System

Abstract

An object of a system according to an embodiment is to optimize business of a shop and to eradicate food waste.SOLUTION: A system according to an embodiment includes a AI camera, a date reference part, a price change part, and a sound generation part. The AI camera automatically detects unsold commodities. The processor is configured to determine a food waste based on the unsold commodity detected by the AI camera. The price change unit changes the price in real time based on the food waste determined by the data check unit. The voice generation part generates a voice from the script on the basis of the price information changed by the price change part and broadcasts a AI announcement.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there is a risk of food waste, such as unsold goods, due to complex business processes and fragmented systems in stores.

[0005] The system according to the embodiment aims to optimize store operations and eliminate food waste. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI camera, a data reference unit, a price change unit, and a voice generation unit. The AI ​​camera automatically detects unsold items. The data reference unit references various store data based on the unsold items detected by the AI ​​camera and determines food waste. The price change unit changes prices in real time based on POS data and inventory information, in accordance with the food waste determined by the data reference unit. The voice generation unit generates voice from a script based on the price information changed by the price change unit, and broadcasts an AI announcement. [Effects of the Invention]

[0007] The system according to the embodiment can optimize store operations and eliminate 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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The store multimodal AI system according to an embodiment of the present invention uses AI cameras to automatically detect unsold items, query various store data to determine food waste, adjust prices in real time based on POS data and inventory information, and generate voices from scripts to broadcast AI announcements. As a result, the store multimodal AI system can optimize store business processes and eliminate food waste.

[0029] The store multimodal AI system according to the embodiment includes an AI camera, a data reference unit, a price change unit, and a voice generation unit. The AI ​​camera automatically detects unsold items. For example, the AI ​​camera analyzes video footage of shelves to detect items that have remained unsold for a certain period of time. The AI ​​camera can also analyze video footage from inside refrigerators to identify unsold items. Furthermore, the AI ​​camera can use machine learning algorithms to learn the characteristics of unsold items and improve accuracy. The data reference unit references various store data to determine food waste based on the unsold items detected by the AI ​​camera. For example, the data reference unit references inventory data to check whether unsold items are out of stock. The data reference unit can also reference sales data to check the sales history of unsold items. Furthermore, the data reference unit references store business data to comprehensively evaluate the sales status of unsold items. The price change unit changes prices in real time based on POS data and inventory information, based on the food waste determined by the data reference unit. For example, if an unsold item is detected, the price change unit automatically discounts the price of the item. The price change unit can also adjust the prices of items with high inventory based on inventory information. Furthermore, the price change unit can change prices for sales promotions based on POS data. The voice generation unit generates voice from a script based on the price information changed by the price change unit and broadcasts an AI announcement. For example, the voice generation unit announces discount information for unsold items. The voice generation unit can also provide product location and discount information based on script data. Furthermore, the voice generation unit can generate natural voices using the generation AI and engage in conversations with customers. This allows the store multimodal AI system according to the embodiment to optimize store business processes and eliminate food waste. For example, food waste can be reduced by detecting unsold items early and appropriately changing their prices. Furthermore, the provision of information within the store can be made more efficient through AI announcements and conversations with customers.

[0030] When detecting unsold items, AI cameras can simultaneously evaluate the freshness and quality of the products, allowing them to prioritize the detection of products that are deteriorating. For example, AI cameras analyze video footage of product shelves and use algorithms to evaluate the freshness and quality of products. For example, they can detect changes in color or shape to identify products that are deteriorating. AI cameras can also prioritize the detection of products that are deteriorating based on their expiration dates. Furthermore, AI cameras can evaluate the appearance and packaging condition of products to identify products that are deteriorating. This allows for the prioritization of detecting products that are deteriorating, further reducing food waste.

