System

Abstract

An object of a system according to an embodiment is to make an appropriate sales plan to reduce food waste and to propose a special sale or a limited-time discount.SOLUTION: A system includes a data collection part, an analysis part, a sales plan planning part, and a proposal part. The data collection unit collects at least one piece of data among a best-before date, a stock status, and a sales history of a product. The analysis unit analyzes the data collected by the data collection unit. The sales plan creation unit creates an appropriate sales plan based on the data analyzed by the analysis unit. The proposal unit proposes a special sale or a limited-time discount based on the sales plan drafted by the sales plan drafting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately develop sales plans or propose special sales to effectively reduce food waste, and there is room for improvement.

[0005] The system according to the embodiment aims to develop an appropriate sales plan to reduce food waste and to propose special sales and limited-time discounts. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a sales plan creation unit, and a proposal unit. The data collection unit collects at least one of data on product expiration dates, inventory status, and sales history. The analysis unit analyzes the data collected by the data collection unit. The sales plan creation unit creates an appropriate sales plan based on the data analyzed by the analysis unit. The proposal unit proposes special sales or limited-time discounts based on the sales plan created by the sales plan creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can create appropriate sales plans to reduce food waste and propose special sales and limited-time discounts. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The Food Loss Stopper AI system according to an embodiment of the present invention works in collaboration with supermarkets, restaurants, food manufacturers, etc., and the generation AI analyzes data such as product expiration dates, inventory status, and sales history to create appropriate sales plans. As a result, the Food Loss Stopper AI system can reduce food waste and achieve efficient inventory management and sales promotion.

[0029] The Food Loss Stopper AI system according to the embodiment includes a data collection unit, an analysis unit, a sales plan creation unit, and a proposal unit. The data collection unit collects at least one of product expiration dates, inventory status, and sales history data. For example, the data collection unit collects expiration date data by scanning product barcodes. The data collection unit can also acquire inventory status data from an inventory management system. The data collection unit can also collect sales history data from a POS system. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit identifies products whose expiration dates are approaching using a generation AI. The analysis unit can also calculate inventory turnover by analyzing inventory status. The analysis unit can also identify best-selling products by analyzing sales history. The sales plan creation unit creates an appropriate sales plan based on the data analyzed by the analysis unit. For example, the sales plan creation unit creates a plan to prioritize sales of products whose expiration dates are approaching. The sales plan creation unit can also create a sales strategy to increase inventory turnover. The sales planning department can also create a plan to prioritize sales of best-selling products. The proposal department can propose special sales or limited-time discounts based on the sales plan created by the sales planning department. For example, the proposal department can propose special sales for products that are approaching their expiration date. The proposal department can also propose limited-time discounts to increase inventory turnover. The proposal department can also propose special sales for best-selling products. In this way, the Food Loss Stopper AI system can reduce food waste and achieve efficient inventory management and sales promotion. For example, the Food Loss Stopper AI system can reduce the amount of food that is wasted by quickly selling off products that are approaching their expiration date. In addition, the system can provide consumers with a great shopping experience through special sales and limited-time discounts.

[0030] The data collection unit updates the expiration date data for each product in real time, allowing the generation AI to always perform analysis based on the latest data. The data collection unit, for example, updates the expiration date data for each product in real time using a barcode scanner or RFID tag and inputs it into the generation AI. This makes it possible to instantly identify products whose expiration dates are approaching. The data collection unit, for example, reads the expiration date data for products using a barcode scanner and registers it in a database. The data collection unit can also automatically update the expiration date data for products using RFID tags. This makes it possible for the generation AI to always perform analysis based on the latest data. This makes it possible to instantly identify products whose expiration dates are approaching.

[0031] The data collection unit also collects at least one environmental data of the product's temperature or humidity, and can predict the expiration date based on the storage conditions. For example, the data collection unit installs a temperature sensor or a humidity sensor to monitor the product's storage environment and provides the data to the generation AI. This makes it possible to predict the expiration date based on the storage conditions. For example, the data collection unit measures the product's storage temperature using a temperature sensor and registers the data in a database. The data collection unit can also measure the product's storage humidity using a humidity sensor and register the data in a database. This allows the generation AI to predict the expiration date based on the product's storage conditions. This makes it possible to predict the expiration date based on the storage conditions.

[0032] When collecting data, the data collection unit can also include the impact of product packaging design and marketing campaigns in the analysis. The data collection unit, for example, collects product packaging design data, and the generation AI makes sales predictions based on that data. For example, the data collection unit analyzes sales trends for products whose designs have been changed. The data collection unit, for example, collects product packaging designs as image data and registers them in a database. The data collection unit can also collect marketing campaign data and provide it to the generation AI. This allows the generation AI to make sales predictions that take into account the impact of product packaging design and marketing campaigns. This makes it possible to make predictions that take into account the impact of package design and marketing campaigns.

[0033] The data collection unit can compare data from different regions or stores and analyze consumption trends by region. The data collection unit, for example, collects sales data from different regions, and the generation AI analyzes consumption trends by region based on that data. For example, it identifies popular and best-selling products in each region. The data collection unit, for example, registers sales data from different regions in a database and provides it to the generation AI. The data collection unit can also collect sales data from different stores and provide it to the generation AI. This allows the generation AI to analyze consumption trends by region. This makes it possible to develop sales strategies that take into account the characteristics of each region and store.

[0034] The sales planning department can consider the influence of seasons or events in the sales plan and create a sales strategy tailored to a specific time of year. The sales planning department, for example, collects seasonal sales data, and the generation AI creates a sales plan tailored to the season based on that data. For example, in the summer, sales of cold drinks and ice cream are strengthened. The sales planning department, for example, registers seasonal sales data in a database and provides it to the generation AI. The sales planning department can also collect event data and provide it to the generation AI. This allows the generation AI to create a sales plan that takes into account the influence of seasons and events. This makes it possible to create a strategy that takes into account the influence of seasons and events.

