System

Abstract

An object of a system according to an embodiment is to reduce food loss by adjusting a price based on a best-before date.SOLUTION: A system includes a price adjustment unit, a weather analysis unit, a store visit prediction analysis unit, and a reward program application unit. The price adjustment unit adjusts the price based on the best-before date. The weather analysis unit analyzes weather data. The store visit prediction analysis part analyzes the store visit prediction data. The Rewards Program Applier applies the Rewards Program.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 adjust prices for food products approaching their expiration date or reduce food waste, so there is room for improvement.

[0005] The system according to the embodiment aims to reduce food waste by adjusting prices based on expiration dates. [Means for solving the problem]

[0006] The system according to the embodiment includes a price adjustment unit, a weather analysis unit, a store visitation prediction analysis unit, and a reward program application unit. The price adjustment unit adjusts prices based on expiration dates. The weather analysis unit analyzes weather data. The store visitation prediction analysis unit analyzes store visitation prediction data. The reward program application unit applies reward programs. [Effects of the Invention]

[0007] The system according to the embodiment can adjust prices based on expiration dates and reduce food waste. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 price adjustment system according to an embodiment of the present invention adaptively adjusts prices based on statistical information such as weather and the expected number and time of customer visits as food approaches its expiration date. This prevents customers from purchasing items with longer expiration dates and prevents products that are close to expiring from remaining unsold, reducing food waste and creating a sense of value. Furthermore, the system utilizes AI to automate the time-consuming process of adjusting prices. Furthermore, by automatically applying campaign services as a reward program for valued customers, it can promote customer loyalty and sustainable consumption.

[0029] A price adjustment system according to an embodiment includes a price adjustment unit, a weather analysis unit, a store visit prediction analysis unit, and a reward program application unit. The price adjustment unit adjusts prices based on expiration dates. For example, products with expiration dates within one week are discounted by 10%, and products with expiration dates within three days are discounted by 20%, gradually lowering prices according to expiration dates. The weather analysis unit analyzes weather data. For example, on rainy days, fewer customers visit the store, so prices can be further reduced to promote sales. The store visit prediction analysis unit analyzes store visit prediction data. For example, it analyzes the expected number of customers and time of visits based on past store visit history and event information, and adjusts prices based on that. The reward program application unit applies a reward program. For example, it automatically issues coupons that can be used on the next purchase to customers who reach a certain purchase amount. As a result, the price adjustment system according to an embodiment adjusts prices based on expiration dates, weather, and store visit prediction data, and applies a reward program, thereby reducing food waste and improving customer loyalty.

[0030] The price adjustment unit can use generation AI to analyze consumer purchasing history for products approaching their expiration date and provide individual discounts to specific consumers. For example, the price adjustment unit can use generation AI to analyze consumer purchasing history for products approaching their expiration date and provide individual discounts to consumers who have purchased similar products in the past. For example, a consumer who has purchased a specific brand of yogurt in the past can be offered yogurt of that brand that is approaching its expiration date at a discounted price. In this way, by providing individual discounts based on the consumer's purchasing history, it is possible to increase consumer purchasing motivation.

[0031] The price adjustment unit uses generation AI to monitor products approaching their expiration date in real time and dynamically adjust prices according to inventory status. For example, the price adjustment unit uses generation AI to monitor products approaching their expiration date in real time and significantly lowers the price if there is a large amount of stock. For example, if there is a large amount of product remaining with an expiration date within three days, the price of that product is set at a 50% discount. This allows for efficient inventory management by dynamically adjusting prices according to inventory status.

[0032] The reward program application unit can use the generation AI to analyze the purchase history of a customer and apply a reward program individually. The reward program application unit can, for example, use the generation AI to analyze the purchase history of a customer and apply a reward program individually. For example, a customer who has frequently purchased products of a specific brand in the past can be offered products of that brand at a discounted price. In this way, applying a reward program based on the customer's purchase history can improve customer loyalty.

[0033] The weather analysis unit can use generation AI to analyze consumer purchasing patterns under specific weather conditions based on weather data and adjust prices based on that. For example, the weather analysis unit can use generation AI to analyze consumer purchasing patterns under specific weather conditions based on weather data and adjust prices based on that. For example, because consumers prefer hot drinks on rainy days, the price of hot drinks is discounted. In this way, adjusting prices based on weather conditions can increase consumer purchasing motivation.

