system

Abstract

We provide the system. [Solution] A means of obtaining inventory information and identifying unsold items, A means of generating cooking procedures based on identified unsold items, Means for providing the generated cooking procedure through a visual medium and a communication device, A means of collecting consumer input and improving cooking procedures, A means of obtaining and electronically displaying identification information of products, including unsold items, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] One of the problems in inventory management in enterprises is the economic loss and increased waste caused by the accumulation of unsold products. In particular, products with a short shelf life have a high risk of remaining unsold, and there is a need to promote sales at an appropriate timing. In addition, there is a need for a mechanism to stimulate the purchasing desire by providing consumers with new cooking methods and recipes. Furthermore, there is also a problem that it is difficult to propose recipes reflecting consumers' preferences with conventional methods.

Means for Solving the Problems

[0005] This invention provides a system that acquires inventory information in real time and identifies unsold products. Based on the identified unsold products, it uses a generation AI to generate simple and delicious cooking instructions. It also includes a mechanism to suggest the generated cooking instructions through visual media in stores and electronic devices used by consumers, and to collect feedback from consumers. By improving the accuracy of the cooking instructions based on this feedback, it is possible to encourage the consumption of unsold products and improve the efficiency of inventory management for companies.

[0006] "Inventory information" refers to data regarding the storage status, quantity, and expiration date of products, and is information necessary for business management.

[0007] "Unsold items" are products that remain in stores without being purchased, and are at risk of being discarded or discounted if left as is.

[0008] "Means of identification" refers to methods and technologies for selecting and identifying unsold products through data analysis and algorithms.

[0009] "Cooking instructions" refer to specific steps or recipes that demonstrate how to use a particular product, and are intended to offer consumers new culinary suggestions.

[0010] "Visual media" refers to devices used to visually display information, including digital signage and monitors.

[0011] An "electronic terminal" is a digital device used to display and operate information, including devices such as smartphones and tablets.

[0012] "Consumer input" refers to feedback and evaluations provided by consumers, including their opinions and impressions of the products and services offered. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. <e000077>It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] [[ID=^^^]]It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0017] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] One embodiment of the present invention is an information provision system that promotes the efficient utilization of unsold goods. This system consists of an inventory management information system, a generation processing server, a digital signage terminal, and an electronic terminal for interacting with consumers.

[0035] Acquisition and analysis of inventory information

[0036] The server works in conjunction with the inventory management information system to obtain real-time information on unsold products. The obtained data is then used by an AI algorithm to identify the unsold items. The identified unsold products are then passed to the recipe generation module on the generation processing server.

[0037] Recipe generation and provision

[0038] The generation processing server generates cooking instructions for consumers based on information about identified unsold products. These instructions are automatically designed to be easy for consumers to prepare and to be delicious. The generated instructions are sent to digital signage terminals installed in the store and displayed as a visual promotion for the unsold products.

[0039] Consumer interface and feedback collection

[0040] Users can receive generated recipes via their smartphones or other electronic devices. Visual instructions and ingredient lists can be viewed through the app. Furthermore, users can provide feedback through the app after cooking, and the server collects this information to improve the accuracy of the algorithm.

[0041] Specific example

[0042] In a supermarket, a server detects from inventory data that a large quantity of tomatoes and herb chicken remain unsold that week. Based on this, the AI ​​generates a cooking recipe for "Tomato Herb Chicken Pasta." Digital signage is used to promote this recipe and related products within the store. Users can view the recipe through an app, purchase the ingredients, and actually cook the dish. Feedback after cooking contributes to future algorithm improvements.

[0043] In this way, it is possible to efficiently utilize unsold products and increase consumer purchasing intent.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server periodically retrieves the latest inventory data from the inventory management information system via an API. This allows it to understand the current storage status and potential unsold status of each product.

[0047] Step 2:

[0048] The server analyzes the acquired inventory data to identify unsold items. In this process, items with approaching expiration dates or those with low past inventory turnover rates are selected as unsold.

[0049] Step 3:

[0050] The server sends data based on unsold products to a generation processing module, which then generates cooking instructions using those products. During this process, an AI algorithm is used to create easy-to-prepare and delicious recipes for consumers.

[0051] Step 4:

[0052] The server transmits the generated cooking instructions to digital signage for in-store display. The displayed content includes selected leftover products and recommended recipes using them.

[0053] Step 5:

[0054] The server simultaneously sends information about the generated recipe to the device app as a push notification. This allows users to receive new cooking ideas in real time.

[0055] Step 6:

[0056] The user opens the app and checks the details of the received recipe. The recipe includes the necessary ingredients and cooking instructions, and the user can add it directly to their shopping list.

[0057] Step 7:

[0058] After the user completes cooking, they can provide feedback on the recipe within the app. This feedback can include a rating of the recipe and suggestions for improvement.

[0059] Step 8:

[0060] The terminal sends the input feedback to the server. The server receives this feedback, saves it in a database, and optimizes the algorithm by incorporating it into the next recipe generation.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] One challenge is that unsold products cannot be efficiently utilized and end up being discarded, resulting in economic losses and environmental problems. Furthermore, consumers are not provided with fresh and useful information, leading to a decrease in their willingness to purchase.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for acquiring inventory information and identifying unsold products, means for generating cooking instructions based on the identified unsold products, and means for providing the generated cooking instructions through a visual medium and an electronic device. This makes it possible to efficiently utilize unsold products and improve consumer purchasing intent.

[0066] "Inventory information" refers to data that shows the inventory status of products, and includes real-time information necessary to identify unsold products.

[0067] "Unsold products" refer to goods that remain unconsumed compared to the planned sales volume, and their efficient utilization is required.

[0068] "Cooking instructions" are guidelines generated based on unsold products, containing the steps consumers need to actually prepare the food.

[0069] "Visual media" refers to digital signage and other display devices, and is a means of providing information to consumers visually.

[0070] "Electronic devices" refer to devices such as smartphones and tablets that consumers use to receive cooking instructions.

[0071] "Consumer responses" refer to information that shows feedback and opinions from consumers after they have cooked according to the provided cooking instructions, and these contribute to future improvements.

[0072] One embodiment of the present invention is an information provision system for efficiently utilizing unsold products. This system consists of an inventory management information system, a generation processing server, a digital signage terminal, and an electronic terminal for interacting with consumers.

[0073] The server works in conjunction with the inventory management information system to periodically retrieve real-time inventory information. This information shows the inventory status of products and is used to identify unsold items. Upon receiving the inventory information, the server uses an AI algorithm to analyze the data, understand trends in unsold inventory, and list products that meet specific criteria.

[0074] Next, the generation server generates cooking instructions based on the data of the identified unsold products. At this stage, a generation AI model is used to create specific prompt statements. These prompt statements include specific instructions such as "Create a recipe for a dish using tomatoes and herb chicken," and cooking steps are created based on these.

[0075] The generated cooking instructions are transmitted to a digital signage terminal. This terminal displays the recipe as a visual promotion within the store. Visual media such as product images and cooking images are used to attract consumers' attention.

[0076] Users can receive recipes generated through the app using electronic devices such as smartphones and tablets. They can view visual instructions and ingredient lists. Furthermore, users can provide feedback via the app after cooking; this information is returned to the server and used to improve future algorithms.

[0077] As a concrete example, if tomatoes and herb chicken are unsold at a certain store, the server will detect this and generate a recipe called "Tomato Herb Chicken Pasta." This recipe will be promoted on digital signage, and users can check it in the app and actually cook it. The prompt given to the generation AI model is "Please come up with a delicious recipe using tomatoes and herb chicken."

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server retrieves real-time inventory information from the inventory management system. This information includes the stock quantity and details of unsold items for each product. Based on the input inventory information, the server analyzes the data and generates a list of unsold products. This list identifies products with low sales rates and excess inventory. A dataset of unsold products is generated as output.

[0081] Step 2:

[0082] The server passes the dataset of unsold products obtained in Step 1 to an AI algorithm for analysis. In this process, a generative AI model is used to create prompt sentences for cooking instructions related to the identified unsold products. The input is the dataset of unsold products, and the prompt sentences include the instruction "Please think of cooking steps using the selected ingredients." As a result, a draft recipe proposed by the AI ​​model is output.

[0083] Step 3:

[0084] The generation processing server sends the prompt text created in step 2 to the AI ​​model to generate specific cooking instructions. Based on the input prompt text, the AI ​​model automatically generates the optimal cooking procedure for the ingredients and compiles it into a detailed recipe. This output is a clearly formatted cooking instruction that makes it easy for consumers to prepare the dishes.

[0085] Step 4:

[0086] The server transmits the generated cooking instructions to a digital signage terminal. The input is the generated recipe information, which the terminal converts into media data for visual display. Images of related products and cooking processes are displayed on the terminal, designed to attract consumer interest. The output functions as a visual promotion for customers.

[0087] Step 5:

[0088] Users can receive these cooking instructions via their device or smartphone and follow the steps to prepare the meal. The input is a request to view the recipe through the app, and the output is a visual instruction sheet and ingredient list displayed on the user's device. After completing the dish, users provide feedback through the app.

[0089] Step 6:

[0090] The server collects feedback from users and uses it to readjust the AI ​​algorithm. Consumer response data is input, and based on this, the algorithm's parameters are modified to improve the accuracy of future recipe generation. The output of this process is optimized cooking procedures and a better user experience for future use.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] The challenge lies in effectively utilizing unsold products in stores while simultaneously increasing consumer purchasing intent. Furthermore, efficient methods are needed for managing inventory and visually promoting unsold products. Additionally, accurate consumer feedback must be collected to continuously improve the quality of the resulting cooking instructions.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for acquiring inventory information and identifying unsold items, means for generating cooking procedures based on the identified unsold items, means for providing the generated cooking procedures through visual media and communication devices, means for collecting consumer input and improving the cooking procedures, and means for acquiring and electronically displaying identification information of products including unsold items. This enables effective utilization of unsold products, increased consumer purchasing intent, and continuous improvement of cooking procedures based on feedback.

[0096] "Inventory information" refers to data about the quantity and condition of goods managed at stores and distribution facilities.

[0097] "Unsold items" refer to products that have not been sold within a certain period or products that remain in inventory due to decreased demand.

[0098] "Cooking instructions" refer to detailed methods and procedures for preparing a dish using specific ingredients.

[0099] "Visual media" refers to tools and devices used to display information visually, such as images and videos.

[0100] A "communication device" is an electronic device used to send and receive information, and includes smartphones and tablets.

[0101] "Consumer input" refers to evaluations and feedback information provided by consumers based on their own experiences and opinions.

[0102] A "generative algorithm" is a computational procedure for automatically generating information such as cooking procedures based on specific rules or models.

[0103] "Identification information" refers to information that clearly identifies the characteristics of a particular product and distinguishes it from other products.

[0104] In order to implement this invention, it is necessary to construct an information provision system for effectively utilizing unsold goods. This system consists of a server, a visual medium, a communication device, and an interface for collecting user input.

[0105] The server connects to an inventory management database and retrieves inventory information in real time. From this information, it identifies products that are likely to remain unsold and manages their identification information. The server also uses a generative AI model to create cooking instructions based on the identified unsold products. These cooking instructions are designed to be easily understood by the user and are visually presented through in-store displays and communication devices via visual media.

[0106] The communication device enables interaction between the user and the system. Users can use their smartphones or tablets to view the provided cooking instructions and related information. User feedback is sent to the server via the communication device and used to improve the generation algorithm. As a concrete example, a food store could use leftover apples to generate a new recipe called "Caramelized Apples," which the server could then display on the store's screen.

[0107] An example of a prompt message would be: "Generate an original dessert recipe using leftover apples from the store. Please suggest an easy-to-make recipe for home use."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server retrieves inventory information in real time from the inventory management database. All product data from the inventory management database is used as input. The server analyzes the inventory quantity and sales history of each product to identify unsold items and extract their identification information. A list of the identification information for unsold items is generated as output.

[0111] Step 2:

[0112] The server uses a generative AI model to automatically generate cooking instructions based on the identification information of unsold products extracted in Step 1. A list of identification information for unsold products is used as input. The server then compares this information with a recipe database to construct appropriate ingredient combinations and cooking processes. The generated cooking instructions are obtained as output.

[0113] Step 3:

[0114] The server transmits the generated cooking instructions to a visual medium and a communication device. The terminal receives this information and displays it visually to the user. The generated cooking instructions are used as input. The terminal uses this data to visually display the instructions on the in-store display or the user's smartphone screen. The output provides the user with cooking instructions that they can visually confirm.