[0035] The sales plan formulation unit can analyze customer purchase history and propose optimal sales plans for individual customers. The sales plan formulation unit, for example, collects customer purchase history data, and the generation AI proposes optimal sales plans for individual customers based on that data. For example, it promotes repeat purchases based on past purchase history. The sales plan formulation unit, for example, registers customer purchase history data in a database and provides it to the generation AI. The sales plan formulation unit can also analyze customer purchase history and propose optimal sales plans for individual customers. This allows the generation AI to propose optimal sales plans for individual customers. This makes it possible to create optimal strategies for individual customers.

[0036] The sales planning department can integrate online and offline sales data and create optimal sales plans for all channels. The sales planning department, for example, collects online and offline sales data, and the generation AI creates a comprehensive sales plan based on that data. For example, online and offline inventory is integrated and managed. The sales planning department, for example, registers sales data from an online shop in a database and provides it to the generation AI. The sales planning department can also collect sales data from physical stores and provide it to the generation AI. This allows the generation AI to integrate online and offline sales data and create optimal sales plans for all channels. This strengthens collaboration between channels.

[0037] The suggestion unit can analyze data on past special sale sales and identify the most effective discount strategy. For example, the suggestion unit collects data on past special sale sales, and the generation AI identifies the most effective discount strategy based on that data. For example, the suggestion unit analyzes sales that were most effective at a specific discount rate or period. For example, the suggestion unit registers data on past special sale sales in a database and provides it to the generation AI. The suggestion unit can also analyze data on past special sale sales and identify the most effective discount strategy. This allows the generation AI to identify a discount strategy based on past success stories. This makes it possible to use a strategy based on past success stories.

[0038] The proposal unit can apply the sale proposals to different product categories or brands. For example, the proposal unit applies the sale proposals to different product categories, and the generation AI creates an optimal discount strategy based on that data. For example, a sale can be held not only on food but also on daily necessities and home appliances. The proposal unit, for example, collects data on different product categories and provides it to the generation AI. The proposal unit can also collect data on different brands and provide it to the generation AI. This allows the generation AI to make sale proposals for different product categories and brands. This enables strategies that take into account the characteristics of each category and brand.

[0039] The proposal unit can link the proposal for the special sale with online advertising or a social media campaign. For example, the proposal unit can link the proposal for the special sale with online advertising, and the generation AI can create an optimal advertising strategy based on that data. For example, information about the special sale can be widely announced through online advertising. For example, the proposal unit can collect data on online advertising and provide it to the generation AI. The proposal unit can also collect data on social media campaigns and provide it to the generation AI. This allows the generation AI to link the proposal for the special sale with online advertising or a social media campaign. This strengthens the connection between online and offline.

[0040] The system can automatically update the list of products whose expiration date is approaching and notify store staff in real time. For example, the system can be built to automatically update the list of products whose expiration date is approaching, and the generation AI can notify store staff in real time based on that data. For example, it can list products whose expiration date is approaching. For example, the system can collect product expiration date data and register it in a database. The system can also automatically update the list of products whose expiration date is approaching and notify store staff. This allows the generation AI to instantly identify products whose expiration date is approaching and notify store staff. This makes it possible to instantly identify products whose expiration date is approaching.

[0041] The system can automatically reallocate inventory of products that are approaching their expiration date and optimize the sales area. For example, the system builds a system that automatically reallocates inventory of products that are approaching their expiration date, and the generation AI optimizes the sales area based on that data. For example, products that are approaching their expiration date are placed in prominent locations. The system, for example, collects product inventory data and registers it in a database. The system can also automatically reallocate inventory of products that are approaching their expiration date and optimize the sales area. This allows the generation AI to reallocate inventory of products that are approaching their expiration date and optimize the sales area. This makes it possible to immediately identify products that are approaching their expiration date.

[0042] The system can also apply the management of products with approaching expiration dates to other consumer goods. For example, the system applies the management system for products with approaching expiration dates to other consumer goods, and the generation AI manages them based on that data. For example, the system collects expiration date data for cosmetics and pharmaceuticals. The system collects expiration date data for cosmetics and pharmaceuticals, and registers it in a database. The system can also apply the management of products with approaching expiration dates to other consumer goods. This allows the generation AI to apply the management of products with approaching expiration dates to other consumer goods as well. This makes the management of consumer goods overall more efficient.

[0043] The system can promote purchases by notifying consumers of information about products that are approaching their expiration date via a consumer app. For example, the system can be built to notify consumers of information about products that are approaching their expiration date via a consumer app, and the generation AI can promote purchases based on that data. For example, the system can distribute information about special sales via the app. For example, the system can notify consumers of information about products that are approaching their expiration date via a consumer app. The system can also distribute information about special sales via the consumer app to promote purchases. In this way, the generation AI can notify consumers of information about products that are approaching their expiration date via the consumer app, promoting purchases. This makes the provision of information to consumers more efficient.

[0044] The system shares data with collaborators in real time, allowing the generation AI to always perform analysis based on the latest data. For example, the system builds a system that shares data with collaborators in real time, and the generation AI performs analysis based on that data. For example, a cloud-based data sharing platform is used. For example, the system shares data with collaborators in real time and registers it in a database. The system also allows the generation AI to always perform analysis based on the latest data. This allows the generation AI to share data with collaborators in real time and always perform analysis based on the latest data. This makes it possible to centrally manage data.

[0045] The system can analyze the collaborating partner's data and propose an optimal sales plan to maximize mutual profits. For example, the system collects collaborating partner's data and the generation AI proposes a sales plan to maximize mutual profits based on that data. For example, the system analyzes inventory data and sales history. For example, the system registers the collaborating partner's data in a database and provides it to the generation AI. The system can also analyze the collaborating partner's data and propose a sales plan to maximize mutual profits. This allows the generation AI to analyze the collaborating partner's data and propose a sales plan to maximize mutual profits. This strengthens collaboration with the collaborating partner and maximizes mutual profits.

