System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting voice, text, image, and video data from a user; means for analyzing the collected data to identify user behavior and preferences; means for generating product recommendations personalized to the user based on the analysis; means for displaying the product recommendations on the user's terminal; means for collecting inventory and sales data and running a demand forecasting algorithm; means for managing inventory based on the demand forecast; means for analyzing a customer service inquiry by natural language processing and generating an appropriate response; and means for transmitting the response to the user's terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The retail industry is required to provide personalized shopping experiences that meet customer needs, efficient inventory management, accurate sales forecasts, and high-quality customer service. However, traditional approaches to these challenges often involve manual data analysis and management, which is inefficient and limits the improvement of customer satisfaction. Addressing these issues is particularly difficult for small and medium-sized businesses, which have limited resources. Therefore, new methods are needed to significantly improve operational efficiency and customer experience across the retail industry. [Means for solving the problem]

[0005] The present invention provides a system including means for collecting voice, text, image, and video data from users, means for analyzing the collected data to identify user behavior and preferences, means for generating personalized product recommendations for users based on the analysis results, means for displaying the product recommendations on the user's device, means for collecting inventory data and sales data and running a demand forecasting algorithm, means for managing inventory based on the demand forecast, means for analyzing customer service inquiries using natural language processing to generate appropriate responses, and means for transmitting the responses to the user's device. This system enables improved customer experience, optimized inventory management, more accurate sales forecasts, and more efficient customer service in the retail industry, thereby significantly improving operational efficiency throughout the retail industry.

[0006] "User" refers to a consumer who uses this system to search for, select, and purchase products.

[0007] "Voice data" refers to data in which the contents of what the user has said are recorded in audio format.

[0008] "Text data" is information expressed as characters, and includes data entered by a user or generated by a server.

[0009] "Image data" is visual information recorded as a still image.

[0010] "Movie data" is visual information recorded as moving images.

[0011] A "terminal" is a device that a user directly operates (e.g., a smartphone, tablet, or PC).

[0012] A "server" is a computer system that analyzes data and controls and manages the entire system.

[0013] "Data analytics" is the process of processing collected data using algorithms and machine learning to extract meaningful information.

[0014] "User behavior" refers to a series of activities performed by a user, such as browsing, searching, and purchasing products.

[0015] "Preferences" are attributes that indicate the user's preferences and interests.

[0016] "Personalized product recommendation" is the process of selecting and suggesting specific products based on a user's individual interests and behavioral data.

[0017] "Inventory data" is information that indicates the current quantity of a product for sale.

[0018] "Sales data" is information indicating past sales performance.

[0019] A "demand forecasting algorithm" is a calculation method for predicting future demand for a product based on past data.

[0020] "Inventory management" is the process of securing and maintaining an adequate amount of merchandise for sale.

[0021] "Customer service" refers to a support service that provides appropriate responses to inquiries and requests from users.

[0022] "Natural language processing" is a technology that allows computers to understand and process human language.

[0023] A "response" is a response from the system to a user's inquiry. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0026] First, the terms used in the following description will be explained.

[0027] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0028] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0029] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0030] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0031] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0038] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0042] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[0046] Data collection and analysis

[0047] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server then converts the voice data into text using voice recognition technology.

[0048] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences, which allows it to understand users' interests and preferences based on their past purchase and browsing history.

[0049] Generating personalized product recommendations

[0050] The server generates a personalized product recommendation list based on the results of analyzing the user's behavior and preferences. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[0051] The generated product recommendation list is sent to the user's device and visually presented, for example, by displaying images and detailed information of the recommended products on the screen of a smartphone.

[0052] Inventory and sales data management

[0053] The server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server then uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, aiming to improve the efficiency of inventory management.

[0054] Customer Service Automation

[0055] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user asks the chatbot, "I'd like to cancel my order," the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. This improves the quality of customer service and ensures efficient responses.

[0056] Specific examples

[0057] A user speaks to their smartphone's voice assistant, saying, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the speech into text. The server analyzes the user's past purchase history and generates a recommended list of sneakers that match the user's preferences. This list is displayed on the user's smartphone, and the user can select and purchase the products they like.

[0058] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms to predict future demand, and, when inventory is low, sends replenishment notifications to warehouses to optimize inventory management.

[0059] Additionally, if a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request, generates a message containing guidelines on the cancellation procedure, and sends it to the user's smartphone, allowing the user to easily complete the cancellation procedure.

[0060] As described above, the present invention is a system that detects user needs in real time, provides a personalized shopping experience, and realizes efficient inventory management and high-quality customer service.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] A user speaks to their smartphone's voice assistant, saying, "I'm looking for some new sneakers."

[0064] Step 2:

[0065] The terminal collects the user's voice data and transmits it to a server via the Internet.

[0066] Step 3:

[0067] The server converts the received voice data into text data using a voice recognition engine.

[0068] Step 4:

[0069] The server analyzes the converted text data and understands the user's request ("I'm looking for sneakers").

[0070] Step 5:

[0071] The server retrieves the user's past purchase history and browsing history from a database and analyzes it.

[0072] Step 6:

[0073] The server uses machine learning algorithms to analyze the user's preferences and generate a personalized list of sneaker recommendations for the user.

[0074] Step 7:

[0075] The server transmits the generated sneaker recommendation list to the user's terminal.

[0076] Step 8:

[0077] The terminal visually displays a list of sneaker recommendations to the user.

[0078] Step 9:

[0079] The user selects a sneaker of interest from the displayed recommendation list and views the detail page.

[0080] Step 10:

[0081] The user clicks the "Buy" button to purchase the selected sneakers.

[0082] Step 11:

[0083] The terminal transmits the user's purchase request to the server.

[0084] Step 12:

[0085] The server processes purchase requests and updates the inventory database.

[0086] Step 13:

[0087] The server collects sales and inventory data and stores it in a database.

[0088] Step 14:

[0089] The server uses machine learning algorithms to forecast sales and predict future demand.

[0090] Step 15:

[0091] The server manages inventory based on the forecast and, if necessary, notifies warehouses and stores of replenishment requests.

[0092] Step 16:

[0093] After making a purchase, the user asks the chatbot, "I would like to cancel my order."

[0094] Step 17:

[0095] The terminal transmits the contents of the user's inquiry to the server.

[0096] Step 18:

[0097] The server analyzes the inquiry using natural language processing and generates a template message containing guidelines for the cancellation procedure.

[0098] Step 19:

[0099] The server sends the generated template message to the user's terminal.

[0100] Step 20:

[0101] The terminal displays guidelines to the user and guides them through the cancellation procedure.

[0102] The above are the specific processing steps of the system according to the present invention, which enable users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service.

[0103] Example 1

[0104] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0105] In today's retail industry, despite the existence of a large amount of data, efficient inventory management and personalized product recommendations remain difficult. Customer service responses are also time-consuming and costly, so fast, high-quality responses are required. Furthermore, inaccurate demand forecasts can lead to excess inventory, making it difficult to prevent economic losses. There is a need to solve these issues and provide an efficient and effective retail assistant system.

[0106] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0107] In this invention, the server includes means for collecting voice, text, image, and video data from a user, means for converting the collected data into text using voice recognition technology, means for analyzing the collected data and identifying user behavior and preferences using a machine learning algorithm, means for generating a personalized product recommendation list for the user based on the analysis results, means for transmitting the product recommendation list to the user's terminal and visually displaying it, means for collecting sales data and customer trend data and storing them in a database, means for using the collected data to perform a demand forecast using a machine learning algorithm, means for calculating the optimal inventory replenishment timing and amount based on the demand forecast and sending a notification, means for analyzing customer service inquiries using natural language processing and generating an appropriate response, and means for transmitting the response to the user's terminal. This enables efficient inventory management and sales forecasting, personalized product recommendations, and high-quality customer service.

[0108] "Voice data" is digital data that records what the user has said.

[0109] "Text data" refers to character string information converted using voice recognition technology or character string information directly input by a user.

[0110] "Image data" refers to a digital image file containing user-provided visual information.

[0111] "Video Data" means a digital video file containing user-provided dynamic visual information.

[0112] "Speech recognition technology" is a technology that recognizes human speech as digital data and converts it into text data.

[0113] A "machine learning algorithm" is a mathematical model that analyzes large amounts of data and finds patterns to make predictions and classifications.

[0114] "User behavior" refers to a behavior history that includes all operations and selections that a user performs within the system.

[0115] "Preferences" refers to information related to products and services that a user prefers, and is primarily based on past behavioral history and purchase history.

[0116] A "product recommendation list" is a list of products that are likely to interest the user, generated based on the analysis results.

[0117] "Visually displaying" means presenting images or text information to the user's visual field on the user's terminal.

[0118] "Sales data" refers to information related to product sales, specifically including the number of products, price, date and time of sale, etc.

[0119] "Customer trend data" refers to data related to customer activity, including customer behavior history, purchase history, site browsing history, etc.

[0120] A "database" is a system for efficiently storing, retrieving, and updating data in a structured format.

[0121] "Demand forecasting" is the process of predicting future consumer demand based on past data.

[0122] "Natural language processing" is the branch of computer science that deals with understanding, interpreting, and generating human language.

[0123] A "response" is a reply message generated in response to a customer inquiry.

[0124] "Terminal" refers to a device used by a user, including a smartphone, tablet, or PC.

[0125] A "notification" is a message intended to inform a user or other system of specific information or action.

[0126] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[0127] Data collection and analysis

[0128] The server collects voice, text, image, and video data from the user. Specifically, when a user says to the voice assistant, "I'm looking for new sneakers," the voice data is sent to the server via the device. The server converts the voice data into text using voice recognition technology (e.g., Google Cloud Speech-to-Text API).

[0129] The server analyzes the collected data and uses machine learning algorithms (e.g., TensorFlow, Scikit-learn) to identify user behavior and preferences, which allows the server to understand the user's interests and preferences based on past purchase and browsing history.

[0130] Generating personalized product recommendations

[0131] The server generates a personalized product recommendation list based on the user's behavior and preference analysis. For example, a sneaker recommendation list is generated and sent to the user's device. The device then visually displays the list, showing product images and detailed information on the screen.

[0132] Inventory and sales data management

[0133] The server continuously collects sales data and customer trend data and stores it in a database (e.g., MongoDB, PostgreSQL). This accumulates data for inventory management and sales forecasting. The server uses machine learning algorithms (e.g., SciPy, Pandas) to forecast demand and calculate the optimal timing and amount of inventory replenishment. For example, if sneaker stock is low, the server sends a notification to the warehouse to instruct it to replenish the stock.

[0134] Customer Service Automation

[0135] When a user makes an inquiry or request a return after a purchase, the server analyzes the request using natural language processing technology (e.g., Amazon Lex, Dialogflow). For example, if a user asks a chatbot, "I would like to cancel my order," the server analyzes the request, generates guidelines for the cancellation procedure based on a template message, and sends them to the user's device. This allows the user to easily proceed with the cancellation procedure.

[0136] Specific examples

[0137] For example, a user might say to a smartphone's voice assistant, "I'm looking for new sneakers." The device sends this voice data to a server, which converts the speech into text using the Google Cloud Speech-to-Text API. The server analyzes the user's past purchase history and uses TensorFlow to generate a recommended list of sneakers that match the user's preferences. This list is then sent to the device and displayed on the smartphone screen. The user can then select and purchase the items they like from the list.

[0138] The server also updates sneaker inventory data and predicts future demand using a sales forecasting algorithm powered by SciPy. If inventory is low, it sends a replenishment notification to the warehouse to optimize inventory management. Furthermore, if a user sends a message to the chatbot saying, "I want to cancel my order," the server uses Amazon Lex to analyze the request and sends guidelines for the cancellation procedure to the user's smartphone.

[0139] The system analyzes data from users in real time and provides personalized services, thereby increasing customer satisfaction and enabling efficient inventory management and sales forecasting.

[0140] Prompt Sentence Examples

[0141] "Generate personalized product recommendation lists using user purchase history and preference data."

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] When a user speaks to a voice assistant, the voice data is collected by the device. For example, if a user says to their smartphone, "I'm looking for new sneakers," the voice recording is stored on the device.

[0145] Input: User's voice data

[0146] Output: Recorded audio data

[0147] Step 2:

[0148] The device sends the recorded audio data to the server using an HTTPS request, uploading the audio data to the server as an attachment.

[0149] Input: Recorded audio data

[0150] Output: Audio data sent to the server

[0151] Step 3:

[0152] The server uses speech recognition technology to convert the received voice data into text data. Specifically, it sends the voice data to the Google Cloud Speech-to-Text API and receives the resulting text data.

[0153] Input: Audio data sent to the server

[0154] Output: Text data converted from audio

[0155] Step 4:

[0156] The server analyzes the text data and uses machine learning algorithms to identify user behavior and preferences. Specifically, the text data is fed into a TensorFlow environment, where it is compared with past purchase and browsing history to update a user preference model.

[0157] Input: Text data converted from speech, past purchase history

[0158] Output: User preference analysis results

[0159] Step 5:

[0160] The server generates a personalized product recommendation list based on the results of the preference analysis. It searches the product database for products that match the user's preferences and compiles them into a list.

[0161] Input: User preference analysis results

[0162] Output: A personalized product recommendation list

[0163] Step 6:

[0164] The generated product recommendation list is sent to the device, which receives the JSON format data from the server and visually displays it along with detailed product information.

[0165] Input: A personalized product recommendation list

[0166] Output: Product recommendation list displayed on the device

[0167] Step 7:

[0168] The server continuously collects sales data and customer behavior data and stores them in a database, such as recording sales information and browsing frequency for each product in real time.

[0169] Input: Sales data, customer trend data

[0170] Output: Sales data and customer trend data stored in a database

[0171] Step 8:

[0172] The server uses machine learning algorithms to forecast demand using past sales data, and SciPy and Pandas are used to analyze the data and predict future demand.

[0173] Input: Sales data stored in the database

[0174] Output: Demand forecast results

[0175] Step 9:

[0176] The server calculates the optimal inventory replenishment timing and quantity based on the demand forecast results, and if it detects that an item is low in stock, it notifies the warehouse to request replenishment.

[0177] Input: Demand forecast results

[0178] Output: Replenishment notification to warehouse

[0179] Step 10:

[0180] If a user makes an inquiry or returns a purchase after purchase, the server uses natural language processing technology to analyze the request and generate an appropriate response. Amazon Lex is used to analyze the user's message.

[0181] Input: User's query message

[0182] Output: Parsed request

[0183] Step 11:

[0184] The response message generated by the server is sent to the terminal in real time, and the user can easily complete the process by following instructions such as cancellation procedures.

[0185] Input: Parsed request, generated response message

[0186] Output: Response message displayed on the terminal

[0187] In this way, this system handles everything from collecting voice data to converting it into text, analyzing it using machine learning, and automating personalized product recommendations, inventory management, and customer service.

[0188] (Application example 1)

[0189] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0190] Current retail systems lack the ability to understand diverse user preferences and make personalized product recommendations. They also lack efficiency in inventory management and demand forecasting, creating a need for improved customer service. Real-time preference analysis and appropriate product recommendations are particularly important for online shopping sites to improve user experience.

[0191] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0192] In this invention, the server includes means for collecting voice, text, image, and video data from users, means for analyzing the collected data to identify user behavior and preferences, and means for generating personalized product recommendations for users based on the analysis results, thereby enabling efficient inventory management and high-quality customer service while providing personalized product recommendations tailored to user needs.

[0193] A "user" is a person who uses this system to receive product recommendations and customer service.

[0194] "Voice data" is digital data of the voice generated when a user speaks to a voice assistant.

[0195] "Text data" refers to voice data converted into text, as well as text information manually entered by the user.

[0196] "Image data" refers to digital data of product images and related images provided by users.

[0197] "Video data" is digital data of examples of product usage and related videos provided by users.

[0198] "Analysis" is the process of analyzing data to identify user behavior and preferences based on collected data.

[0199] "Personalized product recommendations" are product lists generated based on analysis results to suit the individual preferences and needs of each user.

[0200] A "terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[0201] "Inventory data" is digital information that indicates how many items are in stock at a warehouse or store.

[0202] "Sales data" is digital data that includes past sales history and sales information.

[0203] A "demand forecasting algorithm" is a mathematical model or calculation method for predicting future demand based on past sales data.

[0204] "Inventory management" is the process of maintaining appropriate inventory levels based on demand forecasting algorithms.

[0205] "Natural language processing" is an artificial intelligence technology that analyzes natural human language to understand its appropriate meaning.

[0206] A "response" is a reply or guidance message generated by the system in response to a user's inquiry.

[0207] "Speech recognition technology" is a technology that converts voice data into text data.

[0208] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[0209] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server converts the voice data into text using voice recognition technology. Users can also upload image and video data to the server using their device. In this process, "SpeechRecognition" is used as the voice recognition technology and "Spacy" is used for natural language processing.

[0210] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences, including past purchase and browsing history. This analysis process utilizes machine learning libraries such as TensorFlow and scikit-learn.

[0211] The server generates a personalized product recommendation list based on the analysis results. This recommendation list includes products that are likely to interest the user, improving the user experience. The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone.

[0212] In addition, the server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server uses machine learning algorithms to predict future demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores to improve inventory management efficiency.

[0213] Automated customer service is also an important function of this system. When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user inquires about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. This improves the quality of customer service and ensures efficient responses.

[0214] As a concrete example, consider the case where a user says to a smartphone's voice assistant, "I'm looking for new sneakers." At this time, the device sends the voice data to a server, which converts the voice into text. The server then analyzes the user's past purchase history and generates a recommended list of sneakers that match the user's preferences. This list is displayed on the user's smartphone, allowing the user to select and purchase the products they like.

[0215] The server also updates sneaker inventory data and runs sales forecasting algorithms to predict future demand. If inventory is low, it sends a replenishment notification to the warehouse to optimize inventory management. If a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request and generates a message with guidelines on the cancellation procedure, which is sent to the user's smartphone. The user can easily complete the cancellation procedure.

[0216] Example prompt sentence:

[0217] Explain how you can recommend products to a user when they say, "I'm looking for some new sneakers," to their smartphone voice assistant. Show them how you can improve their shopping experience by providing sneaker recommendations.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] A user talks to a voice assistant

[0221] A user inputs a specific voice command into the smartphone's voice assistant, for example, "I'm looking for new sneakers." The input is voice data, and the output is the device's transmission of the voice data to the server.

[0222] Step 2:

[0223] Converting audio data into text data

[0224] The server uses speech recognition technology to convert the received voice data into text data. Specifically, it uses the "SpeechRecognition" library, with the input being voice data and the output being the corresponding text data. The converted text data is "I'm looking for new sneakers."

[0225] Step 3:

[0226] Analyze the user's past purchase history

[0227] The server analyzes the user's past purchase and browsing history based on the text data. This analysis uses machine learning libraries such as "TensorFlow" and "scikit-learn." The input is text data and history data, and the output is the analysis results of the user's preferences.

[0228] Step 4:

[0229] Generate personalized product recommendation lists

[0230] The server generates a personalized product recommendation list based on the analysis results. For example, it includes specific product names such as "Sneaker A" and "Sneaker B." The input is the analysis results, and the output is the product recommendation list.

[0231] Step 5:

[0232] Send and display the product recommendation list to the user's device

[0233] The server sends the generated product recommendation list to the user's smartphone, which then visually displays it. The input is the product recommendation list, and the output is the product list displayed on the user's smartphone screen. Specifically, images and detailed information about the recommended products are displayed.

[0234] Step 6:

[0235] Collect inventory and sales data to forecast demand

[0236] The server collects sales data and customer trend data and builds a database for inventory management and sales forecasting. It uses TensorFlow and scikit-learn to predict future demand and calculate the optimal timing and amount of inventory replenishment. The input is sales data and trend data, and the output is the demand forecast results.

[0237] Step 7:

[0238] Optimize inventory management

[0239] The server notifies warehouses and stores of inventory replenishment based on the demand forecast results, thereby improving the efficiency of inventory management. The input is the demand forecast results, and the output is replenishment notifications to warehouses and stores.

[0240] Step 8:

[0241] Parse customer service inquiries and generate responses

[0242] When a user makes a query to the chatbot, the server uses natural language processing to analyze the request and generate an appropriate response. For example, it responds to a message such as "I would like to cancel my order." The input is the query message, and the output is the analysis result and the generated response message.

[0243] Step 9:

[0244] Sends the generated response to the user's device

[0245] The server generates a response message and sends it to the user's terminal, which then displays it. The input is the response message, and the output is the response message displayed on the user's terminal. For example, guidelines on cancellation procedures are displayed.

[0246] By implementing each of the above steps, personalized product recommendations based on user needs can be made while achieving efficient inventory management and high-quality customer service.