[0115] Step 4:

[0116] The user uses a terminal to review the provided cooking instructions and then performs the actual cooking. The user can provide feedback on the cooking results and experience. The input includes user feedback. The terminal sends this feedback to the server. The output is the user's feedback information.

[0117] Step 5:

[0118] The server analyzes the feedback received from the user and makes adjustments to the generative AI model. User feedback information is used as input. Based on this data, the server re-evaluates the generative AI model's algorithm and makes improvements as needed. The adjusted generative AI model is obtained as output.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] As an embodiment of the present invention, a system for utilizing unsold products incorporating an emotion engine is provided. This system is configured by integrating an inventory management information system, a generation processing server, digital signage, and an electronic terminal with the emotion engine.

[0121] Acquisition and analysis of inventory information

[0122] The server works in conjunction with the inventory management system to periodically retrieve inventory information. It analyzes the inventory data using an algorithm to identify unsold items. The information on unsold items is then passed on to the next process.

[0123] User analysis using an emotion engine

[0124] An emotion engine built into the device analyzes the user's emotions using user input and video footage captured from the device's camera. The emotion engine analyzes the user's facial expressions and voice tone in real time to identify their emotional state.

[0125] Recipe generation and provision

[0126] The generation server generates cooking instructions based on unsold products and the user's emotional state. The generated cooking instructions are customized to the user's current emotions; for example, a user experiencing high stress levels will be offered a simple and relaxing recipe.

[0127] Display and adjustment of information

[0128] The generated cooking instructions are sent to a digital signage terminal and the user's electronic device. The digital signage also features visual promotions utilizing leftover products from the store. Furthermore, the app on the device displays personalized cooking suggestions and ingredient lists tailored to the user's mood.

[0129] Feedback and algorithm improvements

[0130] Users can provide feedback through the app after cooking. This feedback is sent to the server and stored in a database. The server analyzes this data and uses it to improve the accuracy of both the emotion engine and the cooking procedure generation algorithm.

[0131] Specific example

[0132] In a supermarket, a server detects unsold tomatoes and herb chicken. Simultaneously, an emotion engine reads the user's emotional state from their device, indicating a desire to relax. Based on this information, the server generates a simple and relaxing recipe for "Refreshing Tomato and Herb Chicken Salad" and visually promotes it on digital signage. This recipe is displayed on the user's electronic device, facilitating a smooth purchasing and cooking process.

[0133] This system can more effectively promote unsold products and further meet the individual needs of consumers.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The server periodically retrieves the latest inventory information from the inventory management system via API. This allows it to monitor inventory levels and unsold items.

[0137] Step 2:

[0138] The server analyzes the acquired inventory information and applies an algorithm to identify unsold items. This process evaluates data such as expiration dates and inventory turnover rates.

[0139] Step 3:

[0140] The emotion engine built into the device analyzes the user's facial expressions and voice to identify the user's emotional state in real time. This allows the system to understand what emotions the user is currently feeling.

[0141] Step 4:

[0142] The server retrieves data on unsold products and the user's emotional state, and uses this information to generate cooking instructions in the generation processing module. The generated recipe is customized to match the user's emotional state.

[0143] Step 5:

[0144] The server transmits the generated cooking instructions to digital signage terminals, providing visual promotion within the store. This visually presents customers with advantageous offers utilizing leftover products.

[0145] Step 6:

[0146] The server also sends cooking instructions as push notifications to the user's electronic device, allowing the user to receive new recipes at any time.

[0147] Step 7:

[0148] The user opens the app on their electronic device and checks the received recipe details. The recipe includes the necessary ingredients and steps, which the user can refer to to cook.

[0149] Step 8:

[0150] Users submit feedback through the app after cooking. This feedback includes ratings of the recipe and suggestions for improvement.

[0151] Step 9:

[0152] The server collects and analyzes user feedback and stores it in a database. This information will be used to adjust the emotion engine and improve the recipe generation algorithm in future updates.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] In inventory management, it is difficult to efficiently utilize underperforming products and make proposals that meet the individual needs of consumers. Furthermore, because product suggestions cannot take into account consumers' emotional states, the effectiveness of sales promotions is limited.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes means for acquiring inventory information and identifying items that are not selling well based on certain criteria, means for collecting and analyzing user emotions through a terminal, and means for generating cooking content based on the identified items that are not selling well and the analyzed emotional information. This makes it possible to suggest the most suitable products and cooking content based on the consumer's emotional state.

[0158] "Inventory information" refers to detailed data such as the quantity of products or materials, storage location, and expiration date.

[0159] "Items with stagnant sales" refer to goods that have not sold sufficiently within a certain period and remain as inventory.

[0160] "Emotion analysis" is a technology that determines a user's emotional state in real time based on their facial expressions and voice.

[0161] "Cooking instructions" refer to the specific recipes and cooking methods suggested to the user.

[0162] "Visual media" refers to display devices and digital signage used to convey information to consumers through images.

[0163] "Portable devices" refer to electronic devices that users can carry around, such as smartphones and tablets.

[0164] "Consumer input" refers to opinions such as feedback and requests that users provide to the system.

[0165] A "generative algorithm" is a computational method used to automatically create optimal recipes and suggestions based on input data.

[0166] This invention relates to a system for inventory management and cooking suggestions that incorporates emotion analysis. This system integrates a server, terminals, digital signage, and an emotion analysis engine. Each component is described in detail below.

[0167] The server integrates with the inventory management information system to periodically collect inventory information. This data includes product type, quantity, and expiration date, and is analyzed using an algorithm to identify items with slow sales. The results of this analysis are then used as data for subsequent cooking suggestions.

[0168] The device is equipped with an engine for analyzing the user's emotions. Users can communicate their emotional state to the system by facing the device's camera or using voice input. Based on the data acquired from the device, the emotion analysis engine analyzes the user's facial expressions and voice tone in real time to identify their current emotional state. This information is used to customize cooking suggestions.

[0169] The generation server generates recipes based on underperforming items and the user's emotional state. This process utilizes a generation AI model. The AI ​​model automatically creates recipes suitable for the characteristics of the items and the user's emotional state, providing optimal suggestions tailored to the user. For example, if a user wants to relax, a simple, relaxing recipe will be generated.

[0170] The generated cooking instructions are transmitted to digital signage and the user's mobile device. The digital signage displays promotions that visually highlight the appeal of the products. Users can check the cooking instructions and required ingredient list on their own devices, receiving support to ensure a smooth purchasing and cooking process.

[0171] As a concrete example, in a supermarket, the server detects unsold tomatoes and herb chicken, and the terminal's emotion engine reads the user's emotion as "wanting to relax." Based on this information, the server generates a recipe for "Refreshing Tomato and Herb Chicken Salad." This recipe is expressed as a prompt message that reads, "There are unsold tomatoes and herb chicken, and the user wants to relax. Please suggest a simple and relaxing recipe based on these factors."

[0172] This system is designed to achieve both sales promotion and improved customer satisfaction.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The server periodically retrieves inventory information from the inventory management system. Input data includes product name, quantity, and expiration date. After retrieving the inventory data, the server analyzes it using an algorithm to identify items that are not selling well. The analysis then outputs a list of items that are not selling well.

[0176] Step 2:

[0177] The device uses an emotion analysis engine to collect user emotion information. Input includes facial expressions captured by the device's camera and voice input. The emotion analysis engine uses facial recognition and voice analysis technologies to identify the user's most recent emotional state. The output of this process is data on the user's current emotional state.

[0178] Step 3:

[0179] The generation processing server receives a list of underperforming items obtained in Step 1 and user sentiment data analyzed in Step 2 as input. The generation AI model calculates cooking content using the relevant items and generates cooking content optimized for the user's emotional state. The output of this process is a customized cooking content for the user.

[0180] Step 4:

[0181] The server transmits the generated cooking instructions to the digital signage and the user's mobile device. The input includes data on the cooking instructions. The digital signage generates promotional content to visually appeal to the product and outputs it as visual information to the user. The terminal displays the cooking procedure and a list of required ingredients to the user.

[0182] Step 5:

[0183] Users provide feedback via a terminal after cooking. This feedback includes satisfaction levels and suggestions for improvement. The server collects this feedback and stores it in a database. The output provides data that helps improve the sentiment analysis engine and cooking content generation algorithm.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] Unsold goods remaining in stores lead to decreased corporate profits and increased environmental burden due to product waste. Furthermore, consumers may become dissatisfied if they cannot find suitable products or recipes that match their emotional needs. To solve these problems, new methods of offering products that take into account both the product itself and the emotional state of the consumer are needed.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes means for acquiring inventory information and identifying product targets, means for analyzing the consumer's emotional state using emotion analysis means, and means for adapting procedures to the consumer's emotions based on the analyzed emotional state. This enables the effective utilization of unsold products and the provision of appropriate product suggestions and recipes tailored to the consumer's emotions.

[0189] "Inventory information" refers to data about the existence and quantity of products, and is used to identify unsold items.

[0190] "Target products" refers to products selected in a store based on specific criteria, and after identification, various analyses and processes are performed.

[0191] "Instructions" refer to information that combines steps for cooking or using a specific product, and are provided to consumers through visual media or electronic information processing devices.

[0192] "Display means" refers to a device or method for visually providing a consumer with generated procedures or other information, including digital displays.

[0193] "Electronic information processing equipment" refers to devices used for inputting, processing, analyzing, and outputting digital data, and examples include smartphones and tablets.

[0194] "Emotional analysis methods" refer to technologies or devices used to analyze a consumer's emotional state from their facial expressions, voice, etc., and analysis using artificial intelligence is common.

[0195] "Consumer emotional state" refers to the psychological state of consumers, and analysis can identify states such as "wanting to relax" or "feeling stressed."

[0196] "Generation rules" are rules that define algorithms and methods for generating procedures, thereby enabling the creation of efficient and consumer-friendly procedures.

[0197] In implementing this invention, the server first works in conjunction with an inventory information processing system to periodically acquire inventory data for products. The inventory data is processed by an algorithm built into the server to identify unsold products. This identified product information is then used in the next step.

[0198] Next, the emotion analysis engine installed in the device analyzes the user's emotional state through the camera and voice input functions. This engine identifies emotions in real time from the user's facial expressions and voice tone, and sends that data to the server.

[0199] Based on this emotional data and inventory information, the server generates instructions (e.g., cooking methods and usage instructions) using a generative AI model. These instructions are customized according to the consumer's current emotional state; for example, it suggests simple, calming recipes for stressed users and provides easy-to-cook recipes for users who need energy.

[0200] The generated instructions are sent to in-store display devices (e.g., digital signage) and users' electronic information devices (e.g., smartphones). This enables attractive visual promotions utilizing unsold products, and users are shown individually personalized instructions.

[0201] Users can provide feedback after performing a procedure. This feedback is collected by the server and used to improve the accuracy of the sentiment analysis engine and procedure generation rules.

[0202] As a concrete example, consider a scenario where a user discovers unsold tomatoes and herb chicken at a supermarket. The server combines this with the user's emotional state of "wanting to relax," obtained from their device, to generate a simple recipe for "Refreshing Tomato and Herb Chicken Salad," which is then displayed attractively on a visual medium. This recipe is displayed on the user's electronic device, and the purchasing process proceeds smoothly.

[0203] A concrete example of a prompt message for a generative AI model would be: "If the system detects that the user is tired, generate a recipe that can be prepared quickly using the ingredients remaining in the refrigerator. For example, a simple soup or a one-plate meal."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The server periodically retrieves product inventory data from the inventory information processing system. The input is the current data from the inventory management database, which is then analyzed using an algorithm to output a list of unsold products. This process identifies unsold items.

[0207] Step 2:

[0208] The device uses an emotion analysis engine to analyze the user's emotional state. The input consists of the user's facial expressions and voice, collected through the device's camera and microphone. This data is processed in real time, and the resulting emotional data is output as an emotional state. This process is carried out to accurately understand the user's psychological state.

[0209] Step 3:

[0210] The server takes a list of unsold items and the user's emotional state as input and generates cooking instructions using a generative AI model. It executes an algorithm that generates cooking instructions based on the input data and outputs customized cooking instructions. A specific example of its operation is the generation of recipes tailored to the user's emotional state.

[0211] Step 4:

[0212] The server generates cooking instructions and transmits them to in-store display devices and the user's electronic information processing device. The input is the generated cooking instructions, and the output is visually represented promotional information and a display screen on the user's terminal. This operation allows the user to take actions in accordance with the cooking instructions.

[0213] Step 5:

[0214] Users provide feedback after performing the cooking procedure. The input is an evaluation entered through a feedback form, which the server receives and stores in a database. This feedback data is used to improve future algorithms. This process allows the system to continuously improve.