[0046] The system can integrate data from collaborators with data from different industries or fields to discover new business opportunities. For example, the system can integrate data from collaborators with data from different industries, and the generation AI can use that data to discover new business opportunities. For example, data from the food industry and the medical industry can be integrated. For example, the system can register data from collaborators in a database and provide it to the generation AI. The system can also collect data from different industries or fields and provide it to the generation AI. This allows the generation AI to integrate data from collaborators with data from different industries or fields to discover new business opportunities. This strengthens cross-industry collaboration.

[0047] The system can securely share data with collaborators using blockchain technology. For example, the system builds a system that securely shares data with collaborators using blockchain technology, and the generation AI performs analysis based on that data. For example, it prevents data tampering and ensures transparency. For example, the system encrypts data using blockchain technology and registers it in a database. The system can also securely share data with collaborators using blockchain technology. This allows the generation AI to securely share data with collaborators using blockchain technology. This strengthens data security.

[0048] The system can also apply the management of products with approaching expiration dates to other consumer goods. For example, the system applies the management system for products with approaching expiration dates to other consumer goods, and the generation AI manages them based on that data. For example, the system collects expiration date data for cosmetics and pharmaceuticals. The system collects expiration date data for cosmetics and pharmaceuticals, and registers it in a database. The system can also apply the management of products with approaching expiration dates to other consumer goods. This allows the generation AI to apply the management of products with approaching expiration dates to other consumer goods as well. This makes the management of consumer goods overall more efficient.

[0049] The system can promote purchases by notifying consumers of information about products that are approaching their expiration date via a consumer app. For example, the system can be built to notify consumers of information about products that are approaching their expiration date via a consumer app, and the generation AI can promote purchases based on that data. For example, the system can distribute information about special sales via the app. For example, the system can notify consumers of information about products that are approaching their expiration date via a consumer app. The system can also distribute information about special sales via the consumer app to promote purchases. In this way, the generation AI can notify consumers of information about products that are approaching their expiration date via the consumer app, promoting purchases. This makes the provision of information to consumers more efficient.

[0050] The system shares data with collaborators in real time, allowing the generation AI to always perform analysis based on the latest data. For example, the system builds a system that shares data with collaborators in real time, and the generation AI performs analysis based on that data. For example, a cloud-based data sharing platform is used. For example, the system shares data with collaborators in real time and registers it in a database. The system also allows the generation AI to always perform analysis based on the latest data. This allows the generation AI to share data with collaborators in real time and always perform analysis based on the latest data. This makes it possible to centrally manage data.

[0051] The system can analyze the collaborating partner's data and propose an optimal sales plan to maximize mutual profits. For example, the system collects collaborating partner's data and the generation AI proposes a sales plan to maximize mutual profits based on that data. For example, the system analyzes inventory data and sales history. For example, the system registers the collaborating partner's data in a database and provides it to the generation AI. The system can also analyze the collaborating partner's data and propose a sales plan to maximize mutual profits. This allows the generation AI to analyze the collaborating partner's data and propose a sales plan to maximize mutual profits. This strengthens collaboration with the collaborating partner and maximizes mutual profits.

[0052] The system can securely share data with collaborators using blockchain technology. For example, the system builds a system that securely shares data with collaborators using blockchain technology, and the generation AI performs analysis based on that data. For example, it prevents data tampering and ensures transparency. For example, the system encrypts data using blockchain technology and registers it in a database. The system can also securely share data with collaborators using blockchain technology. This allows the generation AI to securely share data with collaborators using blockchain technology. This strengthens data security.

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

[0054] The data collection unit can also include the impact of product packaging design and marketing campaigns in the analysis. For example, product packaging design data is collected and the generation AI makes sales predictions based on that data. For example, the data collection unit analyzes sales trends for products whose designs have been changed. For example, the data collection unit collects product packaging designs as image data and registers them in a database. The data collection unit can also collect marketing campaign data and provide it to the generation AI. This allows the generation AI to make sales predictions that take into account the impact of product packaging design and marketing campaigns. This makes it possible to make predictions that take into account the impact of package design and marketing campaigns.

[0055] The data collection unit can compare data from different regions or stores and analyze consumption trends by region. For example, sales data from different regions is collected, and the generation AI uses that data to analyze consumption trends by region. For example, popular and best-selling products in each region are identified. For example, the data collection unit registers sales data from different regions in a database and provides it to the generation AI. The data collection unit can also collect sales data from different stores and provide it to the generation AI. This allows the generation AI to analyze consumption trends by region. This makes it possible to develop sales strategies that take into account the characteristics of each region and store.

[0056] The sales planning department can consider the influence of seasons or events in the sales plan and create a sales strategy tailored to a specific time of year. For example, seasonal sales data is collected, and the generation AI creates a sales plan tailored to the season based on that data. For example, sales of cold drinks and ice cream are increased in the summer. The sales planning department, for example, registers seasonal sales data in a database and provides it to the generation AI. The sales planning department can also collect event data and provide it to the generation AI. This allows the generation AI to create a sales plan that takes into account the influence of seasons and events. This makes it possible to create a strategy that takes into account the influence of seasons and events.

[0057] The sales plan formulation department can analyze customer purchase history and propose optimal sales plans for individual customers. For example, customer purchase history data is collected, and the generation AI proposes optimal sales plans for individual customers based on that data. For example, repeat purchases are encouraged based on past purchase history. The sales plan formulation department, for example, registers customer purchase history data in a database and provides it to the generation AI. The sales plan formulation department can also analyze customer purchase history and propose optimal sales plans for individual customers. This allows the generation AI to propose optimal sales plans for individual customers. This makes it possible to create optimal strategies for individual customers.

[0058] The suggestion unit can analyze data on past special sale sales and identify the most effective discount strategy. For example, data on past special sale sales is collected, and the generation AI identifies the most effective discount strategy based on that data. For example, the suggestion unit analyzes which sales were most effective at a specific discount rate or period. For example, the suggestion unit registers data on past special sale sales in a database and provides it to the generation AI. The suggestion unit can also analyze data on past special sale sales and identify the most effective discount strategy. This allows the generation AI to identify a discount strategy based on past success stories. This makes it possible to use a strategy based on past success stories.