[0247] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0248] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to make personalized product recommendations, manages inventory, forecasts sales, and provides efficient customer service. It also combines an emotion engine that recognizes users' emotions.

[0249] Data collection and analysis

[0250] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server then converts the voice data into text using voice recognition technology.

[0251] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences. It also uses an emotion engine to analyze the user's emotions and understand their current emotional state from the content of their requests. This allows it to understand the user's preferences from their past purchase history, browsing history, and emotional state.

[0252] Generating personalized product recommendations

[0253] The server generates a personalized product recommendation list based on the user's behavior, preferences, and sentiment analysis results. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[0254] The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone. Engagement messages that take the user's emotional state into account are also displayed simultaneously.

[0255] Inventory and sales data management

[0256] The server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server then uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, aiming to improve the efficiency of inventory management.

[0257] Customer Service Automation

[0258] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user inquires with the chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. The server also uses an emotion engine to analyze the user's emotions and tailor the response to match their emotional state at the time of the inquiry. This improves the quality of customer service and enables efficient responses.

[0259] Specific examples

[0260] A user says to their smartphone's voice assistant, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the speech into text. The server analyzes the user's past purchase history and also analyzes the user's emotions from the voice data. If the user is determined to be in a good mood, the server takes this into account and generates a list of sneaker recommendations with brighter, more attractive recommendation messages. This list and messages are displayed on the user's smartphone, and the user can select and purchase the products they like.

[0261] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms to predict future demand, and, when inventory is low, sends replenishment notifications to warehouses to optimize inventory management.

[0262] If a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request, generates a message containing guidelines on the cancellation procedure, and sends it to the user's smartphone. At this time, the emotion engine grasps the user's emotional state and responds with a calm message. In this way, the user can easily complete the cancellation procedure.

[0263] The above is a concrete implementation of the system according to the present invention. This system allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. Responses that take user emotions into consideration can achieve even higher customer satisfaction.

[0264] The processing flow will be explained below.

[0265] Step 1:

[0266] A user speaks to their smartphone's voice assistant, saying, "I'm looking for some new sneakers."

[0267] Step 2:

[0268] The terminal collects the user's voice data and transmits it to a server via the Internet.

[0269] Step 3:

[0270] The server converts the received voice data into text data using a voice recognition engine.

[0271] Step 4:

[0272] The server analyzes the converted text data and understands the user's request ("I'm looking for sneakers").

[0273] Step 5:

[0274] The server retrieves the user's past purchase history and browsing history from a database and analyzes it.

[0275] Step 6:

[0276] The server uses an emotion engine to analyze the voice data and identify the user's emotional state, for example, determining whether the user is excited or calm based on the tone and rate of the voice.

[0277] Step 7:

[0278] The server uses machine learning algorithms to analyze the user's preferences and generate a personalized list of sneaker recommendations for the user, taking into account the user's emotional state from the emotion engine.

[0279] Step 8:

[0280] The server sends the generated sneaker recommendation list and an engagement message tailored to the user's emotional state to the user's device.

[0281] Step 9:

[0282] The device visually displays a list of sneaker recommendations and engagement messages to the user. For example, if the user is excited, it displays an energetic message, and if the user is calm, it displays a calm message.

[0283] Step 10:

[0284] The user selects a sneaker of interest from the displayed recommendation list and views the detail page.

[0285] Step 11:

[0286] The user clicks the "Buy" button to purchase the selected sneakers.

[0287] Step 12:

[0288] The terminal transmits the user's purchase request to the server.

[0289] Step 13:

[0290] The server processes purchase requests and updates the inventory database.

[0291] Step 14:

[0292] The server collects sales and inventory data and stores it in a database.

[0293] Step 15:

[0294] The server uses machine learning algorithms to forecast sales and predict future demand.

[0295] Step 16:

[0296] The server manages inventory based on the forecast and, if necessary, notifies warehouses and stores of replenishment requests.

[0297] Step 17:

[0298] After making a purchase, the user asks the chatbot, "I would like to cancel my order."

[0299] Step 18:

[0300] The terminal transmits the contents of the user's inquiry to the server.

[0301] Step 19:

[0302] The server analyzes the inquiry using natural language processing and generates a template message containing guidelines for the cancellation procedure.

[0303] Step 20:

[0304] The server uses an emotion engine to analyze the user's emotions and tailor the response to their emotional state at the time of the query, for example, generating a calmer tone of message if the user is emotional.

[0305] Step 21:

[0306] The server sends the generated template message to the user's terminal.

[0307] Step 22:

[0308] The terminal displays guidelines to the user and guides them through the cancellation procedure.

[0309] The above are the specific processing steps of the system based on the present invention. This series of processes allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. By utilizing the emotion engine, it is possible to respond according to the user's emotional state and provide higher customer satisfaction.

[0310] Example 2

[0311] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0312] In today's retail industry, it is necessary to respond to the needs of each individual customer, and to provide efficient inventory management and high-quality customer service. However, conventional systems have difficulty meeting all of these challenges, particularly in terms of personalized product recommendations that take customer emotions into account and efficient inventory management. Furthermore, there is a lack of methods for responding efficiently while maintaining the quality of customer service.

[0313] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for collecting voice, text, image, and video data from a user; means for analyzing the collected data and identifying the user's behavior and preferences; means for identifying the user's emotions as part of the data analysis; means for generating personalized product recommendations for the user based on the analysis results and the emotion identification results; means for displaying the product recommendations on the user's terminal; means for collecting inventory data and sales data and executing a demand forecasting algorithm; means for performing inventory management based on the demand forecast; means for analyzing customer service inquiries using natural language processing and generating appropriate responses; and means for transmitting the responses to the user's terminal. This enables personalized product recommendations that take customer emotions into consideration, efficient inventory management, and high-quality customer service.

[0314] "User" refers to a person who uses the system.

[0315] "Audio, text, image, and video data" refers to various forms of digital data collected from users.

[0316] "Means of collection" refers to methods and devices for obtaining audio, text, image, and video data from users.

[0317] "Means for analyzing data" refers to methods and devices for processing collected digital data and understanding and classifying its contents.

[0318] "Means for identifying user behavior and preferences" refers to a method or apparatus for identifying user behavior patterns and preferences from analyzed data.

[0319] "Means for identifying emotions" refers to a method or device for determining the emotional state of a user from their voice or text.

[0320] "Means for generating personalized product recommendations" refers to a method or device that creates a list of recommended products that are individually suited to a user based on the user's behavior, preferences, and emotions.

[0321] The term "means for displaying product recommendations on a user's terminal" refers to a method or device for displaying the generated product recommendations on an electronic device used by the user.

[0322] "Inventory data" refers to data regarding the quantity and condition of products stored in stores and warehouses.

[0323] "Sales Data" refers to data regarding the sales status of products.

[0324] A "demand forecasting algorithm" refers to a calculation method or model for predicting future demand for a product based on past data.

[0325] "Means for inventory management" refers to methods and devices for replenishing and adjusting inventory based on the results of demand forecasts.

[0326] "Means for analyzing customer service inquiries" refers to a method or apparatus for understanding and appropriately handling user inquiries.

[0327] "Natural language processing" refers to the technology that enables computers to understand and process human language.

[0328] "Means for generating an appropriate response" refers to a method or device for generating an appropriate answer based on the content of the inquiry.

[0329] "Means for sending a response to a user's terminal" refers to a method or device for sending the generated response to the user's electronic device.

[0330] This is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service. It also has an emotion engine that recognizes users' emotions.

[0331] Hardware and Software Configuration

[0332] The system of the present invention comprises several hardware and software components. The main components are listed below.

[0333] Server: A computer used to perform key functions such as data collection, analysis, emotion recognition, product recommendations, inventory management, sales forecasting, and customer service.

[0334] Device: A device (such as a smartphone, tablet, or PC) through which a user interacts with the system via a voice assistant or other input means.

[0335] software:

[0336] Speech recognition technology: Google Speech-to-Text API

[0337] Machine learning algorithms: Scikit-learn

[0338] Natural Language Processing Model: BERT

[0339] Database: MySQL

[0340] Specific operation of the system

[0341] Data collection and analysis

[0342] The server collects voice, text, image, and video data from users. For example, when a user speaks to a smartphone's voice assistant, the voice data is sent to the server via the device. The server converts the voice data into text using Google's voice recognition technology.

[0343] Analyzing data and identifying user preferences

[0344] The server analyzes the collected data, using machine learning algorithms such as Scikit-learn to identify user behavior and preferences, and also uses an emotion engine based on the BERT model to analyze the user's emotions and understand their current emotional state based on the request content.

[0345] Generating personalized product recommendations

[0346] The server generates a personalized product recommendation list based on the user's behavior, preferences, and sentiment analysis results. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[0347] View the recommendation list

[0348] The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone. Engagement messages that take the user's emotional state into account are also displayed simultaneously.

[0349] Inventory and sales data management

[0350] The server continuously collects sales data and customer trend data and stores it in a MySQL database. Demand forecasts are performed using Scikit-learn to calculate the optimal timing and amount of inventory replenishment. Based on this, notifications are sent to warehouses and stores to improve inventory management efficiency.

[0351] Customer Service Automation

[0352] When a user makes an inquiry or request a return after a purchase, the server uses a natural language processing model such as BERT to analyze the request and generate an appropriate response. For example, if a user inquires with a chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. An emotion engine is used to analyze the user's emotions and tailor the response to match the user's emotional state at the time of the inquiry. This results in efficient, high-quality customer service.

[0353] Specific examples

[0354] For example, consider the case where a user says to a smartphone's voice assistant, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the voice data into text using the Google Speech-to-Text API. The server analyzes the user's past purchase history and emotions from the voice data to generate a personalized list of recommended sneakers. This list and a message reflecting the user's emotions are then displayed on the user's smartphone.

[0355] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms using Scikit-learn, and sends replenishment notifications to warehouses when stock is low, optimizing inventory management.

[0356] An example prompt is:

[0357] "Design a program that identifies emotions from user voice data and recommends personalized products."

[0358] "Build a machine learning model to forecast demand based on inventory and sales data."

[0359] "Design a natural language processing system that generates appropriate responses to user queries using a chatbot."

[0360] The above is a concrete implementation of the system according to the present invention. This system allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. Responses that take user emotions into consideration can achieve even higher customer satisfaction.

[0361] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0362] Step 1:

[0363] A user makes a voice request to a smartphone voice assistant.

[0364] Input: Audio data

[0365] Action: The user says to their smartphone, "I'm looking for some new sneakers."

[0366] Output: The audio data is sent to the device.

[0367] Step 2:

[0368] The terminal transmits the voice data to the server.

[0369] Input: Audio data

[0370] Operation: The terminal transmits the user's voice data to the server.

[0371] Output: The server receives the audio data.

[0372] Step 3:

[0373] The server uses voice recognition technology to convert the voice data into text.

[0374] Input: Audio data

[0375] How it works: The server uses speech recognition technology (Google Speech-to-Text API) to convert the voice data into text.

[0376] Output: Text data

[0377] Step 4:

[0378] The server analyzes the collected text data to identify the user's behavior and preferences.

[0379] Input: Text data

[0380] How it works: The server uses Scikit-learn to analyze text data and the user's purchase history to identify the user's preferences.

[0381] Output: User preference data

[0382] Step 5:

[0383] The server uses the BERT model to analyze the user's sentiment.

[0384] Input: Text data

[0385] How it works: The server uses the BERT model to identify a user's emotions from text data. For example, it determines that a user is in a "good mood" based on their statement.

[0386] Output: User emotion data

[0387] Step 6:

[0388] The server generates a personalized product recommendation list based on the user's behavior, preferences, and emotional data.

[0389] Input: User preference data, user emotion data

[0390] How it works: The server uses Scikit-learn algorithms to generate a list of products that match the user's preferences and emotions.

[0391] Output: Product recommendation list

[0392] Step 7:

[0393] The server transmits the generated product recommendation list to the user's terminal.

[0394] Input: Product Recommendation List

[0395] Operation: The server sends a product recommendation list and an engagement message to the user's device.

[0396] Output: The device receives the product recommendation list and the message.

[0397] Step 8:

[0398] The terminal displays a list of recommended products to the user.

[0399] Input: Product recommendation list, engagement message

[0400] How it works: The device displays a list of product recommendations and engagement messages on the user's screen. For example, a list of recommended sneakers is displayed on a smartphone screen.

[0401] Output: User visually confirms the recommended products.

[0402] Step 9:

[0403] The user selects a product from the recommended list and completes the purchase process.

[0404] Input: Product Recommendation List

[0405] How it works: The user selects the product they like from the list and proceeds with the purchase.

[0406] Output: Purchase data

[0407] Step 10:

[0408] The server updates sales data and manages inventory data.

[0409] Input: Purchase data

[0410] How it works: The server saves sales data to a MySQL database and updates inventory data. It uses Scikit-learn to forecast sales and calculate when and how much inventory to replenish.

[0411] Output: Stock replenishment notification

[0412] Step 11:

[0413] The server sends notifications to warehouses and stores to replenish stock.

[0414] Input: Restock notification

[0415] How it works: The server sends notifications to warehouses and stores to replenish inventory, enabling efficient inventory management.

[0416] Output: Warehouses and stores receive instructions to replenish inventory.

[0417] Step 12:

[0418] A user makes an inquiry or returns a product after making a purchase.

[0419] Input: Query text

[0420] Action: The user sends a message to the chatbot saying, "I want to cancel my order."

[0421] Output: The query data is sent to the server.

[0422] Step 13:

[0423] The server uses natural language processing to analyze the query and generate an appropriate response.

[0424] Input: Inquiry data

[0425] How it works: The server uses the BERT model to analyze the query and generate an appropriate response message, such as a message containing cancellation guidelines.

[0426] Output: Response message

[0427] Step 14:

[0428] The server sends the generated response message to the user's terminal.

[0429] Input: Response message

[0430] Operation: The server sends a response message to the user's device, adapting the response to the user's emotion using a tone that takes into account the user's emotion using the emotion engine.

[0431] Output: The terminal receives the response message.

[0432] Step 15:

[0433] The terminal displays a response message to the user.

[0434] Input: Response message

[0435] Action: The device displays a response message on the user's screen, for example, guidelines on the cancellation procedure.

[0436] Output: The user visually confirms the response message.

[0437] (Application example 2)

[0438] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0439] Conventional online shopping systems tend to be unable to fully consider users' preferences and emotions, and tend to recommend products and provide uniform responses. Furthermore, they face challenges in quickly managing inventory and forecasting sales, making it difficult to provide efficient customer service. This can lead to lower user satisfaction and a lack of increased sales.

[0440] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0441] In this invention, the server includes means for collecting voice, text, image, and video data from a user, means for analyzing the collected data and identifying the user's behavior and preferences, means for generating personalized product recommendations for the user based on the analysis results, means for displaying the product recommendations on the user's device, means for collecting inventory data and sales data and executing a demand forecasting algorithm, means for performing inventory management based on the demand forecast, means for analyzing customer service inquiries using natural language processing and generating appropriate responses, means for transmitting the responses to the user's device, means for analyzing the user's emotions and generating product recommendations and engagement messages based on the user's emotional state in real time, and means for making appropriate adjustments in response to the user's inquiries based on the user's emotional state. This enables personalized product recommendations that accurately reflect the user's emotions and preferences, as well as efficient inventory management, sales forecasting, and high-quality customer service.

[0442] "Means for collecting voice, text, image, and video data from users" means equipment or devices that efficiently collect user-provided voice, text, image, and video data and convert it into the format required for subsequent analysis.

[0443] "Means for analyzing data and identifying user behavior and preferences" refers to algorithms or systems that analyze collected data to identify users' past purchase history, browsing history, and user characteristics, and to understand the behavior and preferences of individual users.

[0444] The "means for generating personalized product recommendations" refers to a method or device for listing products that are optimal for the user's preferences and behavior based on the analysis results and recommending them to the user.

[0445] The "means for displaying product recommendations on a user's device" refers to technology or software for visually presenting the generated product recommendation list on a user's device such as a smartphone or tablet.

[0446] "Means for collecting inventory data and sales data and running demand forecasting algorithms" refers to algorithms or systems that collect inventory status and sales information from stores and warehouses and perform calculations to forecast future demand.

[0447] "Means for inventory management based on demand forecasts" refers to methods and techniques for replenishing inventory in response to forecasted demand and for appropriate inventory management.

[0448] "Means for analyzing customer service inquiries using natural language processing and generating appropriate responses" refers to a system that uses natural language processing technology to analyze inquiries from users and generate responses that are optimal for their content.

[0449] "Means for sending responses to the user's device" refers to the technology or system for sending the generated answers and guidelines to the user's smartphone, tablet, etc., and displaying them appropriately.

[0450] "Means for analyzing user emotions and generating product recommendations and engagement messages based on emotional state in real time" refers to a system that analyzes emotions from the user's tone of voice, context, facial expressions, etc., and based on the results, provides a product recommendation list tailored to the user and messages in line with their emotions in real time.

[0451] "Means for making appropriate adjustments in response to a user's inquiry based on the user's emotional state" refers to a method or system for flexibly changing the tone and content of responses to inquiries based on the analyzed emotional state of the user.

[0452] The system of this invention is an AI-driven retail assistant system that collects and analyzes user voice, text, image, and video data to provide personalized product recommendations, manage inventory, and forecast sales. It also incorporates an emotion engine that analyzes user emotions in real time.

[0453] First, when a user speaks to a voice assistant, the voice data is sent to a server via a device such as a smartphone. The server then converts the voice data into text using voice recognition technology. This can be done using Google Speech API or other voice recognition software. If the user types text, that data is also sent to the server.

[0454] The server analyzes the collected audio, text, image, and video data using machine learning algorithms, natural language processing (NLP) libraries, and sentiment analysis engines designed for this purpose, such as NLTK and spaCy.

[0455] Based on the analysis results, the server references the user's past purchase and browsing history to identify preferences and generate a personalized product recommendation list. The server also analyzes the user's current emotional state from the collected data. The generated product recommendation list and engagement message are then displayed on the smartphone screen.

[0456] The server also continuously collects inventory and sales data. This data is stored in a database along with sales data and customer trend data. The server uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, improving inventory management efficiency.

[0457] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing technology to analyze the request and generate an appropriate response. For example, if a user inquires with the chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the user's device. At this time, the emotion engine grasps the user's emotional state and responds appropriately based on their emotional state at the time of the inquiry.

[0458] Specific examples

[0459] When a user says, "I want some new summer sunglasses," the voice data is sent to a server. The server converts the voice into text and analyzes the user's emotions. For example, if the user is speaking in a cheerful tone, a list of recommended sunglasses will be displayed along with a cheerful and upbeat message. This list and message will be displayed on the user's smartphone, allowing the user to select and purchase the product of their choice.

[0460] Prompt Sentence Examples

[0461] "Build an AI model that provides personalized product recommendations based on user sentiment analysis and feedback. Input data is voice, text, and image data, and output is a list of product recommendations and engagement messages."

[0462] In this way, the system based on the present invention provides personalized product recommendations that accurately reflect the user's emotions and preferences, as well as efficient inventory management, sales forecasting, and high-quality customer service.

[0463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0464] Step 1:

[0465] Collect voice, text, image, and video data from users.

[0466] When a user speaks to a smartphone's voice assistant, the voice data is sent to the server via the device. Text input, image and video data are also collected. The input data is voice, text, image and video. The output is this raw data.

[0467] Step 2:

[0468] The server converts the audio data into text.

[0469] The server uses speech recognition technology such as Google Speech API to convert the received voice data into text data. The input is voice data and the output is text data. Specifically, it uses a speech recognition engine to convert the acoustic signal into a string of characters.

[0470] Step 3:

[0471] The server analyzes user data to identify behavior and preferences.

[0472] The server uses machine learning algorithms and NLP libraries (e.g., NLTK and spaCy) to analyze collected text data, past purchase history, and browsing history to identify user behavior and preferences. The input is text data and past recorded data, and the output is the analysis results. Specific operations include data preprocessing, building topic models, and extracting preference patterns.

[0473] Step 4:

[0474] The server analyzes the user's emotions.

[0475] The server uses an emotion analysis engine to analyze the user's emotional state from text and voice data. The input is text and voice data, and the output is the emotion analysis results. Specifically, it extracts features from the text or voice and predicts the emotion label using an emotion classifier.

[0476] Step 5:

[0477] A server generates personalized product recommendations.

[0478] The server lists the products most relevant to the user's preferences based on the analysis results and sentiment analysis results. The input is the preference analysis results and sentiment analysis results, and the output is a product recommendation list. Specifically, the server uses a recommendation algorithm (e.g., collaborative filtering or content-based recommendation) to select products.