[0215] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 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.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0231] One embodiment of the present invention is an information provision system that promotes the efficient utilization of unsold goods. This system consists of an inventory management information system, a generation processing server, a digital signage terminal, and an electronic terminal for interacting with consumers.

[0232] Acquisition and analysis of inventory information

[0233] The server works in conjunction with the inventory management information system to obtain real-time information on unsold products. The obtained data is then used by an AI algorithm to identify the unsold items. The identified unsold products are then passed to the recipe generation module on the generation processing server.

[0234] Recipe generation and provision

[0235] The generation processing server generates cooking instructions for consumers based on information about identified unsold products. These instructions are automatically designed to be easy for consumers to prepare and to be delicious. The generated instructions are sent to digital signage terminals installed in the store and displayed as a visual promotion for the unsold products.

[0236] Consumer interface and feedback collection

[0237] Users can receive generated recipes via their smartphones or other electronic devices. Visual instructions and ingredient lists can be viewed through the app. Furthermore, users can provide feedback through the app after cooking, and the server collects this information to improve the accuracy of the algorithm.

[0238] Specific example

[0239] In a supermarket, a server detects from inventory data that a large quantity of tomatoes and herb chicken remain unsold that week. Based on this, the AI ​​generates a cooking recipe for "Tomato Herb Chicken Pasta." Digital signage is used to promote this recipe and related products within the store. Users can view the recipe through an app, purchase the ingredients, and actually cook the dish. Feedback after cooking contributes to future algorithm improvements.

[0240] In this way, it is possible to efficiently utilize unsold products and increase consumer purchasing intent.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server periodically retrieves the latest inventory data from the inventory management information system via an API. This allows it to understand the current storage status and potential unsold status of each product.

[0244] Step 2:

[0245] The server analyzes the acquired inventory data to identify unsold items. In this process, items with approaching expiration dates or those with low past inventory turnover rates are selected as unsold.

[0246] Step 3:

[0247] The server sends data based on unsold products to a generation processing module, which then generates cooking instructions using those products. During this process, an AI algorithm is used to create easy-to-prepare and delicious recipes for consumers.

[0248] Step 4:

[0249] The server transmits the generated cooking instructions to digital signage for in-store display. The displayed content includes selected leftover products and recommended recipes using them.

[0250] Step 5:

[0251] The server simultaneously sends information about the generated recipe to the device app as a push notification. This allows users to receive new cooking ideas in real time.

[0252] Step 6:

[0253] The user opens the app and checks the details of the received recipe. The recipe includes the necessary ingredients and cooking instructions, and the user can add it directly to their shopping list.

[0254] Step 7:

[0255] After the user completes cooking, they can provide feedback on the recipe within the app. This feedback can include a rating of the recipe and suggestions for improvement.

[0256] Step 8:

[0257] The terminal sends the input feedback to the server. The server receives this feedback, saves it in a database, and optimizes the algorithm by incorporating it into the next recipe generation.

[0258] (Example 1)

[0259] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] One challenge is that unsold products cannot be efficiently utilized and end up being discarded, resulting in economic losses and environmental problems. Furthermore, consumers are not provided with fresh and useful information, leading to a decrease in their willingness to purchase.

[0261] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0262] In this invention, the server includes means for acquiring inventory information and identifying unsold products, means for generating cooking instructions based on the identified unsold products, and means for providing the generated cooking instructions through a visual medium and an electronic device. This makes it possible to efficiently utilize unsold products and improve consumer purchasing intent.

[0263] "Inventory information" refers to data that shows the inventory status of products, and includes real-time information necessary to identify unsold products.

[0264] "Unsold products" refer to goods that remain unconsumed compared to the planned sales volume, and their efficient utilization is required.

[0265] "Cooking instructions" are guidelines generated based on unsold products, containing the steps consumers need to actually prepare the food.

[0266] "Visual media" refers to digital signage and other display devices, and is a means of providing information to consumers visually.

[0267] "Electronic devices" refer to devices such as smartphones and tablets that consumers use to receive cooking instructions.

[0268] "Consumer responses" refer to information that shows feedback and opinions from consumers after they have cooked according to the provided cooking instructions, and these contribute to future improvements.

[0269] One embodiment of the present invention is an information provision system for efficiently utilizing unsold products. This system consists of an inventory management information system, a generation processing server, a digital signage terminal, and an electronic terminal for interacting with consumers.

[0270] The server works in conjunction with the inventory management information system to periodically retrieve real-time inventory information. This information shows the inventory status of products and is used to identify unsold items. Upon receiving the inventory information, the server uses an AI algorithm to analyze the data, understand trends in unsold inventory, and list products that meet specific criteria.

[0271] Next, the generation server generates cooking instructions based on the data of the identified unsold products. At this stage, a generation AI model is used to create specific prompt statements. These prompt statements include specific instructions such as "Create a recipe for a dish using tomatoes and herb chicken," and cooking steps are created based on these.

[0272] The generated cooking instructions are transmitted to a digital signage terminal. This terminal displays the recipe as a visual promotion within the store. Visual media such as product images and cooking images are used to attract consumers' attention.

[0273] Users can receive recipes generated through the app using electronic devices such as smartphones and tablets. They can view visual instructions and ingredient lists. Furthermore, users can provide feedback via the app after cooking; this information is returned to the server and used to improve future algorithms.

[0274] As a concrete example, if tomatoes and herb chicken are unsold at a certain store, the server will detect this and generate a recipe called "Tomato Herb Chicken Pasta." This recipe will be promoted on digital signage, and users can check it in the app and actually cook it. The prompt given to the generation AI model is "Please come up with a delicious recipe using tomatoes and herb chicken."

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The server retrieves real-time inventory information from the inventory management information system. This information includes the inventory levels and sales remaining details of each product. Based on the input inventory information, the server analyzes the data and generates a list of remaining products. This list identifies products with low sales rates and excessive inventory. As output, a dataset of remaining products is generated.

[0278] Step 2:

[0279] The server passes the dataset of remaining products obtained in Step 1 to the AI algorithm for analysis. In this process, a generated AI model is used to create a prompt sentence for cooking instructions related to the identified remaining products. The input is the dataset of remaining products, and the prompt sentence includes an instruction such as "Please consider cooking procedures using the selected ingredients." As a result, a draft recipe proposed by the AI model is output.

[0280] Step 3:

[0281] The generation processing server sends the prompt sentence created in Step 2 to the AI model to generate specific cooking procedures. Based on the input prompt sentence, the AI model automatically generates the optimal cooking procedures for the ingredients and summarizes them as a detailed recipe. This output becomes cooking instructions that are formatted in an easy-to-understand manner so that consumers can easily cook.

[0282] Step 4:

[0283] The server sends the generated cooking instructions to the digital signage terminal. The input is the generated recipe information, and the terminal converts it into media data for visual display. On the terminal, images of related products and the cooking process are displayed, and it is produced to attract the interest of consumers. The output functions as a visual promotion for in-store customers. <�

[0284] <� Step 5:

[0285] The user can receive this cooking instruction through a terminal or smartphone and cook according to the procedure. What is input is a request to view a recipe via an app, and as output, visual instructions and a list of ingredients are displayed on the user's device. After the user completes cooking, they provide feedback through the app.

[0286] Step 6:

[0287] The server collects the feedback obtained from the user and re - adjusts the AI algorithm based on this. What is input is consumer response data, and based on this, the parameters of the algorithm are modified to improve the accuracy of future recipe generation. The output of this process is an optimized cooking procedure and a better user experience from the next time onwards.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] The problem is to effectively utilize unsold goods in a store while increasing consumers' purchasing desire. Also, means for efficiently managing the inventory of unsold goods and visual promotion are required. Furthermore, it is necessary to accurately collect feedback from consumers and continuously improve the quality of the generated cooking procedures.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0292] In this invention, the server includes means for acquiring inventory information and identifying unsold items, means for generating cooking procedures based on the identified unsold items, means for providing the generated cooking procedures through visual media and communication devices, means for collecting consumer input and improving the cooking procedures, and means for acquiring and electronically displaying identification information of products including unsold items. This enables effective utilization of unsold products, increased consumer purchasing intent, and continuous improvement of cooking procedures based on feedback.

[0293] "Inventory information" refers to data about the quantity and condition of goods managed at stores and distribution facilities.

[0294] "Unsold items" refer to products that have not been sold within a certain period or products that remain in inventory due to decreased demand.

[0295] "Cooking instructions" refer to detailed methods and procedures for preparing a dish using specific ingredients.

[0296] "Visual media" refers to tools and devices used to display information visually, such as images and videos.

[0297] A "communication device" is an electronic device used to send and receive information, and includes smartphones and tablets.

[0298] "Consumer input" refers to evaluations and feedback information provided by consumers based on their own experiences and opinions.

[0299] A "generative algorithm" is a computational procedure for automatically generating information such as cooking procedures based on specific rules or models.

[0300] "Identification information" refers to information that clearly identifies the characteristics of a particular product and distinguishes it from other products.

[0301] In order to implement this invention, it is necessary to construct an information provision system for effectively utilizing unsold goods. This system consists of a server, a visual medium, a communication device, and an interface for collecting user input.

[0302] The server connects to an inventory management database and retrieves inventory information in real time. From this information, it identifies products that are likely to remain unsold and manages their identification information. The server also uses a generative AI model to create cooking instructions based on the identified unsold products. These cooking instructions are designed to be easily understood by the user and are visually presented through in-store displays and communication devices via visual media.

[0303] The communication device enables interaction between the user and the system. Users can use their smartphones or tablets to view the provided cooking instructions and related information. User feedback is sent to the server via the communication device and used to improve the generation algorithm. As a concrete example, a food store could use leftover apples to generate a new recipe called "Caramelized Apples," which the server could then display on the store's screen.

[0304] An example of a prompt message would be: "Generate an original dessert recipe using leftover apples from the store. Please suggest an easy-to-make recipe for home use."

[0305] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0306] Step 1:

[0307] The server retrieves inventory information in real time from the inventory management database. All product data from the inventory management database is used as input. The server analyzes the inventory quantity and sales history of each product to identify unsold items and extract their identification information. A list of the identification information for unsold items is generated as output.

[0308] Step 2:

[0309] The server uses the generative AI model to automatically generate cooking procedures based on the identification information of the unsold products extracted in Step 1. A list of the identification information of the unsold products is used as the input. Based on this information, the server matches against the recipe database and constructs an appropriate combination of ingredients and cooking process. The generated cooking procedures are obtained as the output.

[0310] Step 3:

[0311] The server transmits the generated cooking procedures to the visual medium and the communication device. The terminal receives this information and visually displays it to the user. The generated cooking procedures are used as the input. Based on this data, the terminal visually displays it on the in-store display or the user's smartphone screen. Cooking procedures that can be visually confirmed by the user are provided as the output. [[ID=1十七]]

[0312] Step 4:

[0313] The user uses the terminal to check the provided cooking procedures and perform actual cooking. The user can provide feedback on the results and experiences of the cooking. The input includes feedback from the user. The terminal transmits this feedback to the server. The user's feedback information is obtained as the output.

[0314] Step 5:

[0315] The server analyzes the feedback obtained from the user and makes adjustments to the generative AI model. The user's feedback information is used as the input. Based on this data, the server re-evaluates the algorithm of the generative AI model and makes improvements if necessary. The adjusted generative AI model is obtained as the output.

[0316] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0317] As an embodiment of the present invention, a system for utilizing unsold products incorporating an emotion engine is provided. This system is configured by integrating an inventory management information system, a generation processing server, digital signage, and an electronic terminal with the emotion engine.

[0318] Acquisition and analysis of inventory information

[0319] The server works in conjunction with the inventory management system to periodically retrieve inventory information. It analyzes the inventory data using an algorithm to identify unsold items. The information on unsold items is then passed on to the next process.

[0320] User analysis using an emotion engine

[0321] An emotion engine built into the device analyzes the user's emotions using user input and video footage captured from the device's camera. The emotion engine analyzes the user's facial expressions and voice tone in real time to identify their emotional state.

[0322] Recipe generation and provision

[0323] The generation server generates cooking instructions based on unsold products and the user's emotional state. The generated cooking instructions are customized to the user's current emotions; for example, a user experiencing high stress levels will be offered a simple and relaxing recipe.

[0324] Display and adjustment of information

[0325] The generated cooking instructions are sent to a digital signage terminal and the user's electronic device. The digital signage also features visual promotions utilizing leftover products from the store. Furthermore, the app on the device displays personalized cooking suggestions and ingredient lists tailored to the user's mood.

