System

Abstract

To provide a system for proposing an appropriate markdown price or menu for effectively reducing food waste.SOLUTION: A specific processing part 290 of a data processor 290 in the system analyzes stock data and use-by date information to execute markdown price proposal processing for proposing an appropriate markdown price of a food material and menu proposal processing for proposing a menu using the markdown food material proposed by the markdown price proposal processing.SELECTED DRAWING: Figure 2

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not providing adequate discount prices or menu suggestions to effectively reduce food waste.

[0005] The system according to the embodiment aims to reduce food waste and improve profit margins. [Means for solving the problem]

[0006] The system according to the embodiment includes a discounted price proposal unit and a menu proposal unit. The discounted price proposal unit analyzes inventory data and expiration date information to propose appropriate discounted prices. The menu proposal unit proposes menus using discounted ingredients proposed by the discounted price proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce food waste and improve profit margins. [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) An app according to an embodiment of the present invention is an app that aims to reduce food waste, a major factor in reducing profit margins in the food retail industry. This app proposes appropriate discount prices to stores and suggests menus using discounted ingredients to users. This allows the app to contribute to environmental protection, household savings, and increased profits for supermarkets.

[0029] An app according to an embodiment includes a discounted price proposal unit and a menu proposal unit. The discounted price proposal unit analyzes inventory data and expiration date information and proposes an appropriate discounted price. For example, the generation AI analyzes inventory data and expiration date information provided by a store, calculates the most effective price reduction for products approaching their expiration date, and notifies the store. The generation AI proposes discounted prices based on prompts including inventory data and expiration date information. The menu proposal unit proposes menus using discounted ingredients proposed by the discounted price proposal unit. For example, the generation AI proposes menus using discounted ingredients to a user based on information about discounted ingredients. The generation AI proposes menus based on prompts including information about discounted ingredients. This allows the app to reduce food waste, contribute to environmental protection, household savings, and increased supermarket profits.

[0030] The price reduction proposal unit can calculate the most effective price reduction for products approaching their expiration date and notify the store. For example, the price reduction proposal unit uses a generation AI to combine and analyze past sales data and weather data to propose a discount price according to the weather. For example, on rainy days, the demand for fresh food decreases, so the proposal will be to increase the amount of price reduction. This makes it possible to effectively reduce the price of products approaching their expiration date.

[0031] The menu suggestion unit can generate recipes using vegetables and meats that have dropped in price and provide them to users. For example, the menu suggestion unit's generation AI analyzes the store's location and the price trends of nearby competing stores, and proposes discounted prices based on that. For example, in areas with many competing stores, it will propose a larger price reduction. This makes it possible to propose menus that utilize discounted ingredients.

[0032] The price reduction proposal unit can combine and analyze past sales data and weather data to propose price reductions according to the weather. For example, the price reduction proposal unit uses a generation AI to analyze the emotions of store staff and propose price reductions at the timing when the staff can work most efficiently. For example, it uses an emotion estimation function to analyze the emotions of store staff in real time and propose price reductions at the timing when the staff can work most efficiently. This makes it possible to propose effective price reductions according to the weather.

[0033] The price reduction proposal unit can analyze the location of the store and the price trends of nearby competing stores, and propose a discounted price based on that. For example, the generation AI analyzes the location of the store and proposes a discounted price taking into account the price trends of nearby competing stores. For example, in areas with many competing stores, it will propose a larger discount. This makes it possible to implement effective price reductions in line with the price trends of competing stores.

[0034] The menu suggestion unit can analyze the user's past meal history and health data and suggest menus according to the user's health condition. For example, the generation AI in the menu suggestion unit analyzes the user's past meal history and health data and suggests menus according to the user's health condition. For example, it suggests a nutritionally balanced menu based on the user's health data such as weight, blood pressure, and blood sugar level. This makes it possible to suggest menus according to the user's health condition.

[0035] The menu suggestion unit can analyze the user's food allergy information and suggest menus that avoid allergies. For example, the generation AI in the menu suggestion unit analyzes the user's food allergy information and suggests menus that avoid allergies. For example, if the user is allergic to a specific food ingredient, it will suggest a menu that does not include that ingredient. This makes it possible to suggest menus that avoid the user's allergies.