[0059] The proposal unit can apply sale proposals to different product categories or brands. For example, the proposals can be applied to different product categories, and the generation AI can create an optimal discount strategy based on that data. For example, a sale can be held not only on food but also on daily necessities and home appliances. The proposal unit, for example, collects data on different product categories and provides it to the generation AI. The proposal unit can also collect data on different brands and provide it to the generation AI. This allows the generation AI to make sale proposals for different product categories and brands. This makes it possible to create strategies that take into account the characteristics of each category and brand.

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

[0061] Step 1: The data collection unit collects at least one of the following data: expiration date, inventory status, and sales history of the product. For example, the data collection unit may collect expiration date data by scanning the barcode of the product. The data collection unit may also obtain inventory status data from an inventory management system and collect sales history data from a POS system. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it uses generative AI to identify products that are nearing their expiration date, analyzes inventory status to calculate inventory turnover, and analyzes sales history to identify best-selling products. Step 3: The sales planning department creates an appropriate sales plan based on the data analyzed by the analysis department. For example, they may create a plan to prioritize the sale of products that are approaching their expiration date, a sales strategy to increase inventory turnover, or a plan to focus on selling best-selling products. Step 4: The proposal department proposes special sales or limited-time discounts based on the sales plan drawn up by the sales planning department. For example, the proposal department proposes special sales for products that are nearing their expiration date, proposes limited-time discounts to increase inventory turnover, and proposes special sales for best-selling products.

[0062] (Example 2) The Food Loss Stopper AI system according to an embodiment of the present invention works in collaboration with supermarkets, restaurants, food manufacturers, etc., and the generation AI analyzes data such as product expiration dates, inventory status, and sales history to create appropriate sales plans. As a result, the Food Loss Stopper AI system can reduce food waste and achieve efficient inventory management and sales promotion.

[0063] The Food Loss Stopper AI system according to the embodiment includes a data collection unit, an analysis unit, a sales plan creation unit, and a proposal unit. The data collection unit collects at least one of product expiration dates, inventory status, and sales history data. For example, the data collection unit collects expiration date data by scanning product barcodes. The data collection unit can also acquire inventory status data from an inventory management system. The data collection unit can also collect sales history data from a POS system. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit identifies products whose expiration dates are approaching using a generation AI. The analysis unit can also calculate inventory turnover by analyzing inventory status. The analysis unit can also identify best-selling products by analyzing sales history. The sales plan creation unit creates an appropriate sales plan based on the data analyzed by the analysis unit. For example, the sales plan creation unit creates a plan to prioritize sales of products whose expiration dates are approaching. The sales plan creation unit can also create a sales strategy to increase inventory turnover. The sales planning department can also create a plan to prioritize sales of best-selling products. The proposal department can propose special sales or limited-time discounts based on the sales plan created by the sales planning department. For example, the proposal department can propose special sales for products that are approaching their expiration date. The proposal department can also propose limited-time discounts to increase inventory turnover. The proposal department can also propose special sales for best-selling products. In this way, the Food Loss Stopper AI system can reduce food waste and achieve efficient inventory management and sales promotion. For example, the Food Loss Stopper AI system can reduce the amount of food that is wasted by quickly selling off products that are approaching their expiration date. In addition, the system can provide consumers with a great shopping experience through special sales and limited-time discounts.

[0064] The data collection unit updates the expiration date data for each product in real time, allowing the generation AI to always perform analysis based on the latest data. The data collection unit, for example, updates the expiration date data for each product in real time using a barcode scanner or RFID tag and inputs it into the generation AI. This makes it possible to instantly identify products whose expiration dates are approaching. The data collection unit, for example, reads the expiration date data for products using a barcode scanner and registers it in a database. The data collection unit can also automatically update the expiration date data for products using RFID tags. This makes it possible for the generation AI to always perform analysis based on the latest data. This makes it possible to instantly identify products whose expiration dates are approaching.

[0065] The data collection unit also collects at least one environmental data of the product's temperature or humidity, and can predict the expiration date based on the storage conditions. For example, the data collection unit installs a temperature sensor or a humidity sensor to monitor the product's storage environment and provides the data to the generation AI. This makes it possible to predict the expiration date based on the storage conditions. For example, the data collection unit measures the product's storage temperature using a temperature sensor and registers the data in a database. The data collection unit can also measure the product's storage humidity using a humidity sensor and register the data in a database. This allows the generation AI to predict the expiration date based on the product's storage conditions. This makes it possible to predict the expiration date based on the storage conditions.

[0066] The data collection unit can use the emotion estimation function to analyze consumers' purchasing intent and make sales predictions based on the consumers' emotions. For example, to analyze consumers' purchasing intent, the data collection unit uses the emotion estimation function to analyze the consumers' facial expressions and voices and provides the data to the generation AI. This makes it possible to make sales predictions based on emotions. For example, the data collection unit can use a camera to capture the consumers' facial expressions and analyze their emotions using an emotion estimation algorithm. The data collection unit can also use a microphone to record the consumers' voices and estimate their emotions using voice analysis technology. This allows the generation AI to make sales predictions based on the consumers' emotions. This makes it possible to make sales predictions based on emotions.

[0067] When collecting data, the data collection unit can also include the impact of product packaging design and marketing campaigns in the analysis. The data collection unit, for example, collects product packaging design data, and the generation AI makes sales predictions based on that data. For example, the data collection unit analyzes sales trends for products whose designs have been changed. The data collection unit, for example, collects product packaging designs as image data and registers them in a database. The data collection unit can also collect marketing campaign data and provide it to the generation AI. This allows the generation AI to make sales predictions that take into account the impact of product packaging design and marketing campaigns. This makes it possible to make predictions that take into account the impact of package design and marketing campaigns.

[0068] The data collection unit can compare data from different regions or stores and analyze consumption trends by region. The data collection unit, for example, collects sales data from different regions, and the generation AI analyzes consumption trends by region based on that data. For example, it identifies popular and best-selling products in each region. The data collection unit, for example, registers sales data from different regions in a database and provides it to the generation AI. The data collection unit can also collect sales data from different stores and provide it to the generation AI. This allows the generation AI to analyze consumption trends by region. This makes it possible to develop sales strategies that take into account the characteristics of each region and store.