[0034] The store visitor prediction analysis unit can dynamically adjust prices during peak and off-peak times using generation AI based on data on the predicted number of visitors and time. The store visitor prediction analysis unit can dynamically adjust prices during peak and off-peak times using generation AI based on data on the predicted number of visitors and time. For example, prices can be set at normal levels during peak times and discounted during off-peak times. This makes it possible to dynamically adjust prices based on predicted store visit data, thereby increasing consumer purchasing motivation.

[0035] The reward program application unit can use the generation AI to conduct targeted promotions for specific consumer groups and apply reward programs. The reward program application unit, for example, uses the generation AI to conduct targeted promotions for specific consumer groups and apply reward programs. For example, a reward program for organic food can be offered to a health-conscious consumer group. In this way, applying the reward program to a specific consumer group can improve consumer loyalty.

[0036] The reward program application unit can use the generation AI to link the reward program with a specific event or campaign. The reward program application unit, for example, uses the generation AI to link the reward program with a specific event or campaign. For example, the reward program is provided in conjunction with a Christmas sale. In this way, linking the reward program with an event or campaign can increase consumer purchasing motivation.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The price adjustment unit can provide individual discounts to specific consumers based on the consumer's purchasing history. For example, a consumer who has previously purchased a specific brand of yogurt can be offered a discounted version of that brand's yogurt that is close to its expiration date. Also, if a consumer frequently visits the store on a specific day of the week or at a specific time, a discount can be provided that is tailored to that time. Furthermore, if a consumer has a history of participating in a specific event or campaign, a discount can be provided on products related to that event or campaign. In this way, by providing individual discounts based on the consumer's purchasing history, it is possible to increase the consumer's motivation to purchase.

[0039] The weather analysis unit can analyze consumer purchasing patterns under specific weather conditions based on weather data and adjust prices accordingly. For example, on rainy days, consumers prefer hot drinks, so the price of hot drinks can be discounted. On hot days, consumers prefer cold drinks, so the price of cold drinks can be discounted. Furthermore, on snowy days, consumers tend to stay indoors, so the price of delivery services can be discounted. In this way, adjusting prices based on weather conditions can increase consumer purchasing motivation.

[0040] The store visitor prediction analysis unit can dynamically adjust prices during peak and off-peak times based on data on the predicted number of visitors and time. For example, prices can be set at normal rates during peak times and discounted during off-peak times. Also, if there are fewer customers on certain days of the week or during certain times of the day, discounts can be offered to suit those times. Furthermore, if a specific event or campaign is being held, prices can be adjusted during that period. This makes it possible to increase consumer purchasing motivation by dynamically adjusting prices based on store visitor prediction data.

[0041] The reward program application unit can use the generation AI to conduct targeted promotions and apply reward programs to specific consumer groups. For example, a reward program for organic food can be offered to a health-conscious consumer group. It can also offer a reward program for products popular with a consumer group of a specific age or gender. It can also offer a reward program for products popular in a specific region to a consumer group living in that region. In this way, applying reward programs to specific consumer groups can increase consumer loyalty.

[0042] The reward program application unit can use the generation AI to link the reward program with specific events and campaigns. For example, the reward program can be provided in conjunction with a Christmas sale. It can also be provided in conjunction with specific events such as Valentine's Day and Halloween. Furthermore, the reward program can be strengthened during specific campaign periods. In this way, linking the reward program with events and campaigns can increase consumer purchasing motivation.

[0043] The price adjustment unit uses generation AI to monitor products approaching their expiration date in real time and dynamically adjust prices according to inventory status. For example, generation AI can be used to monitor products approaching their expiration date in real time, and if there is a large amount of inventory, the price can be significantly reduced. Specifically, if there is a large amount of product remaining with an expiration date of within three days, the price of that product can be set at a 50% discount. If inventory is low, the price can also be set at the normal rate. Furthermore, if a specific product remains unsold, the price of that product can be gradually reduced. This allows for efficient inventory management by dynamically adjusting prices according to inventory status.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The price adjustment unit adjusts the price based on the expiration date. For example, products with an expiration date of less than one week will receive a 10% discount, and products with an expiration date of less than three days will receive a 20% discount, and so on. Step 2: The weather analysis department analyzes weather data. For example, on rainy days, fewer customers visit the store, so sales can be promoted by further lowering prices. Step 3: The store visitor prediction analysis unit analyzes the store visitor prediction data. For example, it analyzes the expected number of customers and time of visit based on past store visit history and event information, and adjusts prices accordingly. Step 4: The reward program application unit applies the reward program. For example, it automatically issues a coupon that can be used for the next purchase to a customer who has reached a certain purchase amount.