[0031] When detecting unsold items, AI cameras can also analyze product placement and display methods and suggest optimal display methods. For example, AI cameras analyze footage of product shelves and use algorithms to evaluate product placement and display methods. For example, they can analyze whether product placement is affecting unsold items. AI cameras can also suggest optimal display methods based on how products are displayed. Furthermore, AI cameras can evaluate product visibility and accessibility and suggest optimal display methods. This makes it possible to reduce unsold items by optimizing product placement and display methods.

[0032] When detecting unsold items, AI cameras can also use audio or temperature sensors to evaluate the condition of products from multiple angles. For example, when analyzing video footage of product shelves, AI cameras can also use audio sensors to evaluate the condition of products from multiple angles. For example, they can detect the sound of product packaging being torn. AI cameras can also use temperature sensors to measure the temperature of products. For example, they can monitor the temperature of products in a refrigerator and evaluate whether they are being stored at the appropriate temperature. Furthermore, AI cameras can combine data from audio and temperature sensors to comprehensively evaluate the condition of products. This allows for a multi-angle evaluation of product condition, enabling more accurate detection of unsold items.

[0033] The data reference unit can also take into account past sales data or seasonal trends to make more accurate food waste judgments. The data reference unit, for example, analyzes seasonal trends based on past sales data and predicts the risk of food waste. For example, it identifies products that are likely to remain unsold in a particular season. The data reference unit can also identify products that are at high risk of remaining unsold based on past sales data. Furthermore, the data reference unit can evaluate the risk of remaining unsold based on seasonal trends and take appropriate measures. This makes it possible to make more accurate food waste judgments by taking into account past sales data and seasonal trends.

[0034] The data reference unit can predict the risk of future unsold items based on the store data reference results and take measures in advance. The data reference unit, for example, develops an algorithm to predict the risk of future unsold items based on the store data reference results. For example, it identifies products with a high risk of unsold items based on past data. The data reference unit can also take sales promotion measures for products with a high risk of unsold items based on the store data. Furthermore, the data reference unit can predict the risk of unsold items and change prices or carry out promotions in advance, thereby preventing food waste. In this way, food waste can be prevented by predicting the risk of future unsold items and taking measures in advance.

[0035] The price change unit can collect price information from competing stores in real time when changing prices, allowing for competitive pricing. The price change unit, for example, builds a system that collects price information from competing stores in real time and reflects it in its own store's pricing. For example, it uses web scraping technology to collect competitor price information. The price change unit can also set competitive prices based on the price information from competing stores. Furthermore, the price change unit can monitor price information from competing stores in real time and optimize the timing of price changes. This allows for competitive pricing based on the price information from competing stores, maximizing sales.

[0036] The price change unit can analyze the price change history, identify the most effective price change pattern, and reflect it in the next price change. For example, the price change unit stores the price change history in a database and develops an algorithm to identify effective price change patterns. For example, it analyzes past price changes and sales data. The price change unit can also reflect this in the next price change based on the price change history. Furthermore, the price change unit can optimize the timing and range of price changes based on effective price change patterns. This allows the price change history to be analyzed and effective price change patterns to be identified and reflected in the next price change.

[0037] When generating voice, the voice generation unit generates voice customized according to the age or gender of the customer, allowing for more personalized announcements. The voice generation unit, for example, builds a system that generates voice customized according to the age and gender of the customer. For example, it generates a bright tone for younger people and a calm tone for older people. The voice generation unit can also adjust the content of the announcement based on the age and gender of the customer. Furthermore, the voice generation unit can make personalized announcements based on customer attribute data. This allows for more personalized announcements by generating voice customized according to the age and gender of the customer.

[0038] The voice generation unit can optimize the content of AI announcements based on past customer reaction data, allowing for effective information provision. The voice generation unit, for example, builds a system that optimizes the content of AI announcements based on past customer reaction data. For example, it prioritizes the use of announcement content that has received a positive customer reaction. The voice generation unit can also adjust the content of announcements based on customer reaction data. Furthermore, the voice generation unit can collect customer reaction data in real time and optimize the content of announcements. This makes it possible to provide effective information by optimizing the content of AI announcements based on past customer reaction data.