[0326] Feedback and algorithm improvements

[0327] Users can provide feedback through the app after cooking. This feedback is sent to the server and stored in a database. The server analyzes this data and uses it to improve the accuracy of both the emotion engine and the cooking procedure generation algorithm.

[0328] Specific example

[0329] In a supermarket, a server detects unsold tomatoes and herb chicken. Simultaneously, an emotion engine reads the user's emotional state from their device, indicating a desire to relax. Based on this information, the server generates a simple and relaxing recipe for "Refreshing Tomato and Herb Chicken Salad" and visually promotes it on digital signage. This recipe is displayed on the user's electronic device, facilitating a smooth purchasing and cooking process.

[0330] This system can more effectively promote unsold products and further meet the individual needs of consumers.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The server periodically retrieves the latest inventory information from the inventory management system via API. This allows it to monitor inventory levels and unsold items.

[0334] Step 2:

[0335] The server analyzes the acquired inventory information and applies an algorithm to identify unsold items. This process evaluates data such as expiration dates and inventory turnover rates.

[0336] Step 3:

[0337] The emotion engine built into the device analyzes the user's facial expressions and voice to identify the user's emotional state in real time. This allows the system to understand what emotions the user is currently feeling.

[0338] Step 4:

[0339] The server retrieves data on unsold products and the user's emotional state, and uses this information to generate cooking instructions in the generation processing module. The generated recipe is customized to match the user's emotional state.

[0340] Step 5:

[0341] The server transmits the generated cooking instructions to digital signage terminals, providing visual promotion within the store. This visually presents customers with advantageous offers utilizing leftover products.

[0342] Step 6:

[0343] The server also sends cooking instructions as push notifications to the user's electronic device, allowing the user to receive new recipes at any time.

[0344] Step 7:

[0345] The user opens the app on their electronic device and checks the received recipe details. The recipe includes the necessary ingredients and steps, which the user can refer to to cook.

[0346] Step 8:

[0347] Users submit feedback through the app after cooking. This feedback includes ratings of the recipe and suggestions for improvement.

[0348] Step 9:

[0349] The server collects and analyzes user feedback and stores it in a database. This information will be used to adjust the emotion engine and improve the recipe generation algorithm in future updates.

[0350] (Example 2)

[0351] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0352] In inventory management, it is difficult to efficiently utilize underperforming products and make proposals that meet the individual needs of consumers. Furthermore, because product suggestions cannot take into account consumers' emotional states, the effectiveness of sales promotions is limited.

[0353] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0354] In this invention, the server includes means for acquiring inventory information and identifying items that are not selling well based on certain criteria, means for collecting and analyzing user emotions through a terminal, and means for generating cooking content based on the identified items that are not selling well and the analyzed emotional information. This makes it possible to suggest the most suitable products and cooking content based on the consumer's emotional state.

[0355] "Inventory information" refers to detailed data such as the quantity of products or materials, storage location, and expiration date.

[0356] "Items with stagnant sales" refer to goods that have not sold sufficiently within a certain period and remain as inventory.

[0357] "Emotion analysis" is a technology that determines a user's emotional state in real time based on their facial expressions and voice.

[0358] "Cooking instructions" refer to the specific recipes and cooking methods suggested to the user.

[0359] "Visual media" refers to display devices and digital signage used to convey information to consumers through images.

[0360] "Portable devices" refer to electronic devices that users can carry around, such as smartphones and tablets.

[0361] "Consumer input" refers to opinions such as feedback and requests that users provide to the system.

[0362] A "generative algorithm" is a computational method used to automatically create optimal recipes and suggestions based on input data.

[0363] This invention relates to a system for inventory management and cooking suggestions that incorporates emotion analysis. This system integrates a server, terminals, digital signage, and an emotion analysis engine. Each component is described in detail below.

[0364] The server integrates with the inventory management information system to periodically collect inventory information. This data includes product type, quantity, and expiration date, and is analyzed using an algorithm to identify items with slow sales. The results of this analysis are then used as data for subsequent cooking suggestions.

[0365] The device is equipped with an engine for analyzing the user's emotions. Users can communicate their emotional state to the system by facing the device's camera or using voice input. Based on the data acquired from the device, the emotion analysis engine analyzes the user's facial expressions and voice tone in real time to identify their current emotional state. This information is used to customize cooking suggestions.

[0366] The generation server generates recipes based on underperforming items and the user's emotional state. This process utilizes a generation AI model. The AI ​​model automatically creates recipes suitable for the characteristics of the items and the user's emotional state, providing optimal suggestions tailored to the user. For example, if a user wants to relax, a simple, relaxing recipe will be generated.

[0367] The generated cooking instructions are transmitted to digital signage and the user's mobile device. The digital signage displays promotions that visually highlight the appeal of the products. Users can check the cooking instructions and required ingredient list on their own devices, receiving support to ensure a smooth purchasing and cooking process.

[0368] As a concrete example, in a supermarket, the server detects unsold tomatoes and herb chicken, and the terminal's emotion engine reads the user's emotion as "wanting to relax." Based on this information, the server generates a recipe for "Refreshing Tomato and Herb Chicken Salad." This recipe is expressed as a prompt message that reads, "There are unsold tomatoes and herb chicken, and the user wants to relax. Please suggest a simple and relaxing recipe based on these factors."

[0369] This system is designed to achieve both sales promotion and improved customer satisfaction.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The server periodically retrieves inventory information from the inventory management system. Input data includes product name, quantity, and expiration date. After retrieving the inventory data, the server analyzes it using an algorithm to identify items that are not selling well. The analysis then outputs a list of items that are not selling well.

[0373] Step 2:

[0374] The device uses an emotion analysis engine to collect user emotion information. Input includes facial expressions captured by the device's camera and voice input. The emotion analysis engine uses facial recognition and voice analysis technologies to identify the user's most recent emotional state. The output of this process is data on the user's current emotional state.

[0375] Step 3:

[0376] The generation processing server receives a list of underperforming items obtained in Step 1 and user sentiment data analyzed in Step 2 as input. The generation AI model calculates cooking content using the relevant items and generates cooking content optimized for the user's emotional state. The output of this process is a customized cooking content for the user.

[0377] Step 4:

[0378] The server transmits the generated cooking instructions to the digital signage and the user's mobile device. The input includes data on the cooking instructions. The digital signage generates promotional content to visually appeal to the product and outputs it as visual information to the user. The terminal displays the cooking procedure and a list of required ingredients to the user.

[0379] Step 5:

[0380] Users provide feedback via a terminal after cooking. This feedback includes satisfaction levels and suggestions for improvement. The server collects this feedback and stores it in a database. The output provides data that helps improve the sentiment analysis engine and cooking content generation algorithm.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] Unsold goods remaining in stores lead to decreased corporate profits and increased environmental burden due to product waste. Furthermore, consumers may become dissatisfied if they cannot find suitable products or recipes that match their emotional needs. To solve these problems, new methods of offering products that take into account both the product itself and the emotional state of the consumer are needed.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for acquiring inventory information and identifying product targets, means for analyzing the consumer's emotional state using emotion analysis means, and means for adapting procedures to the consumer's emotions based on the analyzed emotional state. This enables the effective utilization of unsold products and the provision of appropriate product suggestions and recipes tailored to the consumer's emotions.

[0386] "Inventory information" refers to data about the existence and quantity of products, and is used to identify unsold items.

[0387] "Target products" refers to products selected in a store based on specific criteria, and after identification, various analyses and processes are performed.

[0388] "Instructions" refer to information that combines steps for cooking or using a specific product, and are provided to consumers through visual media or electronic information processing devices.

[0389] "Display means" refers to a device or method for visually providing a consumer with generated procedures or other information, including digital displays.

[0390] "Electronic information processing equipment" refers to devices used for inputting, processing, analyzing, and outputting digital data, and examples include smartphones and tablets.

[0391] "Emotional analysis methods" refer to technologies or devices used to analyze a consumer's emotional state from their facial expressions, voice, etc., and analysis using artificial intelligence is common.

[0392] "Consumer emotional state" refers to the psychological state of consumers, and analysis can identify states such as "wanting to relax" or "feeling stressed."

[0393] "Generation rules" are rules that define algorithms and methods for generating procedures, thereby enabling the creation of efficient and consumer-friendly procedures.

[0394] In implementing this invention, the server first works in conjunction with an inventory information processing system to periodically acquire inventory data for products. The inventory data is processed by an algorithm built into the server to identify unsold products. This identified product information is then used in the next step.

[0395] Next, the emotion analysis engine installed in the device analyzes the user's emotional state through the camera and voice input functions. This engine identifies emotions in real time from the user's facial expressions and voice tone, and sends that data to the server.

[0396] Based on this emotional data and inventory information, the server generates instructions (e.g., cooking methods and usage instructions) using a generative AI model. These instructions are customized according to the consumer's current emotional state; for example, it suggests simple, calming recipes for stressed users and provides easy-to-cook recipes for users who need energy.

[0397] The generated instructions are sent to in-store display devices (e.g., digital signage) and users' electronic information devices (e.g., smartphones). This enables attractive visual promotions utilizing unsold products, and users are shown individually personalized instructions.

[0398] Users can provide feedback after performing a procedure. This feedback is collected by the server and used to improve the accuracy of the sentiment analysis engine and procedure generation rules.

[0399] As a concrete example, consider a scenario where a user discovers unsold tomatoes and herb chicken at a supermarket. The server combines this with the user's emotional state of "wanting to relax," obtained from their device, to generate a simple recipe for "Refreshing Tomato and Herb Chicken Salad," which is then displayed attractively on a visual medium. This recipe is displayed on the user's electronic device, and the purchasing process proceeds smoothly.

[0400] A concrete example of a prompt message for a generative AI model would be: "If the system detects that the user is tired, generate a recipe that can be prepared quickly using the ingredients remaining in the refrigerator. For example, a simple soup or a one-plate meal."

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The server periodically retrieves product inventory data from the inventory information processing system. The input is the current data from the inventory management database, which is then analyzed using an algorithm to output a list of unsold products. This process identifies unsold items.

[0404] Step 2:

[0405] The device uses an emotion analysis engine to analyze the user's emotional state. The input consists of the user's facial expressions and voice, collected through the device's camera and microphone. This data is processed in real time, and the resulting emotional data is output as an emotional state. This process is carried out to accurately understand the user's psychological state.

[0406] Step 3:

[0407] The server takes a list of unsold items and the user's emotional state as input and generates cooking instructions using a generative AI model. It executes an algorithm that generates cooking instructions based on the input data and outputs customized cooking instructions. A specific example of its operation is the generation of recipes tailored to the user's emotional state.

[0408] Step 4:

[0409] The server generates cooking instructions and transmits them to in-store display devices and the user's electronic information processing device. The input is the generated cooking instructions, and the output is visually represented promotional information and a display screen on the user's terminal. This operation allows the user to take actions in accordance with the cooking instructions.

[0410] Step 5:

[0411] Users provide feedback after performing the cooking procedure. The input is an evaluation entered through a feedback form, which the server receives and stores in a database. This feedback data is used to improve future algorithms. This process allows the system to continuously improve.

[0412] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0413] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0414] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0418] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0419] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0420] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0422] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0423] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0424] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0425] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0426] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0427] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0428] One embodiment of the present invention is an information provision system that promotes the efficient utilization of unsold goods. This system consists of an inventory management information system, a generation processing server, a digital signage terminal, and an electronic terminal for interacting with consumers.

[0429] Acquisition and analysis of inventory information

[0430] The server works in conjunction with the inventory management information system to obtain real-time information on unsold products. The obtained data is then used by an AI algorithm to identify the unsold items. The identified unsold products are then passed to the recipe generation module on the generation processing server.

[0431] Recipe generation and provision

[0432] The generation processing server generates cooking instructions for consumers based on information about identified unsold products. These instructions are automatically designed to be easy for consumers to prepare and to be delicious. The generated instructions are sent to digital signage terminals installed in the store and displayed as a visual promotion for the unsold products.

[0433] Consumer interface and feedback collection

[0434] Users can receive generated recipes via their smartphones or other electronic devices. Visual instructions and ingredient lists can be viewed through the app. Furthermore, users can provide feedback through the app after cooking, and the server collects this information to improve the accuracy of the algorithm.

[0435] Specific example

[0436] In a supermarket, a server detects from inventory data that a large quantity of tomatoes and herb chicken remain unsold that week. Based on this, the AI ​​generates a cooking recipe for "Tomato Herb Chicken Pasta." Digital signage is used to promote this recipe and related products within the store. Users can view the recipe through an app, purchase the ingredients, and actually cook the dish. Feedback after cooking contributes to future algorithm improvements.