[0069] The data collection unit can use the emotion estimation function to analyze consumer reviews or feedback and identify areas for product improvement. The data collection unit, for example, uses the emotion estimation function to analyze consumer reviews and feedback, and the generation AI identifies areas for product improvement based on that data. For example, reviews with a high percentage of negative emotions are analyzed preferentially. The data collection unit, for example, collects data from online reviews and analyzes emotions using an emotion estimation algorithm. The data collection unit can also collect survey data and provide it to the generation AI. This allows the generation AI to analyze consumer reviews and feedback and identify areas for product improvement. This makes it possible to respond immediately to changes in consumer emotions.

[0070] The sales planning department can consider the influence of seasons or events in the sales plan and create a sales strategy tailored to a specific time of year. The sales planning department, for example, collects seasonal sales data, and the generation AI creates a sales plan tailored to the season based on that data. For example, in the summer, sales of cold drinks and ice cream are strengthened. The sales planning department, for example, registers seasonal sales data in a database and provides it to the generation AI. The sales planning department can also collect event data and provide it to the generation AI. This allows the generation AI to create a sales plan that takes into account the influence of seasons and events. This makes it possible to create a strategy that takes into account the influence of seasons and events.

[0071] The sales plan formulation unit can analyze customer purchase history and propose optimal sales plans for individual customers. The sales plan formulation unit, for example, collects customer purchase history data, and the generation AI proposes optimal sales plans for individual customers based on that data. For example, it promotes repeat purchases based on past purchase history. The sales plan formulation unit, for example, registers customer purchase history data in a database and provides it to the generation AI. The sales plan formulation unit can also analyze customer purchase history and propose optimal sales plans for individual customers. This allows the generation AI to propose optimal sales plans for individual customers. This makes it possible to create optimal strategies for individual customers.

[0072] The sales plan formulation unit can use the emotion estimation function to formulate a personalized sales plan based on the customer's emotions. The sales plan formulation unit, for example, uses the emotion estimation function to collect customer emotion data, and the generation AI formulates a personalized sales plan based on that data. For example, a special promotion is proposed for customers with strong positive emotions. The sales plan formulation unit, for example, collects customer emotion data and analyzes the emotions using an emotion estimation algorithm. The sales plan formulation unit can also formulate a personalized sales plan based on the customer's emotions. This allows the generation AI to formulate a personalized sales plan based on the customer's emotions. This makes it possible to respond immediately to changes in customer emotions.

[0073] The sales planning department can integrate online and offline sales data and create optimal sales plans for all channels. The sales planning department, for example, collects online and offline sales data, and the generation AI creates a comprehensive sales plan based on that data. For example, online and offline inventory is integrated and managed. The sales planning department, for example, registers sales data from an online shop in a database and provides it to the generation AI. The sales planning department can also collect sales data from physical stores and provide it to the generation AI. This allows the generation AI to integrate online and offline sales data and create optimal sales plans for all channels. This strengthens collaboration between channels.

[0074] The sales planning unit can use the emotion estimation function to generate marketing messages that increase customer purchasing motivation. The sales planning unit, for example, uses the emotion estimation function to collect customer emotion data, and the generation AI generates marketing messages that increase purchasing motivation based on that data. For example, it creates messages that elicit positive emotions. The sales planning unit, for example, collects customer emotion data and analyzes emotions using an emotion estimation algorithm. The sales planning unit can also generate marketing messages based on customer emotions. This allows the generation AI to generate marketing messages based on customer emotions. This makes it possible to respond immediately to changes in customer emotions.

[0075] The suggestion unit can analyze data on past special sale sales and identify the most effective discount strategy. For example, the suggestion unit collects data on past special sale sales, and the generation AI identifies the most effective discount strategy based on that data. For example, the suggestion unit analyzes sales that were most effective at a specific discount rate or period. For example, the suggestion unit registers data on past special sale sales in a database and provides it to the generation AI. The suggestion unit can also analyze data on past special sale sales and identify the most effective discount strategy. This allows the generation AI to identify a discount strategy based on past success stories. This makes it possible to use a strategy based on past success stories.

[0076] The suggestion unit can use the emotion estimation function to make discount suggestions based on the consumer's emotions. The suggestion unit, for example, uses the emotion estimation function to collect consumer emotion data, and the generation AI makes emotion-based discount suggestions based on that data. For example, the suggestion unit can suggest special discounts to consumers with strong positive emotions. The suggestion unit, for example, collects consumer emotion data and analyzes the emotions using an emotion estimation algorithm. The suggestion unit can also make discount suggestions based on the consumer's emotions. This allows the generation AI to make discount suggestions based on the consumer's emotions. This makes it possible to respond immediately to changes in consumer emotions.

[0077] The proposal unit can apply the sale proposals to different product categories or brands. For example, the proposal unit applies the sale proposals to different product categories, and the generation AI creates an optimal discount strategy based on that data. For example, a sale can be held not only on food but also on daily necessities and home appliances. The proposal unit, for example, collects data on different product categories and provides it to the generation AI. The proposal unit can also collect data on different brands and provide it to the generation AI. This allows the generation AI to make sale proposals for different product categories and brands. This enables strategies that take into account the characteristics of each category and brand.

[0078] The proposal unit can link the proposal for the special sale with online advertising or a social media campaign. For example, the proposal unit can link the proposal for the special sale with online advertising, and the generation AI can create an optimal advertising strategy based on that data. For example, information about the special sale can be widely announced through online advertising. For example, the proposal unit can collect data on online advertising and provide it to the generation AI. The proposal unit can also collect data on social media campaigns and provide it to the generation AI. This allows the generation AI to link the proposal for the special sale with online advertising or a social media campaign. This strengthens the connection between online and offline.