[0039] The speech generation unit can automatically translate the content of AI announcements into different languages, achieving multilingual support. The speech generation unit, for example, builds a system that automatically translates the content of AI announcements into different languages. For example, it supports multiple languages ​​such as English, Japanese, and Chinese. The speech generation unit can also use machine translation algorithms to translate the content of announcements with high accuracy. Furthermore, the speech generation unit can generate speech corresponding to each language based on the translated content of the announcement. This makes it possible to make multilingual announcements by automatically translating into different languages.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The store multimodal AI system can also be equipped with a proposal unit that analyzes customer purchase history and makes personalized product proposals to individual customers. For example, the proposal unit can identify a customer's favorite products based on past purchase history and offer special discounts to that customer if a particular product remains unsold. The proposal unit can also analyze a customer's purchasing patterns, predict which products the customer is likely to purchase the next time they visit the store, and place those products in prominent locations in advance. Furthermore, the proposal unit can suggest related products based on the customer's purchase history and promote cross-selling. This can increase sales by making personalized product proposals to each customer.

[0042] The store multimodal AI system can also be equipped with a behavior analysis unit that monitors customer purchasing behavior in real time and provides promotions based on specific behavioral patterns. For example, if a customer lingers in front of a particular product shelf for a long time, the behavior analysis unit can provide a discount coupon for that product. Furthermore, if a customer picks up and compares multiple products, the behavior analysis unit can provide information on related products. Furthermore, the behavior analysis unit can analyze customer movement patterns, grasp the congestion situation in the store in real time, and provide guidance to avoid congestion. This can increase purchasing motivation by providing promotions based on customer purchasing behavior.

[0043] The store multimodal AI system can also be equipped with a layout optimization unit that optimizes the store layout based on customer purchase history and behavioral data. For example, the layout optimization unit analyzes customer purchase history and optimizes the placement of popular products. The layout optimization unit can also suggest layout changes to avoid congestion based on customer movement data. Furthermore, the layout optimization unit can take seasonal trends into account and place seasonal products in prominent locations. This can improve the customer shopping experience by optimizing the store layout.

[0044] The store multimodal AI system can also be equipped with an inventory optimization unit that optimizes inventory management based on customer purchase history and behavioral data. For example, the inventory optimization unit analyzes customer purchase history and makes demand forecasts. The inventory optimization unit can also evaluate the risk of certain products remaining unsold based on customer behavioral data. Furthermore, the inventory optimization unit can create ordering plans to maintain appropriate inventory levels based on demand forecasts. This can optimize inventory management and reduce food waste.

[0045] The store multimodal AI system can also be equipped with a marketing optimization unit that optimizes the store's marketing strategy based on customer purchase history and behavioral data. For example, the marketing optimization unit can analyze customer purchase history and design effective promotional campaigns. The marketing optimization unit can also identify target customer segments based on customer behavioral data. Furthermore, the marketing optimization unit can take seasonal trends into account and develop appropriate marketing strategies. This can improve sales by optimizing the store's marketing strategy.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The AI ​​camera automatically detects unsold items. For example, the AI ​​camera analyzes video footage of shelves to detect items that have remained unsold for a certain period of time. The AI ​​camera can also analyze video footage from inside refrigerators to identify unsold items. Furthermore, the AI ​​camera can use machine learning algorithms to learn the characteristics of unsold items and improve accuracy. Step 2: The data reference unit references various store data based on the unsold items detected by the AI ​​camera to determine food waste. For example, the data reference unit references inventory data to check whether unsold items are out of stock. The data reference unit can also reference sales data to check the sales history of unsold items. Furthermore, the data reference unit can reference store business data to comprehensively evaluate the sales status of unsold items. Step 3: The price change unit changes prices in real time based on the food waste determined by the data reference unit and the POS data and inventory information. For example, if the price change unit detects an unsold item, it automatically discounts the price of that item. The price change unit can also adjust the prices of items with high inventory based on the inventory information. Furthermore, the price change unit can change prices for sales promotions based on the POS data. Step 4: The voice generation unit generates voice from the script based on the price information changed by the price change unit and broadcasts an AI announcement. For example, the voice generation unit announces discount information for unsold items. The voice generation unit can also provide product location and discount information based on the script data. Furthermore, the voice generation unit uses generation AI to generate natural voices and can converse with customers.