[0437] In this way, it is possible to efficiently utilize unsold products and increase consumer purchasing intent.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The server periodically retrieves the latest inventory data from the inventory management information system via an API. This allows it to understand the current storage status and potential unsold status of each product.

[0441] Step 2:

[0442] The server analyzes the acquired inventory data to identify unsold items. In this process, items with approaching expiration dates or those with low past inventory turnover rates are selected as unsold.

[0443] Step 3:

[0444] The server sends data based on unsold products to a generation processing module, which then generates cooking instructions using those products. During this process, an AI algorithm is used to create easy-to-prepare and delicious recipes for consumers.

[0445] Step 4:

[0446] The server transmits the generated cooking instructions to digital signage for in-store display. The displayed content includes selected leftover products and recommended recipes using them.

[0447] Step 5:

[0448] The server simultaneously sends information about the generated recipe to the device app as a push notification. This allows users to receive new cooking ideas in real time.

[0449] Step 6:

[0450] The user opens the app and checks the details of the received recipe. The recipe includes the necessary ingredients and cooking instructions, and the user can add it directly to their shopping list.

[0451] Step 7:

[0452] After the user completes cooking, they can provide feedback on the recipe within the app. This feedback can include a rating of the recipe and suggestions for improvement.

[0453] Step 8:

[0454] The terminal sends the input feedback to the server. The server receives this feedback, saves it in a database, and optimizes the algorithm by incorporating it into the next recipe generation.

[0455] (Example 1)

[0456] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0457] One challenge is that unsold products cannot be efficiently utilized and end up being discarded, resulting in economic losses and environmental problems. Furthermore, consumers are not provided with fresh and useful information, leading to a decrease in their willingness to purchase.

[0458] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0459] In this invention, the server includes means for acquiring inventory information and identifying unsold products, means for generating cooking instructions based on the identified unsold products, and means for providing the generated cooking instructions through a visual medium and an electronic device. This makes it possible to efficiently utilize unsold products and improve consumer purchasing intent.

[0460] "Inventory information" refers to data that shows the inventory status of products, and includes real-time information necessary to identify unsold products.

[0461] "Unsold products" refer to goods that remain unconsumed compared to the planned sales volume, and their efficient utilization is required.

[0462] "Cooking instructions" are guidelines generated based on unsold products, containing the steps consumers need to actually prepare the food.

[0463] "Visual media" refers to digital signage and other display devices, and is a means of providing information to consumers visually.

[0464] "Electronic devices" refer to devices such as smartphones and tablets that consumers use to receive cooking instructions.

[0465] "Consumer responses" refer to information that shows feedback and opinions from consumers after they have cooked according to the provided cooking instructions, and these contribute to future improvements.

[0466] One embodiment of the present invention is an information provision system for efficiently utilizing unsold products. This system consists of an inventory management information system, a generation processing server, a digital signage terminal, and an electronic terminal for interacting with consumers.

[0467] The server works in conjunction with the inventory management information system to periodically retrieve real-time inventory information. This information shows the inventory status of products and is used to identify unsold items. Upon receiving the inventory information, the server uses an AI algorithm to analyze the data, understand trends in unsold inventory, and list products that meet specific criteria.

[0468] Next, the generation server generates cooking instructions based on the data of the identified unsold products. At this stage, a generation AI model is used to create specific prompt statements. These prompt statements include specific instructions such as "Create a recipe for a dish using tomatoes and herb chicken," and cooking steps are created based on these.

[0469] The generated cooking instructions are transmitted to a digital signage terminal. This terminal displays the recipe as a visual promotion within the store. Visual media such as product images and cooking images are used to attract consumers' attention.

[0470] Users can receive recipes generated through the app using electronic devices such as smartphones and tablets. They can view visual instructions and ingredient lists. Furthermore, users can provide feedback via the app after cooking; this information is returned to the server and used to improve future algorithms.

[0471] As a concrete example, if tomatoes and herb chicken are unsold at a certain store, the server will detect this and generate a recipe called "Tomato Herb Chicken Pasta." This recipe will be promoted on digital signage, and users can check it in the app and actually cook it. The prompt given to the generation AI model is "Please come up with a delicious recipe using tomatoes and herb chicken."

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The server retrieves real-time inventory information from the inventory management system. This information includes the stock quantity and details of unsold items for each product. Based on the input inventory information, the server analyzes the data and generates a list of unsold products. This list identifies products with low sales rates and excess inventory. A dataset of unsold products is generated as output.

[0475] Step 2:

[0476] The server passes the dataset of unsold products obtained in Step 1 to an AI algorithm for analysis. In this process, a generative AI model is used to create prompt sentences for cooking instructions related to the identified unsold products. The input is the dataset of unsold products, and the prompt sentences include the instruction "Please think of cooking steps using the selected ingredients." As a result, a draft recipe proposed by the AI ​​model is output.

[0477] Step 3:

[0478] The generation processing server sends the prompt text created in step 2 to the AI ​​model to generate specific cooking instructions. Based on the input prompt text, the AI ​​model automatically generates the optimal cooking procedure for the ingredients and compiles it into a detailed recipe. This output is a clearly formatted cooking instruction that makes it easy for consumers to prepare the dishes.

[0479] Step 4:

[0480] The server transmits the generated cooking instructions to a digital signage terminal. The input is the generated recipe information, which the terminal converts into media data for visual display. Images of related products and cooking processes are displayed on the terminal, designed to attract consumer interest. The output functions as a visual promotion for customers.

[0481] Step 5:

[0482] Users can receive these cooking instructions via their device or smartphone and follow the steps to prepare the meal. The input is a request to view the recipe through the app, and the output is a visual instruction sheet and ingredient list displayed on the user's device. After completing the dish, users provide feedback through the app.

[0483] Step 6:

[0484] The server collects feedback from users and uses it to readjust the AI ​​algorithm. Consumer response data is input, and based on this, the algorithm's parameters are modified to improve the accuracy of future recipe generation. The output of this process is optimized cooking procedures and a better user experience for future use.

[0485] (Application Example 1)

[0486] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] The challenge lies in effectively utilizing unsold products in stores while simultaneously increasing consumer purchasing intent. Furthermore, efficient methods are needed for managing inventory and visually promoting unsold products. Additionally, accurate consumer feedback must be collected to continuously improve the quality of the resulting cooking instructions.

[0488] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0489] In this invention, the server includes means for acquiring inventory information and identifying unsold items, means for generating cooking procedures based on the identified unsold items, means for providing the generated cooking procedures through visual media and communication devices, means for collecting consumer input and improving the cooking procedures, and means for acquiring and electronically displaying identification information of products including unsold items. This enables effective utilization of unsold products, increased consumer purchasing intent, and continuous improvement of cooking procedures based on feedback.

[0490] "Inventory information" refers to data about the quantity and condition of goods managed at stores and distribution facilities.

[0491] "Unsold items" refer to products that have not been sold within a certain period or products that remain in inventory due to decreased demand.

[0492] "Cooking instructions" refer to detailed methods and procedures for preparing a dish using specific ingredients.

[0493] "Visual media" refers to tools and devices used to display information visually, such as images and videos.

[0494] A "communication device" is an electronic device used to send and receive information, and includes smartphones and tablets.

[0495] "Consumer input" refers to evaluations and feedback information provided by consumers based on their own experiences and opinions.

[0496] A "generative algorithm" is a computational procedure for automatically generating information such as cooking procedures based on specific rules or models.

[0497] "Identification information" refers to information that clearly identifies the characteristics of a particular product and distinguishes it from other products.

[0498] In order to implement this invention, it is necessary to construct an information provision system for effectively utilizing unsold goods. This system consists of a server, a visual medium, a communication device, and an interface for collecting user input.

[0499] The server connects to an inventory management database and retrieves inventory information in real time. From this information, it identifies products that are likely to remain unsold and manages their identification information. The server also uses a generative AI model to create cooking instructions based on the identified unsold products. These cooking instructions are designed to be easily understood by the user and are visually presented through in-store displays and communication devices via visual media.

[0500] The communication device enables interaction between the user and the system. Users can use their smartphones or tablets to view the provided cooking instructions and related information. User feedback is sent to the server via the communication device and used to improve the generation algorithm. As a concrete example, a food store could use leftover apples to generate a new recipe called "Caramelized Apples," which the server could then display on the store's screen.

[0501] An example of a prompt message would be: "Generate an original dessert recipe using leftover apples from the store. Please suggest an easy-to-make recipe for home use."

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The server retrieves inventory information in real time from the inventory management database. All product data from the inventory management database is used as input. The server analyzes the inventory quantity and sales history of each product to identify unsold items and extract their identification information. A list of the identification information for unsold items is generated as output.

[0505] Step 2:

[0506] The server uses a generative AI model to automatically generate cooking instructions based on the identification information of unsold products extracted in Step 1. A list of identification information for unsold products is used as input. The server then compares this information with a recipe database to construct appropriate ingredient combinations and cooking processes. The generated cooking instructions are obtained as output.

[0507] Step 3:

[0508] The server transmits the generated cooking instructions to a visual medium and a communication device. The terminal receives this information and displays it visually to the user. The generated cooking instructions are used as input. The terminal uses this data to visually display the instructions on the in-store display or the user's smartphone screen. The output provides the user with cooking instructions that they can visually confirm.

[0509] Step 4:

[0510] The user uses a terminal to review the provided cooking instructions and then performs the actual cooking. The user can provide feedback on the cooking results and experience. The input includes user feedback. The terminal sends this feedback to the server. The output is the user's feedback information.

[0511] Step 5:

[0512] The server analyzes the feedback received from the user and makes adjustments to the generative AI model. User feedback information is used as input. Based on this data, the server re-evaluates the generative AI model's algorithm and makes improvements as needed. The adjusted generative AI model is obtained as output.

[0513] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0514] As an embodiment of the present invention, a system for utilizing unsold products incorporating an emotion engine is provided. This system is configured by integrating an inventory management information system, a generation processing server, digital signage, and an electronic terminal with the emotion engine.

[0515] Acquisition and analysis of inventory information

[0516] The server works in conjunction with the inventory management system to periodically retrieve inventory information. It analyzes the inventory data using an algorithm to identify unsold items. The information on unsold items is then passed on to the next process.

[0517] User analysis using an emotion engine

[0518] An emotion engine built into the device analyzes the user's emotions using user input and video footage captured from the device's camera. The emotion engine analyzes the user's facial expressions and voice tone in real time to identify their emotional state.

[0519] Recipe generation and provision

[0520] The generation server generates cooking instructions based on unsold products and the user's emotional state. The generated cooking instructions are customized to the user's current emotions; for example, a user experiencing high stress levels will be offered a simple and relaxing recipe.

[0521] Display and adjustment of information

[0522] The generated cooking instructions are sent to a digital signage terminal and the user's electronic device. The digital signage also features visual promotions utilizing leftover products from the store. Furthermore, the app on the device displays personalized cooking suggestions and ingredient lists tailored to the user's mood.

[0523] Feedback and algorithm improvements

[0524] Users can provide feedback through the app after cooking. This feedback is sent to the server and stored in a database. The server analyzes this data and uses it to improve the accuracy of both the emotion engine and the cooking procedure generation algorithm.

[0525] Specific example

[0526] In a supermarket, a server detects unsold tomatoes and herb chicken. Simultaneously, an emotion engine reads the user's emotional state from their device, indicating a desire to relax. Based on this information, the server generates a simple and relaxing recipe for "Refreshing Tomato and Herb Chicken Salad" and visually promotes it on digital signage. This recipe is displayed on the user's electronic device, facilitating a smooth purchasing and cooking process.

[0527] This system can more effectively promote unsold products and further meet the individual needs of consumers.

[0528] The following describes the processing flow.

[0529] Step 1:

[0530] The server periodically retrieves the latest inventory information from the inventory management system via API. This allows it to monitor inventory levels and unsold items.

[0531] Step 2:

[0532] The server analyzes the acquired inventory information and applies an algorithm to identify unsold items. This process evaluates data such as expiration dates and inventory turnover rates.

[0533] Step 3:

[0534] The emotion engine built into the device analyzes the user's facial expressions and voice to identify the user's emotional state in real time. This allows the system to understand what emotions the user is currently feeling.

[0535] Step 4:

[0536] The server retrieves data on unsold products and the user's emotional state, and uses this information to generate cooking instructions in the generation processing module. The generated recipe is customized to match the user's emotional state.

