specific product, sales data and demand information for that product are collected. The information collection unit can also collect related information by taking into account the activities of the user's followers and friends. For example, it can collect product and trend information that the user's followers are interested in. Furthermore, the information collection unit can adjust the information to be collected based on the user's social media trends. This allows related information to be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the information collection unit may be performed using AI or without AI. For example, the information collection unit can input the user's social media post data into a generation AI and cause the generation AI to collect related information.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. The analysis unit, for example, uses an impact score or a prioritization algorithm to evaluate the importance of the collected information. Specifically, the analysis unit performs a detailed analysis on information with high importance. For example, important sales data or information related to demand forecasts can be analyzed in detail. Furthermore, the analysis unit can perform a simplified analysis on information with low importance. For example, general trend information or reference data can be analyzed simply. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above-described processing in the analysis unit can be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the analysis unit can input the collected information into a generation AI and have the generation AI adjust the level of detail of the analysis based on the importance.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit uses a category classification algorithm to classify the categories of information. Specifically, the analysis unit applies the optimal analysis algorithm for each product category. For example, a demand forecasting algorithm is applied to the food category, and an inventory turnover algorithm is applied to the electronics category. The analysis unit can also apply a weather data analysis algorithm to weather information. For example, it can analyze temperature and precipitation data to evaluate the impact of the weather. Furthermore, the analysis unit can apply an event analysis algorithm to event information. For example, it can analyze local event data to evaluate the impact of the event. This enables more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the analysis unit can input the category of information into the generation AI and have the generation AI apply an analysis algorithm according to the category.

[0043] During analysis, the analysis unit can determine analysis priorities based on when the information was collected. For example, the analysis unit considers the collection date and the freshness of the data to evaluate when the information was collected. Specifically, the analysis unit prioritizes the most recent information. For example, it prioritizes analysis of information related to the most recent sales data and demand forecasts. The analysis unit can also emphasize the most recent information while referring to past information. For example, it performs trend analysis based on past sales data and analyzes it in combination with the most recent data. Furthermore, the analysis unit can optimally allocate analysis resources according to when the information was collected. This enables efficient analysis by determining analysis priorities based on when the information was collected. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the analysis unit can input the time when the information was collected into the generation AI and have the generation AI determine the analysis priorities based on the time when the information was collected.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. The analysis unit, for example, uses correlation analysis or relevance scores to evaluate the relevance of information. Specifically, the analysis unit prioritizes analysis of highly relevant information. For example, sales data and information related to demand forecasts are prioritized for analysis. In addition, the analysis unit can postpone analysis of less relevant information. For example, general trend information and reference data are postponed. Furthermore, the analysis unit can optimally adjust the order of analysis based on the relevance of information. This enables efficient analysis by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the analysis unit can input the relevance of information into the generation AI and have the generation AI adjust the order of analysis based on the relevance.

[0045] When placing an order, the order unit can analyze past order history and select the optimal order method. The order unit, for example, uses an analysis tool to analyze the past order history. Specifically, it analyzes the past order history using order data logs and analysis tools. The order unit can also select the most efficient order method from the past order history. For example, it can concentrate orders in a specific time period based on the past order history. The order unit can also analyze the past order history and eliminate unnecessary orders. This enables efficient ordering by analyzing the past order history. Some or all of the above-mentioned processing in the order unit may be performed using AI, or may be performed without using AI. For example, the order unit can input the past order history into a generation AI and have the generation AI select the optimal order method.

[0046] When placing an order, the order unit can customize the order method based on the current inventory status. For example, the order unit considers inventory levels and inventory turnover to evaluate the current inventory status. Specifically, when inventory is low, the order unit selects an order method that allows for quick replenishment. For example, an urgent order or immediate delivery is selected. Also, when inventory is high, a method of refraining from ordering can be selected. For example, delaying the timing of the order or reducing the order quantity. Furthermore, the order unit can adjust the timing of the order depending on the inventory status. This enables appropriate inventory management by customizing the order method based on the current inventory status. Some or all of the above-mentioned processing in the order unit may be performed using AI, or may be performed without using AI. For example, the order unit can input the current inventory status into the generation AI and have the generation AI customize the order method based on the inventory status.