[0046] (Example 2) The price adjustment system according to an embodiment of the present invention adaptively adjusts prices based on statistical information such as weather and the expected number and time of customer visits as food approaches its expiration date. This prevents customers from purchasing items with longer expiration dates and prevents products that are close to expiring from remaining unsold, reducing food waste and creating a sense of value. Furthermore, the system utilizes AI to automate the time-consuming process of adjusting prices. Furthermore, by automatically applying campaign services as a reward program for valued customers, it can promote customer loyalty and sustainable consumption.

[0047] A price adjustment system according to an embodiment includes a price adjustment unit, a weather analysis unit, a store visit prediction analysis unit, and a reward program application unit. The price adjustment unit adjusts prices based on expiration dates. For example, products with expiration dates within one week are discounted by 10%, and products with expiration dates within three days are discounted by 20%, gradually lowering prices according to expiration dates. The weather analysis unit analyzes weather data. For example, on rainy days, fewer customers visit the store, so prices can be further reduced to promote sales. The store visit prediction analysis unit analyzes store visit prediction data. For example, it analyzes the expected number of customers and time of visits based on past store visit history and event information, and adjusts prices based on that. The reward program application unit applies a reward program. For example, it automatically issues coupons that can be used on the next purchase to customers who reach a certain purchase amount. As a result, the price adjustment system according to an embodiment adjusts prices based on expiration dates, weather, and store visit prediction data, and applies a reward program, thereby reducing food waste and improving customer loyalty.

[0048] The price adjustment unit can use generation AI to analyze consumer purchasing history for products approaching their expiration date and provide individual discounts to specific consumers. For example, the price adjustment unit can use generation AI to analyze consumer purchasing history for products approaching their expiration date and provide individual discounts to consumers who have purchased similar products in the past. For example, a consumer who has purchased a specific brand of yogurt in the past can be offered yogurt of that brand that is approaching its expiration date at a discounted price. In this way, by providing individual discounts based on the consumer's purchasing history, it is possible to increase consumer purchasing motivation.

[0049] The price adjustment unit uses generation AI to monitor products approaching their expiration date in real time and dynamically adjust prices according to inventory status. For example, the price adjustment unit uses generation AI to monitor products approaching their expiration date in real time and significantly lowers the price if there is a large amount of stock. For example, if there is a large amount of product remaining with an expiration date within three days, the price of that product is set at a 50% discount. This allows for efficient inventory management by dynamically adjusting prices according to inventory status.

[0050] The price adjustment unit can use the emotion estimation function to analyze how consumers feel about products with an approaching expiration date and adjust the price based on that emotion. For example, if a consumer feels anxious or worried about a product with an approaching expiration date, the price adjustment unit can use the emotion estimation function to significantly lower the price to alleviate that emotion. For example, if a consumer feels anxious about a product with an expiration date within one week, the price of the product can be discounted by 30%. In this way, adjusting the price based on the consumer's emotion can increase consumer purchasing motivation.

[0051] The reward program application unit can use the generation AI to analyze the purchase history of a customer and apply a reward program individually. The reward program application unit can, for example, use the generation AI to analyze the purchase history of a customer and apply a reward program individually. For example, a customer who has frequently purchased products of a specific brand in the past can be offered products of that brand at a discounted price. In this way, applying a reward program based on the customer's purchase history can improve customer loyalty.

[0052] The reward program application unit can use the emotion estimation function to analyze what emotions customers have toward the reward program and adjust the reward program based on those emotions. For example, the reward program application unit can use the emotion estimation function to analyze what emotions customers have toward the reward program and adjust the reward program based on those emotions. For example, if a customer has positive emotions toward a reward program, the reward program can be strengthened. In this way, adjusting the reward program based on the customer's emotions can improve customer satisfaction.

[0053] The weather analysis unit can use generation AI to analyze consumer purchasing patterns under specific weather conditions based on weather data and adjust prices based on that. For example, the weather analysis unit can use generation AI to analyze consumer purchasing patterns under specific weather conditions based on weather data and adjust prices based on that. For example, because consumers prefer hot drinks on rainy days, the price of hot drinks is discounted. In this way, adjusting prices based on weather conditions can increase consumer purchasing motivation.

[0054] The store visitor prediction analysis unit can dynamically adjust prices during peak and off-peak times using generation AI based on data on the predicted number of visitors and time. The store visitor prediction analysis unit can dynamically adjust prices during peak and off-peak times using generation AI based on data on the predicted number of visitors and time. For example, prices can be set at normal levels during peak times and discounted during off-peak times. This makes it possible to dynamically adjust prices based on predicted store visit data, thereby increasing consumer purchasing motivation.