[0479] Step 6:

[0480] The server displays the product recommendation list on the user's terminal.

[0481] The generated product recommendation list and engagement message are displayed on the user's smartphone screen. The input is the product recommendation list and engagement message, and the output is the content displayed on the device screen. Specifically, the system updates the GUI components of the smartphone app.

[0482] Step 7:

[0483] The server collects inventory and sales data and runs demand forecasting algorithms.

[0484] The server continuously collects inventory and sales data from stores and warehouses, and runs a demand forecasting algorithm based on this data. The inputs are inventory and sales data, and the output is the demand forecast results. Specific operations include analyzing sales history data and calculating forecasts using a demand forecasting model.

[0485] Step 8:

[0486] The server manages inventory based on the demand forecast results.

[0487] The server calculates the optimal inventory replenishment timing and quantity based on the demand forecast results. The input is the demand forecast results, and the output is the replenishment notification. Specific operations include generating replenishment orders in conjunction with the inventory management system.

[0488] Step 9:

[0489] The server analyzes customer service inquiries using natural language processing and generates appropriate responses.

[0490] When a user makes a query, the content is sent to the server. The server uses NLP technology to analyze the query and generate an appropriate response. The input is the query content (text data), and the output is a response message. Specific operations include analyzing the query text and generating a template response.

[0491] Step 10:

[0492] The server sends a response message to the user's terminal.

[0493] The generated response message is sent to the user's smartphone and displayed immediately. The input is the response message, and the output is the content displayed on the user's device. Specifically, the data is sent to the device using a message sending API.

[0494] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0495] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0496] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0497] [Second embodiment]

[0498] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0499] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0500] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0501] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0502] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0503] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0504] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0505] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0506] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0507] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0508] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0509] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0510] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[0511] Data collection and analysis

[0512] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server then converts the voice data into text using voice recognition technology.

[0513] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences, which allows it to understand users' interests and preferences based on their past purchase and browsing history.

[0514] Generating personalized product recommendations

[0515] The server generates a personalized product recommendation list based on the results of analyzing the user's behavior and preferences. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[0516] The generated product recommendation list is sent to the user's device and visually presented, for example, by displaying images and detailed information of the recommended products on the screen of a smartphone.

[0517] Inventory and sales data management

[0518] The server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server then uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, aiming to improve the efficiency of inventory management.

[0519] Customer Service Automation

[0520] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user asks the chatbot, "I'd like to cancel my order," the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. This improves the quality of customer service and ensures efficient responses.

[0521] Specific examples

[0522] A user speaks to their smartphone's voice assistant, saying, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the speech into text. The server analyzes the user's past purchase history and generates a recommended list of sneakers that match the user's preferences. This list is displayed on the user's smartphone, and the user can select and purchase the products they like.

[0523] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms to predict future demand, and, when inventory is low, sends replenishment notifications to warehouses to optimize inventory management.

[0524] Additionally, if a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request, generates a message containing guidelines on the cancellation procedure, and sends it to the user's smartphone, allowing the user to easily complete the cancellation procedure.

[0525] As described above, the present invention is a system that detects user needs in real time, provides a personalized shopping experience, and realizes efficient inventory management and high-quality customer service.

[0526] The processing flow will be explained below.

[0527] Step 1:

[0528] A user speaks to their smartphone's voice assistant, saying, "I'm looking for some new sneakers."

[0529] Step 2:

[0530] The terminal collects the user's voice data and transmits it to a server via the Internet.

[0531] Step 3:

[0532] The server converts the received voice data into text data using a voice recognition engine.

[0533] Step 4:

[0534] The server analyzes the converted text data and understands the user's request ("I'm looking for sneakers").

[0535] Step 5:

[0536] The server retrieves the user's past purchase history and browsing history from a database and analyzes it.

[0537] Step 6:

[0538] The server uses machine learning algorithms to analyze the user's preferences and generate a personalized list of sneaker recommendations for the user.

[0539] Step 7:

[0540] The server transmits the generated sneaker recommendation list to the user's terminal.

[0541] Step 8:

[0542] The terminal visually displays a list of sneaker recommendations to the user.

[0543] Step 9:

[0544] The user selects a sneaker of interest from the displayed recommendation list and views the detail page.

[0545] Step 10:

[0546] The user clicks the "Buy" button to purchase the selected sneakers.

[0547] Step 11:

[0548] The terminal transmits the user's purchase request to the server.

[0549] Step 12:

[0550] The server processes purchase requests and updates the inventory database.

[0551] Step 13:

[0552] The server collects sales and inventory data and stores it in a database.

[0553] Step 14:

[0554] The server uses machine learning algorithms to forecast sales and predict future demand.

[0555] Step 15:

[0556] The server manages inventory based on the forecast and, if necessary, notifies warehouses and stores of replenishment requests.

[0557] Step 16:

[0558] After making a purchase, the user asks the chatbot, "I would like to cancel my order."

[0559] Step 17:

[0560] The terminal transmits the contents of the user's inquiry to the server.

[0561] Step 18:

[0562] The server analyzes the inquiry using natural language processing and generates a template message containing guidelines for the cancellation procedure.

[0563] Step 19:

[0564] The server sends the generated template message to the user's terminal.

[0565] Step 20:

[0566] The terminal displays guidelines to the user and guides them through the cancellation procedure.

[0567] The above are the specific processing steps of the system according to the present invention, which enable users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service.

[0568] Example 1

[0569] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0570] In today's retail industry, despite the existence of a large amount of data, efficient inventory management and personalized product recommendations remain difficult. Customer service responses are also time-consuming and costly, so fast, high-quality responses are required. Furthermore, inaccurate demand forecasts can lead to excess inventory, making it difficult to prevent economic losses. There is a need to solve these issues and provide an efficient and effective retail assistant system.

[0571] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0572] In this invention, the server includes means for collecting voice, text, image, and video data from a user, means for converting the collected data into text using voice recognition technology, means for analyzing the collected data and identifying user behavior and preferences using a machine learning algorithm, means for generating a personalized product recommendation list for the user based on the analysis results, means for transmitting the product recommendation list to the user's terminal and visually displaying it, means for collecting sales data and customer trend data and storing them in a database, means for using the collected data to perform a demand forecast using a machine learning algorithm, means for calculating the optimal inventory replenishment timing and amount based on the demand forecast and sending a notification, means for analyzing customer service inquiries using natural language processing and generating an appropriate response, and means for transmitting the response to the user's terminal. This enables efficient inventory management and sales forecasting, personalized product recommendations, and high-quality customer service.

[0573] "Voice data" is digital data that records what the user has said.

[0574] "Text data" refers to character string information converted using voice recognition technology or character string information directly input by a user.

[0575] "Image data" refers to a digital image file containing user-provided visual information.

[0576] "Video Data" means a digital video file containing user-provided dynamic visual information.

[0577] "Speech recognition technology" is a technology that recognizes human speech as digital data and converts it into text data.

[0578] A "machine learning algorithm" is a mathematical model that analyzes large amounts of data and finds patterns to make predictions and classifications.

[0579] "User behavior" refers to a behavior history that includes all operations and selections that a user performs within the system.

[0580] "Preferences" refers to information related to products and services that a user prefers, and is primarily based on past behavioral history and purchase history.

[0581] A "product recommendation list" is a list of products that are likely to interest the user, generated based on the analysis results.

[0582] "Visually displaying" means presenting images or text information to the user's visual field on the user's terminal.

[0583] "Sales data" refers to information related to product sales, specifically including the number of products, price, date and time of sale, etc.

[0584] "Customer trend data" refers to data related to customer activity, including customer behavior history, purchase history, site browsing history, etc.

[0585] A "database" is a system for efficiently storing, retrieving, and updating data in a structured format.

[0586] "Demand forecasting" is the process of predicting future consumer demand based on past data.

[0587] "Natural language processing" is the branch of computer science that deals with understanding, interpreting, and generating human language.

[0588] A "response" is a reply message generated in response to a customer inquiry.

[0589] "Terminal" refers to a device used by a user, including a smartphone, tablet, or PC.

[0590] A "notification" is a message intended to inform a user or other system of specific information or action.

[0591] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[0592] Data collection and analysis

[0593] The server collects voice, text, image, and video data from the user. Specifically, when a user says to the voice assistant, "I'm looking for new sneakers," the voice data is sent to the server via the device. The server converts the voice data into text using voice recognition technology (e.g., Google Cloud Speech-to-Text API).

[0594] The server analyzes the collected data and uses machine learning algorithms (e.g., TensorFlow, Scikit-learn) to identify user behavior and preferences, which allows the server to understand the user's interests and preferences based on past purchase and browsing history.

[0595] Generating personalized product recommendations

[0596] The server generates a personalized product recommendation list based on the user's behavior and preference analysis. For example, a sneaker recommendation list is generated and sent to the user's device. The device then visually displays the list, showing product images and detailed information on the screen.

[0597] Inventory and sales data management

[0598] The server continuously collects sales data and customer trend data and stores it in a database (e.g., MongoDB, PostgreSQL). This accumulates data for inventory management and sales forecasting. The server uses machine learning algorithms (e.g., SciPy, Pandas) to forecast demand and calculate the optimal timing and amount of inventory replenishment. For example, if sneaker stock is low, the server sends a notification to the warehouse to instruct it to replenish the stock.

[0599] Customer Service Automation

[0600] When a user makes an inquiry or request a return after a purchase, the server analyzes the request using natural language processing technology (e.g., Amazon Lex, Dialogflow). For example, if a user asks a chatbot, "I would like to cancel my order," the server analyzes the request, generates guidelines for the cancellation procedure based on a template message, and sends them to the user's device. This allows the user to easily proceed with the cancellation procedure.

[0601] Specific examples

[0602] For example, a user might say to a smartphone's voice assistant, "I'm looking for new sneakers." The device sends this voice data to a server, which converts the speech into text using the Google Cloud Speech-to-Text API. The server analyzes the user's past purchase history and uses TensorFlow to generate a recommended list of sneakers that match the user's preferences. This list is then sent to the device and displayed on the smartphone screen. The user can then select and purchase the items they like from the list.

[0603] The server also updates sneaker inventory data and predicts future demand using a sales forecasting algorithm powered by SciPy. If inventory is low, it sends a replenishment notification to the warehouse to optimize inventory management. Furthermore, if a user sends a message to the chatbot saying, "I want to cancel my order," the server uses Amazon Lex to analyze the request and sends guidelines for the cancellation procedure to the user's smartphone.

[0604] The system analyzes data from users in real time and provides personalized services, thereby increasing customer satisfaction and enabling efficient inventory management and sales forecasting.

[0605] Prompt Sentence Examples

[0606] "Generate personalized product recommendation lists using user purchase history and preference data."

[0607] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0608] Step 1:

[0609] When a user speaks to a voice assistant, the voice data is collected by the device. For example, if a user says to their smartphone, "I'm looking for new sneakers," the voice recording is stored on the device.

[0610] Input: User's voice data

[0611] Output: Recorded audio data

[0612] Step 2:

[0613] The device sends the recorded audio data to the server using an HTTPS request, uploading the audio data to the server as an attachment.

[0614] Input: Recorded audio data

[0615] Output: Audio data sent to the server

[0616] Step 3:

[0617] The server uses speech recognition technology to convert the received voice data into text data. Specifically, it sends the voice data to the Google Cloud Speech-to-Text API and receives the resulting text data.

[0618] Input: Audio data sent to the server

[0619] Output: Text data converted from audio

[0620] Step 4:

[0621] The server analyzes the text data and uses machine learning algorithms to identify user behavior and preferences. Specifically, the text data is fed into a TensorFlow environment, where it is compared with past purchase and browsing history to update a user preference model.

[0622] Input: Text data converted from speech, past purchase history

[0623] Output: User preference analysis results

[0624] Step 5:

[0625] The server generates a personalized product recommendation list based on the results of the preference analysis. It searches the product database for products that match the user's preferences and compiles them into a list.

[0626] Input: User preference analysis results

[0627] Output: A personalized product recommendation list

[0628] Step 6:

[0629] The generated product recommendation list is sent to the device, which receives the JSON format data from the server and visually displays it along with detailed product information.

[0630] Input: A personalized product recommendation list

[0631] Output: Product recommendation list displayed on the device

[0632] Step 7:

[0633] The server continuously collects sales data and customer behavior data and stores them in a database, such as recording sales information and browsing frequency for each product in real time.

[0634] Input: Sales data, customer trend data

[0635] Output: Sales data and customer trend data stored in a database

[0636] Step 8:

[0637] The server uses machine learning algorithms to forecast demand using past sales data, and SciPy and Pandas are used to analyze the data and predict future demand.

[0638] Input: Sales data stored in the database

[0639] Output: Demand forecast results

[0640] Step 9:

[0641] The server calculates the optimal inventory replenishment timing and quantity based on the demand forecast results, and if it detects that an item is low in stock, it notifies the warehouse to request replenishment.

[0642] Input: Demand forecast results

[0643] Output: Replenishment notification to warehouse

[0644] Step 10:

[0645] If a user makes an inquiry or returns a purchase after purchase, the server uses natural language processing technology to analyze the request and generate an appropriate response. Amazon Lex is used to analyze the user's message.

[0646] Input: User's query message

[0647] Output: Parsed request

[0648] Step 11:

[0649] The response message generated by the server is sent to the terminal in real time, and the user can easily complete the process by following instructions such as cancellation procedures.

[0650] Input: Parsed request, generated response message

[0651] Output: Response message displayed on the terminal

[0652] In this way, this system handles everything from collecting voice data to converting it into text, analyzing it using machine learning, and automating personalized product recommendations, inventory management, and customer service.

[0653] (Application example 1)

[0654] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0655] Current retail systems lack the ability to understand diverse user preferences and make personalized product recommendations. They also lack efficiency in inventory management and demand forecasting, creating a need for improved customer service. Real-time preference analysis and appropriate product recommendations are particularly important for online shopping sites to improve user experience.

[0656] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0657] In this invention, the server includes means for collecting voice, text, image, and video data from users, means for analyzing the collected data to identify user behavior and preferences, and means for generating personalized product recommendations for users based on the analysis results, thereby enabling efficient inventory management and high-quality customer service while providing personalized product recommendations tailored to user needs.

[0658] A "user" is a person who uses this system to receive product recommendations and customer service.

[0659] "Voice data" is digital data of the voice generated when a user speaks to a voice assistant.

[0660] "Text data" refers to voice data converted into text, as well as text information manually entered by the user.

[0661] "Image data" refers to digital data of product images and related images provided by users.

[0662] "Video data" is digital data of examples of product usage and related videos provided by users.

[0663] "Analysis" is the process of analyzing data to identify user behavior and preferences based on collected data.

[0664] "Personalized product recommendations" are product lists generated based on analysis results to suit the individual preferences and needs of each user.

[0665] A "terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[0666] "Inventory data" is digital information that indicates how many items are in stock at a warehouse or store.

[0667] "Sales data" is digital data that includes past sales history and sales information.

[0668] A "demand forecasting algorithm" is a mathematical model or calculation method for predicting future demand based on past sales data.

[0669] "Inventory management" is the process of maintaining appropriate inventory levels based on demand forecasting algorithms.

[0670] "Natural language processing" is an artificial intelligence technology that analyzes natural human language to understand its appropriate meaning.

[0671] A "response" is a reply or guidance message generated by the system in response to a user's inquiry.

[0672] "Speech recognition technology" is a technology that converts voice data into text data.

[0673] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[0674] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server converts the voice data into text using voice recognition technology. Users can also upload image and video data to the server using their device. In this process, "SpeechRecognition" is used as the voice recognition technology and "Spacy" is used for natural language processing.

[0675] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences, including past purchase and browsing history. This analysis process utilizes machine learning libraries such as TensorFlow and scikit-learn.

[0676] The server generates a personalized product recommendation list based on the analysis results. This recommendation list includes products that are likely to interest the user, improving the user experience. The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone.

[0677] In addition, the server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server uses machine learning algorithms to predict future demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores to improve inventory management efficiency.

[0678] Automated customer service is also an important function of this system. When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user inquires about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. This improves the quality of customer service and ensures efficient responses.

[0679] As a concrete example, consider the case where a user says to a smartphone's voice assistant, "I'm looking for new sneakers." At this time, the device sends the voice data to a server, which converts the voice into text. The server then analyzes the user's past purchase history and generates a recommended list of sneakers that match the user's preferences. This list is displayed on the user's smartphone, allowing the user to select and purchase the products they like.

[0680] The server also updates sneaker inventory data and runs sales forecasting algorithms to predict future demand. If inventory is low, it sends a replenishment notification to the warehouse to optimize inventory management. If a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request and generates a message with guidelines on the cancellation procedure, which is sent to the user's smartphone. The user can easily complete the cancellation procedure.

[0681] Example prompt sentence:

[0682] Explain how you can recommend products to a user when they say, "I'm looking for some new sneakers," to their smartphone voice assistant. Show them how you can improve their shopping experience by providing sneaker recommendations.

[0683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0684] Step 1:

[0685] A user talks to a voice assistant

[0686] A user inputs a specific voice command into the smartphone's voice assistant, for example, "I'm looking for new sneakers." The input is voice data, and the output is the device's transmission of the voice data to the server.

[0687] Step 2:

[0688] Converting audio data into text data

[0689] The server uses speech recognition technology to convert the received voice data into text data. Specifically, it uses the "SpeechRecognition" library, with the input being voice data and the output being the corresponding text data. The converted text data is "I'm looking for new sneakers."

[0690] Step 3:

[0691] Analyze the user's past purchase history

[0692] The server analyzes the user's past purchase and browsing history based on the text data. This analysis uses machine learning libraries such as "TensorFlow" and "scikit-learn." The input is text data and history data, and the output is the analysis results of the user's preferences.

[0693] Step 4:

[0694] Generate personalized product recommendation lists

[0695] The server generates a personalized product recommendation list based on the analysis results. For example, it includes specific product names such as "Sneaker A" and "Sneaker B." The input is the analysis results, and the output is the product recommendation list.

[0696] Step 5:

[0697] Send and display the product recommendation list to the user's device

[0698] The server sends the generated product recommendation list to the user's smartphone, which then visually displays it. The input is the product recommendation list, and the output is the product list displayed on the user's smartphone screen. Specifically, images and detailed information about the recommended products are displayed.

[0699] Step 6:

[0700] Collect inventory and sales data to forecast demand

[0701] The server collects sales data and customer trend data and builds a database for inventory management and sales forecasting. It uses TensorFlow and scikit-learn to predict future demand and calculate the optimal timing and amount of inventory replenishment. The input is sales data and trend data, and the output is the demand forecast results.

[0702] Step 7:

[0703] Optimize inventory management

[0704] The server notifies warehouses and stores of inventory replenishment based on the demand forecast results, thereby improving the efficiency of inventory management. The input is the demand forecast results, and the output is replenishment notifications to warehouses and stores.

[0705] Step 8:

[0706] Parse customer service inquiries and generate responses

[0707] When a user makes a query to the chatbot, the server uses natural language processing to analyze the request and generate an appropriate response. For example, it responds to a message such as "I would like to cancel my order." The input is the query message, and the output is the analysis result and the generated response message.

[0708] Step 9:

[0709] Sends the generated response to the user's device

[0710] The server generates a response message and sends it to the user's terminal, which then displays it. The input is the response message, and the output is the response message displayed on the user's terminal. For example, guidelines on cancellation procedures are displayed.

[0711] By implementing each of the above steps, personalized product recommendations based on user needs can be made while achieving efficient inventory management and high-quality customer service.

[0712] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0713] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to make personalized product recommendations, manages inventory, forecasts sales, and provides efficient customer service. It also combines an emotion engine that recognizes users' emotions.

[0714] Data collection and analysis

[0715] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server then converts the voice data into text using voice recognition technology.

[0716] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences. It also uses an emotion engine to analyze the user's emotions and understand their current emotional state from the content of their requests. This allows it to understand the user's preferences from their past purchase history, browsing history, and emotional state.

[0717] Generating personalized product recommendations

[0718] The server generates a personalized product recommendation list based on the user's behavior, preferences, and sentiment analysis results. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[0719] The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone. Engagement messages that take the user's emotional state into account are also displayed simultaneously.

[0720] Inventory and sales data management

[0721] The server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server then uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, aiming to improve the efficiency of inventory management.

[0722] Customer Service Automation

[0723] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user inquires with the chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. The server also uses an emotion engine to analyze the user's emotions and tailor the response to match their emotional state at the time of the inquiry. This improves the quality of customer service and enables efficient responses.