[0047] When placing an order, the order unit can select the optimal order method by taking into account the user's geographic location information. The order unit, for example, uses GPS data or location information services to collect the user's geographic location information. Specifically, the order unit selects the optimal supplier based on the user's current location. For example, if the user is in a specific area, the order unit selects a supplier close to that area. The order unit can also select the optimal order method by taking into account the user's travel history. For example, the order unit selects the optimal supplier based on information about areas the user has previously visited. Furthermore, the order unit can select the optimal order timing based on the user's geographic location information. This allows the optimal order method to be selected by taking the user's geographic location information into account. Some or all of the above-described processing in the order unit may be performed using AI, or may be performed without using AI. For example, the order unit can input the user's GPS data into the generation AI and have the generation AI select the optimal order method.

[0048] When placing an order, the order unit can analyze the user's social media activity and suggest an order method. For example, the order unit uses analysis of post content and engagement data to analyze the user's social media activity. Specifically, the order unit analyzes the user's social media posts and suggests an optimal order method. For example, if the user posts about a specific product, the order unit suggests an order method for that product. The order unit can also suggest an optimal order method by taking into account the activities of the user's followers and friends. For example, the order unit suggests an order method based on products that the user's followers are interested in and trend information. Furthermore, the order unit can adjust the order method based on the user's social media trends. In this way, the optimal order method can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the order unit may be performed using AI, or may be performed without using AI. For example, the order unit can input the user's social media post data into a generation AI and have the generation AI suggest an optimal order method.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The information collection unit can analyze a user's purchasing history and predict future demand based on past purchasing patterns. For example, it can collect data on products a specific user has purchased in the past and analyze seasonal purchasing trends. The information collection unit can also suggest related products based on the user's purchasing history. For example, it can suggest new products or complementary products related to products previously purchased. Furthermore, the information collection unit can offer specific promotions or discounts based on the user's purchasing history. This makes it possible to utilize the user's purchasing history to provide more personalized services.

[0051] The analysis unit can predict the product life cycle based on the collected information and perform appropriate inventory management. For example, it can predict the timing of new product introductions and the growth, maturity, and decline stages of a product, and adjust inventory levels accordingly. The analysis unit can also plan promotion and marketing strategies based on the product life cycle. For example, it can conduct aggressive promotions during the growth stage and maintain a stable supply during the maturity stage. Furthermore, the analysis unit can take measures to minimize waste risks according to the product life cycle. This enables inventory management that takes product life cycles into account.

[0052] The ordering unit can make customized ordering suggestions to specific users based on the user's purchasing history. For example, the ordering unit can suggest regular purchases of a specific product to a user who has frequently purchased that product in the past. The ordering unit can also suggest ordering related products together based on the user's purchasing history. For example, it can suggest products that go well with products purchased in the past. Furthermore, the ordering unit can offer specific promotions and discounts based on the user's purchasing history. This makes it possible to make more personalized ordering suggestions by utilizing the user's purchasing history.

[0053] The analysis unit can predict product demand and calculate appropriate inventory levels based on the collected information. For example, it can analyze past sales data and seasonal demand patterns to predict future demand. The analysis unit can also plan promotions and marketing strategies based on the demand forecast. For example, it can implement promotions in accordance with periods of high demand. Furthermore, the analysis unit can take measures to minimize waste risk based on the demand forecast. This enables inventory management that takes demand forecasts into account.

[0054] The ordering department can select the most suitable supplier based on the collected information. For example, it can compare price information and inventory status from multiple suppliers to select the most cost-effective supplier. The ordering department can also select a supplier taking into consideration the supplier's reliability and delivery time. For example, it can select a reliable supplier based on past transaction history and delivery performance. Furthermore, the ordering department can select a supplier taking into consideration the supplier's geographic location. This allows for the selection of the most suitable supplier, enabling cost reduction and efficient inventory management.

[0055] The information collection unit can forecast demand for specific products based on the collected information. For example, it can analyze past sales data and seasonal demand patterns to predict future demand. The information collection unit can also plan promotions and marketing strategies based on the demand forecast. For example, it can implement promotions in accordance with periods of high demand. Furthermore, the information collection unit can take measures to minimize waste risk based on the demand forecast. This enables inventory management that takes demand forecasts into account.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The information collection unit collects information. This information includes, for example, information about the store's location, weather information, information about the day of the week and general product purchasing trends, information about the lowest-priced seller of purchased inventory, and event information. Specifically, the data collected includes the store's location, the local population, traffic conditions, temperature, precipitation, wind speed, the tendency for certain products to sell well on certain days of the week, seasonal product purchasing trends, price information and inventory status from multiple suppliers, and local events and festivals. Step 2: The analysis unit uses the generation AI to analyze the information collected by the information collection unit and determine the optimal number of products to purchase each day. For example, it calculates the optimal purchase quantity based on the collected data and adjusts the purchase quantity based on the tendency for certain products to sell well on certain days of the week. It also predicts fluctuations in demand based on weather and event information and optimizes the purchase quantity. Step 3: The ordering department automatically places orders based on the optimal number of purchased products determined by the analysis department. For example, a system can be created to automatically place orders based on the purchase quantity determined by the generation AI. This will improve the efficiency of ordering operations and prevent human error.