[0537] Step 5:

[0538] The server transmits the generated cooking instructions to digital signage terminals, providing visual promotion within the store. This visually presents customers with advantageous offers utilizing leftover products.

[0539] Step 6:

[0540] The server also sends cooking instructions as push notifications to the user's electronic device, allowing the user to receive new recipes at any time.

[0541] Step 7:

[0542] The user opens the app on their electronic device and checks the received recipe details. The recipe includes the necessary ingredients and steps, which the user can refer to to cook.

[0543] Step 8:

[0544] Users submit feedback through the app after cooking. This feedback includes ratings of the recipe and suggestions for improvement.

[0545] Step 9:

[0546] The server collects and analyzes user feedback and stores it in a database. This information will be used to adjust the emotion engine and improve the recipe generation algorithm in future updates.

[0547] (Example 2)

[0548] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] In inventory management, it is difficult to efficiently utilize underperforming products and make proposals that meet the individual needs of consumers. Furthermore, because product suggestions cannot take into account consumers' emotional states, the effectiveness of sales promotions is limited.

[0550] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0551] In this invention, the server includes means for acquiring inventory information and identifying items that are not selling well based on certain criteria, means for collecting and analyzing user emotions through a terminal, and means for generating cooking content based on the identified items that are not selling well and the analyzed emotional information. This makes it possible to suggest the most suitable products and cooking content based on the consumer's emotional state.

[0552] "Inventory information" refers to detailed data such as the quantity of products or materials, storage location, and expiration date.

[0553] "Items with stagnant sales" refer to goods that have not sold sufficiently within a certain period and remain as inventory.

[0554] "Emotion analysis" is a technology that determines a user's emotional state in real time based on their facial expressions and voice.

[0555] "Cooking instructions" refer to the specific recipes and cooking methods suggested to the user.

[0556] "Visual media" refers to display devices and digital signage used to convey information to consumers through images.

[0557] "Portable devices" refer to electronic devices that users can carry around, such as smartphones and tablets.

[0558] "Consumer input" refers to opinions such as feedback and requests that users provide to the system.

[0559] A "generative algorithm" is a computational method used to automatically create optimal recipes and suggestions based on input data.

[0560] This invention relates to a system for inventory management and cooking suggestions that incorporates emotion analysis. This system integrates a server, terminals, digital signage, and an emotion analysis engine. Each component is described in detail below.

[0561] The server integrates with the inventory management information system to periodically collect inventory information. This data includes product type, quantity, and expiration date, and is analyzed using an algorithm to identify items with slow sales. The results of this analysis are then used as data for subsequent cooking suggestions.

[0562] The device is equipped with an engine for analyzing the user's emotions. Users can communicate their emotional state to the system by facing the device's camera or using voice input. Based on the data acquired from the device, the emotion analysis engine analyzes the user's facial expressions and voice tone in real time to identify their current emotional state. This information is used to customize cooking suggestions.

[0563] The generation server generates recipes based on underperforming items and the user's emotional state. This process utilizes a generation AI model. The AI ​​model automatically creates recipes suitable for the characteristics of the items and the user's emotional state, providing optimal suggestions tailored to the user. For example, if a user wants to relax, a simple, relaxing recipe will be generated.

[0564] The generated cooking instructions are transmitted to digital signage and the user's mobile device. The digital signage displays promotions that visually highlight the appeal of the products. Users can check the cooking instructions and required ingredient list on their own devices, receiving support to ensure a smooth purchasing and cooking process.

[0565] As a concrete example, in a supermarket, the server detects unsold tomatoes and herb chicken, and the terminal's emotion engine reads the user's emotion as "wanting to relax." Based on this information, the server generates a recipe for "Refreshing Tomato and Herb Chicken Salad." This recipe is expressed as a prompt message that reads, "There are unsold tomatoes and herb chicken, and the user wants to relax. Please suggest a simple and relaxing recipe based on these factors."

[0566] This system is designed to achieve both sales promotion and improved customer satisfaction.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] The server periodically retrieves inventory information from the inventory management system. Input data includes product name, quantity, and expiration date. After retrieving the inventory data, the server analyzes it using an algorithm to identify items that are not selling well. The analysis then outputs a list of items that are not selling well.

[0570] Step 2:

[0571] The device uses an emotion analysis engine to collect user emotion information. Input includes facial expressions captured by the device's camera and voice input. The emotion analysis engine uses facial recognition and voice analysis technologies to identify the user's most recent emotional state. The output of this process is data on the user's current emotional state.

[0572] Step 3:

[0573] The generation processing server receives a list of underperforming items obtained in Step 1 and user sentiment data analyzed in Step 2 as input. The generation AI model calculates cooking content using the relevant items and generates cooking content optimized for the user's emotional state. The output of this process is a customized cooking content for the user.

[0574] Step 4:

[0575] The server transmits the generated cooking instructions to the digital signage and the user's mobile device. The input includes data on the cooking instructions. The digital signage generates promotional content to visually appeal to the product and outputs it as visual information to the user. The terminal displays the cooking procedure and a list of required ingredients to the user.

[0576] Step 5:

[0577] Users provide feedback via a terminal after cooking. This feedback includes satisfaction levels and suggestions for improvement. The server collects this feedback and stores it in a database. The output provides data that helps improve the sentiment analysis engine and cooking content generation algorithm.

[0578] (Application Example 2)

[0579] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0580] Unsold goods remaining in stores lead to decreased corporate profits and increased environmental burden due to product waste. Furthermore, consumers may become dissatisfied if they cannot find suitable products or recipes that match their emotional needs. To solve these problems, new methods of offering products that take into account both the product itself and the emotional state of the consumer are needed.

[0581] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0582] In this invention, the server includes means for acquiring inventory information and identifying product targets, means for analyzing the consumer's emotional state using emotion analysis means, and means for adapting procedures to the consumer's emotions based on the analyzed emotional state. This enables the effective utilization of unsold products and the provision of appropriate product suggestions and recipes tailored to the consumer's emotions.

[0583] "Inventory information" refers to data about the existence and quantity of products, and is used to identify unsold items.

[0584] "Target products" refers to products selected in a store based on specific criteria, and after identification, various analyses and processes are performed.

[0585] "Instructions" refer to information that combines steps for cooking or using a specific product, and are provided to consumers through visual media or electronic information processing devices.

[0586] "Display means" refers to a device or method for visually providing a consumer with generated procedures or other information, including digital displays.

[0587] "Electronic information processing equipment" refers to devices used for inputting, processing, analyzing, and outputting digital data, and examples include smartphones and tablets.

[0588] "Emotional analysis methods" refer to technologies or devices used to analyze a consumer's emotional state from their facial expressions, voice, etc., and analysis using artificial intelligence is common.

[0589] "Consumer emotional state" refers to the psychological state of consumers, and analysis can identify states such as "wanting to relax" or "feeling stressed."

[0590] "Generation rules" are rules that define algorithms and methods for generating procedures, thereby enabling the creation of efficient and consumer-friendly procedures.

[0591] In implementing this invention, the server first works in conjunction with an inventory information processing system to periodically acquire inventory data for products. The inventory data is processed by an algorithm built into the server to identify unsold products. This identified product information is then used in the next step.

[0592] Next, the emotion analysis engine installed in the device analyzes the user's emotional state through the camera and voice input functions. This engine identifies emotions in real time from the user's facial expressions and voice tone, and sends that data to the server.

[0593] Based on this emotional data and inventory information, the server generates instructions (e.g., cooking methods and usage instructions) using a generative AI model. These instructions are customized according to the consumer's current emotional state; for example, it suggests simple, calming recipes for stressed users and provides easy-to-cook recipes for users who need energy.

[0594] The generated instructions are sent to in-store display devices (e.g., digital signage) and users' electronic information devices (e.g., smartphones). This enables attractive visual promotions utilizing unsold products, and users are shown individually personalized instructions.

[0595] Users can provide feedback after performing a procedure. This feedback is collected by the server and used to improve the accuracy of the sentiment analysis engine and procedure generation rules.

[0596] As a concrete example, consider a scenario where a user discovers unsold tomatoes and herb chicken at a supermarket. The server combines this with the user's emotional state of "wanting to relax," obtained from their device, to generate a simple recipe for "Refreshing Tomato and Herb Chicken Salad," which is then displayed attractively on a visual medium. This recipe is displayed on the user's electronic device, and the purchasing process proceeds smoothly.

[0597] A concrete example of a prompt message for a generative AI model would be: "If the system detects that the user is tired, generate a recipe that can be prepared quickly using the ingredients remaining in the refrigerator. For example, a simple soup or a one-plate meal."

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The server periodically retrieves product inventory data from the inventory information processing system. The input is the current data from the inventory management database, which is then analyzed using an algorithm to output a list of unsold products. This process identifies unsold items.

[0601] Step 2:

[0602] The device uses an emotion analysis engine to analyze the user's emotional state. The input consists of the user's facial expressions and voice, collected through the device's camera and microphone. This data is processed in real time, and the resulting emotional data is output as an emotional state. This process is carried out to accurately understand the user's psychological state.

[0603] Step 3:

[0604] The server takes a list of unsold items and the user's emotional state as input and generates cooking instructions using a generative AI model. It executes an algorithm that generates cooking instructions based on the input data and outputs customized cooking instructions. A specific example of its operation is the generation of recipes tailored to the user's emotional state.

[0605] Step 4:

[0606] The server generates cooking instructions and transmits them to in-store display devices and the user's electronic information processing device. The input is the generated cooking instructions, and the output is visually represented promotional information and a display screen on the user's terminal. This operation allows the user to take actions in accordance with the cooking instructions.

[0607] Step 5:

[0608] Users provide feedback after performing the cooking procedure. The input is an evaluation entered through a feedback form, which the server receives and stores in a database. This feedback data is used to improve future algorithms. This process allows the system to continuously improve.

[0609] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0610] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0611] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0612] [Fourth Embodiment]

[0613] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0614] As shown in Figure 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.

[0615] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0616] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0617] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0618] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0619] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0620] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0621] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0622] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0623] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0624] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0625] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0626] One embodiment of the present invention is an information provision system that promotes the efficient utilization of unsold goods. This system consists of an inventory management information system, a generation processing server, a digital signage terminal, and an electronic terminal for interacting with consumers.

[0627] Acquisition and analysis of inventory information

[0628] The server works in conjunction with the inventory management information system to obtain real-time information on unsold products. The obtained data is then used by an AI algorithm to identify the unsold items. The identified unsold products are then passed to the recipe generation module on the generation processing server.

[0629] Recipe generation and provision

[0630] The generation processing server generates cooking instructions for consumers based on information about identified unsold products. These instructions are automatically designed to be easy for consumers to prepare and to be delicious. The generated instructions are sent to digital signage terminals installed in the store and displayed as a visual promotion for the unsold products.

[0631] Consumer interface and feedback collection

[0632] Users can receive generated recipes via their smartphones or other electronic devices. Visual instructions and ingredient lists can be viewed through the app. Furthermore, users can provide feedback through the app after cooking, and the server collects this information to improve the accuracy of the algorithm.

[0633] Specific example

[0634] In a supermarket, a server detects from inventory data that a large quantity of tomatoes and herb chicken remain unsold that week. Based on this, the AI ​​generates a cooking recipe for "Tomato Herb Chicken Pasta." Digital signage is used to promote this recipe and related products within the store. Users can view the recipe through an app, purchase the ingredients, and actually cook the dish. Feedback after cooking contributes to future algorithm improvements.

[0635] In this way, it is possible to efficiently utilize unsold products and increase consumer purchasing intent.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The server periodically retrieves the latest inventory data from the inventory management information system via an API. This allows it to understand the current storage status and potential unsold status of each product.

[0639] Step 2:

[0640] The server analyzes the acquired inventory data to identify unsold items. In this process, items with approaching expiration dates or those with low past inventory turnover rates are selected as unsold.

[0641] Step 3:

[0642] The server sends data based on unsold products to a generation processing module, which then generates cooking instructions using those products. During this process, an AI algorithm is used to create easy-to-prepare and delicious recipes for consumers.

[0643] Step 4:

[0644] The server transmits the generated cooking instructions to digital signage for in-store display. The displayed content includes selected leftover products and recommended recipes using them.

[0645] Step 5:

[0646] The server simultaneously sends information about the generated recipe to the device app as a push notification. This allows users to receive new cooking ideas in real time.

[0647] Step 6:

[0648] The user opens the app and checks the details of the received recipe. The recipe includes the necessary ingredients and cooking instructions, and the user can add it directly to their shopping list.

[0649] Step 7:

[0650] After the user completes cooking, they can provide feedback on the recipe within the app. This feedback can include a rating of the recipe and suggestions for improvement.