[0055] The store visit prediction analysis unit can use the emotion estimation function to analyze consumer emotions under specific weather conditions and adjust prices based on those emotions. The store visit prediction analysis unit can, for example, use the emotion estimation function to analyze consumer emotions under specific weather conditions and adjust prices based on those emotions. For example, on rainy days, consumers feel depressed, so prices are discounted to increase purchasing motivation. In this way, adjusting prices based on consumer emotions can increase consumer purchasing motivation.

[0056] The reward program application unit can use the generation AI to conduct targeted promotions for specific consumer groups and apply reward programs. The reward program application unit, for example, uses the generation AI to conduct targeted promotions for specific consumer groups and apply reward programs. For example, a reward program for organic food can be offered to a health-conscious consumer group. In this way, applying the reward program to a specific consumer group can improve consumer loyalty.

[0057] The reward program application unit can use the generation AI to link the reward program with a specific event or campaign. The reward program application unit, for example, uses the generation AI to link the reward program with a specific event or campaign. For example, the reward program is provided in conjunction with a Christmas sale. In this way, linking the reward program with an event or campaign can increase consumer purchasing motivation.

[0058] The reward program application unit can use the emotion estimation function to monitor in real time how customers feel about the reward program and run promotions based on those emotions. For example, the reward program application unit can use the emotion estimation function to monitor in real time how customers feel about the reward program and run promotions based on those emotions. For example, if a customer feels positive about the reward program, an additional reward can be provided on the spot. In this way, customer satisfaction can be improved by running promotions based on the customer's emotions.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The price adjustment unit can provide individual discounts to specific consumers based on the consumer's purchasing history. For example, a consumer who has previously purchased a specific brand of yogurt can be offered a discounted version of that brand's yogurt that is close to its expiration date. Also, if a consumer frequently visits the store on a specific day of the week or at a specific time, a discount can be provided that is tailored to that time. Furthermore, if a consumer has a history of participating in a specific event or campaign, a discount can be provided on products related to that event or campaign. In this way, by providing individual discounts based on the consumer's purchasing history, it is possible to increase the consumer's motivation to purchase.

[0061] The reward program application unit uses the emotion estimation function to analyze the customer's emotions toward the reward program and adjust the reward program based on those emotions. For example, if the customer has positive emotions toward the reward program, the program can be strengthened. Specifically, if the customer is satisfied with the reward program, an additional coupon that can be used for the next purchase can be provided. Also, if the customer is dissatisfied with the reward program, the cause can be identified and measures to improve the program can be taken. Furthermore, if the customer shows interest in the reward program, detailed information about the program can be provided. This makes it possible to improve customer satisfaction by adjusting the reward program based on the customer's emotions.

[0062] The weather analysis unit can analyze consumer purchasing patterns under specific weather conditions based on weather data and adjust prices accordingly. For example, on rainy days, consumers prefer hot drinks, so the price of hot drinks can be discounted. On hot days, consumers prefer cold drinks, so the price of cold drinks can be discounted. Furthermore, on snowy days, consumers tend to stay indoors, so the price of delivery services can be discounted. In this way, adjusting prices based on weather conditions can increase consumer purchasing motivation.

[0063] The store visitor prediction analysis unit can dynamically adjust prices during peak and off-peak times based on data on the predicted number of visitors and time. For example, prices can be set at normal rates during peak times and discounted during off-peak times. Also, if there are fewer customers on certain days of the week or during certain times of the day, discounts can be offered to suit those times. Furthermore, if a specific event or campaign is being held, prices can be adjusted during that period. This makes it possible to increase consumer purchasing motivation by dynamically adjusting prices based on store visitor prediction data.

[0064] The reward program application unit can use the generation AI to conduct targeted promotions and apply reward programs to specific consumer groups. For example, a reward program for organic food can be offered to a health-conscious consumer group. It can also offer a reward program for products popular with a consumer group of a specific age or gender. It can also offer a reward program for products popular in a specific region to a consumer group living in that region. In this way, applying reward programs to specific consumer groups can increase consumer loyalty.