[0058] (Example 2) An inventory management and ordering system according to an embodiment of the present invention collects information on the area where a store is located, the weather in that area, the day of the week, general product purchasing trends, the lowest-priced seller of stock, events, and so on, and uses a generation AI to determine the optimal number of merchandise to be purchased each day. This system builds a mechanism for reducing waste and unnecessary inventory and appropriately automating ordering operations, and provides this system to various industries. For example, information on the area where a store is located is collected. Specifically, data on the store's location, the local population, and traffic conditions are collected. Next, weather information for the area is collected. For example, data on temperature, precipitation, wind speed, etc. are collected. Furthermore, information on the day of the week and general product purchasing trends is collected. For example, data on the tendency for certain products to sell well on certain days of the week and seasonal product purchasing trends are collected. Next, information on the lowest-priced seller of stock is collected. For example, data on price information and inventory status from multiple suppliers is collected. Furthermore, event information is collected. For example, data on local events and festivals is collected. This information is input into the generation AI to determine the optimal number of merchandise to be purchased each day. The generation AI analyzes the collected data and calculates the optimal purchase quantity. For example, if a particular product tends to sell well on a particular day of the week, it adjusts the purchase quantity to suit that day. It also predicts demand fluctuations based on weather and event information and optimizes the purchase quantity. Furthermore, it automates ordering operations as appropriate based on the inventory management described above. For example, a system can be built to automatically place orders based on the purchase quantity determined by the generation AI. This improves the efficiency of ordering operations and prevents human error. This system can be applied to a variety of industries, including retail, food service, and manufacturing, which require inventory management and ordering operations. This reduces waste and unnecessary inventory and improves operational efficiency. This allows inventory management and ordering systems to reduce waste and unnecessary inventory and automate ordering operations as appropriate.

[0059] The inventory management and ordering system according to the embodiment includes an information collection unit, an analysis unit, and an ordering unit. The information collection unit collects information. Examples of the information include, but are not limited to, information about the area where the store is located, weather information, information about the day of the week and general product purchasing trends, information about the lowest-priced seller of stocked inventory, and event information. The information collection unit, for example, collects information about the area where the store is located. Specifically, the information collection unit collects data about the location of the store, the population of the area, traffic conditions, and the like. The information collection unit also collects weather information about the area. For example, data such as temperature, precipitation, and wind speed can be collected. The information collection unit also collects information about the day of the week and general product purchasing trends. For example, data such as the tendency for certain products to sell well on certain days of the week and seasonal product purchasing trends can be collected. Next, the information collection unit collects information about the lowest-priced seller of stocked inventory. For example, data such as price information and inventory status can be collected from multiple suppliers. The information collection unit also collects event information. For example, data about events and festivals held in the area can be collected. The analysis unit uses a generation AI to analyze the information collected by the information collection unit and derive the optimal number of merchandise to purchase each day. The analysis unit, for example, calculates the optimal purchase quantity based on the collected data. For example, if a particular product tends to sell well on a particular day of the week, the analysis unit adjusts the purchase quantity to suit that day. The analysis unit can also predict demand fluctuations based on weather information and event information and optimize the purchase quantity. Some or all of the above-described processing in the analysis unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). The ordering unit automatically places an order based on the optimal number of merchandise derived by the analysis unit. The ordering unit, for example, establishes a system for automatically placing an order based on the purchase quantity derived by the generation AI. This improves the efficiency of ordering operations and prevents human error. Some or all of the above-described processing in the ordering unit may be performed using AI or without AI. As a result, the inventory management and ordering system according to the embodiment automates the information collection, analysis, and ordering processes, enabling efficient inventory management and ordering.