[0651] Step 8:

[0652] The terminal sends the input feedback to the server. The server receives this feedback, saves it in a database, and optimizes the algorithm by incorporating it into the next recipe generation.

[0653] (Example 1)

[0654] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] One challenge is that unsold products cannot be efficiently utilized and end up being discarded, resulting in economic losses and environmental problems. Furthermore, consumers are not provided with fresh and useful information, leading to a decrease in their willingness to purchase.

[0656] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0657] In this invention, the server includes means for acquiring inventory information and identifying unsold products, means for generating cooking instructions based on the identified unsold products, and means for providing the generated cooking instructions through a visual medium and an electronic device. This makes it possible to efficiently utilize unsold products and improve consumer purchasing intent.

[0658] "Inventory information" refers to data that shows the inventory status of products, and includes real-time information necessary to identify unsold products.

[0659] "Unsold products" refer to goods that remain unconsumed compared to the planned sales volume, and their efficient utilization is required.

[0660] "Cooking instructions" are guidelines generated based on unsold products, containing the steps consumers need to actually prepare the food.

[0661] "Visual media" refers to digital signage and other display devices, and is a means of providing information to consumers visually.

[0662] "Electronic devices" refer to devices such as smartphones and tablets that consumers use to receive cooking instructions.

[0663] "Consumer responses" refer to information that shows feedback and opinions from consumers after they have cooked according to the provided cooking instructions, and these contribute to future improvements.

[0664] One embodiment of the present invention is an information provision system for efficiently utilizing unsold products. This system consists of an inventory management information system, a generation processing server, a digital signage terminal, and an electronic terminal for interacting with consumers.

[0665] The server works in conjunction with the inventory management information system to periodically retrieve real-time inventory information. This information shows the inventory status of products and is used to identify unsold items. Upon receiving the inventory information, the server uses an AI algorithm to analyze the data, understand trends in unsold inventory, and list products that meet specific criteria.

[0666] Next, the generation server generates cooking instructions based on the data of the identified unsold products. At this stage, a generation AI model is used to create specific prompt statements. These prompt statements include specific instructions such as "Create a recipe for a dish using tomatoes and herb chicken," and cooking steps are created based on these.

[0667] The generated cooking instructions are transmitted to a digital signage terminal. This terminal displays the recipe as a visual promotion within the store. Visual media such as product images and cooking images are used to attract consumers' attention.

[0668] Users can receive recipes generated through the app using electronic devices such as smartphones and tablets. They can view visual instructions and ingredient lists. Furthermore, users can provide feedback via the app after cooking; this information is returned to the server and used to improve future algorithms.

[0669] As a concrete example, if tomatoes and herb chicken are unsold at a certain store, the server will detect this and generate a recipe called "Tomato Herb Chicken Pasta." This recipe will be promoted on digital signage, and users can check it in the app and actually cook it. The prompt given to the generation AI model is "Please come up with a delicious recipe using tomatoes and herb chicken."

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] The server retrieves real-time inventory information from the inventory management system. This information includes the stock quantity and details of unsold items for each product. Based on the input inventory information, the server analyzes the data and generates a list of unsold products. This list identifies products with low sales rates and excess inventory. A dataset of unsold products is generated as output.

[0673] Step 2:

[0674] The server passes the dataset of unsold products obtained in Step 1 to an AI algorithm for analysis. In this process, a generative AI model is used to create prompt sentences for cooking instructions related to the identified unsold products. The input is the dataset of unsold products, and the prompt sentences include the instruction "Please think of cooking steps using the selected ingredients." As a result, a draft recipe proposed by the AI ​​model is output.

[0675] Step 3:

[0676] The generation processing server sends the prompt text created in step 2 to the AI ​​model to generate specific cooking instructions. Based on the input prompt text, the AI ​​model automatically generates the optimal cooking procedure for the ingredients and compiles it into a detailed recipe. This output is a clearly formatted cooking instruction that makes it easy for consumers to prepare the dishes.

[0677] Step 4:

[0678] The server transmits the generated cooking instructions to a digital signage terminal. The input is the generated recipe information, which the terminal converts into media data for visual display. Images of related products and cooking processes are displayed on the terminal, designed to attract consumer interest. The output functions as a visual promotion for customers.

[0679] Step 5:

[0680] Users can receive these cooking instructions via their device or smartphone and follow the steps to prepare the meal. The input is a request to view the recipe through the app, and the output is a visual instruction sheet and ingredient list displayed on the user's device. After completing the dish, users provide feedback through the app.

[0681] Step 6:

[0682] The server collects feedback from users and uses it to readjust the AI ​​algorithm. Consumer response data is input, and based on this, the algorithm's parameters are modified to improve the accuracy of future recipe generation. The output of this process is optimized cooking procedures and a better user experience for future use.

[0683] (Application Example 1)

[0684] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] The challenge lies in effectively utilizing unsold products in stores while simultaneously increasing consumer purchasing intent. Furthermore, efficient methods are needed for managing inventory and visually promoting unsold products. Additionally, accurate consumer feedback must be collected to continuously improve the quality of the resulting cooking instructions.

[0686] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0687] In this invention, the server includes means for acquiring inventory information and identifying unsold items, means for generating cooking procedures based on the identified unsold items, means for providing the generated cooking procedures through visual media and communication devices, means for collecting consumer input and improving the cooking procedures, and means for acquiring and electronically displaying identification information of products including unsold items. This enables effective utilization of unsold products, increased consumer purchasing intent, and continuous improvement of cooking procedures based on feedback.

[0688] "Inventory information" refers to data about the quantity and condition of goods managed at stores and distribution facilities.

[0689] "Unsold items" refer to products that have not been sold within a certain period or products that remain in inventory due to decreased demand.

[0690] "Cooking instructions" refer to detailed methods and procedures for preparing a dish using specific ingredients.

[0691] "Visual media" refers to tools and devices used to display information visually, such as images and videos.

[0692] A "communication device" is an electronic device used to send and receive information, and includes smartphones and tablets.

[0693] "Consumer input" refers to evaluations and feedback information provided by consumers based on their own experiences and opinions.

[0694] A "generative algorithm" is a computational procedure for automatically generating information such as cooking procedures based on specific rules or models.

[0695] "Identification information" refers to information that clearly identifies the characteristics of a particular product and distinguishes it from other products.

[0696] In order to implement this invention, it is necessary to construct an information provision system for effectively utilizing unsold goods. This system consists of a server, a visual medium, a communication device, and an interface for collecting user input.

[0697] The server connects to an inventory management database and retrieves inventory information in real time. From this information, it identifies products that are likely to remain unsold and manages their identification information. The server also uses a generative AI model to create cooking instructions based on the identified unsold products. These cooking instructions are designed to be easily understood by the user and are visually presented through in-store displays and communication devices via visual media.

[0698] The communication device enables interaction between the user and the system. Users can use their smartphones or tablets to view the provided cooking instructions and related information. User feedback is sent to the server via the communication device and used to improve the generation algorithm. As a concrete example, a food store could use leftover apples to generate a new recipe called "Caramelized Apples," which the server could then display on the store's screen.

[0699] An example of a prompt message would be: "Generate an original dessert recipe using leftover apples from the store. Please suggest an easy-to-make recipe for home use."

[0700] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0701] Step 1:

[0702] The server retrieves inventory information in real time from the inventory management database. All product data from the inventory management database is used as input. The server analyzes the inventory quantity and sales history of each product to identify unsold items and extract their identification information. A list of the identification information for unsold items is generated as output.

[0703] Step 2:

[0704] The server uses a generative AI model to automatically generate cooking instructions based on the identification information of unsold products extracted in Step 1. A list of identification information for unsold products is used as input. The server then compares this information with a recipe database to construct appropriate ingredient combinations and cooking processes. The generated cooking instructions are obtained as output.

[0705] Step 3:

[0706] The server transmits the generated cooking instructions to a visual medium and a communication device. The terminal receives this information and displays it visually to the user. The generated cooking instructions are used as input. The terminal uses this data to visually display the instructions on the in-store display or the user's smartphone screen. The output provides the user with cooking instructions that they can visually confirm.

[0707] Step 4:

[0708] The user uses a terminal to review the provided cooking instructions and then performs the actual cooking. The user can provide feedback on the cooking results and experience. The input includes user feedback. The terminal sends this feedback to the server. The output is the user's feedback information.

[0709] Step 5:

[0710] The server analyzes the feedback received from the user and makes adjustments to the generative AI model. User feedback information is used as input. Based on this data, the server re-evaluates the generative AI model's algorithm and makes improvements as needed. The adjusted generative AI model is obtained as output.

[0711] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0712] As an embodiment of the present invention, a system for utilizing unsold products incorporating an emotion engine is provided. This system is configured by integrating an inventory management information system, a generation processing server, digital signage, and an electronic terminal with the emotion engine.

[0713] Acquisition and analysis of inventory information

[0714] The server works in conjunction with the inventory management system to periodically retrieve inventory information. It analyzes the inventory data using an algorithm to identify unsold items. The information on unsold items is then passed on to the next process.

[0715] User analysis using an emotion engine

[0716] An emotion engine built into the device analyzes the user's emotions using user input and video footage captured from the device's camera. The emotion engine analyzes the user's facial expressions and voice tone in real time to identify their emotional state.

[0717] Recipe generation and provision

[0718] The generation server generates cooking instructions based on unsold products and the user's emotional state. The generated cooking instructions are customized to the user's current emotions; for example, a user experiencing high stress levels will be offered a simple and relaxing recipe.

[0719] Display and adjustment of information

[0720] The generated cooking instructions are sent to a digital signage terminal and the user's electronic device. The digital signage also features visual promotions utilizing leftover products from the store. Furthermore, the app on the device displays personalized cooking suggestions and ingredient lists tailored to the user's mood.

[0721] Feedback and algorithm improvements

[0722] Users can provide feedback through the app after cooking. This feedback is sent to the server and stored in a database. The server analyzes this data and uses it to improve the accuracy of both the emotion engine and the cooking procedure generation algorithm.

[0723] Specific example

[0724] In a supermarket, a server detects unsold tomatoes and herb chicken. Simultaneously, an emotion engine reads the user's emotional state from their device, indicating a desire to relax. Based on this information, the server generates a simple and relaxing recipe for "Refreshing Tomato and Herb Chicken Salad" and visually promotes it on digital signage. This recipe is displayed on the user's electronic device, facilitating a smooth purchasing and cooking process.

[0725] This system can more effectively promote unsold products and further meet the individual needs of consumers.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The server periodically retrieves the latest inventory information from the inventory management system via API. This allows it to monitor inventory levels and unsold items.

[0729] Step 2:

[0730] The server analyzes the acquired inventory information and applies an algorithm to identify unsold items. This process evaluates data such as expiration dates and inventory turnover rates.

[0731] Step 3:

[0732] The emotion engine built into the device analyzes the user's facial expressions and voice to identify the user's emotional state in real time. This allows the system to understand what emotions the user is currently feeling.

[0733] Step 4:

[0734] The server retrieves data on unsold products and the user's emotional state, and uses this information to generate cooking instructions in the generation processing module. The generated recipe is customized to match the user's emotional state.

[0735] Step 5:

[0736] The server transmits the generated cooking instructions to digital signage terminals, providing visual promotion within the store. This visually presents customers with advantageous offers utilizing leftover products.

[0737] Step 6:

[0738] The server also sends cooking instructions as push notifications to the user's electronic device, allowing the user to receive new recipes at any time.

[0739] Step 7:

[0740] The user opens the app on their electronic device and checks the received recipe details. The recipe includes the necessary ingredients and steps, which the user can refer to to cook.

[0741] Step 8:

[0742] Users submit feedback through the app after cooking. This feedback includes ratings of the recipe and suggestions for improvement.

[0743] Step 9:

[0744] The server collects and analyzes user feedback and stores it in a database. This information will be used to adjust the emotion engine and improve the recipe generation algorithm in future updates.

[0745] (Example 2)

[0746] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0747] In inventory management, it is difficult to efficiently utilize underperforming products and make proposals that meet the individual needs of consumers. Furthermore, because product suggestions cannot take into account consumers' emotional states, the effectiveness of sales promotions is limited.

[0748] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0749] In this invention, the server includes means for acquiring inventory information and identifying items that are not selling well based on certain criteria, means for collecting and analyzing user emotions through a terminal, and means for generating cooking content based on the identified items that are not selling well and the analyzed emotional information. This makes it possible to suggest the most suitable products and cooking content based on the consumer's emotional state.

[0750] "Inventory information" refers to detailed data such as the quantity of products or materials, storage location, and expiration date.