[0036] The price reduction proposal unit can link with the store's inventory data to make proposals to optimize the display location of products subject to price reduction. For example, the price reduction proposal unit uses a generation AI to analyze the store's inventory data and make proposals to optimize the display location of products subject to price reduction. For example, placing discounted products in prominent locations can promote sales. In this way, sales can be promoted by optimizing the display location of products subject to price reduction.

[0037] The price reduction proposal unit can propose a promotion method for the discounted product at the same time as proposing a discounted price. For example, the generation AI in the price reduction proposal unit proposes a promotion method using an in-store announcement at the same time as proposing a discounted price. For example, announcing discounted products at a specific time period can attract customer attention. This can promote sales by proposing a promotion method for the discounted product.

[0038] The price reduction proposal unit can analyze product quality data and propose a discount price according to the quality. For example, the generation AI analyzes product appearance data in addition to inventory data and expiration date information, and proposes a discount price according to the quality. For example, it proposes a larger price reduction for products with external scratches. This makes it possible to effectively reduce prices according to the quality of the product.

[0039] The price reduction proposal unit can learn the effects of past price reductions and propose the most effective price reduction pattern. For example, the generation AI can learn the effects of past price reductions and propose the most effective price reduction pattern. For example, based on past data, it can identify a pattern in which a specific price reduction amount will most promote sales. This makes it possible to propose the optimal price reduction pattern based on past data.

[0040] The price reduction proposal unit can make proposals to change the package design of products subject to price reduction based on inventory data and expiration date information. For example, the generation AI in the price reduction proposal unit makes proposals to change the package design of products subject to price reduction based on inventory data and expiration date information. For example, it proposes a package with an eye-catching design for products with an approaching expiration date. This makes it possible to increase the attention of discounted products by changing the package design.

[0041] The price reduction proposal unit can propose layout changes for discounted products at the same time as proposing a discounted price. For example, the generation AI can propose layout changes for discounted products at the same time as proposing a discounted price. For example, placing discounted products in a prominent location can promote sales. In this way, by proposing layout changes for discounted products, sales can be promoted.

[0042] The menu suggestion unit can suggest menus that use ingredients that have already been purchased in conjunction with the user's ingredient purchase history. For example, the generation AI in the menu suggestion unit analyzes the user's ingredient purchase history and suggests menus that use ingredients that have already been purchased. For example, it suggests recipes that use ingredients that are in the refrigerator. This makes it possible to suggest menus that use ingredients that have already been purchased.

[0043] The menu suggestion unit can suggest menus according to the user's mealtimes. For example, the generation AI analyzes the user's mealtimes and suggests menus suitable for those times. For example, it suggests a nutritionally balanced menu for breakfast. This makes it possible to suggest menus according to mealtimes.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The price reduction proposal unit can also analyze the customer demographic data of a store and propose optimal discount prices for specific customer demographics. For example, a store with a large number of young customers may propose a larger discount, while a store with a large number of senior customers may propose a smaller discount. This makes it possible to effectively reduce prices according to the customer demographic.

[0046] The menu suggestion unit can learn the user's food preferences and suggest menus customized for each individual user. For example, it can analyze the trends of the user's past menu choices and suggest menus that use the user's favorite seasonings and ingredients. This makes it possible to suggest menus that suit the user's preferences.

[0047] The price reduction proposal unit can also propose price reductions based on the store's sales targets. For example, if the sales target has not been achieved at the end of the month, it will propose a larger price reduction. This allows for effective price reductions according to the sales target.

[0048] The menu suggestion unit can also analyze the user's meal frequency and suggest menus according to the frequency of meals. For example, it can suggest a wide variety of menus to a user who eats frequently, and suggest easy-to-prepare menus to a user who eats infrequently. This makes it possible to suggest menus according to the frequency of meals.

[0049] The price reduction proposal unit can also analyze the store's inventory turnover rate and propose a discount price according to the inventory turnover rate. For example, it can propose a larger discount for products with a low inventory turnover rate. This makes it possible to effectively reduce prices according to the inventory turnover rate.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The price reduction proposal unit analyzes inventory data and expiration date information and proposes an appropriate discount price. For example, the generation AI analyzes inventory data and expiration date information provided by the store, calculates the most effective price reduction for products whose expiration date is approaching, and notifies the store. The generation AI proposes a discount price based on prompts including inventory data and expiration date information. Step 2: The menu suggestion unit suggests a menu using the discounted ingredients suggested by the discount price suggestion unit. For example, the generation AI suggests a menu using the discounted ingredients to the user based on information about the discounted ingredients. The generation AI suggests a menu based on a prompt that includes information about the discounted ingredients.