[0060] The information collection unit can collect information about the area where the store is located. Information about the area where the store is located includes, but is not limited to, the location of the store, the population of the area, and traffic conditions. For example, the information collection unit collects information about the location of the store. Specifically, the information collection unit collects information about the store's address and geographical location. The information collection unit can also collect information about the population of the area. For example, the information collection unit collects information such as demographic data and population density of the area. The information collection unit can also collect information about traffic conditions. For example, the information collection unit collects data such as traffic volume and public transportation operation status of the area. By collecting information about the area where the store is located, inventory management according to the characteristics of the area becomes possible. Some or all of the above-mentioned processing by the information collection unit may be performed using AI, or may be performed without using AI. For example, the information collection unit can input demographic data about the area into the generation AI and cause the generation AI to analyze the characteristics of the area.

[0061] The information collection unit can collect weather information for the region. Weather information includes, but is not limited to, temperature, precipitation, wind speed, etc. For example, the information collection unit collects temperature information. Specifically, it collects temperature data and temperature fluctuation patterns for the region. The information collection unit can also collect precipitation information. For example, it collects information such as precipitation data and precipitation frequency for the region. The information collection unit can also collect wind speed information. For example, it collects information such as wind speed data and wind strength fluctuation patterns for the region. By collecting weather information, inventory management according to the weather becomes possible. Some or all of the above-described processing by the information collection unit may be performed using AI, or may be performed without using AI. For example, the information collection unit can input temperature data for the region into the generation AI and cause the generation AI to analyze the temperature fluctuation patterns.

[0062] The information collecting unit can collect information on days of the week or general product purchasing trends. Information based on days of the week includes, but is not limited to, sales data for specific days of the week and demand patterns for each day of the week. For example, the information collecting unit collects sales data for specific days of the week. Specifically, it collects product sales quantities and sales data for specific days of the week. The information collecting unit can also collect demand patterns for each day of the week. For example, it collects data on the tendency for specific products to sell well on specific days of the week and patterns of sales fluctuations for each day of the week. Information based on product purchasing trends includes, but is not limited to, past purchase history and trend analysis. For example, the information collecting unit collects past purchase history. Specifically, it collects information such as customer purchase history data and product sales history. The information collecting unit can also perform trend analysis. For example, it collects and analyzes data on general trends and popular products. By collecting information on days of the week and product purchasing trends, demand forecasting becomes possible. Some or all of the above-described processing by the information collecting unit may be performed using AI or without AI. For example, the information collection unit can input sales data for a specific day of the week into the generation AI and have the generation AI analyze demand patterns for each day of the week.

[0063] The information collection unit can collect information on the lowest-priced supplier of inventory. Information on the lowest-priced supplier of inventory includes, but is not limited to, price information and inventory status from multiple suppliers. For example, the information collection unit collects price information from multiple suppliers. Specifically, it collects price lists from each supplier and data from price comparison sites. The information collection unit can also collect inventory status from suppliers. For example, it collects information such as inventory levels and inventory turnover rates from each supplier. This allows for cost reduction by collecting information on the supplier with the lowest price. Some or all of the above-described processing by the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input price information from multiple suppliers into the generation AI and have the generation AI perform an analysis to identify the supplier with the lowest price.

[0064] The information collection unit can collect event information. Event information includes, but is not limited to, events and festivals held in the local area. For example, the information collection unit collects information on events held in the local area. Specifically, the information collection unit collects information on local event calendars and event announcements. The information collection unit can also collect information on seasonal events. For example, the information collection unit collects data on seasonal events and promotional events. By collecting event information, inventory management according to events becomes possible. Some or all of the above-described processing in the information collection unit may be performed using AI, or may be performed without using AI. For example, the information collection unit can input a local event calendar into the generation AI and cause the generation AI to execute a process to analyze the impact of the event.

[0065] The analysis unit can analyze the collected information and calculate an appropriate purchase quantity. Calculation of an appropriate purchase quantity includes, but is not limited to, a demand forecasting model and inventory turnover. The analysis unit calculates the purchase quantity using, for example, a demand forecasting model. Specifically, the analysis unit predicts future demand based on past sales data and demand fluctuation patterns and derives an optimal purchase quantity. The analysis unit can also adjust the purchase quantity taking inventory turnover into account. For example, a larger amount of stock is purchased for products with a faster inventory turnover, and a smaller amount is purchased for products with a slower inventory turnover. This allows the optimal purchase quantity to be derived by analyzing the collected information. Some or all of the above-described processing in the analysis unit can be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the analysis unit can input the collected information into a generation AI and have the generation AI calculate the optimal purchase quantity.