[0048] (Example 2) The store multimodal AI system according to an embodiment of the present invention uses AI cameras to automatically detect unsold items, query various store data to determine food waste, adjust prices in real time based on POS data and inventory information, and generate voices from scripts to broadcast AI announcements. As a result, the store multimodal AI system can optimize store business processes and eliminate food waste.

[0049] The store multimodal AI system according to the embodiment includes an AI camera, a data reference unit, a price change unit, and a voice generation unit. The AI ​​camera automatically detects unsold items. For example, the AI ​​camera analyzes video footage of shelves to detect items that have remained unsold for a certain period of time. The AI ​​camera can also analyze video footage from inside refrigerators to identify unsold items. Furthermore, the AI ​​camera can use machine learning algorithms to learn the characteristics of unsold items and improve accuracy. The data reference unit references various store data to determine food waste based on the unsold items detected by the AI ​​camera. For example, the data reference unit references inventory data to check whether unsold items are out of stock. The data reference unit can also reference sales data to check the sales history of unsold items. Furthermore, the data reference unit references store business data to comprehensively evaluate the sales status of unsold items. The price change unit changes prices in real time based on POS data and inventory information, based on the food waste determined by the data reference unit. For example, if an unsold item is detected, the price change unit automatically discounts the price of the item. The price change unit can also adjust the prices of items with high inventory based on inventory information. Furthermore, the price change unit can change prices for sales promotions based on POS data. The voice generation unit generates voice from a script based on the price information changed by the price change unit and broadcasts an AI announcement. For example, the voice generation unit announces discount information for unsold items. The voice generation unit can also provide product location and discount information based on script data. Furthermore, the voice generation unit can generate natural voices using the generation AI and engage in conversations with customers. This allows the store multimodal AI system according to the embodiment to optimize store business processes and eliminate food waste. For example, food waste can be reduced by detecting unsold items early and appropriately changing their prices. Furthermore, the provision of information within the store can be made more efficient through AI announcements and conversations with customers.

[0050] When detecting unsold items, AI cameras can simultaneously evaluate the freshness and quality of the products, allowing them to prioritize the detection of products that are deteriorating. For example, AI cameras analyze video footage of product shelves and use algorithms to evaluate the freshness and quality of products. For example, they can detect changes in color or shape to identify products that are deteriorating. AI cameras can also prioritize the detection of products that are deteriorating based on their expiration dates. Furthermore, AI cameras can evaluate the appearance and packaging condition of products to identify products that are deteriorating. This allows for the prioritization of detecting products that are deteriorating, further reducing food waste.

[0051] When detecting unsold items, AI cameras can also analyze product placement and display methods and suggest optimal display methods. For example, AI cameras analyze footage of product shelves and use algorithms to evaluate product placement and display methods. For example, they can analyze whether product placement is affecting unsold items. AI cameras can also suggest optimal display methods based on how products are displayed. Furthermore, AI cameras can evaluate product visibility and accessibility and suggest optimal display methods. This makes it possible to reduce unsold items by optimizing product placement and display methods.

[0052] When detecting unsold items, AI cameras can also use audio or temperature sensors to evaluate the condition of products from multiple angles. For example, when analyzing video footage of product shelves, AI cameras can also use audio sensors to evaluate the condition of products from multiple angles. For example, they can detect the sound of product packaging being torn. AI cameras can also use temperature sensors to measure the temperature of products. For example, they can monitor the temperature of products in a refrigerator and evaluate whether they are being stored at the appropriate temperature. Furthermore, AI cameras can combine data from audio and temperature sensors to comprehensively evaluate the condition of products. This allows for a multi-angle evaluation of product condition, enabling more accurate detection of unsold items.