[0724] Specific examples

[0725] A user says to their smartphone's voice assistant, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the speech into text. The server analyzes the user's past purchase history and also analyzes the user's emotions from the voice data. If the user is determined to be in a good mood, the server takes this into account and generates a list of sneaker recommendations with brighter, more attractive recommendation messages. This list and messages are displayed on the user's smartphone, and the user can select and purchase the products they like.

[0726] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms to predict future demand, and, when inventory is low, sends replenishment notifications to warehouses to optimize inventory management.

[0727] If a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request, generates a message containing guidelines on the cancellation procedure, and sends it to the user's smartphone. At this time, the emotion engine grasps the user's emotional state and responds with a calm message. In this way, the user can easily complete the cancellation procedure.

[0728] The above is a concrete implementation of the system according to the present invention. This system allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. Responses that take user emotions into consideration can achieve even higher customer satisfaction.

[0729] The processing flow will be explained below.

[0730] Step 1:

[0731] A user speaks to their smartphone's voice assistant, saying, "I'm looking for some new sneakers."

[0732] Step 2:

[0733] The terminal collects the user's voice data and transmits it to a server via the Internet.

[0734] Step 3:

[0735] The server converts the received voice data into text data using a voice recognition engine.

[0736] Step 4:

[0737] The server analyzes the converted text data and understands the user's request ("I'm looking for sneakers").

[0738] Step 5:

[0739] The server retrieves the user's past purchase history and browsing history from a database and analyzes it.

[0740] Step 6:

[0741] The server uses an emotion engine to analyze the voice data and identify the user's emotional state, for example, determining whether the user is excited or calm based on the tone and rate of the voice.

[0742] Step 7:

[0743] The server uses machine learning algorithms to analyze the user's preferences and generate a personalized list of sneaker recommendations for the user, taking into account the user's emotional state from the emotion engine.

[0744] Step 8:

[0745] The server sends the generated sneaker recommendation list and an engagement message tailored to the user's emotional state to the user's device.

[0746] Step 9:

[0747] The device visually displays a list of sneaker recommendations and engagement messages to the user. For example, if the user is excited, it displays an energetic message, and if the user is calm, it displays a calm message.

[0748] Step 10:

[0749] The user selects a sneaker of interest from the displayed recommendation list and views the detail page.

[0750] Step 11:

[0751] The user clicks the "Buy" button to purchase the selected sneakers.

[0752] Step 12:

[0753] The terminal transmits the user's purchase request to the server.

[0754] Step 13:

[0755] The server processes purchase requests and updates the inventory database.

[0756] Step 14:

[0757] The server collects sales and inventory data and stores it in a database.

[0758] Step 15:

[0759] The server uses machine learning algorithms to forecast sales and predict future demand.

[0760] Step 16:

[0761] The server manages inventory based on the forecast and, if necessary, notifies warehouses and stores of replenishment requests.

[0762] Step 17:

[0763] After making a purchase, the user asks the chatbot, "I would like to cancel my order."

[0764] Step 18:

[0765] The terminal transmits the contents of the user's inquiry to the server.

[0766] Step 19:

[0767] The server analyzes the inquiry using natural language processing and generates a template message containing guidelines for the cancellation procedure.

[0768] Step 20:

[0769] The server uses an emotion engine to analyze the user's emotions and tailor the response to their emotional state at the time of the query, for example, generating a calmer tone of message if the user is emotional.

[0770] Step 21:

[0771] The server sends the generated template message to the user's terminal.

[0772] Step 22:

[0773] The terminal displays guidelines to the user and guides them through the cancellation procedure.

[0774] The above are the specific processing steps of the system based on the present invention. This series of processes allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. By utilizing the emotion engine, it is possible to respond according to the user's emotional state and provide higher customer satisfaction.

[0775] Example 2

[0776] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0777] In today's retail industry, it is necessary to respond to the needs of each individual customer, and to provide efficient inventory management and high-quality customer service. However, conventional systems have difficulty meeting all of these challenges, particularly in terms of personalized product recommendations that take customer emotions into account and efficient inventory management. Furthermore, there is a lack of methods for responding efficiently while maintaining the quality of customer service.

[0778] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for collecting voice, text, image, and video data from a user; means for analyzing the collected data and identifying the user's behavior and preferences; means for identifying the user's emotions as part of the data analysis; means for generating personalized product recommendations for the user based on the analysis results and the emotion identification results; means for displaying the product recommendations on the user's terminal; means for collecting inventory data and sales data and executing a demand forecasting algorithm; means for performing inventory management based on the demand forecast; means for analyzing customer service inquiries using natural language processing and generating appropriate responses; and means for transmitting the responses to the user's terminal. This enables personalized product recommendations that take customer emotions into consideration, efficient inventory management, and high-quality customer service.

[0779] "User" refers to a person who uses the system.

[0780] "Audio, text, image, and video data" refers to various forms of digital data collected from users.

[0781] "Means of collection" refers to methods and devices for obtaining audio, text, image, and video data from users.

[0782] "Means for analyzing data" refers to methods and devices for processing collected digital data and understanding and classifying its contents.

[0783] "Means for identifying user behavior and preferences" refers to a method or apparatus for identifying user behavior patterns and preferences from analyzed data.

[0784] "Means for identifying emotions" refers to a method or device for determining the emotional state of a user from their voice or text.

[0785] "Means for generating personalized product recommendations" refers to a method or device that creates a list of recommended products that are individually suited to a user based on the user's behavior, preferences, and emotions.

[0786] The term "means for displaying product recommendations on a user's terminal" refers to a method or device for displaying the generated product recommendations on an electronic device used by the user.

[0787] "Inventory data" refers to data regarding the quantity and condition of products stored in stores and warehouses.

[0788] "Sales Data" refers to data regarding the sales status of products.

[0789] A "demand forecasting algorithm" refers to a calculation method or model for predicting future demand for a product based on past data.

[0790] "Means for inventory management" refers to methods and devices for replenishing and adjusting inventory based on the results of demand forecasts.

[0791] "Means for analyzing customer service inquiries" refers to a method or apparatus for understanding and appropriately handling user inquiries.

[0792] "Natural language processing" refers to the technology that enables computers to understand and process human language.

[0793] "Means for generating an appropriate response" refers to a method or device for generating an appropriate answer based on the content of the inquiry.

[0794] "Means for sending a response to a user's terminal" refers to a method or device for sending the generated response to the user's electronic device.

[0795] This is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service. It also has an emotion engine that recognizes users' emotions.

[0796] Hardware and Software Configuration

[0797] The system of the present invention comprises several hardware and software components. The main components are listed below.

[0798] Server: A computer used to perform key functions such as data collection, analysis, emotion recognition, product recommendations, inventory management, sales forecasting, and customer service.

[0799] Device: A device (such as a smartphone, tablet, or PC) through which a user interacts with the system via a voice assistant or other input means.

[0800] software:

[0801] Speech recognition technology: Google Speech-to-Text API

[0802] Machine learning algorithms: Scikit-learn

[0803] Natural Language Processing Model: BERT

[0804] Database: MySQL

[0805] Specific operation of the system

[0806] Data collection and analysis

[0807] The server collects voice, text, image, and video data from users. For example, when a user speaks to a smartphone's voice assistant, the voice data is sent to the server via the device. The server converts the voice data into text using Google's voice recognition technology.

[0808] Analyzing data and identifying user preferences

[0809] The server analyzes the collected data, using machine learning algorithms such as Scikit-learn to identify user behavior and preferences, and also uses an emotion engine based on the BERT model to analyze the user's emotions and understand their current emotional state based on the request content.

[0810] Generating personalized product recommendations

[0811] The server generates a personalized product recommendation list based on the user's behavior, preferences, and sentiment analysis results. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[0812] View the recommendation list

[0813] The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone. Engagement messages that take the user's emotional state into account are also displayed simultaneously.

[0814] Inventory and sales data management

[0815] The server continuously collects sales data and customer trend data and stores it in a MySQL database. Demand forecasts are performed using Scikit-learn to calculate the optimal timing and amount of inventory replenishment. Based on this, notifications are sent to warehouses and stores to improve inventory management efficiency.

[0816] Customer Service Automation

[0817] When a user makes an inquiry or request a return after a purchase, the server uses a natural language processing model such as BERT to analyze the request and generate an appropriate response. For example, if a user inquires with a chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. An emotion engine is used to analyze the user's emotions and tailor the response to match the user's emotional state at the time of the inquiry. This results in efficient, high-quality customer service.

[0818] Specific examples

[0819] For example, consider the case where a user says to a smartphone's voice assistant, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the voice data into text using the Google Speech-to-Text API. The server analyzes the user's past purchase history and emotions from the voice data to generate a personalized list of recommended sneakers. This list and a message reflecting the user's emotions are then displayed on the user's smartphone.

[0820] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms using Scikit-learn, and sends replenishment notifications to warehouses when stock is low, optimizing inventory management.

[0821] An example prompt is:

[0822] "Design a program that identifies emotions from user voice data and recommends personalized products."

[0823] "Build a machine learning model to forecast demand based on inventory and sales data."

[0824] "Design a natural language processing system that generates appropriate responses to user queries using a chatbot."

[0825] The above is a concrete implementation of the system according to the present invention. This system allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. Responses that take user emotions into consideration can achieve even higher customer satisfaction.

[0826] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0827] Step 1:

[0828] A user makes a voice request to a smartphone voice assistant.

[0829] Input: Audio data

[0830] Action: The user says to their smartphone, "I'm looking for some new sneakers."

[0831] Output: The audio data is sent to the device.

[0832] Step 2:

[0833] The terminal transmits the voice data to the server.

[0834] Input: Audio data

[0835] Operation: The terminal transmits the user's voice data to the server.

[0836] Output: The server receives the audio data.

[0837] Step 3:

[0838] The server uses voice recognition technology to convert the voice data into text.

[0839] Input: Audio data

[0840] How it works: The server uses speech recognition technology (Google Speech-to-Text API) to convert the voice data into text.

[0841] Output: Text data

[0842] Step 4:

[0843] The server analyzes the collected text data to identify the user's behavior and preferences.

[0844] Input: Text data

[0845] How it works: The server uses Scikit-learn to analyze text data and the user's purchase history to identify the user's preferences.

[0846] Output: User preference data

[0847] Step 5:

[0848] The server uses the BERT model to analyze the user's sentiment.

[0849] Input: Text data

[0850] How it works: The server uses the BERT model to identify a user's emotions from text data. For example, it determines that a user is in a "good mood" based on their statement.

[0851] Output: User emotion data

[0852] Step 6:

[0853] The server generates a personalized product recommendation list based on the user's behavior, preferences, and emotional data.

[0854] Input: User preference data, user emotion data

[0855] How it works: The server uses Scikit-learn algorithms to generate a list of products that match the user's preferences and emotions.

[0856] Output: Product recommendation list

[0857] Step 7:

[0858] The server transmits the generated product recommendation list to the user's terminal.

[0859] Input: Product Recommendation List

[0860] Operation: The server sends a product recommendation list and an engagement message to the user's device.

[0861] Output: The device receives the product recommendation list and the message.

[0862] Step 8:

[0863] The terminal displays a list of recommended products to the user.

[0864] Input: Product recommendation list, engagement message

[0865] How it works: The device displays a list of product recommendations and engagement messages on the user's screen. For example, a list of recommended sneakers is displayed on a smartphone screen.

[0866] Output: User visually confirms the recommended products.

[0867] Step 9:

[0868] The user selects a product from the recommended list and completes the purchase process.

[0869] Input: Product Recommendation List

[0870] How it works: The user selects the product they like from the list and proceeds with the purchase.

[0871] Output: Purchase data

[0872] Step 10:

[0873] The server updates sales data and manages inventory data.

[0874] Input: Purchase data

[0875] How it works: The server saves sales data to a MySQL database and updates inventory data. It uses Scikit-learn to forecast sales and calculate when and how much inventory to replenish.

[0876] Output: Stock replenishment notification

[0877] Step 11:

[0878] The server sends notifications to warehouses and stores to replenish stock.

[0879] Input: Restock notification

[0880] How it works: The server sends notifications to warehouses and stores to replenish inventory, enabling efficient inventory management.

[0881] Output: Warehouses and stores receive instructions to replenish inventory.

[0882] Step 12:

[0883] A user makes an inquiry or returns a product after making a purchase.

[0884] Input: Query text

[0885] Action: The user sends a message to the chatbot saying, "I want to cancel my order."

[0886] Output: The query data is sent to the server.

[0887] Step 13:

[0888] The server uses natural language processing to analyze the query and generate an appropriate response.

[0889] Input: Inquiry data

[0890] How it works: The server uses the BERT model to analyze the query and generate an appropriate response message, such as a message containing cancellation guidelines.

[0891] Output: Response message

[0892] Step 14:

[0893] The server sends the generated response message to the user's terminal.

[0894] Input: Response message

[0895] Operation: The server sends a response message to the user's device, adapting the response to the user's emotion using a tone that takes into account the user's emotion using the emotion engine.

[0896] Output: The terminal receives the response message.

[0897] Step 15:

[0898] The terminal displays a response message to the user.

[0899] Input: Response message

[0900] Action: The device displays a response message on the user's screen, for example, guidelines on the cancellation procedure.

[0901] Output: The user visually confirms the response message.

[0902] (Application example 2)

[0903] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0904] Conventional online shopping systems tend to be unable to fully consider users' preferences and emotions, and tend to recommend products and provide uniform responses. Furthermore, they face challenges in quickly managing inventory and forecasting sales, making it difficult to provide efficient customer service. This can lead to lower user satisfaction and a lack of increased sales.

[0905] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0906] In this invention, the server includes means for collecting voice, text, image, and video data from a user, means for analyzing the collected data and identifying the user's behavior and preferences, means for generating personalized product recommendations for the user based on the analysis results, means for displaying the product recommendations on the user's device, means for collecting inventory data and sales data and executing a demand forecasting algorithm, means for performing inventory management based on the demand forecast, means for analyzing customer service inquiries using natural language processing and generating appropriate responses, means for transmitting the responses to the user's device, means for analyzing the user's emotions and generating product recommendations and engagement messages based on the user's emotional state in real time, and means for making appropriate adjustments in response to the user's inquiries based on the user's emotional state. This enables personalized product recommendations that accurately reflect the user's emotions and preferences, as well as efficient inventory management, sales forecasting, and high-quality customer service.

[0907] "Means for collecting voice, text, image, and video data from users" means equipment or devices that efficiently collect user-provided voice, text, image, and video data and convert it into the format required for subsequent analysis.

[0908] "Means for analyzing data and identifying user behavior and preferences" refers to algorithms or systems that analyze collected data to identify users' past purchase history, browsing history, and user characteristics, and to understand the behavior and preferences of individual users.

[0909] The "means for generating personalized product recommendations" refers to a method or device for listing products that are optimal for the user's preferences and behavior based on the analysis results and recommending them to the user.

[0910] The "means for displaying product recommendations on a user's device" refers to technology or software for visually presenting the generated product recommendation list on a user's device such as a smartphone or tablet.

[0911] "Means for collecting inventory data and sales data and running demand forecasting algorithms" refers to algorithms or systems that collect inventory status and sales information from stores and warehouses and perform calculations to forecast future demand.

[0912] "Means for inventory management based on demand forecasts" refers to methods and techniques for replenishing inventory in response to forecasted demand and for appropriate inventory management.

[0913] "Means for analyzing customer service inquiries using natural language processing and generating appropriate responses" refers to a system that uses natural language processing technology to analyze inquiries from users and generate responses that are optimal for their content.

[0914] "Means for sending responses to the user's device" refers to the technology or system for sending the generated answers and guidelines to the user's smartphone, tablet, etc., and displaying them appropriately.

[0915] "Means for analyzing user emotions and generating product recommendations and engagement messages based on emotional state in real time" refers to a system that analyzes emotions from the user's tone of voice, context, facial expressions, etc., and based on the results, provides a product recommendation list tailored to the user and messages in line with their emotions in real time.

[0916] "Means for making appropriate adjustments in response to a user's inquiry based on the user's emotional state" refers to a method or system for flexibly changing the tone and content of responses to inquiries based on the analyzed emotional state of the user.

[0917] The system of this invention is an AI-driven retail assistant system that collects and analyzes user voice, text, image, and video data to provide personalized product recommendations, manage inventory, and forecast sales. It also incorporates an emotion engine that analyzes user emotions in real time.

[0918] First, when a user speaks to a voice assistant, the voice data is sent to a server via a device such as a smartphone. The server then converts the voice data into text using voice recognition technology. This can be done using Google Speech API or other voice recognition software. If the user types text, that data is also sent to the server.

[0919] The server analyzes the collected audio, text, image, and video data using machine learning algorithms, natural language processing (NLP) libraries, and sentiment analysis engines designed for this purpose, such as NLTK and spaCy.

[0920] Based on the analysis results, the server references the user's past purchase and browsing history to identify preferences and generate a personalized product recommendation list. The server also analyzes the user's current emotional state from the collected data. The generated product recommendation list and engagement message are then displayed on the smartphone screen.

[0921] The server also continuously collects inventory and sales data. This data is stored in a database along with sales data and customer trend data. The server uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, improving inventory management efficiency.

[0922] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing technology to analyze the request and generate an appropriate response. For example, if a user inquires with the chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the user's device. At this time, the emotion engine grasps the user's emotional state and responds appropriately based on their emotional state at the time of the inquiry.

[0923] Specific examples

[0924] When a user says, "I want some new summer sunglasses," the voice data is sent to a server. The server converts the voice into text and analyzes the user's emotions. For example, if the user is speaking in a cheerful tone, a list of recommended sunglasses will be displayed along with a cheerful and upbeat message. This list and message will be displayed on the user's smartphone, allowing the user to select and purchase the product of their choice.

[0925] Prompt Sentence Examples

[0926] "Build an AI model that provides personalized product recommendations based on user sentiment analysis and feedback. Input data is voice, text, and image data, and output is a list of product recommendations and engagement messages."

[0927] In this way, the system based on the present invention provides personalized product recommendations that accurately reflect the user's emotions and preferences, as well as efficient inventory management, sales forecasting, and high-quality customer service.

[0928] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0929] Step 1:

[0930] Collect voice, text, image, and video data from users.

[0931] When a user speaks to a smartphone's voice assistant, the voice data is sent to the server via the device. Text input, image and video data are also collected. The input data is voice, text, image and video. The output is this raw data.

[0932] Step 2:

[0933] The server converts the audio data into text.

[0934] The server uses speech recognition technology such as Google Speech API to convert the received voice data into text data. The input is voice data and the output is text data. Specifically, it uses a speech recognition engine to convert the acoustic signal into a string of characters.

[0935] Step 3:

[0936] The server analyzes user data to identify behavior and preferences.

[0937] The server uses machine learning algorithms and NLP libraries (e.g., NLTK and spaCy) to analyze collected text data, past purchase history, and browsing history to identify user behavior and preferences. The input is text data and past recorded data, and the output is the analysis results. Specific operations include data preprocessing, building topic models, and extracting preference patterns.

[0938] Step 4:

[0939] The server analyzes the user's emotions.

[0940] The server uses an emotion analysis engine to analyze the user's emotional state from text and voice data. The input is text and voice data, and the output is the emotion analysis results. Specifically, it extracts features from the text or voice and predicts the emotion label using an emotion classifier.

[0941] Step 5:

[0942] A server generates personalized product recommendations.

[0943] The server lists the products most relevant to the user's preferences based on the analysis results and sentiment analysis results. The input is the preference analysis results and sentiment analysis results, and the output is a product recommendation list. Specifically, the server uses a recommendation algorithm (e.g., collaborative filtering or content-based recommendation) to select products.

[0944] Step 6:

[0945] The server displays the product recommendation list on the user's terminal.

[0946] The generated product recommendation list and engagement message are displayed on the user's smartphone screen. The input is the product recommendation list and engagement message, and the output is the content displayed on the device screen. Specifically, the system updates the GUI components of the smartphone app.

[0947] Step 7:

[0948] The server collects inventory and sales data and runs demand forecasting algorithms.

[0949] The server continuously collects inventory and sales data from stores and warehouses, and runs a demand forecasting algorithm based on this data. The inputs are inventory and sales data, and the output is the demand forecast results. Specific operations include analyzing sales history data and calculating forecasts using a demand forecasting model.

[0950] Step 8:

[0951] The server manages inventory based on the demand forecast results.

[0952] The server calculates the optimal inventory replenishment timing and quantity based on the demand forecast results. The input is the demand forecast results, and the output is the replenishment notification. Specific operations include generating replenishment orders in conjunction with the inventory management system.

[0953] Step 9:

[0954] The server analyzes customer service inquiries using natural language processing and generates appropriate responses.

[0955] When a user makes a query, the content is sent to the server. The server uses NLP technology to analyze the query and generate an appropriate response. The input is the query content (text data), and the output is a response message. Specific operations include analyzing the query text and generating a template response.