[0066] The ordering unit can automatically place an order based on the purchase quantity derived by the generation AI. The ordering unit, for example, constructs a system for automatically placing an order based on the purchase quantity derived by the generation AI. Specifically, the ordering system receives the calculation results of the generation AI and automatically orders the required products. The ordering unit can also adjust the timing of the order. For example, it can be set to automatically place an order when inventory is low. This improves the efficiency of ordering operations by automatically placing an order based on the purchase quantity derived by the generation AI. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, or may be performed without using AI. For example, the ordering unit can place an order using an AI model that inputs the calculation results of the generation AI and outputs order instructions.

[0067] The information collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The information collection unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. Specifically, it analyzes the user's facial expressions, voice, and text data to estimate the emotions. The information collection unit can also adjust the timing of information collection based on the estimated user emotions. For example, if the user is stressed, the frequency of information collection can be reduced to reduce the burden. If the user is relaxed, the frequency of information collection can be increased to collect detailed data. Furthermore, if the user is in a hurry, only important information can be collected preferentially. This allows the user's burden to be reduced by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the information collection unit can be performed using AI or without AI. For example, the information collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0068] The information collection unit can analyze past information collection history and select the optimal information collection method. The information collection unit, for example, uses an analysis tool to analyze the past information collection history. Specifically, the information collection unit analyzes the past information collection history using a log of collected data and an analysis tool. The information collection unit can also select the most efficient collection method from the past information collection history. For example, the information collection unit can concentrate information collection in a specific time period based on the past information collection history. The information collection unit can also analyze the past information collection history and eliminate unnecessary information collection. This enables efficient information collection by analyzing the past information collection history. Some or all of the above-mentioned processing in the information collection unit may be performed using AI, or may be performed without using AI. For example, the information collection unit can input the past information collection history into a generation AI and have the generation AI select the optimal information collection method.

[0069] The information collection unit can adjust the type of information to be collected based on specific events or seasons when collecting information. For example, the information collection unit refers to an event calendar or seasonal sales data to adjust the type of information to be collected based on specific events or seasons. Specifically, the type of information to be collected is adjusted according to seasonal demand. For example, information on cooling products and beverages is prioritized in summer, and information on heating products and cold weather gear is prioritized in winter. The information collection unit can also prioritize collecting information related to specific events. For example, information on products related to local festivals and promotional events is collected. Furthermore, the information collection unit can expand the range of information to be collected based on seasons and events. This allows for more appropriate information collection by adjusting the type of information to be collected depending on events and seasons. Some or all of the above-described processing by the information collection unit may be performed using AI or without AI. For example, the information collection unit can input an event calendar into the generation AI and cause the generation AI to perform an analysis to adjust the type of information to be collected.

[0070] The information collection unit can estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. The information collection unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. Specifically, it analyzes the user's facial expressions, voice, and text data to estimate the emotions. The information collection unit can also prioritize the information to be collected based on the estimated user emotions. For example, if the user is stressed, it can prioritize collecting only important information. If the user is relaxed, it can prioritize collecting detailed information. Furthermore, if the user is in a hurry, it can prioritize information that can be collected quickly. This enables more appropriate information collection by prioritizing the information to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the information collection unit can be performed using AI or without AI. For example, the information collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0071] When collecting information, the information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The information collection unit, for example, uses GPS data or location information services to collect the user's geographical location information. Specifically, the information collection unit prioritizes collecting information on related areas based on the user's current location. For example, if the user is in a specific area, sales data and demand information for that area are collected. The information collection unit can also collect highly relevant information by taking into account the user's movement history. For example, relevant information is collected based on information on areas the user has previously visited. Furthermore, the information collection unit can select the optimal information collection point based on the user's geographical location information. This allows highly relevant information to be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the information collection unit may be performed using AI or without AI. For example, the information collection unit can input the user's GPS data into the generation AI and cause the generation AI to collect highly relevant information.