[0053] The data reference unit can also take into account past sales data or seasonal trends to make more accurate food waste judgments. The data reference unit, for example, analyzes seasonal trends based on past sales data and predicts the risk of food waste. For example, it identifies products that are likely to remain unsold in a particular season. The data reference unit can also identify products that are at high risk of remaining unsold based on past sales data. Furthermore, the data reference unit can evaluate the risk of remaining unsold based on seasonal trends and take appropriate measures. This makes it possible to make more accurate food waste judgments by taking into account past sales data and seasonal trends.

[0054] The data reference unit can predict the risk of future unsold items based on the store data reference results and take measures in advance. The data reference unit, for example, develops an algorithm to predict the risk of future unsold items based on the store data reference results. For example, it identifies products with a high risk of unsold items based on past data. The data reference unit can also take sales promotion measures for products with a high risk of unsold items based on the store data. Furthermore, the data reference unit can predict the risk of unsold items and change prices or carry out promotions in advance, thereby preventing food waste. In this way, food waste can be prevented by predicting the risk of future unsold items and taking measures in advance.

[0055] The price change unit can collect price information from competing stores in real time when changing prices, allowing for competitive pricing. The price change unit, for example, builds a system that collects price information from competing stores in real time and reflects it in its own store's pricing. For example, it uses web scraping technology to collect competitor price information. The price change unit can also set competitive prices based on the price information from competing stores. Furthermore, the price change unit can monitor price information from competing stores in real time and optimize the timing of price changes. This allows for competitive pricing based on the price information from competing stores, maximizing sales.

[0056] The price change unit can analyze the price change history, identify the most effective price change pattern, and reflect it in the next price change. For example, the price change unit stores the price change history in a database and develops an algorithm to identify effective price change patterns. For example, it analyzes past price changes and sales data. The price change unit can also reflect this in the next price change based on the price change history. Furthermore, the price change unit can optimize the timing and range of price changes based on effective price change patterns. This allows the price change history to be analyzed and effective price change patterns to be identified and reflected in the next price change.

[0057] When generating voice, the voice generation unit generates voice customized according to the age or gender of the customer, allowing for more personalized announcements. The voice generation unit, for example, builds a system that generates voice customized according to the age and gender of the customer. For example, it generates a bright tone for younger people and a calm tone for older people. The voice generation unit can also adjust the content of the announcement based on the age and gender of the customer. Furthermore, the voice generation unit can make personalized announcements based on customer attribute data. This allows for more personalized announcements by generating voice customized according to the age and gender of the customer.

[0058] The voice generation unit can optimize the content of AI announcements based on past customer reaction data, allowing for effective information provision. The voice generation unit, for example, builds a system that optimizes the content of AI announcements based on past customer reaction data. For example, it prioritizes the use of announcement content that has received a positive customer reaction. The voice generation unit can also adjust the content of announcements based on customer reaction data. Furthermore, the voice generation unit can collect customer reaction data in real time and optimize the content of announcements. This makes it possible to provide effective information by optimizing the content of AI announcements based on past customer reaction data.

[0059] The speech generation unit can automatically translate the content of AI announcements into different languages, achieving multilingual support. The speech generation unit, for example, builds a system that automatically translates the content of AI announcements into different languages. For example, it supports multiple languages ​​such as English, Japanese, and Chinese. The speech generation unit can also use machine translation algorithms to translate the content of announcements with high accuracy. Furthermore, the speech generation unit can generate speech corresponding to each language based on the translated content of the announcement. This makes it possible to make multilingual announcements by automatically translating into different languages.