[0956] Step 10:

[0957] The server sends a response message to the user's terminal.

[0958] The generated response message is sent to the user's smartphone and displayed immediately. The input is the response message, and the output is the content displayed on the user's device. Specifically, the data is sent to the device using a message sending API.

[0959] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0960] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0961] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0962] [Third embodiment]

[0963] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0964] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0965] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0966] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0967] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0968] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0969] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0970] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0971] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0972] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0973] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0974] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0975] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[0976] Data collection and analysis

[0977] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server then converts the voice data into text using voice recognition technology.

[0978] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences, which allows it to understand users' interests and preferences based on their past purchase and browsing history.

[0979] Generating personalized product recommendations

[0980] The server generates a personalized product recommendation list based on the results of analyzing the user's behavior and preferences. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[0981] The generated product recommendation list is sent to the user's device and visually presented, for example, by displaying images and detailed information of the recommended products on the screen of a smartphone.

[0982] Inventory and sales data management

[0983] The server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server then uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, aiming to improve the efficiency of inventory management.

[0984] Customer Service Automation

[0985] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user asks the chatbot, "I'd like to cancel my order," the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. This improves the quality of customer service and ensures efficient responses.

[0986] Specific examples

[0987] A user speaks to their smartphone's voice assistant, saying, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the speech into text. The server analyzes the user's past purchase history and generates a recommended list of sneakers that match the user's preferences. This list is displayed on the user's smartphone, and the user can select and purchase the products they like.

[0988] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms to predict future demand, and, when inventory is low, sends replenishment notifications to warehouses to optimize inventory management.

[0989] Additionally, if a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request, generates a message containing guidelines on the cancellation procedure, and sends it to the user's smartphone, allowing the user to easily complete the cancellation procedure.

[0990] As described above, the present invention is a system that detects user needs in real time, provides a personalized shopping experience, and realizes efficient inventory management and high-quality customer service.

[0991] The processing flow will be explained below.

[0992] Step 1:

[0993] A user speaks to their smartphone's voice assistant, saying, "I'm looking for some new sneakers."

[0994] Step 2:

[0995] The terminal collects the user's voice data and transmits it to a server via the Internet.

[0996] Step 3:

[0997] The server converts the received voice data into text data using a voice recognition engine.

[0998] Step 4:

[0999] The server analyzes the converted text data and understands the user's request ("I'm looking for sneakers").

[1000] Step 5:

[1001] The server retrieves the user's past purchase history and browsing history from a database and analyzes it.

[1002] Step 6:

[1003] The server uses machine learning algorithms to analyze the user's preferences and generate a personalized list of sneaker recommendations for the user.

[1004] Step 7:

[1005] The server transmits the generated sneaker recommendation list to the user's terminal.

[1006] Step 8:

[1007] The terminal visually displays a list of sneaker recommendations to the user.

[1008] Step 9:

[1009] The user selects a sneaker of interest from the displayed recommendation list and views the detail page.

[1010] Step 10:

[1011] The user clicks the "Buy" button to purchase the selected sneakers.

[1012] Step 11:

[1013] The terminal transmits the user's purchase request to the server.

[1014] Step 12:

[1015] The server processes purchase requests and updates the inventory database.

[1016] Step 13:

[1017] The server collects sales and inventory data and stores it in a database.

[1018] Step 14:

[1019] The server uses machine learning algorithms to forecast sales and predict future demand.

[1020] Step 15:

[1021] The server manages inventory based on the forecast and, if necessary, notifies warehouses and stores of replenishment requests.

[1022] Step 16:

[1023] After making a purchase, the user asks the chatbot, "I would like to cancel my order."

[1024] Step 17:

[1025] The terminal transmits the contents of the user's inquiry to the server.

[1026] Step 18:

[1027] The server analyzes the inquiry using natural language processing and generates a template message containing guidelines for the cancellation procedure.

[1028] Step 19:

[1029] The server sends the generated template message to the user's terminal.

[1030] Step 20:

[1031] The terminal displays guidelines to the user and guides them through the cancellation procedure.

[1032] The above are the specific processing steps of the system according to the present invention, which enable users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service.

[1033] Example 1

[1034] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1035] In today's retail industry, despite the existence of a large amount of data, efficient inventory management and personalized product recommendations remain difficult. Customer service responses are also time-consuming and costly, so fast, high-quality responses are required. Furthermore, inaccurate demand forecasts can lead to excess inventory, making it difficult to prevent economic losses. There is a need to solve these issues and provide an efficient and effective retail assistant system.

[1036] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1037] In this invention, the server includes means for collecting voice, text, image, and video data from a user, means for converting the collected data into text using voice recognition technology, means for analyzing the collected data and identifying user behavior and preferences using a machine learning algorithm, means for generating a personalized product recommendation list for the user based on the analysis results, means for transmitting the product recommendation list to the user's terminal and visually displaying it, means for collecting sales data and customer trend data and storing them in a database, means for using the collected data to perform a demand forecast using a machine learning algorithm, means for calculating the optimal inventory replenishment timing and amount based on the demand forecast and sending a notification, means for analyzing customer service inquiries using natural language processing and generating an appropriate response, and means for transmitting the response to the user's terminal. This enables efficient inventory management and sales forecasting, personalized product recommendations, and high-quality customer service.

[1038] "Voice data" is digital data that records what the user has said.

[1039] "Text data" refers to character string information converted using voice recognition technology or character string information directly input by a user.

[1040] "Image data" refers to a digital image file containing user-provided visual information.

[1041] "Video Data" means a digital video file containing user-provided dynamic visual information.

[1042] "Speech recognition technology" is a technology that recognizes human speech as digital data and converts it into text data.

[1043] A "machine learning algorithm" is a mathematical model that analyzes large amounts of data and finds patterns to make predictions and classifications.

[1044] "User behavior" refers to a behavior history that includes all operations and selections that a user performs within the system.

[1045] "Preferences" refers to information related to products and services that a user prefers, and is primarily based on past behavioral history and purchase history.

[1046] A "product recommendation list" is a list of products that are likely to interest the user, generated based on the analysis results.

[1047] "Visually displaying" means presenting images or text information to the user's visual field on the user's terminal.

[1048] "Sales data" refers to information related to product sales, specifically including the number of products, price, date and time of sale, etc.

[1049] "Customer trend data" refers to data related to customer activity, including customer behavior history, purchase history, site browsing history, etc.

[1050] A "database" is a system for efficiently storing, retrieving, and updating data in a structured format.

[1051] "Demand forecasting" is the process of predicting future consumer demand based on past data.

[1052] "Natural language processing" is the branch of computer science that deals with understanding, interpreting, and generating human language.

[1053] A "response" is a reply message generated in response to a customer inquiry.

[1054] "Terminal" refers to a device used by a user, including a smartphone, tablet, or PC.

[1055] A "notification" is a message intended to inform a user or other system of specific information or action.

[1056] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[1057] Data collection and analysis

[1058] The server collects voice, text, image, and video data from the user. Specifically, when a user says to the voice assistant, "I'm looking for new sneakers," the voice data is sent to the server via the device. The server converts the voice data into text using voice recognition technology (e.g., Google Cloud Speech-to-Text API).

[1059] The server analyzes the collected data and uses machine learning algorithms (e.g., TensorFlow, Scikit-learn) to identify user behavior and preferences, which allows the server to understand the user's interests and preferences based on past purchase and browsing history.

[1060] Generating personalized product recommendations

[1061] The server generates a personalized product recommendation list based on the user's behavior and preference analysis. For example, a sneaker recommendation list is generated and sent to the user's device. The device then visually displays the list, showing product images and detailed information on the screen.

[1062] Inventory and sales data management

[1063] The server continuously collects sales data and customer trend data and stores it in a database (e.g., MongoDB, PostgreSQL). This accumulates data for inventory management and sales forecasting. The server uses machine learning algorithms (e.g., SciPy, Pandas) to forecast demand and calculate the optimal timing and amount of inventory replenishment. For example, if sneaker stock is low, the server sends a notification to the warehouse to instruct it to replenish the stock.

[1064] Customer Service Automation

[1065] When a user makes an inquiry or request a return after a purchase, the server analyzes the request using natural language processing technology (e.g., Amazon Lex, Dialogflow). For example, if a user asks a chatbot, "I would like to cancel my order," the server analyzes the request, generates guidelines for the cancellation procedure based on a template message, and sends them to the user's device. This allows the user to easily proceed with the cancellation procedure.

[1066] Specific examples

[1067] For example, a user might say to a smartphone's voice assistant, "I'm looking for new sneakers." The device sends this voice data to a server, which converts the speech into text using the Google Cloud Speech-to-Text API. The server analyzes the user's past purchase history and uses TensorFlow to generate a recommended list of sneakers that match the user's preferences. This list is then sent to the device and displayed on the smartphone screen. The user can then select and purchase the items they like from the list.

[1068] The server also updates sneaker inventory data and predicts future demand using a sales forecasting algorithm powered by SciPy. If inventory is low, it sends a replenishment notification to the warehouse to optimize inventory management. Furthermore, if a user sends a message to the chatbot saying, "I want to cancel my order," the server uses Amazon Lex to analyze the request and sends guidelines for the cancellation procedure to the user's smartphone.

[1069] The system analyzes data from users in real time and provides personalized services, thereby increasing customer satisfaction and enabling efficient inventory management and sales forecasting.

[1070] Prompt Sentence Examples

[1071] "Generate personalized product recommendation lists using user purchase history and preference data."

[1072] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1073] Step 1:

[1074] When a user speaks to a voice assistant, the voice data is collected by the device. For example, if a user says to their smartphone, "I'm looking for new sneakers," the voice recording is stored on the device.

[1075] Input: User's voice data

[1076] Output: Recorded audio data

[1077] Step 2:

[1078] The device sends the recorded audio data to the server using an HTTPS request, uploading the audio data to the server as an attachment.

[1079] Input: Recorded audio data

[1080] Output: Audio data sent to the server

[1081] Step 3:

[1082] The server uses speech recognition technology to convert the received voice data into text data. Specifically, it sends the voice data to the Google Cloud Speech-to-Text API and receives the resulting text data.

[1083] Input: Audio data sent to the server

[1084] Output: Text data converted from audio

[1085] Step 4:

[1086] The server analyzes the text data and uses machine learning algorithms to identify user behavior and preferences. Specifically, the text data is fed into a TensorFlow environment, where it is compared with past purchase and browsing history to update a user preference model.

[1087] Input: Text data converted from speech, past purchase history

[1088] Output: User preference analysis results

[1089] Step 5:

[1090] The server generates a personalized product recommendation list based on the results of the preference analysis. It searches the product database for products that match the user's preferences and compiles them into a list.

[1091] Input: User preference analysis results

[1092] Output: A personalized product recommendation list

[1093] Step 6:

[1094] The generated product recommendation list is sent to the device, which receives the JSON format data from the server and visually displays it along with detailed product information.

[1095] Input: A personalized product recommendation list

[1096] Output: Product recommendation list displayed on the device

[1097] Step 7:

[1098] The server continuously collects sales data and customer behavior data and stores them in a database, such as recording sales information and browsing frequency for each product in real time.

[1099] Input: Sales data, customer trend data

[1100] Output: Sales data and customer trend data stored in a database

[1101] Step 8:

[1102] The server uses machine learning algorithms to forecast demand using past sales data, and SciPy and Pandas are used to analyze the data and predict future demand.

[1103] Input: Sales data stored in the database

[1104] Output: Demand forecast results

[1105] Step 9:

[1106] The server calculates the optimal inventory replenishment timing and quantity based on the demand forecast results, and if it detects that an item is low in stock, it notifies the warehouse to request replenishment.

[1107] Input: Demand forecast results

[1108] Output: Replenishment notification to warehouse

[1109] Step 10:

[1110] If a user makes an inquiry or returns a purchase after purchase, the server uses natural language processing technology to analyze the request and generate an appropriate response. Amazon Lex is used to analyze the user's message.

[1111] Input: User's query message

[1112] Output: Parsed request

[1113] Step 11:

[1114] The response message generated by the server is sent to the terminal in real time, and the user can easily complete the process by following instructions such as cancellation procedures.

[1115] Input: Parsed request, generated response message

[1116] Output: Response message displayed on the terminal

[1117] In this way, this system handles everything from collecting voice data to converting it into text, analyzing it using machine learning, and automating personalized product recommendations, inventory management, and customer service.

[1118] (Application example 1)

[1119] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1120] Current retail systems lack the ability to understand diverse user preferences and make personalized product recommendations. They also lack efficiency in inventory management and demand forecasting, creating a need for improved customer service. Real-time preference analysis and appropriate product recommendations are particularly important for online shopping sites to improve user experience.

[1121] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1122] In this invention, the server includes means for collecting voice, text, image, and video data from users, means for analyzing the collected data to identify user behavior and preferences, and means for generating personalized product recommendations for users based on the analysis results, thereby enabling efficient inventory management and high-quality customer service while providing personalized product recommendations tailored to user needs.

[1123] A "user" is a person who uses this system to receive product recommendations and customer service.

[1124] "Voice data" is digital data of the voice generated when a user speaks to a voice assistant.

[1125] "Text data" refers to voice data converted into text, as well as text information manually entered by the user.

[1126] "Image data" refers to digital data of product images and related images provided by users.

[1127] "Video data" is digital data of examples of product usage and related videos provided by users.

[1128] "Analysis" is the process of analyzing data to identify user behavior and preferences based on collected data.

[1129] "Personalized product recommendations" are product lists generated based on analysis results to suit the individual preferences and needs of each user.

[1130] A "terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[1131] "Inventory data" is digital information that indicates how many items are in stock at a warehouse or store.

[1132] "Sales data" is digital data that includes past sales history and sales information.

[1133] A "demand forecasting algorithm" is a mathematical model or calculation method for predicting future demand based on past sales data.

[1134] "Inventory management" is the process of maintaining appropriate inventory levels based on demand forecasting algorithms.

[1135] "Natural language processing" is an artificial intelligence technology that analyzes natural human language to understand its appropriate meaning.

[1136] A "response" is a reply or guidance message generated by the system in response to a user's inquiry.

[1137] "Speech recognition technology" is a technology that converts voice data into text data.

[1138] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[1139] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server converts the voice data into text using voice recognition technology. Users can also upload image and video data to the server using their device. In this process, "SpeechRecognition" is used as the voice recognition technology and "Spacy" is used for natural language processing.

[1140] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences, including past purchase and browsing history. This analysis process utilizes machine learning libraries such as TensorFlow and scikit-learn.

[1141] The server generates a personalized product recommendation list based on the analysis results. This recommendation list includes products that are likely to interest the user, improving the user experience. The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone.

[1142] In addition, the server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server uses machine learning algorithms to predict future demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores to improve inventory management efficiency.

[1143] Automated customer service is also an important function of this system. When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user inquires about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. This improves the quality of customer service and ensures efficient responses.

[1144] As a concrete example, consider the case where a user says to a smartphone's voice assistant, "I'm looking for new sneakers." At this time, the device sends the voice data to a server, which converts the voice into text. The server then analyzes the user's past purchase history and generates a recommended list of sneakers that match the user's preferences. This list is displayed on the user's smartphone, allowing the user to select and purchase the products they like.

[1145] The server also updates sneaker inventory data and runs sales forecasting algorithms to predict future demand. If inventory is low, it sends a replenishment notification to the warehouse to optimize inventory management. If a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request and generates a message with guidelines on the cancellation procedure, which is sent to the user's smartphone. The user can easily complete the cancellation procedure.

[1146] Example prompt sentence:

[1147] Explain how you can recommend products to a user when they say, "I'm looking for some new sneakers," to their smartphone voice assistant. Show them how you can improve their shopping experience by providing sneaker recommendations.

[1148] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1149] Step 1:

[1150] A user talks to a voice assistant

[1151] A user inputs a specific voice command into the smartphone's voice assistant, for example, "I'm looking for new sneakers." The input is voice data, and the output is the device's transmission of the voice data to the server.

[1152] Step 2:

[1153] Converting audio data into text data

[1154] The server uses speech recognition technology to convert the received voice data into text data. Specifically, it uses the "SpeechRecognition" library, with the input being voice data and the output being the corresponding text data. The converted text data is "I'm looking for new sneakers."

[1155] Step 3:

[1156] Analyze the user's past purchase history

[1157] The server analyzes the user's past purchase and browsing history based on the text data. This analysis uses machine learning libraries such as "TensorFlow" and "scikit-learn." The input is text data and history data, and the output is the analysis results of the user's preferences.

[1158] Step 4:

[1159] Generate personalized product recommendation lists

[1160] The server generates a personalized product recommendation list based on the analysis results. For example, it includes specific product names such as "Sneaker A" and "Sneaker B." The input is the analysis results, and the output is the product recommendation list.

[1161] Step 5:

[1162] Send and display the product recommendation list to the user's device

[1163] The server sends the generated product recommendation list to the user's smartphone, which then visually displays it. The input is the product recommendation list, and the output is the product list displayed on the user's smartphone screen. Specifically, images and detailed information about the recommended products are displayed.

[1164] Step 6:

[1165] Collect inventory and sales data to forecast demand

[1166] The server collects sales data and customer trend data and builds a database for inventory management and sales forecasting. It uses TensorFlow and scikit-learn to predict future demand and calculate the optimal timing and amount of inventory replenishment. The input is sales data and trend data, and the output is the demand forecast results.

[1167] Step 7:

[1168] Optimize inventory management

[1169] The server notifies warehouses and stores of inventory replenishment based on the demand forecast results, thereby improving the efficiency of inventory management. The input is the demand forecast results, and the output is replenishment notifications to warehouses and stores.

[1170] Step 8:

[1171] Parse customer service inquiries and generate responses

[1172] When a user makes a query to the chatbot, the server uses natural language processing to analyze the request and generate an appropriate response. For example, it responds to a message such as "I would like to cancel my order." The input is the query message, and the output is the analysis result and the generated response message.

[1173] Step 9:

[1174] Sends the generated response to the user's device

[1175] The server generates a response message and sends it to the user's terminal, which then displays it. The input is the response message, and the output is the response message displayed on the user's terminal. For example, guidelines on cancellation procedures are displayed.

[1176] By implementing each of the above steps, personalized product recommendations based on user needs can be made while achieving efficient inventory management and high-quality customer service.

[1177] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1178] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to make personalized product recommendations, manages inventory, forecasts sales, and provides efficient customer service. It also combines an emotion engine that recognizes users' emotions.

[1179] Data collection and analysis

[1180] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server then converts the voice data into text using voice recognition technology.

[1181] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences. It also uses an emotion engine to analyze the user's emotions and understand their current emotional state from the content of their requests. This allows it to understand the user's preferences from their past purchase history, browsing history, and emotional state.

[1182] Generating personalized product recommendations

[1183] The server generates a personalized product recommendation list based on the user's behavior, preferences, and sentiment analysis results. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[1184] The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone. Engagement messages that take the user's emotional state into account are also displayed simultaneously.

[1185] Inventory and sales data management

[1186] The server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server then uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, aiming to improve the efficiency of inventory management.

[1187] Customer Service Automation

[1188] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user inquires with the chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. The server also uses an emotion engine to analyze the user's emotions and tailor the response to match their emotional state at the time of the inquiry. This improves the quality of customer service and enables efficient responses.

[1189] Specific examples

[1190] A user says to their smartphone's voice assistant, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the speech into text. The server analyzes the user's past purchase history and also analyzes the user's emotions from the voice data. If the user is determined to be in a good mood, the server takes this into account and generates a list of sneaker recommendations with brighter, more attractive recommendation messages. This list and messages are displayed on the user's smartphone, and the user can select and purchase the products they like.

[1191] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms to predict future demand, and, when inventory is low, sends replenishment notifications to warehouses to optimize inventory management.

[1192] If a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request, generates a message containing guidelines on the cancellation procedure, and sends it to the user's smartphone. At this time, the emotion engine grasps the user's emotional state and responds with a calm message. In this way, the user can easily complete the cancellation procedure.

[1193] The above is a concrete implementation of the system according to the present invention. This system allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. Responses that take user emotions into consideration can achieve even higher customer satisfaction.

[1194] The processing flow will be explained below.

[1195] Step 1:

[1196] A user speaks to their smartphone's voice assistant, saying, "I'm looking for some new sneakers."

[1197] Step 2:

[1198] The terminal collects the user's voice data and transmits it to a server via the Internet.

[1199] Step 3:

[1200] The server converts the received voice data into text data using a voice recognition engine.

[1201] Step 4:

[1202] The server analyzes the converted text data and understands the user's request ("I'm looking for sneakers").