[0072] The information collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the information collection unit uses analysis of post content and engagement data to analyze the user's social media activities. Specifically, the information collection unit analyzes the user's social media posts and collects related information. For example, if the user posts about a specific product, sales data and demand information for that product are collected. The information collection unit can also collect related information by taking into account the activities of the user's followers and friends. For example, it can collect product and trend information that the user's followers are interested in. Furthermore, the information collection unit can adjust the information to be collected based on the user's social media trends. This allows related information to be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the information collection unit may be performed using AI or without AI. For example, the information collection unit can input the user's social media post data into a generation AI and cause the generation AI to collect related information.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. Specifically, the analysis unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The analysis unit can also adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is stressed, visually easy-to-understand analysis results can be provided. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using AI or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. The analysis unit, for example, uses an impact score or a prioritization algorithm to evaluate the importance of the collected information. Specifically, the analysis unit performs a detailed analysis on information with high importance. For example, important sales data or information related to demand forecasts can be analyzed in detail. Furthermore, the analysis unit can perform a simplified analysis on information with low importance. For example, general trend information or reference data can be analyzed simply. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above-described processing in the analysis unit can be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the analysis unit can input the collected information into a generation AI and have the generation AI adjust the level of detail of the analysis based on the importance.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit uses a category classification algorithm to classify the categories of information. Specifically, the analysis unit applies the optimal analysis algorithm for each product category. For example, a demand forecasting algorithm is applied to the food category, and an inventory turnover algorithm is applied to the electronics category. The analysis unit can also apply a weather data analysis algorithm to weather information. For example, it can analyze temperature and precipitation data to evaluate the impact of the weather. Furthermore, the analysis unit can apply an event analysis algorithm to event information. For example, it can analyze local event data to evaluate the impact of the event. This enables more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the analysis unit can input the category of information into the generation AI and have the generation AI apply an analysis algorithm according to the category.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. Specifically, the analysis unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The analysis unit can also adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can determine analysis priorities based on when the information was collected. For example, the analysis unit considers the collection date and the freshness of the data to evaluate when the information was collected. Specifically, the analysis unit prioritizes the most recent information. For example, it prioritizes analysis of information related to the most recent sales data and demand forecasts. The analysis unit can also emphasize the most recent information while referring to past information. For example, it performs trend analysis based on past sales data and analyzes it in combination with the most recent data. Furthermore, the analysis unit can optimally allocate analysis resources according to when the information was collected. This enables efficient analysis by determining analysis priorities based on when the information was collected. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the analysis unit can input the time when the information was collected into the generation AI and have the generation AI determine the analysis priorities based on the time when the information was collected.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. The analysis unit, for example, uses correlation analysis or relevance scores to evaluate the relevance of information. Specifically, the analysis unit prioritizes analysis of highly relevant information. For example, sales data and information related to demand forecasts are prioritized for analysis. In addition, the analysis unit can postpone analysis of less relevant information. For example, general trend information and reference data are postponed. Furthermore, the analysis unit can optimally adjust the order of analysis based on the relevance of information. This enables efficient analysis by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the analysis unit can input the relevance of information into the generation AI and have the generation AI adjust the order of analysis based on the relevance.

[0079] The ordering unit can estimate the user's emotions and adjust the ordering method based on the estimated user emotions. The ordering unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. Specifically, the ordering unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The ordering unit can also adjust the ordering method based on the estimated user emotions. For example, if the user is relaxed, detailed ordering options can be provided. If the user is in a hurry, a method that allows for quick ordering can be provided. Furthermore, if the user is stressed, a simple ordering method can be provided. This allows for more appropriate ordering by adjusting the ordering method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the ordering unit can be performed using AI, or without AI. For example, the ordering unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] When placing an order, the order unit can analyze past order history and select the optimal order method. The order unit, for example, uses an analysis tool to analyze the past order history. Specifically, it analyzes the past order history using order data logs and analysis tools. The order unit can also select the most efficient order method from the past order history. For example, it can concentrate orders in a specific time period based on the past order history. The order unit can also analyze the past order history and eliminate unnecessary orders. This enables efficient ordering by analyzing the past order history. Some or all of the above-mentioned processing in the order unit may be performed using AI, or may be performed without using AI. For example, the order unit can input the past order history into a generation AI and have the generation AI select the optimal order method.

[0081] When placing an order, the order unit can customize the order method based on the current inventory status. For example, the order unit considers inventory levels and inventory turnover to evaluate the current inventory status. Specifically, when inventory is low, the order unit selects an order method that allows for quick replenishment. For example, an urgent order or immediate delivery is selected. Also, when inventory is high, a method of refraining from ordering can be selected. For example, delaying the timing of the order or reducing the order quantity. Furthermore, the order unit can adjust the timing of the order depending on the inventory status. This enables appropriate inventory management by customizing the order method based on the current inventory status. Some or all of the above-mentioned processing in the order unit may be performed using AI, or may be performed without using AI. For example, the order unit can input the current inventory status into the generation AI and have the generation AI customize the order method based on the inventory status.