[0060] The voice generation unit can use the emotion estimation function to analyze customer emotions toward AI announcements in real time and generate announcement content that elicits positive emotions. The voice generation unit, for example, uses the emotion estimation function to build a system that analyzes customer emotions toward AI announcements in real time. For example, it analyzes the customer's facial expressions and voice and calculates an emotion score. The voice generation unit can also generate announcement content that elicits positive emotions based on customer emotion data. Furthermore, the voice generation unit can use an emotion estimation algorithm to monitor customer emotions in real time and adjust the announcement content. This makes it possible to analyze customer emotions in real time and generate announcement content that elicits positive emotions, thereby improving customer satisfaction.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The store multimodal AI system can also be equipped with a proposal unit that analyzes customer purchase history and makes personalized product proposals to individual customers. For example, the proposal unit can identify a customer's favorite products based on past purchase history and offer special discounts to that customer if a particular product remains unsold. The proposal unit can also analyze a customer's purchasing patterns, predict which products the customer is likely to purchase the next time they visit the store, and place those products in prominent locations in advance. Furthermore, the proposal unit can suggest related products based on the customer's purchase history and promote cross-selling. This can increase sales by making personalized product proposals to each customer.

[0063] The store multimodal AI system can also be equipped with an environmental adjustment unit that estimates customer emotions and adjusts the background music and lighting in the store based on the estimated emotions. For example, the environmental adjustment unit can analyze the customer's facial expressions and voice and play calming background music if the customer is relaxed. The environmental adjustment unit can also enhance the relaxation effect by softening the lighting if the customer is feeling stressed. Furthermore, the environmental adjustment unit can adjust the temperature and fragrance in the store based on the customer's emotional data, providing a comfortable shopping environment. This makes it possible to improve customer satisfaction by adjusting the environment according to the customer's emotions.

[0064] The store multimodal AI system can also be equipped with a behavior analysis unit that monitors customer purchasing behavior in real time and provides promotions based on specific behavioral patterns. For example, if a customer lingers in front of a particular product shelf for a long time, the behavior analysis unit can provide a discount coupon for that product. Furthermore, if a customer picks up and compares multiple products, the behavior analysis unit can provide information on related products. Furthermore, the behavior analysis unit can analyze customer movement patterns, grasp the congestion situation in the store in real time, and provide guidance to avoid congestion. This can increase purchasing motivation by providing promotions based on customer purchasing behavior.

[0065] The store multimodal AI system can also be equipped with a customer service adjustment unit that estimates the customer's emotions and adjusts the customer service robot's response based on the estimated emotions. For example, if the customer is excited, the customer service adjustment unit will speak to them in a calm tone. If the customer is confused, the customer service adjustment unit can also reassure them by carefully explaining things. Furthermore, the customer service adjustment unit can suggest products at the appropriate time based on the customer's emotional data. This makes it possible to improve customer satisfaction by providing customer service that is appropriate for the customer's emotions.

[0066] The store multimodal AI system can also be equipped with a layout optimization unit that optimizes the store layout based on customer purchase history and behavioral data. For example, the layout optimization unit analyzes customer purchase history and optimizes the placement of popular products. The layout optimization unit can also suggest layout changes to avoid congestion based on customer movement data. Furthermore, the layout optimization unit can take seasonal trends into account and place seasonal products in prominent locations. This can improve the customer shopping experience by optimizing the store layout.

[0067] The store multimodal AI system can further include a signage adjustment unit that estimates customer emotions and changes the content of the digital signage based on the estimated emotions. For example, if a customer shows interest, the signage adjustment unit can display advertisements for related products. If a customer appears bored, the signage adjustment unit can also display entertaining content. Furthermore, the signage adjustment unit can promote specific products or services based on the customer's emotional data. This makes it possible to attract customer interest by providing digital signage content that matches the customer's emotions.

[0068] The store multimodal AI system can also be equipped with an inventory optimization unit that optimizes inventory management based on customer purchase history and behavioral data. For example, the inventory optimization unit analyzes customer purchase history and makes demand forecasts. The inventory optimization unit can also evaluate the risk of certain products remaining unsold based on customer behavioral data. Furthermore, the inventory optimization unit can create ordering plans to maintain appropriate inventory levels based on demand forecasts. This can optimize inventory management and reduce food waste.