[1203] Step 5:

[1204] The server retrieves the user's past purchase history and browsing history from a database and analyzes it.

[1205] Step 6:

[1206] The server uses an emotion engine to analyze the voice data and identify the user's emotional state, for example, determining whether the user is excited or calm based on the tone and rate of the voice.

[1207] Step 7:

[1208] The server uses machine learning algorithms to analyze the user's preferences and generate a personalized list of sneaker recommendations for the user, taking into account the user's emotional state from the emotion engine.

[1209] Step 8:

[1210] The server sends the generated sneaker recommendation list and an engagement message tailored to the user's emotional state to the user's device.

[1211] Step 9:

[1212] The device visually displays a list of sneaker recommendations and engagement messages to the user. For example, if the user is excited, it displays an energetic message, and if the user is calm, it displays a calm message.

[1213] Step 10:

[1214] The user selects a sneaker of interest from the displayed recommendation list and views the detail page.

[1215] Step 11:

[1216] The user clicks the "Buy" button to purchase the selected sneakers.

[1217] Step 12:

[1218] The terminal transmits the user's purchase request to the server.

[1219] Step 13:

[1220] The server processes purchase requests and updates the inventory database.

[1221] Step 14:

[1222] The server collects sales and inventory data and stores it in a database.

[1223] Step 15:

[1224] The server uses machine learning algorithms to forecast sales and predict future demand.

[1225] Step 16:

[1226] The server manages inventory based on the forecast and, if necessary, notifies warehouses and stores of replenishment requests.

[1227] Step 17:

[1228] After making a purchase, the user asks the chatbot, "I would like to cancel my order."

[1229] Step 18:

[1230] The terminal transmits the contents of the user's inquiry to the server.

[1231] Step 19:

[1232] The server analyzes the inquiry using natural language processing and generates a template message containing guidelines for the cancellation procedure.

[1233] Step 20:

[1234] The server uses an emotion engine to analyze the user's emotions and tailor the response to their emotional state at the time of the query, for example, generating a calmer tone of message if the user is emotional.

[1235] Step 21:

[1236] The server sends the generated template message to the user's terminal.

[1237] Step 22:

[1238] The terminal displays guidelines to the user and guides them through the cancellation procedure.

[1239] The above are the specific processing steps of the system based on the present invention. This series of processes allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. By utilizing the emotion engine, it is possible to respond according to the user's emotional state and provide higher customer satisfaction.

[1240] Example 2

[1241] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1242] In today's retail industry, it is necessary to respond to the needs of each individual customer, and to provide efficient inventory management and high-quality customer service. However, conventional systems have difficulty meeting all of these challenges, particularly in terms of personalized product recommendations that take customer emotions into account and efficient inventory management. Furthermore, there is a lack of methods for responding efficiently while maintaining the quality of customer service.

[1243] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for collecting voice, text, image, and video data from a user; means for analyzing the collected data and identifying the user's behavior and preferences; means for identifying the user's emotions as part of the data analysis; means for generating personalized product recommendations for the user based on the analysis results and the emotion identification results; means for displaying the product recommendations on the user's terminal; means for collecting inventory data and sales data and executing a demand forecasting algorithm; means for performing inventory management based on the demand forecast; means for analyzing customer service inquiries using natural language processing and generating appropriate responses; and means for transmitting the responses to the user's terminal. This enables personalized product recommendations that take customer emotions into consideration, efficient inventory management, and high-quality customer service.

[1244] "User" refers to a person who uses the system.

[1245] "Audio, text, image, and video data" refers to various forms of digital data collected from users.

[1246] "Means of collection" refers to methods and devices for obtaining audio, text, image, and video data from users.

[1247] "Means for analyzing data" refers to methods and devices for processing collected digital data and understanding and classifying its contents.

[1248] "Means for identifying user behavior and preferences" refers to a method or apparatus for identifying user behavior patterns and preferences from analyzed data.

[1249] "Means for identifying emotions" refers to a method or device for determining the emotional state of a user from their voice or text.

[1250] "Means for generating personalized product recommendations" refers to a method or device that creates a list of recommended products that are individually suited to a user based on the user's behavior, preferences, and emotions.

[1251] The term "means for displaying product recommendations on a user's terminal" refers to a method or device for displaying the generated product recommendations on an electronic device used by the user.

[1252] "Inventory data" refers to data regarding the quantity and condition of products stored in stores and warehouses.

[1253] "Sales Data" refers to data regarding the sales status of products.

[1254] A "demand forecasting algorithm" refers to a calculation method or model for predicting future demand for a product based on past data.

[1255] "Means for inventory management" refers to methods and devices for replenishing and adjusting inventory based on the results of demand forecasts.

[1256] "Means for analyzing customer service inquiries" refers to a method or apparatus for understanding and appropriately handling user inquiries.

[1257] "Natural language processing" refers to the technology that enables computers to understand and process human language.

[1258] "Means for generating an appropriate response" refers to a method or device for generating an appropriate answer based on the content of the inquiry.

[1259] "Means for sending a response to a user's terminal" refers to a method or device for sending the generated response to the user's electronic device.

[1260] This is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service. It also has an emotion engine that recognizes users' emotions.

[1261] Hardware and Software Configuration

[1262] The system of the present invention comprises several hardware and software components. The main components are listed below.

[1263] Server: A computer used to perform key functions such as data collection, analysis, emotion recognition, product recommendations, inventory management, sales forecasting, and customer service.

[1264] Device: A device (such as a smartphone, tablet, or PC) through which a user interacts with the system via a voice assistant or other input means.

[1265] software:

[1266] Speech recognition technology: Google Speech-to-Text API

[1267] Machine learning algorithms: Scikit-learn

[1268] Natural Language Processing Model: BERT

[1269] Database: MySQL

[1270] Specific operation of the system

[1271] Data collection and analysis

[1272] The server collects voice, text, image, and video data from users. For example, when a user speaks to a smartphone's voice assistant, the voice data is sent to the server via the device. The server converts the voice data into text using Google's voice recognition technology.

[1273] Analyzing data and identifying user preferences

[1274] The server analyzes the collected data, using machine learning algorithms such as Scikit-learn to identify user behavior and preferences, and also uses an emotion engine based on the BERT model to analyze the user's emotions and understand their current emotional state based on the request content.

[1275] Generating personalized product recommendations

[1276] The server generates a personalized product recommendation list based on the user's behavior, preferences, and sentiment analysis results. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[1277] View the recommendation list

[1278] The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone. Engagement messages that take the user's emotional state into account are also displayed simultaneously.

[1279] Inventory and sales data management

[1280] The server continuously collects sales data and customer trend data and stores it in a MySQL database. Demand forecasts are performed using Scikit-learn to calculate the optimal timing and amount of inventory replenishment. Based on this, notifications are sent to warehouses and stores to improve inventory management efficiency.

[1281] Customer Service Automation

[1282] When a user makes an inquiry or request a return after a purchase, the server uses a natural language processing model such as BERT to analyze the request and generate an appropriate response. For example, if a user inquires with a chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. An emotion engine is used to analyze the user's emotions and tailor the response to match the user's emotional state at the time of the inquiry. This results in efficient, high-quality customer service.

[1283] Specific examples

[1284] For example, consider the case where a user says to a smartphone's voice assistant, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the voice data into text using the Google Speech-to-Text API. The server analyzes the user's past purchase history and emotions from the voice data to generate a personalized list of recommended sneakers. This list and a message reflecting the user's emotions are then displayed on the user's smartphone.

[1285] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms using Scikit-learn, and sends replenishment notifications to warehouses when stock is low, optimizing inventory management.

[1286] An example prompt is:

[1287] "Design a program that identifies emotions from user voice data and recommends personalized products."

[1288] "Build a machine learning model to forecast demand based on inventory and sales data."

[1289] "Design a natural language processing system that generates appropriate responses to user queries using a chatbot."

[1290] The above is a concrete implementation of the system according to the present invention. This system allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. Responses that take user emotions into consideration can achieve even higher customer satisfaction.

[1291] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1292] Step 1:

[1293] A user makes a voice request to a smartphone voice assistant.

[1294] Input: Audio data

[1295] Action: The user says to their smartphone, "I'm looking for some new sneakers."

[1296] Output: The audio data is sent to the device.

[1297] Step 2:

[1298] The terminal transmits the voice data to the server.

[1299] Input: Audio data

[1300] Operation: The terminal transmits the user's voice data to the server.

[1301] Output: The server receives the audio data.

[1302] Step 3:

[1303] The server uses voice recognition technology to convert the voice data into text.

[1304] Input: Audio data

[1305] How it works: The server uses speech recognition technology (Google Speech-to-Text API) to convert the voice data into text.

[1306] Output: Text data

[1307] Step 4:

[1308] The server analyzes the collected text data to identify the user's behavior and preferences.

[1309] Input: Text data

[1310] How it works: The server uses Scikit-learn to analyze text data and the user's purchase history to identify the user's preferences.

[1311] Output: User preference data

[1312] Step 5:

[1313] The server uses the BERT model to analyze the user's sentiment.

[1314] Input: Text data

[1315] How it works: The server uses the BERT model to identify a user's emotions from text data. For example, it determines that a user is in a "good mood" based on their statement.

[1316] Output: User emotion data

[1317] Step 6:

[1318] The server generates a personalized product recommendation list based on the user's behavior, preferences, and emotional data.

[1319] Input: User preference data, user emotion data

[1320] How it works: The server uses Scikit-learn algorithms to generate a list of products that match the user's preferences and emotions.

[1321] Output: Product recommendation list

[1322] Step 7:

[1323] The server transmits the generated product recommendation list to the user's terminal.

[1324] Input: Product Recommendation List

[1325] Operation: The server sends a product recommendation list and an engagement message to the user's device.

[1326] Output: The device receives the product recommendation list and the message.

[1327] Step 8:

[1328] The terminal displays a list of recommended products to the user.

[1329] Input: Product recommendation list, engagement message

[1330] How it works: The device displays a list of product recommendations and engagement messages on the user's screen. For example, a list of recommended sneakers is displayed on a smartphone screen.

[1331] Output: User visually confirms the recommended products.

[1332] Step 9:

[1333] The user selects a product from the recommended list and completes the purchase process.

[1334] Input: Product Recommendation List

[1335] How it works: The user selects the product they like from the list and proceeds with the purchase.

[1336] Output: Purchase data

[1337] Step 10:

[1338] The server updates sales data and manages inventory data.

[1339] Input: Purchase data

[1340] How it works: The server saves sales data to a MySQL database and updates inventory data. It uses Scikit-learn to forecast sales and calculate when and how much inventory to replenish.

[1341] Output: Stock replenishment notification

[1342] Step 11:

[1343] The server sends notifications to warehouses and stores to replenish stock.

[1344] Input: Restock notification

[1345] How it works: The server sends notifications to warehouses and stores to replenish inventory, enabling efficient inventory management.

[1346] Output: Warehouses and stores receive instructions to replenish inventory.

[1347] Step 12:

[1348] A user makes an inquiry or returns a product after making a purchase.

[1349] Input: Query text

[1350] Action: The user sends a message to the chatbot saying, "I want to cancel my order."

[1351] Output: The query data is sent to the server.

[1352] Step 13:

[1353] The server uses natural language processing to analyze the query and generate an appropriate response.

[1354] Input: Inquiry data

[1355] How it works: The server uses the BERT model to analyze the query and generate an appropriate response message, such as a message containing cancellation guidelines.

[1356] Output: Response message

[1357] Step 14:

[1358] The server sends the generated response message to the user's terminal.

[1359] Input: Response message

[1360] Operation: The server sends a response message to the user's device, adapting the response to the user's emotion using a tone that takes into account the user's emotion using the emotion engine.

[1361] Output: The terminal receives the response message.

[1362] Step 15:

[1363] The terminal displays a response message to the user.

[1364] Input: Response message

[1365] Action: The device displays a response message on the user's screen, for example, guidelines on the cancellation procedure.

[1366] Output: The user visually confirms the response message.

[1367] (Application example 2)

[1368] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1369] Conventional online shopping systems tend to be unable to fully consider users' preferences and emotions, and tend to recommend products and provide uniform responses. Furthermore, they face challenges in quickly managing inventory and forecasting sales, making it difficult to provide efficient customer service. This can lead to lower user satisfaction and a lack of increased sales.

[1370] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1371] In this invention, the server includes means for collecting voice, text, image, and video data from a user, means for analyzing the collected data and identifying the user's behavior and preferences, means for generating personalized product recommendations for the user based on the analysis results, means for displaying the product recommendations on the user's device, means for collecting inventory data and sales data and executing a demand forecasting algorithm, means for performing inventory management based on the demand forecast, means for analyzing customer service inquiries using natural language processing and generating appropriate responses, means for transmitting the responses to the user's device, means for analyzing the user's emotions and generating product recommendations and engagement messages based on the user's emotional state in real time, and means for making appropriate adjustments in response to the user's inquiries based on the user's emotional state. This enables personalized product recommendations that accurately reflect the user's emotions and preferences, as well as efficient inventory management, sales forecasting, and high-quality customer service.

[1372] "Means for collecting voice, text, image, and video data from users" means equipment or devices that efficiently collect user-provided voice, text, image, and video data and convert it into the format required for subsequent analysis.

[1373] "Means for analyzing data and identifying user behavior and preferences" refers to algorithms or systems that analyze collected data to identify users' past purchase history, browsing history, and user characteristics, and to understand the behavior and preferences of individual users.

[1374] The "means for generating personalized product recommendations" refers to a method or device for listing products that are optimal for the user's preferences and behavior based on the analysis results and recommending them to the user.

[1375] The "means for displaying product recommendations on a user's device" refers to technology or software for visually presenting the generated product recommendation list on a user's device such as a smartphone or tablet.

[1376] "Means for collecting inventory data and sales data and running demand forecasting algorithms" refers to algorithms or systems that collect inventory status and sales information from stores and warehouses and perform calculations to forecast future demand.

[1377] "Means for inventory management based on demand forecasts" refers to methods and techniques for replenishing inventory in response to forecasted demand and for appropriate inventory management.

[1378] "Means for analyzing customer service inquiries using natural language processing and generating appropriate responses" refers to a system that uses natural language processing technology to analyze inquiries from users and generate responses that are optimal for their content.

[1379] "Means for sending responses to the user's device" refers to the technology or system for sending the generated answers and guidelines to the user's smartphone, tablet, etc., and displaying them appropriately.

[1380] "Means for analyzing user emotions and generating product recommendations and engagement messages based on emotional state in real time" refers to a system that analyzes emotions from the user's tone of voice, context, facial expressions, etc., and based on the results, provides a product recommendation list tailored to the user and messages in line with their emotions in real time.

[1381] "Means for making appropriate adjustments in response to a user's inquiry based on the user's emotional state" refers to a method or system for flexibly changing the tone and content of responses to inquiries based on the analyzed emotional state of the user.

[1382] The system of this invention is an AI-driven retail assistant system that collects and analyzes user voice, text, image, and video data to provide personalized product recommendations, manage inventory, and forecast sales. It also incorporates an emotion engine that analyzes user emotions in real time.

[1383] First, when a user speaks to a voice assistant, the voice data is sent to a server via a device such as a smartphone. The server then converts the voice data into text using voice recognition technology. This can be done using Google Speech API or other voice recognition software. If the user types text, that data is also sent to the server.

[1384] The server analyzes the collected audio, text, image, and video data using machine learning algorithms, natural language processing (NLP) libraries, and sentiment analysis engines designed for this purpose, such as NLTK and spaCy.

[1385] Based on the analysis results, the server references the user's past purchase and browsing history to identify preferences and generate a personalized product recommendation list. The server also analyzes the user's current emotional state from the collected data. The generated product recommendation list and engagement message are then displayed on the smartphone screen.

[1386] The server also continuously collects inventory and sales data. This data is stored in a database along with sales data and customer trend data. The server uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, improving inventory management efficiency.

[1387] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing technology to analyze the request and generate an appropriate response. For example, if a user inquires with the chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the user's device. At this time, the emotion engine grasps the user's emotional state and responds appropriately based on their emotional state at the time of the inquiry.

[1388] Specific examples

[1389] When a user says, "I want some new summer sunglasses," the voice data is sent to a server. The server converts the voice into text and analyzes the user's emotions. For example, if the user is speaking in a cheerful tone, a list of recommended sunglasses will be displayed along with a cheerful and upbeat message. This list and message will be displayed on the user's smartphone, allowing the user to select and purchase the product of their choice.

[1390] Prompt Sentence Examples

[1391] "Build an AI model that provides personalized product recommendations based on user sentiment analysis and feedback. Input data is voice, text, and image data, and output is a list of product recommendations and engagement messages."

[1392] In this way, the system based on the present invention provides personalized product recommendations that accurately reflect the user's emotions and preferences, as well as efficient inventory management, sales forecasting, and high-quality customer service.

[1393] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1394] Step 1:

[1395] Collect voice, text, image, and video data from users.

[1396] When a user speaks to a smartphone's voice assistant, the voice data is sent to the server via the device. Text input, image and video data are also collected. The input data is voice, text, image and video. The output is this raw data.

[1397] Step 2:

[1398] The server converts the audio data into text.

[1399] The server uses speech recognition technology such as Google Speech API to convert the received voice data into text data. The input is voice data and the output is text data. Specifically, it uses a speech recognition engine to convert the acoustic signal into a string of characters.

[1400] Step 3:

[1401] The server analyzes user data to identify behavior and preferences.

[1402] The server uses machine learning algorithms and NLP libraries (e.g., NLTK and spaCy) to analyze collected text data, past purchase history, and browsing history to identify user behavior and preferences. The input is text data and past recorded data, and the output is the analysis results. Specific operations include data preprocessing, building topic models, and extracting preference patterns.

[1403] Step 4:

[1404] The server analyzes the user's emotions.

[1405] The server uses an emotion analysis engine to analyze the user's emotional state from text and voice data. The input is text and voice data, and the output is the emotion analysis results. Specifically, it extracts features from the text or voice and predicts the emotion label using an emotion classifier.

[1406] Step 5:

[1407] A server generates personalized product recommendations.

[1408] The server lists the products most relevant to the user's preferences based on the analysis results and sentiment analysis results. The input is the preference analysis results and sentiment analysis results, and the output is a product recommendation list. Specifically, the server uses a recommendation algorithm (e.g., collaborative filtering or content-based recommendation) to select products.

[1409] Step 6:

[1410] The server displays the product recommendation list on the user's terminal.

[1411] The generated product recommendation list and engagement message are displayed on the user's smartphone screen. The input is the product recommendation list and engagement message, and the output is the content displayed on the device screen. Specifically, the system updates the GUI components of the smartphone app.

[1412] Step 7:

[1413] The server collects inventory and sales data and runs demand forecasting algorithms.

[1414] The server continuously collects inventory and sales data from stores and warehouses, and runs a demand forecasting algorithm based on this data. The inputs are inventory and sales data, and the output is the demand forecast results. Specific operations include analyzing sales history data and calculating forecasts using a demand forecasting model.

[1415] Step 8:

[1416] The server manages inventory based on the demand forecast results.

[1417] The server calculates the optimal inventory replenishment timing and quantity based on the demand forecast results. The input is the demand forecast results, and the output is the replenishment notification. Specific operations include generating replenishment orders in conjunction with the inventory management system.

[1418] Step 9:

[1419] The server analyzes customer service inquiries using natural language processing and generates appropriate responses.

[1420] When a user makes a query, the content is sent to the server. The server uses NLP technology to analyze the query and generate an appropriate response. The input is the query content (text data), and the output is a response message. Specific operations include analyzing the query text and generating a template response.

[1421] Step 10:

[1422] The server sends a response message to the user's terminal.

[1423] The generated response message is sent to the user's smartphone and displayed immediately. The input is the response message, and the output is the content displayed on the user's device. Specifically, the data is sent to the device using a message sending API.

[1424] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1425] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1426] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1427] [Fourth embodiment]

[1428] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1429] 7, a 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.

[1430] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1431] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1432] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1433] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1434] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1435] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1436] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1437] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1438] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1439] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1440] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1441] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[1442] Data collection and analysis

[1443] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server then converts the voice data into text using voice recognition technology.

[1444] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences, which allows it to understand users' interests and preferences based on their past purchase and browsing history.

[1445] Generating personalized product recommendations

[1446] The server generates a personalized product recommendation list based on the results of analyzing the user's behavior and preferences. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[1447] The generated product recommendation list is sent to the user's device and visually presented, for example, by displaying images and detailed information of the recommended products on the screen of a smartphone.

[1448] Inventory and sales data management

[1449] The server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server then uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, aiming to improve the efficiency of inventory management.