[0082] The ordering unit can estimate the user's emotions and prioritize orders based on the estimated user emotions. The ordering unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. Specifically, it analyzes the user's facial expressions, voice, and text data to estimate emotions. The ordering unit can also prioritize orders based on the estimated user emotions. For example, if the user is relaxed, it can provide detailed ordering options. If the user is in a hurry, it can provide a method for quickly placing orders. Furthermore, if the user is stressed, it can provide a simple ordering method. This enables more appropriate ordering by prioritizing orders based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the ordering unit can be performed using AI, or without AI. For example, the ordering unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0083] When placing an order, the order unit can select the optimal order method by taking into account the user's geographic location information. The order unit, for example, uses GPS data or location information services to collect the user's geographic location information. Specifically, the order unit selects the optimal supplier based on the user's current location. For example, if the user is in a specific area, the order unit selects a supplier close to that area. The order unit can also select the optimal order method by taking into account the user's travel history. For example, the order unit selects the optimal supplier based on information about areas the user has previously visited. Furthermore, the order unit can select the optimal order timing based on the user's geographic location information. This allows the optimal order method to be selected by taking the user's geographic location information into account. Some or all of the above-described processing in the order unit may be performed using AI, or may be performed without using AI. For example, the order unit can input the user's GPS data into the generation AI and have the generation AI select the optimal order method.

[0084] When placing an order, the order unit can analyze the user's social media activity and suggest an order method. For example, the order unit uses analysis of post content and engagement data to analyze the user's social media activity. Specifically, the order unit analyzes the user's social media posts and suggests an optimal order method. For example, if the user posts about a specific product, the order unit suggests an order method for that product. The order unit can also suggest an optimal order method by taking into account the activities of the user's followers and friends. For example, the order unit suggests an order method based on products that the user's followers are interested in and trend information. Furthermore, the order unit can adjust the order method based on the user's social media trends. In this way, the optimal order method can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the order unit may be performed using AI, or may be performed without using AI. For example, the order unit can input the user's social media post data into a generation AI and have the generation AI suggest an optimal order method. === Hard Collateral 1-1 === Each of the multiple elements including the information collection unit, analysis unit, and order unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information collection unit collects store location information, weather information, etc. using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and calculates the optimal purchase quantity. The order unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and establishes a system for automatically placing orders based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the information collection unit, analysis unit, and order unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information collection unit collects store location information, weather information, and the like using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and calculates the optimal purchase quantity. The order unit, realized, for example, by the specific processing unit 290 of the data processing device 12, establishes a system for automatically placing orders based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the information collection unit, analysis unit, and order unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the information collection unit collects store location information, weather information, and the like using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and calculates the optimal purchase quantity. The order unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and establishes a system for automatically placing orders based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the information collection unit, analysis unit, and order unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information collection unit collects store location information, weather information, and the like using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and calculates the optimal purchase quantity. The order unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and establishes a system for automatically placing orders based on the analysis results.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The information collection unit can analyze a user's purchasing history and predict future demand based on past purchasing patterns. For example, it can collect data on products a specific user has purchased in the past and analyze seasonal purchasing trends. The information collection unit can also suggest related products based on the user's purchasing history. For example, it can suggest new products or complementary products related to products previously purchased. Furthermore, the information collection unit can offer specific promotions or discounts based on the user's purchasing history. This makes it possible to utilize the user's purchasing history to provide more personalized services.

[0087] The analysis unit can predict the product life cycle based on the collected information and perform appropriate inventory management. For example, it can predict the timing of new product introductions and the growth, maturity, and decline stages of a product, and adjust inventory levels accordingly. The analysis unit can also plan promotion and marketing strategies based on the product life cycle. For example, it can conduct aggressive promotions during the growth stage and maintain a stable supply during the maturity stage. Furthermore, the analysis unit can take measures to minimize waste risks according to the product life cycle. This enables inventory management that takes product life cycles into account.

[0088] The ordering unit can make customized ordering suggestions to specific users based on the user's purchasing history. For example, the ordering unit can suggest regular purchases of a specific product to a user who has frequently purchased that product in the past. The ordering unit can also suggest ordering related products together based on the user's purchasing history. For example, it can suggest products that go well with products purchased in the past. Furthermore, the ordering unit can offer specific promotions and discounts based on the user's purchasing history. This makes it possible to make more personalized ordering suggestions by utilizing the user's purchasing history.

[0089] The information collection unit can estimate the user's emotions and adjust the type of information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, only important information can be collected with priority. If the user is relaxed, detailed information can be collected. Furthermore, if the user is in a hurry, information that can be collected quickly can be collected with priority. This allows for more appropriate information collection by adjusting the type of information to be collected according to the user's emotions.