[0069] The store multimodal AI system can also be equipped with a cash register optimization unit that estimates customer emotions and takes measures to shorten checkout waiting times based on the estimated emotions. For example, if a customer appears irritated, the cash register optimization unit opens an additional cash register. Alternatively, if the customer appears relaxed, the cash register optimization unit can continue normal cash register operations. Furthermore, the cash register optimization unit can monitor checkout waiting times in real time based on customer emotion data and take appropriate measures. This makes it possible to improve customer satisfaction by optimizing checkout waiting times according to customer emotions.

[0070] The store multimodal AI system can also be equipped with a marketing optimization unit that optimizes the store's marketing strategy based on customer purchase history and behavioral data. For example, the marketing optimization unit can analyze customer purchase history and design effective promotional campaigns. The marketing optimization unit can also identify target customer segments based on customer behavioral data. Furthermore, the marketing optimization unit can take seasonal trends into account and develop appropriate marketing strategies. This can improve sales by optimizing the store's marketing strategy.

[0071] The store multimodal AI system can further include a guidance display adjustment unit that estimates customer emotions and adjusts in-store guidance displays based on the estimated emotions. For example, if a customer is lost, the guidance display adjustment unit provides easy-to-understand guidance displays. Also, if a customer is in a hurry, the guidance display adjustment unit can provide guidance on the shortest route. Furthermore, the guidance display adjustment unit can provide appropriate guidance displays in real time based on customer emotion data. This can improve customer convenience by providing guidance displays that correspond to the customer's emotions.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The AI ​​camera automatically detects unsold items. For example, the AI ​​camera analyzes video footage of shelves to detect items that have remained unsold for a certain period of time. The AI ​​camera can also analyze video footage from inside refrigerators to identify unsold items. Furthermore, the AI ​​camera can use machine learning algorithms to learn the characteristics of unsold items and improve accuracy. Step 2: The data reference unit references various store data based on the unsold items detected by the AI ​​camera to determine food waste. For example, the data reference unit references inventory data to check whether unsold items are out of stock. The data reference unit can also reference sales data to check the sales history of unsold items. Furthermore, the data reference unit can reference store business data to comprehensively evaluate the sales status of unsold items. Step 3: The price change unit changes prices in real time based on the food waste determined by the data reference unit and the POS data and inventory information. For example, if the price change unit detects an unsold item, it automatically discounts the price of that item. The price change unit can also adjust the prices of items with high inventory based on the inventory information. Furthermore, the price change unit can change prices for sales promotions based on the POS data. Step 4: The voice generation unit generates voice from the script based on the price information changed by the price change unit and broadcasts an AI announcement. For example, the voice generation unit announces discount information for unsold items. The voice generation unit can also provide product location and discount information based on the script data. Furthermore, the voice generation unit uses generation AI to generate natural voices and can converse with customers.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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. AI cameras that automatically detect unsold items, a data reference unit that references various store data based on the unsold items detected by the AI ​​camera and determines food waste; a price change unit that changes prices in real time based on the food loss determined by the data reference unit and the POS data and inventory information; a voice generating unit that generates voice from a script based on the price information changed by the price changing unit and broadcasts the AI ​​announcement. A system characterized by:

2. The AI ​​camera is When detecting unsold products, the freshness and quality of the products are also evaluated at the same time, and products that are deteriorating are detected first.

2. The system of claim 1.

3. The data reference unit Taking into account past sales data and seasonal trends, food waste can be determined more accurately.

2. The system of claim 1.

4. The price change unit: When changing prices, price information from competing stores is collected in real time to set competitive prices.

2. The system of claim 1.

5. The voice generation unit When generating voice, create customized voices based on the customer's age or gender for more personalized announcements 2. The system of claim 1.

6. The voice generation unit Using emotion estimation functionality, the system analyzes customer emotions toward the AI ​​announcement in real time and generates announcement content that elicits positive emotions.

2. The system of claim 1.

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