[0052] (Example 2) An app according to an embodiment of the present invention is an app that aims to reduce food waste, a major factor in reducing profit margins in the food retail industry. This app proposes appropriate discount prices to stores and suggests menus using discounted ingredients to users. This allows the app to contribute to environmental protection, household savings, and increased profits for supermarkets.

[0053] An app according to an embodiment includes a discounted price proposal unit and a menu proposal unit. The discounted price proposal unit analyzes inventory data and expiration date information and proposes an appropriate discounted price. For example, the generation AI analyzes inventory data and expiration date information provided by a store, calculates the most effective price reduction for products approaching their expiration date, and notifies the store. The generation AI proposes discounted prices based on prompts including inventory data and expiration date information. The menu proposal unit proposes menus using discounted ingredients proposed by the discounted price proposal unit. For example, the generation AI proposes menus using discounted ingredients to a user based on information about discounted ingredients. The generation AI proposes menus based on prompts including information about discounted ingredients. This allows the app to reduce food waste, contribute to environmental protection, household savings, and increased supermarket profits.

[0054] The price reduction proposal unit can calculate the most effective price reduction for products approaching their expiration date and notify the store. For example, the price reduction proposal unit uses a generation AI to combine and analyze past sales data and weather data to propose a discount price according to the weather. For example, on rainy days, the demand for fresh food decreases, so the proposal will be to increase the amount of price reduction. This makes it possible to effectively reduce the price of products approaching their expiration date.

[0055] The menu suggestion unit can generate recipes using vegetables and meats that have dropped in price and provide them to users. For example, the menu suggestion unit's generation AI analyzes the store's location and the price trends of nearby competing stores, and proposes discounted prices based on that. For example, in areas with many competing stores, it will propose a larger price reduction. This makes it possible to propose menus that utilize discounted ingredients.

[0056] The price reduction proposal unit can combine and analyze past sales data and weather data to propose price reductions according to the weather. For example, the price reduction proposal unit uses a generation AI to analyze the emotions of store staff and propose price reductions at the timing when the staff can work most efficiently. For example, it uses an emotion estimation function to analyze the emotions of store staff in real time and propose price reductions at the timing when the staff can work most efficiently. This makes it possible to propose effective price reductions according to the weather.

[0057] The price reduction proposal unit can analyze the location of the store and the price trends of nearby competing stores, and propose a discounted price based on that. For example, the generation AI analyzes the location of the store and proposes a discounted price taking into account the price trends of nearby competing stores. For example, in areas with many competing stores, it will propose a larger discount. This makes it possible to implement effective price reductions in line with the price trends of competing stores.

[0058] The menu suggestion unit can analyze the user's past meal history and health data and suggest menus according to the user's health condition. For example, the generation AI in the menu suggestion unit analyzes the user's past meal history and health data and suggests menus according to the user's health condition. For example, it suggests a nutritionally balanced menu based on the user's health data such as weight, blood pressure, and blood sugar level. This makes it possible to suggest menus according to the user's health condition.

[0059] The menu suggestion unit can analyze the user's food allergy information and suggest menus that avoid allergies. For example, the generation AI in the menu suggestion unit analyzes the user's food allergy information and suggests menus that avoid allergies. For example, if the user is allergic to a specific food ingredient, it will suggest a menu that does not include that ingredient. This makes it possible to suggest menus that avoid the user's allergies.

[0060] The menu suggestion unit can analyze the user's current emotional state and suggest a menu that matches their mood. For example, the generation AI in the menu suggestion unit can analyze the user's current emotional state and suggest a menu that matches their mood. For example, if the user is feeling stressed, it can suggest a menu that uses ingredients that have a relaxing effect. This makes it possible to suggest menus that match the user's emotional state.

[0061] The price reduction proposal unit can link with the store's inventory data to make proposals to optimize the display location of products subject to price reduction. For example, the price reduction proposal unit uses a generation AI to analyze the store's inventory data and make proposals to optimize the display location of products subject to price reduction. For example, placing discounted products in prominent locations can promote sales. In this way, sales can be promoted by optimizing the display location of products subject to price reduction.