[0079] The suggestion unit can use the emotion estimation function to generate promotional messages based on consumer emotions. The suggestion unit, for example, uses the emotion estimation function to collect consumer emotion data, and the generation AI generates emotion-based promotional messages based on that data. For example, it creates a message that elicits positive emotions. The suggestion unit, for example, collects consumer emotion data and analyzes emotions using an emotion estimation algorithm. The suggestion unit can also generate promotional messages based on consumer emotions. This allows the generation AI to generate promotional messages based on consumer emotions. This makes it possible to respond immediately to changes in consumer emotions.

[0080] The system can automatically update the list of products whose expiration date is approaching and notify store staff in real time. For example, the system can be built to automatically update the list of products whose expiration date is approaching, and the generation AI can notify store staff in real time based on that data. For example, it can list products whose expiration date is approaching. For example, the system can collect product expiration date data and register it in a database. The system can also automatically update the list of products whose expiration date is approaching and notify store staff. This allows the generation AI to instantly identify products whose expiration date is approaching and notify store staff. This makes it possible to instantly identify products whose expiration date is approaching.

[0081] The system can automatically reallocate inventory of products that are approaching their expiration date and optimize the sales area. For example, the system builds a system that automatically reallocates inventory of products that are approaching their expiration date, and the generation AI optimizes the sales area based on that data. For example, products that are approaching their expiration date are placed in prominent locations. The system, for example, collects product inventory data and registers it in a database. The system can also automatically reallocate inventory of products that are approaching their expiration date and optimize the sales area. This allows the generation AI to reallocate inventory of products that are approaching their expiration date and optimize the sales area. This makes it possible to immediately identify products that are approaching their expiration date.

[0082] The system can use an emotion estimation function to suggest product placement based on consumer emotions. For example, the system uses the emotion estimation function to collect consumer emotion data, and the generation AI uses that data to suggest product placement based on emotions. For example, placement that elicits positive emotions. For example, the system collects consumer emotion data and analyzes emotions using an emotion estimation algorithm. The system can also suggest product placement based on consumer emotions. This allows the generation AI to suggest product placement based on consumer emotions. This makes it possible to respond immediately to changes in consumer emotions.

[0083] The system can also apply the management of products with approaching expiration dates to other consumer goods. For example, the system applies the management system for products with approaching expiration dates to other consumer goods, and the generation AI manages them based on that data. For example, the system collects expiration date data for cosmetics and pharmaceuticals. The system collects expiration date data for cosmetics and pharmaceuticals, and registers it in a database. The system can also apply the management of products with approaching expiration dates to other consumer goods. This allows the generation AI to apply the management of products with approaching expiration dates to other consumer goods as well. This makes the management of consumer goods overall more efficient.

[0084] The system can promote purchases by notifying consumers of information about products that are approaching their expiration date via a consumer app. For example, the system can be built to notify consumers of information about products that are approaching their expiration date via a consumer app, and the generation AI can promote purchases based on that data. For example, the system can distribute information about special sales via the app. For example, the system can notify consumers of information about products that are approaching their expiration date via a consumer app. The system can also distribute information about special sales via the consumer app to promote purchases. In this way, the generation AI can notify consumers of information about products that are approaching their expiration date via the consumer app, promoting purchases. This makes the provision of information to consumers more efficient.

[0085] The system shares data with collaborators in real time, allowing the generation AI to always perform analysis based on the latest data. For example, the system builds a system that shares data with collaborators in real time, and the generation AI performs analysis based on that data. For example, a cloud-based data sharing platform is used. For example, the system shares data with collaborators in real time and registers it in a database. The system also allows the generation AI to always perform analysis based on the latest data. This allows the generation AI to share data with collaborators in real time and always perform analysis based on the latest data. This makes it possible to centrally manage data.

[0086] The system can analyze the collaborating partner's data and propose an optimal sales plan to maximize mutual profits. For example, the system collects collaborating partner's data and the generation AI proposes a sales plan to maximize mutual profits based on that data. For example, the system analyzes inventory data and sales history. For example, the system registers the collaborating partner's data in a database and provides it to the generation AI. The system can also analyze the collaborating partner's data and propose a sales plan to maximize mutual profits. This allows the generation AI to analyze the collaborating partner's data and propose a sales plan to maximize mutual profits. This strengthens collaboration with the collaborating partner and maximizes mutual profits.

[0087] The system can use the emotion estimation function to make business efficiency improvement proposals based on the emotions of collaborating employees. For example, the system can use the emotion estimation function to collect emotional data from collaborating employees, and the generation AI can then make business efficiency proposals based on that data. For example, the system can propose business improvement measures that elicit positive emotions. For example, the system can collect emotional data from collaborating employees and analyze their emotions using an emotion estimation algorithm. The system can also make business efficiency proposals based on the emotions of collaborating employees. This allows the generation AI to make business efficiency proposals based on the emotions of collaborating employees. This allows for immediate response to changes in employees' emotions.

[0088] The system can integrate data from collaborators with data from different industries or fields to discover new business opportunities. For example, the system can integrate data from collaborators with data from different industries, and the generation AI can use that data to discover new business opportunities. For example, data from the food industry and the medical industry can be integrated. For example, the system can register data from collaborators in a database and provide it to the generation AI. The system can also collect data from different industries or fields and provide it to the generation AI. This allows the generation AI to integrate data from collaborators with data from different industries or fields to discover new business opportunities. This strengthens cross-industry collaboration.

[0089] The system can securely share data with collaborators using blockchain technology. For example, the system builds a system that securely shares data with collaborators using blockchain technology, and the generation AI performs analysis based on that data. For example, it prevents data tampering and ensures transparency. For example, the system encrypts data using blockchain technology and registers it in a database. The system can also securely share data with collaborators using blockchain technology. This allows the generation AI to securely share data with collaborators using blockchain technology. This strengthens data security.