[0090] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be prioritized. If the user is in a hurry, a concise analysis that covers the main points can be prioritized. Furthermore, if the user is stressed, a visually easy-to-understand analysis can be prioritized. In this way, by determining the priority of analysis according to the user's emotions, more appropriate analysis results can be provided.

[0091] The ordering unit can estimate the user's emotions and adjust the timing of ordering based on the estimated user emotions. For example, if the user is relaxed, detailed ordering options can be provided. If the user is in a hurry, a method for quickly placing an order can be provided. Furthermore, if the user is feeling stressed, a simple ordering method can be provided. This allows for more appropriate ordering by adjusting the timing of ordering according to the user's emotions.

[0092] The information collection unit can estimate the user's emotions and adjust the frequency of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of information collection can be reduced to reduce the burden. Also, if the user is relaxed, the frequency of information collection can be increased to collect detailed data. Furthermore, if the user is in a hurry, only important information can be collected preferentially. In this way, the burden on the user can be reduced by adjusting the frequency of information collection according to the user's emotions.

[0093] The analysis unit can predict product demand and calculate appropriate inventory levels based on the collected information. For example, it can analyze past sales data and seasonal demand patterns to predict future demand. The analysis unit can also plan promotions and marketing strategies based on the demand forecast. For example, it can implement promotions in accordance with periods of high demand. Furthermore, the analysis unit can take measures to minimize waste risk based on the demand forecast. This enables inventory management that takes demand forecasts into account.

[0094] The ordering department can select the most suitable supplier based on the collected information. For example, it can compare price information and inventory status from multiple suppliers to select the most cost-effective supplier. The ordering department can also select a supplier taking into consideration the supplier's reliability and delivery time. For example, it can select a reliable supplier based on past transaction history and delivery performance. Furthermore, the ordering department can select a supplier taking into consideration the supplier's geographic location. This allows for the selection of the most suitable supplier, enabling cost reduction and efficient inventory management.

[0095] The information collection unit can forecast demand for specific products based on the collected information. For example, it can analyze past sales data and seasonal demand patterns to predict future demand. The information collection unit can also plan promotions and marketing strategies based on the demand forecast. For example, it can implement promotions in accordance with periods of high demand. Furthermore, the information collection unit can take measures to minimize waste risk based on the demand forecast. This enables inventory management that takes demand forecasts into account.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The information collection unit collects information. This information includes, for example, information about the store's location, weather information, information about the day of the week and general product purchasing trends, information about the lowest-priced seller of purchased inventory, and event information. Specifically, the data collected includes the store's location, the local population, traffic conditions, temperature, precipitation, wind speed, the tendency for certain products to sell well on certain days of the week, seasonal product purchasing trends, price information and inventory status from multiple suppliers, and local events and festivals. Step 2: The analysis unit uses the generation AI to analyze the information collected by the information collection unit and determine the optimal number of products to purchase each day. For example, it calculates the optimal purchase quantity based on the collected data and adjusts the purchase quantity based on the tendency for certain products to sell well on certain days of the week. It also predicts fluctuations in demand based on weather and event information and optimizes the purchase quantity. Step 3: The ordering department automatically places an order based on the optimal number of purchased products determined by the analysis department. For example, a system can be created to automatically place an order based on the purchase quantity determined by the generation AI. This will improve the efficiency of ordering operations and prevent human error.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0100] 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.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 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.

[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 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.

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

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

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

[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 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.

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

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] [Explanation of symbols]

[0170] 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. an information collection unit that collects information; an analysis unit that analyzes the information collected by the information collection unit and derives an appropriate number of daily purchased commodities; an ordering unit that automatically places an order based on the optimum number of purchased merchandise derived by the analysis unit; A system characterized by:

2. The information collecting unit Collect information about the store location 2. The system of claim 1.

3. The information collecting unit Gather local weather information 2. The system of claim 1.

4. The information collecting unit Collect information on day of the week or purchasing trends across the world 2. The system of claim 1.

5. The information collecting unit Collect information on lowest priced sellers of purchased inventory 2. The system of claim 1.

6. The information collecting unit Collecting event information 2. The system of claim 1.

7. The analysis unit Analyze the collected information and calculate the appropriate purchase quantity 2. The system of claim 1.

8. The ordering unit Automatically place orders based on the purchase quantity determined by the generation AI 2. The system of claim 1.

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