[0062] The price reduction proposal unit can propose a promotion method for the discounted product at the same time as proposing a discounted price. For example, the generation AI in the price reduction proposal unit proposes a promotion method using an in-store announcement at the same time as proposing a discounted price. For example, announcing discounted products at a specific time period can attract customer attention. This can promote sales by proposing a promotion method for the discounted product.

[0063] The price reduction proposal unit can use the emotion estimation function to propose the best timing for a price reduction in real time to increase the customer's willingness to buy. For example, the price reduction proposal unit uses the emotion estimation function to analyze the customer's willingness to buy in real time and propose the best timing for a price reduction. For example, the price reduction can be carried out during a time period when the customer's emotions are positive. This makes it possible to propose the best timing for a price reduction in order to increase the customer's willingness to buy.

[0064] The price reduction proposal unit can analyze product quality data and propose a discount price according to the quality. For example, the generation AI analyzes product appearance data in addition to inventory data and expiration date information, and proposes a discount price according to the quality. For example, it proposes a larger price reduction for products with external scratches. This makes it possible to effectively reduce prices according to the quality of the product.

[0065] The price reduction proposal unit can learn the effects of past price reductions and propose the most effective price reduction pattern. For example, the generation AI can learn the effects of past price reductions and propose the most effective price reduction pattern. For example, based on past data, it can identify a pattern in which a specific price reduction amount will most promote sales. This makes it possible to propose the optimal price reduction pattern based on past data.

[0066] The discount price proposal unit can analyze the customer's purchase history and emotional data to propose a discount price that will most satisfy the customer. The discount price proposal unit can, for example, use an emotion estimation function to analyze the customer's purchase history and emotional data to propose a discount price that will most satisfy the customer. For example, the discount amount that will satisfy the customer can be identified based on the past purchase history and emotional data. This makes it possible to propose an optimal discount price that will increase customer satisfaction.

[0067] The price reduction proposal unit can make proposals to change the package design of products subject to price reduction based on inventory data and expiration date information. For example, the generation AI in the price reduction proposal unit makes proposals to change the package design of products subject to price reduction based on inventory data and expiration date information. For example, it proposes a package with an eye-catching design for products with an approaching expiration date. This makes it possible to increase the attention of discounted products by changing the package design.

[0068] The price reduction proposal unit can propose layout changes for discounted products at the same time as proposing a discounted price. For example, the generation AI can propose layout changes for discounted products at the same time as proposing a discounted price. For example, placing discounted products in a prominent location can promote sales. In this way, by proposing layout changes for discounted products, sales can be promoted.

[0069] The discounted price proposal unit can use the emotion estimation function to propose in real time the placement of discounted products to increase the customer's willingness to buy. For example, the discounted price proposal unit uses the emotion estimation function to analyze the customer's willingness to buy in real time and propose the optimal placement of discounted products. For example, discounted products are placed in places where the customer's emotions are positive. This makes it possible to propose the optimal placement of discounted products to increase the customer's willingness to buy.

[0070] The menu suggestion unit can suggest menus that use ingredients that have already been purchased in conjunction with the user's ingredient purchase history. For example, the generation AI in the menu suggestion unit analyzes the user's ingredient purchase history and suggests menus that use ingredients that have already been purchased. For example, it suggests recipes that use ingredients that are in the refrigerator. This makes it possible to suggest menus that use ingredients that have already been purchased.

[0071] The menu suggestion unit can suggest menus according to the user's mealtimes. For example, the generation AI analyzes the user's mealtimes and suggests menus suitable for those times. For example, it suggests a nutritionally balanced menu for breakfast. This makes it possible to suggest menus according to mealtimes.

[0072] The menu suggestion unit can use the emotion estimation function to suggest combinations of ingredients that correspond to the user's emotional state. For example, the menu suggestion unit uses the emotion estimation function to analyze the user's emotional state and suggest combinations of ingredients that correspond to the emotion. For example, if the user is tired, the menu suggestion unit suggests a menu that combines ingredients that have a relaxing effect. This makes it possible to suggest combinations of ingredients that correspond to the user's emotional state.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The price reduction proposal unit can also analyze the customer demographic data of a store and propose optimal discount prices for specific customer demographics. For example, a store with a large number of young customers may propose a larger discount, while a store with a large number of senior customers may propose a smaller discount. This makes it possible to effectively reduce prices according to the customer demographic.