[0751] "Items with stagnant sales" refer to goods that have not sold sufficiently within a certain period and remain as inventory.

[0752] "Emotion analysis" is a technology that determines a user's emotional state in real time based on their facial expressions and voice.

[0753] "Cooking instructions" refer to the specific recipes and cooking methods suggested to the user.

[0754] "Visual media" refers to display devices and digital signage used to convey information to consumers through images.

[0755] "Portable devices" refer to electronic devices that users can carry around, such as smartphones and tablets.

[0756] "Consumer input" refers to opinions such as feedback and requests that users provide to the system.

[0757] A "generative algorithm" is a computational method used to automatically create optimal recipes and suggestions based on input data.

[0758] This invention relates to a system for inventory management and cooking suggestions that incorporates emotion analysis. This system integrates a server, terminals, digital signage, and an emotion analysis engine. Each component is described in detail below.

[0759] The server integrates with the inventory management information system to periodically collect inventory information. This data includes product type, quantity, and expiration date, and is analyzed using an algorithm to identify items with slow sales. The results of this analysis are then used as data for subsequent cooking suggestions.

[0760] The device is equipped with an engine for analyzing the user's emotions. Users can communicate their emotional state to the system by facing the device's camera or using voice input. Based on the data acquired from the device, the emotion analysis engine analyzes the user's facial expressions and voice tone in real time to identify their current emotional state. This information is used to customize cooking suggestions.

[0761] The generation server generates recipes based on underperforming items and the user's emotional state. This process utilizes a generation AI model. The AI ​​model automatically creates recipes suitable for the characteristics of the items and the user's emotional state, providing optimal suggestions tailored to the user. For example, if a user wants to relax, a simple, relaxing recipe will be generated.

[0762] The generated cooking instructions are transmitted to digital signage and the user's mobile device. The digital signage displays promotions that visually highlight the appeal of the products. Users can check the cooking instructions and required ingredient list on their own devices, receiving support to ensure a smooth purchasing and cooking process.

[0763] As a concrete example, in a supermarket, the server detects unsold tomatoes and herb chicken, and the terminal's emotion engine reads the user's emotion as "wanting to relax." Based on this information, the server generates a recipe for "Refreshing Tomato and Herb Chicken Salad." This recipe is expressed as a prompt message that reads, "There are unsold tomatoes and herb chicken, and the user wants to relax. Please suggest a simple and relaxing recipe based on these factors."

[0764] This system is designed to achieve both sales promotion and improved customer satisfaction.

[0765] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0766] Step 1:

[0767] The server periodically retrieves inventory information from the inventory management system. Input data includes product name, quantity, and expiration date. After retrieving the inventory data, the server analyzes it using an algorithm to identify items that are not selling well. The analysis then outputs a list of items that are not selling well.

[0768] Step 2:

[0769] The device uses an emotion analysis engine to collect user emotion information. Input includes facial expressions captured by the device's camera and voice input. The emotion analysis engine uses facial recognition and voice analysis technologies to identify the user's most recent emotional state. The output of this process is data on the user's current emotional state.

[0770] Step 3:

[0771] The generation processing server receives a list of underperforming items obtained in Step 1 and user sentiment data analyzed in Step 2 as input. The generation AI model calculates cooking content using the relevant items and generates cooking content optimized for the user's emotional state. The output of this process is a customized cooking content for the user.

[0772] Step 4:

[0773] The server transmits the generated cooking instructions to the digital signage and the user's mobile device. The input includes data on the cooking instructions. The digital signage generates promotional content to visually appeal to the product and outputs it as visual information to the user. The terminal displays the cooking procedure and a list of required ingredients to the user.

[0774] Step 5:

[0775] Users provide feedback via a terminal after cooking. This feedback includes satisfaction levels and suggestions for improvement. The server collects this feedback and stores it in a database. The output provides data that helps improve the sentiment analysis engine and cooking content generation algorithm.

[0776] (Application Example 2)

[0777] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0778] Unsold goods remaining in stores lead to decreased corporate profits and increased environmental burden due to product waste. Furthermore, consumers may become dissatisfied if they cannot find suitable products or recipes that match their emotional needs. To solve these problems, new methods of offering products that take into account both the product itself and the emotional state of the consumer are needed.

[0779] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0780] In this invention, the server includes means for acquiring inventory information and identifying product targets, means for analyzing the consumer's emotional state using emotion analysis means, and means for adapting procedures to the consumer's emotions based on the analyzed emotional state. This enables the effective utilization of unsold products and the provision of appropriate product suggestions and recipes tailored to the consumer's emotions.

[0781] "Inventory information" refers to data about the existence and quantity of products, and is used to identify unsold items.

[0782] "Target products" refers to products selected in a store based on specific criteria, and after identification, various analyses and processes are performed.

[0783] "Instructions" refer to information that combines steps for cooking or using a specific product, and are provided to consumers through visual media or electronic information processing devices.

[0784] "Display means" refers to a device or method for visually providing a consumer with generated procedures or other information, including digital displays.

[0785] "Electronic information processing equipment" refers to devices used for inputting, processing, analyzing, and outputting digital data, and examples include smartphones and tablets.

[0786] "Emotional analysis methods" refer to technologies or devices used to analyze a consumer's emotional state from their facial expressions, voice, etc., and analysis using artificial intelligence is common.

[0787] "Consumer emotional state" refers to the psychological state of consumers, and analysis can identify states such as "wanting to relax" or "feeling stressed."

[0788] "Generation rules" are rules that define algorithms and methods for generating procedures, thereby enabling the creation of efficient and consumer-friendly procedures.

[0789] In implementing this invention, the server first works in conjunction with an inventory information processing system to periodically acquire inventory data for products. The inventory data is processed by an algorithm built into the server to identify unsold products. This identified product information is then used in the next step.

[0790] Next, the emotion analysis engine installed in the device analyzes the user's emotional state through the camera and voice input functions. This engine identifies emotions in real time from the user's facial expressions and voice tone, and sends that data to the server.

[0791] Based on this emotional data and inventory information, the server generates instructions (e.g., cooking methods and usage instructions) using a generative AI model. These instructions are customized according to the consumer's current emotional state; for example, it suggests simple, calming recipes for stressed users and provides easy-to-cook recipes for users who need energy.

[0792] The generated instructions are sent to in-store display devices (e.g., digital signage) and users' electronic information devices (e.g., smartphones). This enables attractive visual promotions utilizing unsold products, and users are shown individually personalized instructions.

[0793] Users can provide feedback after performing a procedure. This feedback is collected by the server and used to improve the accuracy of the sentiment analysis engine and procedure generation rules.

[0794] As a concrete example, consider a scenario where a user discovers unsold tomatoes and herb chicken at a supermarket. The server combines this with the user's emotional state of "wanting to relax," obtained from their device, to generate a simple recipe for "Refreshing Tomato and Herb Chicken Salad," which is then displayed attractively on a visual medium. This recipe is displayed on the user's electronic device, and the purchasing process proceeds smoothly.

[0795] A concrete example of a prompt message for a generative AI model would be: "If the system detects that the user is tired, generate a recipe that can be prepared quickly using the ingredients remaining in the refrigerator. For example, a simple soup or a one-plate meal."

[0796] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0797] Step 1:

[0798] The server periodically retrieves product inventory data from the inventory information processing system. The input is the current data from the inventory management database, which is then analyzed using an algorithm to output a list of unsold products. This process identifies unsold items.

[0799] Step 2:

[0800] The device uses an emotion analysis engine to analyze the user's emotional state. The input consists of the user's facial expressions and voice, collected through the device's camera and microphone. This data is processed in real time, and the resulting emotional data is output as an emotional state. This process is carried out to accurately understand the user's psychological state.

[0801] Step 3:

[0802] The server takes a list of unsold items and the user's emotional state as input and generates cooking instructions using a generative AI model. It executes an algorithm that generates cooking instructions based on the input data and outputs customized cooking instructions. A specific example of its operation is the generation of recipes tailored to the user's emotional state.

[0803] Step 4:

[0804] The server generates cooking instructions and transmits them to in-store display devices and the user's electronic information processing device. The input is the generated cooking instructions, and the output is visually represented promotional information and a display screen on the user's terminal. This operation allows the user to take actions in accordance with the cooking instructions.

[0805] Step 5:

[0806] Users provide feedback after performing the cooking procedure. The input is an evaluation entered through a feedback form, which the server receives and stores in a database. This feedback data is used to improve future algorithms. This process allows the system to continuously improve.

[0807] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0808] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0809] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0810] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0811] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0812] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0813] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0814] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0815] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0816] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0817] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0818] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0819] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0820] 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.

[0821] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0822] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0823] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0824] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0825] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0826] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0827] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0828] The following is further disclosed regarding the embodiments described above.

[0829] (Claim 1)

[0830] A means of obtaining inventory information and identifying unsold items,

[0831] A means of generating cooking procedures based on identified unsold items,

[0832] A means of providing the generated cooking instructions through visual media and electronic devices,

[0833] A means of collecting consumer input and improving cooking procedures,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising means for analyzing inventory information in real time and accumulating trends regarding unsold items.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising means for evaluating the cooking procedure using consumer input and adjusting the generation algorithm.

[0839] "Example 1"

[0840] (Claim 1)

[0841] A means of obtaining inventory information and identifying unsold products,

[0842] A means of generating cooking instructions based on identified unsold products,

[0843] Means for providing the generated cooking instructions through a visual medium and electronic device,

[0844] A means of collecting consumer feedback and improving cooking instructions,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, comprising means for analyzing inventory information in real time and accumulating trends regarding unsold products.

[0848] (Claim 3)

[0849] The system according to claim 1, comprising means for evaluating cooking instructions using consumer responses and adjusting the production process.

[0850] "Application Example 1"

[0851] (Claim 1)

[0852] A means of obtaining inventory information and identifying unsold items,

[0853] A means of generating cooking procedures based on identified unsold items,

[0854] Means for providing the generated cooking procedure through a visual medium and a communication device,

[0855] A means of collecting consumer input and improving cooking procedures,

[0856] A means of obtaining and electronically displaying identification information of products, including unsold items,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, comprising means for analyzing inventory information in real time and accumulating trends regarding unsold items, and means for providing consumers with product information and displaying visual information, including photographs and nutritional information, on a communication device.

[0860] (Claim 3)

[0861] The system according to claim 1, comprising means for evaluating cooking procedures using consumer input and adjusting the generation algorithm, and means for proposing alternative cooking methods related to unsold items.

[0862] "Example 2 of combining an emotion engine"

[0863] (Claim 1)

[0864] A means of obtaining inventory information and identifying items whose sales are stalled based on certain criteria,

[0865] A means of collecting and analyzing user emotions through the device,

[0866] A means of generating cooking content based on identified items whose sales are stagnant and analyzed emotional information,

[0867] A means of providing the generated cooking content through visual media and mobile devices, and promoting the use of the product,

[0868] A means of collecting consumer input and improving cooking methods,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, comprising means for analyzing inventory information in real time and accumulating trends regarding items whose sales are stagnant.

[0872] (Claim 3)

[0873] The system according to claim 1, further comprising means for evaluating the cooking content using consumer input and adjusting the generation algorithm.

[0874] "Application example 2 when combining with an emotional engine"

[0875] (Claim 1)

[0876] A means of obtaining inventory information and identifying the target product,

[0877] A means of generating procedures based on identified product targets,

[0878] A means for providing the generated procedure through a display means and an electronic information processing device,

[0879] A means of analyzing the emotional state of consumers using emotion analysis methods,

[0880] A means of adapting procedures to the consumer's emotions based on the analyzed emotional state,

[0881] A means of collecting data from consumers and improving procedures,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, comprising means for acquiring inventory information hourly and recording trends related to the product.

[0885] (Claim 3)

[0886] The system according to claim 1, comprising means for evaluating procedures using consumer data and adjusting generation rules. [Explanation of Symbols]

[0887] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining inventory information and identifying unsold items, A means of generating cooking procedures based on identified unsold items, Means for providing the generated cooking procedure through a visual medium and a communication device, A means of collecting consumer input and improving cooking procedures, A means of obtaining and electronically displaying identification information of products, including unsold items, A system that includes this.

2. The system according to claim 1, comprising means for analyzing inventory information in real time and accumulating trends regarding unsold items, and means for providing consumers with product information and displaying visual information, including photographs and nutritional information, on a communication device.

3. The system according to claim 1, comprising means for evaluating cooking procedures using consumer input and adjusting the generation algorithm, and means for proposing alternative cooking methods related to unsold items.

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