[0090] The system can use the emotion estimation function to propose new product development based on the emotions of collaborating customers. For example, the system uses the emotion estimation function to collect emotional data from collaborating customers, and the generation AI proposes new product development based on that data. For example, a product that elicits positive emotions is developed. For example, the system collects emotional data from collaborating customers and analyzes their emotions using an emotion estimation algorithm. The system can also propose new product development based on the emotions of collaborating customers. This allows the generation AI to propose new product development based on the emotions of collaborating customers. This makes it possible to respond immediately to changes in customer emotions.

[0091] The system can also apply the management of products with approaching expiration dates to other consumer goods. For example, the system applies the management system for products with approaching expiration dates to other consumer goods, and the generation AI manages them based on that data. For example, the system collects expiration date data for cosmetics and pharmaceuticals. The system collects expiration date data for cosmetics and pharmaceuticals, and registers it in a database. The system can also apply the management of products with approaching expiration dates to other consumer goods. This allows the generation AI to apply the management of products with approaching expiration dates to other consumer goods as well. This makes the management of consumer goods overall more efficient.

[0092] The system can promote purchases by notifying consumers of information about products that are approaching their expiration date via a consumer app. For example, the system can be built to notify consumers of information about products that are approaching their expiration date via a consumer app, and the generation AI can promote purchases based on that data. For example, the system can distribute information about special sales via the app. For example, the system can notify consumers of information about products that are approaching their expiration date via a consumer app. The system can also distribute information about special sales via the consumer app to promote purchases. In this way, the generation AI can notify consumers of information about products that are approaching their expiration date via the consumer app, promoting purchases. This makes the provision of information to consumers more efficient.

[0093] The system can use the emotion estimation function to make product recommendations based on consumer emotions. For example, the system collects consumer emotion data using the emotion estimation function, and the generation AI makes emotion-based product recommendations based on that data. For example, it can suggest products that elicit positive emotions. For example, the system collects consumer emotion data and analyzes emotions using an emotion estimation algorithm. The system can also make product recommendations based on consumer emotions. This allows the generation AI to make product recommendations based on consumer emotions. This makes it possible to respond immediately to changes in consumer emotions.

[0094] The system shares data with collaborators in real time, allowing the generation AI to always perform analysis based on the latest data. For example, the system builds a system that shares data with collaborators in real time, and the generation AI performs analysis based on that data. For example, a cloud-based data sharing platform is used. For example, the system shares data with collaborators in real time and registers it in a database. The system also allows the generation AI to always perform analysis based on the latest data. This allows the generation AI to share data with collaborators in real time and always perform analysis based on the latest data. This makes it possible to centrally manage data.

[0095] The system can analyze the collaborating partner's data and propose an optimal sales plan to maximize mutual profits. For example, the system collects collaborating partner's data and the generation AI proposes a sales plan to maximize mutual profits based on that data. For example, the system analyzes inventory data and sales history. For example, the system registers the collaborating partner's data in a database and provides it to the generation AI. The system can also analyze the collaborating partner's data and propose a sales plan to maximize mutual profits. This allows the generation AI to analyze the collaborating partner's data and propose a sales plan to maximize mutual profits. This strengthens collaboration with the collaborating partner and maximizes mutual profits.

[0096] The system can use the emotion estimation function to make business efficiency improvement proposals based on the emotions of collaborating employees. For example, the system can use the emotion estimation function to collect emotional data from collaborating employees, and the generation AI can then make business efficiency proposals based on that data. For example, the system can propose business improvement measures that elicit positive emotions. For example, the system can collect emotional data from collaborating employees and analyze their emotions using an emotion estimation algorithm. The system can also make business efficiency proposals based on the emotions of collaborating employees. This allows the generation AI to make business efficiency proposals based on the emotions of collaborating employees. This allows for immediate response to changes in employees' emotions.

[0097] The system can securely share data with collaborators using blockchain technology. For example, the system builds a system that securely shares data with collaborators using blockchain technology, and the generation AI performs analysis based on that data. For example, it prevents data tampering and ensures transparency. For example, the system encrypts data using blockchain technology and registers it in a database. The system can also securely share data with collaborators using blockchain technology. This allows the generation AI to securely share data with collaborators using blockchain technology. This strengthens data security.

[0098] The system can use the emotion estimation function to propose new product development based on the emotions of collaborating customers. For example, the system uses the emotion estimation function to collect emotional data from collaborating customers, and the generation AI proposes new product development based on that data. For example, a product that elicits positive emotions is developed. For example, the system collects emotional data from collaborating customers and analyzes their emotions using an emotion estimation algorithm. The system can also propose new product development based on the emotions of collaborating customers. This allows the generation AI to propose new product development based on the emotions of collaborating customers. This makes it possible to respond immediately to changes in customer emotions.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The data collection unit can also include the impact of product packaging design and marketing campaigns in the analysis. For example, product packaging design data is collected and the generation AI makes sales predictions based on that data. For example, the data collection unit analyzes sales trends for products whose designs have been changed. For example, the data collection unit collects product packaging designs as image data and registers them in a database. The data collection unit can also collect marketing campaign data and provide it to the generation AI. This allows the generation AI to make sales predictions that take into account the impact of product packaging design and marketing campaigns. This makes it possible to make predictions that take into account the impact of package design and marketing campaigns.

[0101] The data collection unit can compare data from different regions or stores and analyze consumption trends by region. For example, sales data from different regions is collected, and the generation AI uses that data to analyze consumption trends by region. For example, popular and best-selling products in each region are identified. For example, the data collection unit registers sales data from different regions in a database and provides it to the generation AI. The data collection unit can also collect sales data from different stores and provide it to the generation AI. This allows the generation AI to analyze consumption trends by region. This makes it possible to develop sales strategies that take into account the characteristics of each region and store.

[0102] The data collection unit can use the emotion estimation function to analyze consumer reviews or feedback and identify areas for product improvement. For example, the emotion estimation function can be used to analyze consumer reviews and feedback, and the generation AI can use that data to identify areas for product improvement. For example, reviews with a high percentage of negative emotions can be analyzed preferentially. The data collection unit can, for example, collect online review data and analyze emotions using an emotion estimation algorithm. The data collection unit can also collect survey data and provide it to the generation AI. This allows the generation AI to analyze consumer reviews and feedback and identify areas for product improvement. This allows for immediate response to changes in consumer emotions.