[0065] The price adjustment unit uses the emotion estimation function to analyze how consumers feel about products with an approaching expiration date and adjust the price based on that emotion. For example, if a consumer feels anxious or worried about a product with an approaching expiration date, the price can be significantly reduced to alleviate that emotion. Specifically, if a consumer feels anxious about a product with an expiration date within one week, the price of that product can be discounted by 30%. Also, if a consumer shows interest in a product with an approaching expiration date, the price of that product can be slightly discounted. Furthermore, if a consumer feels positive about a product with an approaching expiration date, the price of that product can be set at the normal price. In this way, adjusting the price based on the consumer's emotion can increase consumer purchasing motivation.

[0066] The reward program application unit can use the generation AI to link the reward program with specific events and campaigns. For example, the reward program can be provided in conjunction with a Christmas sale. It can also be provided in conjunction with specific events such as Valentine's Day and Halloween. Furthermore, the reward program can be strengthened during specific campaign periods. In this way, linking the reward program with events and campaigns can increase consumer purchasing motivation.

[0067] The store visit prediction analysis unit can use the emotion estimation function to analyze consumer emotions under specific weather conditions and adjust prices based on those emotions. For example, because consumers tend to feel depressed on rainy days, discounts can be offered to increase their purchasing motivation. Specifically, hot drinks and soups can be discounted on rainy days. Also, because consumers are more likely to go out on sunny days, discounts can be offered on products that can be enjoyed outdoors. Furthermore, because consumers tend to stay indoors on snowy days, discounts can be offered on delivery service prices. In this way, adjusting prices based on consumer emotions can increase consumer purchasing motivation.

[0068] The reward program application unit uses the emotion estimation function to monitor in real time how customers feel about the reward program and can conduct promotions based on those emotions. For example, if a customer has positive emotions about the reward program, additional rewards can be provided on the spot. Specifically, if a customer is satisfied with the reward program, a coupon that can be used for the next purchase can be provided on the spot. Also, if a customer shows interest in a reward program, detailed information about the program can be provided. Furthermore, if a customer is dissatisfied with the reward program, the cause can be identified and measures to improve the program can be taken. This makes it possible to improve customer satisfaction by conducting promotions based on customer emotions.

[0069] The price adjustment unit uses generation AI to monitor products approaching their expiration date in real time and dynamically adjust prices according to inventory status. For example, generation AI can be used to monitor products approaching their expiration date in real time, and if there is a large amount of inventory, the price can be significantly reduced. Specifically, if there is a large amount of product remaining with an expiration date of within three days, the price of that product can be set at a 50% discount. If inventory is low, the price can also be set at the normal rate. Furthermore, if a specific product remains unsold, the price of that product can be gradually reduced. This allows for efficient inventory management by dynamically adjusting prices according to inventory status.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The price adjustment unit adjusts the price based on the expiration date. For example, products with an expiration date of less than one week will receive a 10% discount, and products with an expiration date of less than three days will receive a 20% discount, and so on. Step 2: The weather analysis department analyzes weather data. For example, on rainy days, fewer customers visit the store, so sales can be promoted by further lowering prices. Step 3: The store visitor prediction analysis unit analyzes the store visitor prediction data. For example, it analyzes the expected number of customers and time of visit based on past store visit history and event information, and adjusts prices accordingly. Step 4: The reward program application unit applies the reward program. For example, it automatically issues a coupon that can be used for the next purchase to a customer who has reached a certain purchase amount.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 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 price adjustment unit that adjusts the price based on the expiration date; a weather analysis unit that analyzes weather data; a store visitor prediction analysis unit that analyzes store visitor prediction data; a reward program application unit that applies a reward program; A system characterized by:

2. The price adjustment unit Use generative AI to monitor products approaching their expiration date in real time and dynamically adjust prices based on stock availability. The system of claim 1 .

3. The weather analysis unit Based on the weather data, generative AI is used to analyze consumer purchasing patterns under specific weather conditions and adjust the prices accordingly. The system of claim 1 .

4. The store visit prediction analysis unit Dynamically adjust prices during peak and off-peak periods using generative AI based on predicted visitor numbers and time data. The system of claim 1 .

5. The reward program application unit: Using generative AI, analyze customer purchase history and apply the reward program individually. The system of claim 1 .

6. The price adjustment unit Using emotion estimation, the company analyzes how consumers feel about products that are nearing their expiration date and adjusts the price accordingly. The system of claim 1 .

7. The reward program application unit: Using a sentiment estimation function to analyze how customers feel about the rewards program and adjust the rewards program based on that sentiment. The system of claim 1 .

8. The store visit prediction analysis unit Using sentiment estimation to analyze consumer sentiment under specific weather conditions and adjust the price accordingly The system of claim 1 .

Images