[0075] The menu suggestion unit can learn the user's food preferences and suggest menus customized for each individual user. For example, it can analyze the trends of the user's past menu choices and suggest menus that use the user's favorite seasonings and ingredients. This makes it possible to suggest menus that suit the user's preferences.

[0076] The price reduction proposal unit can also propose price reductions based on the store's sales targets. For example, if the sales target has not been achieved at the end of the month, it will propose a larger price reduction. This allows for effective price reductions according to the sales target.

[0077] The menu suggestion unit can also analyze the user's meal frequency and suggest menus according to the frequency of meals. For example, it can suggest a wide variety of menus to a user who eats frequently, and suggest easy-to-prepare menus to a user who eats infrequently. This makes it possible to suggest menus according to the frequency of meals.

[0078] The price reduction proposal unit can also analyze the store's inventory turnover rate and propose a discount price according to the inventory turnover rate. For example, it can propose a larger discount for products with a low inventory turnover rate. This makes it possible to effectively reduce prices according to the inventory turnover rate.

[0079] The menu suggestion unit can also analyze the user's emotional state and suggest combinations of ingredients according to the emotion. For example, if the user is feeling stressed, it can suggest a menu that combines ingredients that have a relaxing effect. This makes it possible to suggest combinations of ingredients according to the user's emotional state.

[0080] The price reduction proposal unit can also use the emotion estimation function to propose the best time to reduce the price in real time to increase the customer's willingness to buy. For example, the price reduction can be carried out during times when the customer's emotions are positive. This makes it possible to propose the optimal time to reduce the price to increase the customer's willingness to buy.

[0081] The menu suggestion unit can also analyze the user's emotional state and suggest a menu that matches their mood. For example, if the user is tired, it can suggest a menu that uses ingredients that have a relaxing effect. This makes it possible to suggest a menu that matches the user's emotional state.

[0082] The price reduction proposal unit can also use the emotion estimation function to propose in real time the placement of discounted products to increase customer purchasing motivation. For example, discounted products can be placed in places where customers have positive emotions. This makes it possible to propose the optimal placement of discounted products to increase customer purchasing motivation.

[0083] The menu suggestion unit can also analyze the user's emotional state and suggest combinations of ingredients that correspond to the user's emotions. For example, if the user is tired, the unit can suggest a menu that combines ingredients that have a relaxing effect. This allows the unit to suggest combinations of ingredients that correspond to the user's emotional state.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The price reduction proposal unit analyzes inventory data and expiration date information and proposes an appropriate discount price. For example, the generation AI analyzes inventory data and expiration date information provided by the store, calculates the most effective price reduction for products whose expiration date is approaching, and notifies the store. The generation AI proposes a discount price based on prompts including inventory data and expiration date information. Step 2: The menu suggestion unit suggests a menu using the discounted ingredients suggested by the discount price suggestion unit. For example, the generation AI suggests a menu using the discounted ingredients to the user based on information about the discounted ingredients. The generation AI suggests a menu based on a prompt that includes information about the discounted ingredients.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 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. The price reduction proposal department analyzes inventory data and expiration date information to propose appropriate discount prices, and a menu suggestion unit that suggests a menu using the discounted ingredients suggested by the discounted price suggestion unit; A system characterized by:

2. The discount price proposal unit Calculate the most effective price reduction for products approaching their expiration date and notify stores 2. The system of claim 1.

3. The discount price proposal unit Linking with the store's inventory data, the system makes suggestions for optimizing the display locations of the products subject to price reductions.

2. The system of claim 1.

4. The discount price proposal unit Analyze product quality data and propose discounted prices according to quality 2. The system of claim 1.

5. The menu suggestion unit Linking with the user's food purchase history, the system suggests the above-mentioned menu using the purchased ingredients.

2. The system of claim 1.

6. The menu suggestion unit Analyze the user's current emotional state and suggest the menu that matches their mood 2. The system of claim 1.

7. The discount price proposal unit Propose the timing of price reductions in real time to increase customer purchasing motivation 2. The system of claim 1.

8. The menu suggestion unit Suggesting combinations of ingredients according to the user's emotional state 2. The system of claim 1.

Images