[0103] The sales planning department can consider the influence of seasons or events in the sales plan and create a sales strategy tailored to a specific time of year. For example, seasonal sales data is collected, and the generation AI creates a sales plan tailored to the season based on that data. For example, sales of cold drinks and ice cream are increased in the summer. The sales planning department, for example, registers seasonal sales data in a database and provides it to the generation AI. The sales planning department can also collect event data and provide it to the generation AI. This allows the generation AI to create a sales plan that takes into account the influence of seasons and events. This makes it possible to create a strategy that takes into account the influence of seasons and events.

[0104] The sales plan formulation department can analyze customer purchase history and propose optimal sales plans for individual customers. For example, customer purchase history data is collected, and the generation AI proposes optimal sales plans for individual customers based on that data. For example, repeat purchases are encouraged based on past purchase history. The sales plan formulation department, for example, registers customer purchase history data in a database and provides it to the generation AI. The sales plan formulation department can also analyze customer purchase history and propose optimal sales plans for individual customers. This allows the generation AI to propose optimal sales plans for individual customers. This makes it possible to create optimal strategies for individual customers.

[0105] The sales plan formulation unit can use the emotion estimation function to formulate a personalized sales plan based on the customer's emotions. For example, the emotion estimation function is used to collect customer emotion data, and the generation AI formulates a personalized sales plan based on that data. For example, a special promotion may be proposed to customers with strong positive emotions. The sales plan formulation unit, for example, collects customer emotion data and analyzes the emotions using an emotion estimation algorithm. The sales plan formulation unit can also formulate a personalized sales plan based on the customer's emotions. This allows the generation AI to formulate a personalized sales plan based on the customer's emotions. This makes it possible to respond immediately to changes in customer emotions.

[0106] The suggestion unit can analyze data on past special sale sales and identify the most effective discount strategy. For example, data on past special sale sales is collected, and the generation AI identifies the most effective discount strategy based on that data. For example, the suggestion unit analyzes which sales were most effective at a specific discount rate or period. For example, the suggestion unit registers data on past special sale sales in a database and provides it to the generation AI. The suggestion unit can also analyze data on past special sale sales and identify the most effective discount strategy. This allows the generation AI to identify a discount strategy based on past success stories. This makes it possible to use a strategy based on past success stories.

[0107] The suggestion unit can use the emotion estimation function to make discount suggestions based on the consumer's emotions. For example, the emotion estimation function can be used to collect consumer emotion data, and the generation AI can use that data to make emotion-based discount suggestions. For example, a special discount can be proposed to consumers with strong positive emotions. The suggestion unit, for example, collects consumer emotion data and analyzes the emotions using an emotion estimation algorithm. The suggestion unit can also make discount suggestions based on the consumer's emotions. This allows the generation AI to make discount suggestions based on the consumer's emotions. This makes it possible to respond immediately to changes in consumer emotions.

[0108] The proposal unit can apply sale proposals to different product categories or brands. For example, the proposals can be applied to different product categories, and the generation AI can create an optimal discount strategy based on that data. For example, a sale can be held not only on food but also on daily necessities and home appliances. The proposal unit, for example, collects data on different product categories and provides it to the generation AI. The proposal unit can also collect data on different brands and provide it to the generation AI. This allows the generation AI to make sale proposals for different product categories and brands. This makes it possible to create strategies that take into account the characteristics of each category and brand.

[0109] The suggestion unit can use the emotion estimation function to generate promotional messages based on consumer emotions. For example, the emotion estimation function can be used to collect consumer emotion data, and the generation AI can use that data to generate emotion-based promotional messages. For example, a message that elicits positive emotions can be created. The suggestion unit, for example, collects consumer emotion data and analyzes emotions using an emotion estimation algorithm. The suggestion unit can also generate promotional messages based on consumer emotions. This allows the generation AI to generate promotional messages based on consumer emotions. This makes it possible to respond immediately to changes in consumer emotions.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The data collection unit collects at least one of the following data: expiration date, inventory status, and sales history of the product. For example, the data collection unit may collect expiration date data by scanning the barcode of the product. The data collection unit may also obtain inventory status data from an inventory management system and collect sales history data from a POS system. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it uses generative AI to identify products that are approaching their expiration date, analyzes inventory status to calculate inventory turnover, and analyzes sales history to identify best-selling products. Step 3: The sales planning department creates an appropriate sales plan based on the data analyzed by the analysis department. For example, they may create a plan to prioritize the sale of products that are approaching their expiration date, a sales strategy to increase inventory turnover, or a plan to focus on selling best-selling products. Step 4: The proposal department proposes special sales or limited-time discounts based on the sales plan drawn up by the sales planning department. For example, the proposal department proposes special sales for products that are nearing their expiration date, proposes limited-time discounts to increase inventory turnover, and proposes special sales for best-selling products.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] 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.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The 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.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0156] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0157] 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.

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects at least one of data on product expiration dates, inventory status, and sales history; an analysis unit that analyzes the data collected by the data collection unit; a sales planning unit that creates an appropriate sales plan based on the data analyzed by the analysis unit; a proposal unit that proposes special sales or limited-time discounts based on the sales plan formulated by the sales plan formulation unit. A system characterized by:

2. The data collection unit At least one environmental data of the temperature or humidity of the product is also collected, and the expiration date is predicted based on the storage conditions.

2. The system of claim 1.

3. The data collection unit Comparing data from different regions or stores and analyzing consumption trends for each region 2. The system of claim 1.

4. The sales planning unit Analyzing the customer's purchase history and proposing an optimal sales plan for each individual customer 2. The system of claim 1.

5. The proposal unit The data from past sales is analyzed to identify the most effective discount strategies.

2. The system of claim 1.

6. The system comprises: Propose product placement based on consumer sentiment 2. The system of claim 1.

7. The system comprises: Propose business efficiency improvements based on the feelings of employees at partner companies 2. The system of claim 1.

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