[1450] Customer Service Automation

[1451] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user asks the chatbot, "I'd like to cancel my order," the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. This improves the quality of customer service and ensures efficient responses.

[1452] Specific examples

[1453] A user speaks to their smartphone's voice assistant, saying, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the speech into text. The server analyzes the user's past purchase history and generates a recommended list of sneakers that match the user's preferences. This list is displayed on the user's smartphone, and the user can select and purchase the products they like.

[1454] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms to predict future demand, and, when inventory is low, sends replenishment notifications to warehouses to optimize inventory management.

[1455] Additionally, if a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request, generates a message containing guidelines on the cancellation procedure, and sends it to the user's smartphone, allowing the user to easily complete the cancellation procedure.

[1456] As described above, the present invention is a system that detects user needs in real time, provides a personalized shopping experience, and realizes efficient inventory management and high-quality customer service.

[1457] The processing flow will be explained below.

[1458] Step 1:

[1459] A user speaks to their smartphone's voice assistant, saying, "I'm looking for some new sneakers."

[1460] Step 2:

[1461] The terminal collects the user's voice data and transmits it to a server via the Internet.

[1462] Step 3:

[1463] The server converts the received voice data into text data using a voice recognition engine.

[1464] Step 4:

[1465] The server analyzes the converted text data and understands the user's request ("I'm looking for sneakers").

[1466] Step 5:

[1467] The server retrieves the user's past purchase history and browsing history from a database and analyzes it.

[1468] Step 6:

[1469] The server uses machine learning algorithms to analyze the user's preferences and generate a personalized list of sneaker recommendations for the user.

[1470] Step 7:

[1471] The server transmits the generated sneaker recommendation list to the user's terminal.

[1472] Step 8:

[1473] The terminal visually displays a list of sneaker recommendations to the user.

[1474] Step 9:

[1475] The user selects a sneaker of interest from the displayed recommendation list and views the detail page.

[1476] Step 10:

[1477] The user clicks the "Buy" button to purchase the selected sneakers.

[1478] Step 11:

[1479] The terminal transmits the user's purchase request to the server.

[1480] Step 12:

[1481] The server processes purchase requests and updates the inventory database.

[1482] Step 13:

[1483] The server collects sales and inventory data and stores it in a database.

[1484] Step 14:

[1485] The server uses machine learning algorithms to forecast sales and predict future demand.

[1486] Step 15:

[1487] The server manages inventory based on the forecast and, if necessary, notifies warehouses and stores of replenishment requests.

[1488] Step 16:

[1489] After making a purchase, the user asks the chatbot, "I would like to cancel my order."

[1490] Step 17:

[1491] The terminal transmits the contents of the user's inquiry to the server.

[1492] Step 18:

[1493] The server analyzes the inquiry using natural language processing and generates a template message containing guidelines for the cancellation procedure.

[1494] Step 19:

[1495] The server sends the generated template message to the user's terminal.

[1496] Step 20:

[1497] The terminal displays guidelines to the user and guides them through the cancellation procedure.

[1498] The above are the specific processing steps of the system according to the present invention, which enable users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service.

[1499] Example 1

[1500] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1501] In today's retail industry, despite the existence of a large amount of data, efficient inventory management and personalized product recommendations remain difficult. Customer service responses are also time-consuming and costly, so fast, high-quality responses are required. Furthermore, inaccurate demand forecasts can lead to excess inventory, making it difficult to prevent economic losses. There is a need to solve these issues and provide an efficient and effective retail assistant system.

[1502] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1503] In this invention, the server includes means for collecting voice, text, image, and video data from a user, means for converting the collected data into text using voice recognition technology, means for analyzing the collected data and identifying user behavior and preferences using a machine learning algorithm, means for generating a personalized product recommendation list for the user based on the analysis results, means for transmitting the product recommendation list to the user's terminal and visually displaying it, means for collecting sales data and customer trend data and storing them in a database, means for using the collected data to perform a demand forecast using a machine learning algorithm, means for calculating the optimal inventory replenishment timing and amount based on the demand forecast and sending a notification, means for analyzing customer service inquiries using natural language processing and generating an appropriate response, and means for transmitting the response to the user's terminal. This enables efficient inventory management and sales forecasting, personalized product recommendations, and high-quality customer service.

[1504] "Voice data" is digital data that records what the user has said.

[1505] "Text data" refers to character string information converted using voice recognition technology or character string information directly input by a user.

[1506] "Image data" refers to a digital image file containing user-provided visual information.

[1507] "Video Data" means a digital video file containing user-provided dynamic visual information.

[1508] "Speech recognition technology" is a technology that recognizes human speech as digital data and converts it into text data.

[1509] A "machine learning algorithm" is a mathematical model that analyzes large amounts of data and finds patterns to make predictions and classifications.

[1510] "User behavior" refers to a behavior history that includes all operations and selections that a user performs within the system.

[1511] "Preferences" refers to information related to products and services that a user prefers, and is primarily based on past behavioral history and purchase history.

[1512] A "product recommendation list" is a list of products that are likely to interest the user, generated based on the analysis results.

[1513] "Visually displaying" means presenting images or text information to the user's visual field on the user's terminal.

[1514] "Sales data" refers to information related to product sales, specifically including the number of products, price, date and time of sale, etc.

[1515] "Customer trend data" refers to data related to customer activity, including customer behavior history, purchase history, site browsing history, etc.

[1516] A "database" is a system for efficiently storing, retrieving, and updating data in a structured format.

[1517] "Demand forecasting" is the process of predicting future consumer demand based on past data.

[1518] "Natural language processing" is the branch of computer science that deals with understanding, interpreting, and generating human language.

[1519] A "response" is a reply message generated in response to a customer inquiry.

[1520] "Terminal" refers to a device used by a user, including a smartphone, tablet, or PC.

[1521] A "notification" is a message intended to inform a user or other system of specific information or action.

[1522] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[1523] Data collection and analysis

[1524] The server collects voice, text, image, and video data from the user. Specifically, when a user says to the voice assistant, "I'm looking for new sneakers," the voice data is sent to the server via the device. The server converts the voice data into text using voice recognition technology (e.g., Google Cloud Speech-to-Text API).

[1525] The server analyzes the collected data and uses machine learning algorithms (e.g., TensorFlow, Scikit-learn) to identify user behavior and preferences, which allows the server to understand the user's interests and preferences based on past purchase and browsing history.

[1526] Generating personalized product recommendations

[1527] The server generates a personalized product recommendation list based on the user's behavior and preference analysis. For example, a sneaker recommendation list is generated and sent to the user's device. The device then visually displays the list, showing product images and detailed information on the screen.

[1528] Inventory and sales data management

[1529] The server continuously collects sales data and customer trend data and stores it in a database (e.g., MongoDB, PostgreSQL). This accumulates data for inventory management and sales forecasting. The server uses machine learning algorithms (e.g., SciPy, Pandas) to forecast demand and calculate the optimal timing and amount of inventory replenishment. For example, if sneaker stock is low, the server sends a notification to the warehouse to instruct it to replenish the stock.

[1530] Customer Service Automation

[1531] When a user makes an inquiry or request a return after a purchase, the server analyzes the request using natural language processing technology (e.g., Amazon Lex, Dialogflow). For example, if a user asks a chatbot, "I would like to cancel my order," the server analyzes the request, generates guidelines for the cancellation procedure based on a template message, and sends them to the user's device. This allows the user to easily proceed with the cancellation procedure.

[1532] Specific examples

[1533] For example, a user might say to a smartphone's voice assistant, "I'm looking for new sneakers." The device sends this voice data to a server, which converts the speech into text using the Google Cloud Speech-to-Text API. The server analyzes the user's past purchase history and uses TensorFlow to generate a recommended list of sneakers that match the user's preferences. This list is then sent to the device and displayed on the smartphone screen. The user can then select and purchase the items they like from the list.

[1534] The server also updates sneaker inventory data and predicts future demand using a sales forecasting algorithm powered by SciPy. If inventory is low, it sends a replenishment notification to the warehouse to optimize inventory management. Furthermore, if a user sends a message to the chatbot saying, "I want to cancel my order," the server uses Amazon Lex to analyze the request and sends guidelines for the cancellation procedure to the user's smartphone.

[1535] The system analyzes data from users in real time and provides personalized services, thereby increasing customer satisfaction and enabling efficient inventory management and sales forecasting.

[1536] Prompt Sentence Examples

[1537] "Generate personalized product recommendation lists using user purchase history and preference data."

[1538] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1539] Step 1:

[1540] When a user speaks to a voice assistant, the voice data is collected by the device. For example, if a user says to their smartphone, "I'm looking for new sneakers," the voice recording is stored on the device.

[1541] Input: User's voice data

[1542] Output: Recorded audio data

[1543] Step 2:

[1544] The device sends the recorded audio data to the server using an HTTPS request, uploading the audio data to the server as an attachment.

[1545] Input: Recorded audio data

[1546] Output: Audio data sent to the server

[1547] Step 3:

[1548] The server uses speech recognition technology to convert the received voice data into text data. Specifically, it sends the voice data to the Google Cloud Speech-to-Text API and receives the resulting text data.

[1549] Input: Audio data sent to the server

[1550] Output: Text data converted from audio

[1551] Step 4:

[1552] The server analyzes the text data and uses machine learning algorithms to identify user behavior and preferences. Specifically, the text data is fed into a TensorFlow environment, where it is compared with past purchase and browsing history to update a user preference model.

[1553] Input: Text data converted from speech, past purchase history

[1554] Output: User preference analysis results

[1555] Step 5:

[1556] The server generates a personalized product recommendation list based on the results of the preference analysis. It searches the product database for products that match the user's preferences and compiles them into a list.

[1557] Input: User preference analysis results

[1558] Output: A personalized product recommendation list

[1559] Step 6:

[1560] The generated product recommendation list is sent to the device, which receives the JSON format data from the server and visually displays it along with detailed product information.

[1561] Input: A personalized product recommendation list

[1562] Output: Product recommendation list displayed on the device

[1563] Step 7:

[1564] The server continuously collects sales data and customer behavior data and stores them in a database, such as recording sales information and browsing frequency for each product in real time.

[1565] Input: Sales data, customer trend data

[1566] Output: Sales data and customer trend data stored in a database

[1567] Step 8:

[1568] The server uses machine learning algorithms to forecast demand using past sales data, and SciPy and Pandas are used to analyze the data and predict future demand.

[1569] Input: Sales data stored in the database

[1570] Output: Demand forecast results

[1571] Step 9:

[1572] The server calculates the optimal inventory replenishment timing and quantity based on the demand forecast results, and if it detects that an item is low in stock, it notifies the warehouse to request replenishment.

[1573] Input: Demand forecast results

[1574] Output: Replenishment notification to warehouse

[1575] Step 10:

[1576] If a user makes an inquiry or returns a purchase after purchase, the server uses natural language processing technology to analyze the request and generate an appropriate response. Amazon Lex is used to analyze the user's message.

[1577] Input: User's query message

[1578] Output: Parsed request

[1579] Step 11:

[1580] The response message generated by the server is sent to the terminal in real time, and the user can easily complete the process by following instructions such as cancellation procedures.

[1581] Input: Parsed request, generated response message

[1582] Output: Response message displayed on the terminal

[1583] In this way, this system handles everything from collecting voice data to converting it into text, analyzing it using machine learning, and automating personalized product recommendations, inventory management, and customer service.

[1584] (Application example 1)

[1585] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1586] Current retail systems lack the ability to understand diverse user preferences and make personalized product recommendations. They also lack efficiency in inventory management and demand forecasting, creating a need for improved customer service. Real-time preference analysis and appropriate product recommendations are particularly important for online shopping sites to improve user experience.

[1587] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1588] In this invention, the server includes means for collecting voice, text, image, and video data from users, means for analyzing the collected data to identify user behavior and preferences, and means for generating personalized product recommendations for users based on the analysis results, thereby enabling efficient inventory management and high-quality customer service while providing personalized product recommendations tailored to user needs.

[1589] A "user" is a person who uses this system to receive product recommendations and customer service.

[1590] "Voice data" is digital data of the voice generated when a user speaks to a voice assistant.

[1591] "Text data" refers to voice data converted into text, as well as text information manually entered by the user.

[1592] "Image data" refers to digital data of product images and related images provided by users.

[1593] "Video data" is digital data of examples of product usage and related videos provided by users.

[1594] "Analysis" is the process of analyzing data to identify user behavior and preferences based on collected data.

[1595] "Personalized product recommendations" are product lists generated based on analysis results to suit the individual preferences and needs of each user.

[1596] A "terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[1597] "Inventory data" is digital information that indicates how many items are in stock at a warehouse or store.

[1598] "Sales data" is digital data that includes past sales history and sales information.

[1599] A "demand forecasting algorithm" is a mathematical model or calculation method for predicting future demand based on past sales data.

[1600] "Inventory management" is the process of maintaining appropriate inventory levels based on demand forecasting algorithms.

[1601] "Natural language processing" is an artificial intelligence technology that analyzes natural human language to understand its appropriate meaning.

[1602] A "response" is a reply or guidance message generated by the system in response to a user's inquiry.

[1603] "Speech recognition technology" is a technology that converts voice data into text data.

[1604] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service.

[1605] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server converts the voice data into text using voice recognition technology. Users can also upload image and video data to the server using their device. In this process, "SpeechRecognition" is used as the voice recognition technology and "Spacy" is used for natural language processing.

[1606] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences, including past purchase and browsing history. This analysis process utilizes machine learning libraries such as TensorFlow and scikit-learn.

[1607] The server generates a personalized product recommendation list based on the analysis results. This recommendation list includes products that are likely to interest the user, improving the user experience. The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone.

[1608] In addition, the server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server uses machine learning algorithms to predict future demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores to improve inventory management efficiency.

[1609] Automated customer service is also an important function of this system. When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user inquires about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. This improves the quality of customer service and ensures efficient responses.

[1610] As a concrete example, consider the case where a user says to a smartphone's voice assistant, "I'm looking for new sneakers." At this time, the device sends the voice data to a server, which converts the voice into text. The server then analyzes the user's past purchase history and generates a recommended list of sneakers that match the user's preferences. This list is displayed on the user's smartphone, allowing the user to select and purchase the products they like.

[1611] The server also updates sneaker inventory data and runs sales forecasting algorithms to predict future demand. If inventory is low, it sends a replenishment notification to the warehouse to optimize inventory management. If a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request and generates a message with guidelines on the cancellation procedure, which is sent to the user's smartphone. The user can easily complete the cancellation procedure.

[1612] Example prompt sentence:

[1613] Explain how you can recommend products to a user when they say, "I'm looking for some new sneakers," to their smartphone voice assistant. Show them how you can improve their shopping experience by providing sneaker recommendations.

[1614] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1615] Step 1:

[1616] A user talks to a voice assistant

[1617] A user inputs a specific voice command into the smartphone's voice assistant, for example, "I'm looking for new sneakers." The input is voice data, and the output is the device's transmission of the voice data to the server.

[1618] Step 2:

[1619] Converting audio data into text data

[1620] The server uses speech recognition technology to convert the received voice data into text data. Specifically, it uses the "SpeechRecognition" library, with the input being voice data and the output being the corresponding text data. The converted text data is "I'm looking for new sneakers."

[1621] Step 3:

[1622] Analyze the user's past purchase history

[1623] The server analyzes the user's past purchase and browsing history based on the text data. This analysis uses machine learning libraries such as "TensorFlow" and "scikit-learn." The input is text data and history data, and the output is the analysis results of the user's preferences.

[1624] Step 4:

[1625] Generate personalized product recommendation lists

[1626] The server generates a personalized product recommendation list based on the analysis results. For example, it includes specific product names such as "Sneaker A" and "Sneaker B." The input is the analysis results, and the output is the product recommendation list.

[1627] Step 5:

[1628] Send and display the product recommendation list to the user's device

[1629] The server sends the generated product recommendation list to the user's smartphone, which then visually displays it. The input is the product recommendation list, and the output is the product list displayed on the user's smartphone screen. Specifically, images and detailed information about the recommended products are displayed.

[1630] Step 6:

[1631] Collect inventory and sales data to forecast demand

[1632] The server collects sales data and customer trend data and builds a database for inventory management and sales forecasting. It uses TensorFlow and scikit-learn to predict future demand and calculate the optimal timing and amount of inventory replenishment. The input is sales data and trend data, and the output is the demand forecast results.

[1633] Step 7:

[1634] Optimize inventory management

[1635] The server notifies warehouses and stores of inventory replenishment based on the demand forecast results, thereby improving the efficiency of inventory management. The input is the demand forecast results, and the output is replenishment notifications to warehouses and stores.

[1636] Step 8:

[1637] Parse customer service inquiries and generate responses

[1638] When a user makes a query to the chatbot, the server uses natural language processing to analyze the request and generate an appropriate response. For example, it responds to a message such as "I would like to cancel my order." The input is the query message, and the output is the analysis result and the generated response message.

[1639] Step 9:

[1640] Sends the generated response to the user's device

[1641] The server generates a response message and sends it to the user's terminal, which then displays it. The input is the response message, and the output is the response message displayed on the user's terminal. For example, guidelines on cancellation procedures are displayed.

[1642] By implementing each of the above steps, personalized product recommendations based on user needs can be made while achieving efficient inventory management and high-quality customer service.

[1643] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1644] This invention is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to make personalized product recommendations, manages inventory, forecasts sales, and provides efficient customer service. It also combines an emotion engine that recognizes users' emotions.

[1645] Data collection and analysis

[1646] The server collects voice, text, image, and video data from the user. For example, when a user speaks to a voice assistant, the voice data is sent to the server via the device. The server then converts the voice data into text using voice recognition technology.

[1647] The server analyzes the collected data and uses machine learning algorithms to identify user behavior and preferences. It also uses an emotion engine to analyze the user's emotions and understand their current emotional state from the content of their requests. This allows it to understand the user's preferences from their past purchase history, browsing history, and emotional state.

[1648] Generating personalized product recommendations

[1649] The server generates a personalized product recommendation list based on the user's behavior, preferences, and sentiment analysis results. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[1650] The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone. Engagement messages that take the user's emotional state into account are also displayed simultaneously.

[1651] Inventory and sales data management

[1652] The server continuously collects sales data and customer trend data to build a database for inventory management and sales forecasting. The server then uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, aiming to improve the efficiency of inventory management.

[1653] Customer Service Automation

[1654] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing to analyze the request and generate an appropriate response. For example, if a user inquires with the chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. The server also uses an emotion engine to analyze the user's emotions and tailor the response to match their emotional state at the time of the inquiry. This improves the quality of customer service and enables efficient responses.

[1655] Specific examples

[1656] A user says to their smartphone's voice assistant, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the speech into text. The server analyzes the user's past purchase history and also analyzes the user's emotions from the voice data. If the user is determined to be in a good mood, the server takes this into account and generates a list of sneaker recommendations with brighter, more attractive recommendation messages. This list and messages are displayed on the user's smartphone, and the user can select and purchase the products they like.

[1657] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms to predict future demand, and, when inventory is low, sends replenishment notifications to warehouses to optimize inventory management.

[1658] If a user sends a message to the chatbot saying, "I want to cancel my order," the server analyzes the request, generates a message containing guidelines on the cancellation procedure, and sends it to the user's smartphone. At this time, the emotion engine grasps the user's emotional state and responds with a calm message. In this way, the user can easily complete the cancellation procedure.

[1659] The above is a concrete implementation of the system according to the present invention. This system allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. Responses that take user emotions into consideration can achieve even higher customer satisfaction.

[1660] The processing flow will be explained below.

[1661] Step 1:

[1662] A user speaks to their smartphone's voice assistant, saying, "I'm looking for some new sneakers."

[1663] Step 2:

[1664] The terminal collects the user's voice data and transmits it to a server via the Internet.

[1665] Step 3:

[1666] The server converts the received voice data into text data using a voice recognition engine.

[1667] Step 4:

[1668] The server analyzes the converted text data and understands the user's request ("I'm looking for sneakers").

[1669] Step 5:

[1670] The server retrieves the user's past purchase history and browsing history from a database and analyzes it.

[1671] Step 6:

[1672] The server uses an emotion engine to analyze the voice data and identify the user's emotional state, for example, determining whether the user is excited or calm based on the tone and rate of the voice.

[1673] Step 7:

[1674] The server uses machine learning algorithms to analyze the user's preferences and generate a personalized list of sneaker recommendations for the user, taking into account the user's emotional state from the emotion engine.

[1675] Step 8:

[1676] The server sends the generated sneaker recommendation list and an engagement message tailored to the user's emotional state to the user's device.

[1677] Step 9:

[1678] The device visually displays a list of sneaker recommendations and engagement messages to the user. For example, if the user is excited, it displays an energetic message, and if the user is calm, it displays a calm message.

[1679] Step 10:

[1680] The user selects a sneaker of interest from the displayed recommendation list and views the detail page.

[1681] Step 11:

[1682] The user clicks the "Buy" button to purchase the selected sneakers.

[1683] Step 12:

[1684] The terminal transmits the user's purchase request to the server.

[1685] Step 13:

[1686] The server processes purchase requests and updates the inventory database.

[1687] Step 14:

[1688] The server collects sales and inventory data and stores it in a database.

[1689] Step 15:

[1690] The server uses machine learning algorithms to forecast sales and predict future demand.

[1691] Step 16:

[1692] The server manages inventory based on the forecast and, if necessary, notifies warehouses and stores of replenishment requests.

[1693] Step 17:

[1694] After making a purchase, the user asks the chatbot, "I would like to cancel my order."

[1695] Step 18:

[1696] The terminal transmits the contents of the user's inquiry to the server.

[1697] Step 19:

[1698] The server analyzes the inquiry using natural language processing and generates a template message containing guidelines for the cancellation procedure.

[1699] Step 20:

[1700] The server uses an emotion engine to analyze the user's emotions and tailor the response to their emotional state at the time of the query, for example, generating a calmer tone of message if the user is emotional.

[1701] Step 21:

[1702] The server sends the generated template message to the user's terminal.

[1703] Step 22:

[1704] The terminal displays guidelines to the user and guides them through the cancellation procedure.

[1705] The above are the specific processing steps of the system based on the present invention. This series of processes allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. By utilizing the emotion engine, it is possible to respond according to the user's emotional state and provide higher customer satisfaction.

[1706] Example 2

[1707] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1708] In today's retail industry, it is necessary to respond to the needs of each individual customer, and to provide efficient inventory management and high-quality customer service. However, conventional systems have difficulty meeting all of these challenges, particularly in terms of personalized product recommendations that take customer emotions into account and efficient inventory management. Furthermore, there is a lack of methods for responding efficiently while maintaining the quality of customer service.

[1709] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for collecting voice, text, image, and video data from a user; means for analyzing the collected data and identifying the user's behavior and preferences; means for identifying the user's emotions as part of the data analysis; means for generating personalized product recommendations for the user based on the analysis results and the emotion identification results; means for displaying the product recommendations on the user's terminal; means for collecting inventory data and sales data and executing a demand forecasting algorithm; means for performing inventory management based on the demand forecast; means for analyzing customer service inquiries using natural language processing and generating appropriate responses; and means for transmitting the responses to the user's terminal. This enables personalized product recommendations that take customer emotions into consideration, efficient inventory management, and high-quality customer service.

[1710] "User" refers to a person who uses the system.

[1711] "Audio, text, image, and video data" refers to various forms of digital data collected from users.

[1712] "Means of collection" refers to methods and devices for obtaining audio, text, image, and video data from users.

[1713] "Means for analyzing data" refers to methods and devices for processing collected digital data and understanding and classifying its contents.

[1714] "Means for identifying user behavior and preferences" refers to a method or apparatus for identifying user behavior patterns and preferences from analyzed data.

[1715] "Means for identifying emotions" refers to a method or device for determining the emotional state of a user from their voice or text.

[1716] "Means for generating personalized product recommendations" refers to a method or device that creates a list of recommended products that are individually suited to a user based on the user's behavior, preferences, and emotions.

[1717] The term "means for displaying product recommendations on a user's terminal" refers to a method or device for displaying the generated product recommendations on an electronic device used by the user.

[1718] "Inventory data" refers to data regarding the quantity and condition of products stored in stores and warehouses.

[1719] "Sales Data" refers to data regarding the sales status of products.

[1720] A "demand forecasting algorithm" refers to a calculation method or model for predicting future demand for a product based on past data.

[1721] "Means for inventory management" refers to methods and devices for replenishing and adjusting inventory based on the results of demand forecasts.

[1722] "Means for analyzing customer service inquiries" refers to a method or apparatus for understanding and appropriately handling user inquiries.

[1723] "Natural language processing" refers to the technology that enables computers to understand and process human language.

[1724] "Means for generating an appropriate response" refers to a method or device for generating an appropriate answer based on the content of the inquiry.

[1725] "Means for sending a response to a user's terminal" refers to a method or device for sending the generated response to the user's electronic device.

[1726] This is an AI-driven retail assistant system that collects and analyzes users' voice, text, image, and video data to provide personalized product recommendations, manage inventory, forecast sales, and provide efficient customer service. It also has an emotion engine that recognizes users' emotions.

[1727] Hardware and Software Configuration

[1728] The system of the present invention comprises several hardware and software components. The main components are listed below.

[1729] Server: A computer used to perform key functions such as data collection, analysis, emotion recognition, product recommendations, inventory management, sales forecasting, and customer service.

[1730] Device: A device (such as a smartphone, tablet, or PC) through which a user interacts with the system via a voice assistant or other input means.

[1731] software:

[1732] Speech recognition technology: Google Speech-to-Text API

[1733] Machine learning algorithms: Scikit-learn

[1734] Natural Language Processing Model: BERT

[1735] Database: MySQL

[1736] Specific operation of the system

[1737] Data collection and analysis

[1738] The server collects voice, text, image, and video data from users. For example, when a user speaks to a smartphone's voice assistant, the voice data is sent to the server via the device. The server converts the voice data into text using Google's voice recognition technology.

[1739] Analyzing data and identifying user preferences

[1740] The server analyzes the collected data, using machine learning algorithms such as Scikit-learn to identify user behavior and preferences, and also uses an emotion engine based on the BERT model to analyze the user's emotions and understand their current emotional state based on the request content.

[1741] Generating personalized product recommendations

[1742] The server generates a personalized product recommendation list based on the user's behavior, preferences, and sentiment analysis results. This recommendation list contains products that are likely to interest the user, and is an important element for improving the user experience.

[1743] View the recommendation list

[1744] The generated product recommendation list is sent to the user's device and presented visually. For example, images and detailed information of the recommended products are displayed on the screen of a smartphone. Engagement messages that take the user's emotional state into account are also displayed simultaneously.

[1745] Inventory and sales data management

[1746] The server continuously collects sales data and customer trend data and stores it in a MySQL database. Demand forecasts are performed using Scikit-learn to calculate the optimal timing and amount of inventory replenishment. Based on this, notifications are sent to warehouses and stores to improve inventory management efficiency.

[1747] Customer Service Automation

[1748] When a user makes an inquiry or request a return after a purchase, the server uses a natural language processing model such as BERT to analyze the request and generate an appropriate response. For example, if a user inquires with a chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the terminal. An emotion engine is used to analyze the user's emotions and tailor the response to match the user's emotional state at the time of the inquiry. This results in efficient, high-quality customer service.

[1749] Specific examples

[1750] For example, consider the case where a user says to a smartphone's voice assistant, "I'm looking for new sneakers." The device sends the voice data to a server, which converts the voice data into text using the Google Speech-to-Text API. The server analyzes the user's past purchase history and emotions from the voice data to generate a personalized list of recommended sneakers. This list and a message reflecting the user's emotions are then displayed on the user's smartphone.

[1751] Additionally, the server updates sneaker inventory data, runs sales forecasting algorithms using Scikit-learn, and sends replenishment notifications to warehouses when stock is low, optimizing inventory management.

[1752] An example prompt is:

[1753] "Design a program that identifies emotions from user voice data and recommends personalized products."

[1754] "Build a machine learning model to forecast demand based on inventory and sales data."

[1755] "Design a natural language processing system that generates appropriate responses to user queries using a chatbot."

[1756] The above is a concrete implementation of the system according to the present invention. This system allows users to enjoy a personalized shopping experience while receiving efficient inventory management and high-quality customer service. Responses that take user emotions into consideration can achieve even higher customer satisfaction.

[1757] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1758] Step 1:

[1759] A user makes a voice request to a smartphone voice assistant.

[1760] Input: Audio data

[1761] Action: The user says to their smartphone, "I'm looking for some new sneakers."

[1762] Output: The audio data is sent to the device.

[1763] Step 2:

[1764] The terminal transmits the voice data to the server.

[1765] Input: Audio data

[1766] Operation: The terminal transmits the user's voice data to the server.

[1767] Output: The server receives the audio data.

[1768] Step 3:

[1769] The server uses voice recognition technology to convert the voice data into text.

[1770] Input: Audio data

[1771] How it works: The server uses speech recognition technology (Google Speech-to-Text API) to convert the voice data into text.

[1772] Output: Text data

[1773] Step 4:

[1774] The server analyzes the collected text data to identify the user's behavior and preferences.

[1775] Input: Text data

[1776] How it works: The server uses Scikit-learn to analyze text data and the user's purchase history to identify the user's preferences.

[1777] Output: User preference data

[1778] Step 5:

[1779] The server uses the BERT model to analyze the user's sentiment.

[1780] Input: Text data

[1781] How it works: The server uses the BERT model to identify a user's emotions from text data. For example, it determines that a user is in a "good mood" based on their statement.

[1782] Output: User emotion data

[1783] Step 6:

[1784] The server generates a personalized product recommendation list based on the user's behavior, preferences, and emotional data.

[1785] Input: User preference data, user emotion data

[1786] How it works: The server uses Scikit-learn algorithms to generate a list of products that match the user's preferences and emotions.

[1787] Output: Product recommendation list

[1788] Step 7:

[1789] The server transmits the generated product recommendation list to the user's terminal.

[1790] Input: Product Recommendation List

[1791] Operation: The server sends a product recommendation list and an engagement message to the user's device.

[1792] Output: The device receives the product recommendation list and the message.

[1793] Step 8:

[1794] The terminal displays a list of recommended products to the user.

[1795] Input: Product recommendation list, engagement message

[1796] How it works: The device displays a list of product recommendations and engagement messages on the user's screen. For example, a list of recommended sneakers is displayed on a smartphone screen.

[1797] Output: User visually confirms the recommended products.

[1798] Step 9:

[1799] The user selects a product from the recommended list and completes the purchase process.

[1800] Input: Product Recommendation List

[1801] How it works: The user selects the product they like from the list and proceeds with the purchase.

[1802] Output: Purchase data

[1803] Step 10:

[1804] The server updates sales data and manages inventory data.

[1805] Input: Purchase data

[1806] How it works: The server saves sales data to a MySQL database and updates inventory data. It uses Scikit-learn to forecast sales and calculate when and how much inventory to replenish.

[1807] Output: Stock replenishment notification

[1808] Step 11:

[1809] The server sends notifications to warehouses and stores to replenish stock.

[1810] Input: Restock notification

[1811] How it works: The server sends notifications to warehouses and stores to replenish inventory, enabling efficient inventory management.

[1812] Output: Warehouses and stores receive instructions to replenish inventory.

[1813] Step 12:

[1814] A user makes an inquiry or returns a product after making a purchase.

[1815] Input: Query text

[1816] Action: The user sends a message to the chatbot saying, "I want to cancel my order."

[1817] Output: The query data is sent to the server.

[1818] Step 13:

[1819] The server uses natural language processing to analyze the query and generate an appropriate response.

[1820] Input: Inquiry data

[1821] How it works: The server uses the BERT model to analyze the query and generate an appropriate response message, such as a message containing cancellation guidelines.

[1822] Output: Response message

[1823] Step 14:

[1824] The server sends the generated response message to the user's terminal.

[1825] Input: Response message

[1826] Operation: The server sends a response message to the user's device, adapting the response to the user's emotion using a tone that takes into account the user's emotion using the emotion engine.

[1827] Output: The terminal receives the response message.

[1828] Step 15:

[1829] The terminal displays a response message to the user.

[1830] Input: Response message

[1831] Action: The device displays a response message on the user's screen, for example, guidelines on the cancellation procedure.

[1832] Output: The user visually confirms the response message.

[1833] (Application example 2)

[1834] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1835] Conventional online shopping systems tend to be unable to fully consider users' preferences and emotions, and tend to recommend products and provide uniform responses. Furthermore, they face challenges in quickly managing inventory and forecasting sales, making it difficult to provide efficient customer service. This can lead to lower user satisfaction and a lack of increased sales.

[1836] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1837] In this invention, the server includes means for collecting voice, text, image, and video data from a user, means for analyzing the collected data and identifying the user's behavior and preferences, means for generating personalized product recommendations for the user based on the analysis results, means for displaying the product recommendations on the user's device, means for collecting inventory data and sales data and executing a demand forecasting algorithm, means for performing inventory management based on the demand forecast, means for analyzing customer service inquiries using natural language processing and generating appropriate responses, means for transmitting the responses to the user's device, means for analyzing the user's emotions and generating product recommendations and engagement messages based on the user's emotional state in real time, and means for making appropriate adjustments in response to the user's inquiries based on the user's emotional state. This enables personalized product recommendations that accurately reflect the user's emotions and preferences, as well as efficient inventory management, sales forecasting, and high-quality customer service.

[1838] "Means for collecting voice, text, image, and video data from users" means equipment or devices that efficiently collect user-provided voice, text, image, and video data and convert it into the format required for subsequent analysis.

[1839] "Means for analyzing data and identifying user behavior and preferences" refers to algorithms or systems that analyze collected data to identify users' past purchase history, browsing history, and user characteristics, and to understand the behavior and preferences of individual users.

[1840] The "means for generating personalized product recommendations" refers to a method or device for listing products that are optimal for the user's preferences and behavior based on the analysis results and recommending them to the user.

[1841] The "means for displaying product recommendations on a user's device" refers to technology or software for visually presenting the generated product recommendation list on a user's device such as a smartphone or tablet.

[1842] "Means for collecting inventory data and sales data and running demand forecasting algorithms" refers to algorithms or systems that collect inventory status and sales information from stores and warehouses and perform calculations to forecast future demand.

[1843] "Means for inventory management based on demand forecasts" refers to methods and techniques for replenishing inventory in response to forecasted demand and for appropriate inventory management.

[1844] "Means for analyzing customer service inquiries using natural language processing and generating appropriate responses" refers to a system that uses natural language processing technology to analyze inquiries from users and generate responses that are optimal for their content.

[1845] "Means for sending responses to the user's device" refers to the technology or system for sending the generated answers and guidelines to the user's smartphone, tablet, etc., and displaying them appropriately.

[1846] "Means for analyzing user emotions and generating product recommendations and engagement messages based on emotional state in real time" refers to a system that analyzes emotions from the user's tone of voice, context, facial expressions, etc., and based on the results, provides a product recommendation list tailored to the user and messages in line with their emotions in real time.

[1847] "Means for making appropriate adjustments in response to a user's inquiry based on the user's emotional state" refers to a method or system for flexibly changing the tone and content of responses to inquiries based on the analyzed emotional state of the user.

[1848] The system of this invention is an AI-driven retail assistant system that collects and analyzes user voice, text, image, and video data to provide personalized product recommendations, manage inventory, and forecast sales. It also incorporates an emotion engine that analyzes user emotions in real time.

[1849] First, when a user speaks to a voice assistant, the voice data is sent to a server via a device such as a smartphone. The server then converts the voice data into text using voice recognition technology. This can be done using Google Speech API or other voice recognition software. If the user types text, that data is also sent to the server.

[1850] The server analyzes the collected audio, text, image, and video data using machine learning algorithms, natural language processing (NLP) libraries, and sentiment analysis engines designed for this purpose, such as NLTK and spaCy.

[1851] Based on the analysis results, the server references the user's past purchase and browsing history to identify preferences and generate a personalized product recommendation list. The server also analyzes the user's current emotional state from the collected data. The generated product recommendation list and engagement message are then displayed on the smartphone screen.

[1852] The server also continuously collects inventory and sales data. This data is stored in a database along with sales data and customer trend data. The server uses machine learning algorithms to forecast demand and calculate the optimal timing and amount of inventory replenishment. Based on this, it notifies warehouses and stores, improving inventory management efficiency.

[1853] When a user makes an inquiry or request a return after a purchase, the server uses natural language processing technology to analyze the request and generate an appropriate response. For example, if a user inquires with the chatbot about canceling an order, the server analyzes the request, generates a template message containing guidelines for the cancellation procedure, and sends it to the user's device. At this time, the emotion engine grasps the user's emotional state and responds appropriately based on their emotional state at the time of the inquiry.

[1854] Specific examples

[1855] When a user says, "I want some new summer sunglasses," the voice data is sent to a server. The server converts the voice into text and analyzes the user's emotions. For example, if the user is speaking in a cheerful tone, a list of recommended sunglasses will be displayed along with a cheerful and upbeat message. This list and message will be displayed on the user's smartphone, allowing the user to select and purchase the product of their choice.

[1856] Prompt Sentence Examples

[1857] "Build an AI model that provides personalized product recommendations based on user sentiment analysis and feedback. Input data is voice, text, and image data, and output is a list of product recommendations and engagement messages."

[1858] In this way, the system based on the present invention provides personalized product recommendations that accurately reflect the user's emotions and preferences, as well as efficient inventory management, sales forecasting, and high-quality customer service.

[1859] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1860] Step 1:

[1861] Collect voice, text, image, and video data from users.

[1862] When a user speaks to a smartphone's voice assistant, the voice data is sent to the server via the device. Text input, image and video data are also collected. The input data is voice, text, image and video. The output is this raw data.

[1863] Step 2:

[1864] The server converts the audio data into text.

[1865] The server uses speech recognition technology such as Google Speech API to convert the received voice data into text data. The input is voice data and the output is text data. Specifically, it uses a speech recognition engine to convert the acoustic signal into a string of characters.

[1866] Step 3:

[1867] The server analyzes user data to identify behavior and preferences.

[1868] The server uses machine learning algorithms and NLP libraries (e.g., NLTK and spaCy) to analyze collected text data, past purchase history, and browsing history to identify user behavior and preferences. The input is text data and past recorded data, and the output is the analysis results. Specific operations include data preprocessing, building topic models, and extracting preference patterns.

[1869] Step 4:

[1870] The server analyzes the user's emotions.

[1871] The server uses an emotion analysis engine to analyze the user's emotional state from text and voice data. The input is text and voice data, and the output is the emotion analysis results. Specifically, it extracts features from the text or voice and predicts the emotion label using an emotion classifier.

[1872] Step 5:

[1873] A server generates personalized product recommendations.

[1874] The server lists the products most relevant to the user's preferences based on the analysis results and sentiment analysis results. The input is the preference analysis results and sentiment analysis results, and the output is a product recommendation list. Specifically, the server uses a recommendation algorithm (e.g., collaborative filtering or content-based recommendation) to select products.

[1875] Step 6:

[1876] The server displays the product recommendation list on the user's terminal.

[1877] The generated product recommendation list and engagement message are displayed on the user's smartphone screen. The input is the product recommendation list and engagement message, and the output is the content displayed on the device screen. Specifically, the system updates the GUI components of the smartphone app.

[1878] Step 7:

[1879] The server collects inventory and sales data and runs demand forecasting algorithms.

[1880] The server continuously collects inventory and sales data from stores and warehouses, and runs a demand forecasting algorithm based on this data. The inputs are inventory and sales data, and the output is the demand forecast results. Specific operations include analyzing sales history data and calculating forecasts using a demand forecasting model.

[1881] Step 8:

[1882] The server manages inventory based on the demand forecast results.

[1883] The server calculates the optimal inventory replenishment timing and quantity based on the demand forecast results. The input is the demand forecast results, and the output is the replenishment notification. Specific operations include generating replenishment orders in conjunction with the inventory management system.

[1884] Step 9:

[1885] The server analyzes customer service inquiries using natural language processing and generates appropriate responses.

[1886] When a user makes a query, the content is sent to the server. The server uses NLP technology to analyze the query and generate an appropriate response. The input is the query content (text data), and the output is a response message. Specific operations include analyzing the query text and generating a template response.

[1887] Step 10:

[1888] The server sends a response message to the user's terminal.

[1889] The generated response message is sent to the user's smartphone and displayed immediately. The input is the response message, and the output is the content displayed on the user's device. Specifically, the data is sent to the device using a message sending API.

[1890] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1891] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1892] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1893] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1894] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1895] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1896] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emot...

Claims

1. means for collecting voice, text, image, and video data from a user; means for analyzing the collected data to identify user behavior and preferences; means for generating personalized product recommendations for a user based on the analysis results; means for displaying the product recommendations on a user's terminal; a means for collecting inventory and sales data and running demand forecasting algorithms; a means for performing inventory management based on the demand forecast; means for analyzing customer service inquiries using natural language processing and generating appropriate responses; means for transmitting the response to a user terminal; A system including:

2. 10. The system of claim 1, wherein the means for identifying user behavior and preferences uses a machine learning algorithm.

3. 2. The system of claim 1, wherein the demand forecasting algorithm forecasts demand based on past sales data.

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