system

Abstract

We provide the system. [Solution] A means of receiving and analyzing user input data, A means of using a generative model to infer the user's potential needs based on the aforementioned analysis results, A means for generating a list of recommended products or services that meet the aforementioned inferred needs, A means of presenting the aforementioned recommendation list to the user and providing detailed information in an interactive format, A means of receiving the user's intention to purchase and carrying out the purchase procedure, A means of providing after-sales support information after purchase, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional e-commerce system, a user has to search for products based on their own subjective opinions and knowledge. As a result, there are many cases where potential needs are not fully understood. For this reason, there is a problem that the user cannot encounter the products and services they really want, and the purchasing behavior is not efficient. Furthermore, since a general product recommendation system depends on the user's past behavior, it is difficult to reflect trends and new needs. Therefore, it is required to effectively draw out the potential needs of users and propose appropriate products and services.

Means for Solving the Problems

[0005] This invention solves these problems by utilizing a generative model that acquires user input data and analyzes the user's potential needs using natural language processing technology. Based on the analysis results, it generates and presents a list of recommended products or services that meet the user's needs. Furthermore, it provides detailed information in an interactive format to enable the user to form a more concrete purchasing intention. By comprehensively handling the purchasing process and subsequent after-sales support, it provides a system that offers an integrated purchasing experience.

[0006] "User input data" refers to information that users directly enter into the system, including requests and questions regarding products and services.

[0007] "Analysis results" refer to semantic information obtained from user input data using natural language processing technology, and reflect potential needs and intentions.

[0008] A "generative model" is an algorithm and program used to analyze user input data and predict their needs using machine learning and artificial intelligence techniques.

[0009] "Latent needs" are demands or desires that users are not aware of or do not explicitly express, but which they wish to have fulfilled.

[0010] A "recommended list of products or services" is a list of products or services selected based on the user's needs and presented to the user as potential purchase options.

[0011] A "dialogue-based" interface is a form of interface in which the user and the system exchange information in a natural conversational manner, enabling question-and-answer sessions and information provision.

[0012] A "purchase process" refers to a series of steps necessary to complete the purchase of a product or service selected by the user.

[0013] "After-sales support" refers to the support and information provided after the purchase of a product or service, with the aim of improving user satisfaction. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system of the present invention is designed to help users efficiently find the goods and services they desire on an e-commerce platform. This system consists of a server operating in a cloud environment, terminals accessed by users, and a network that mediates between them.

[0036] First, the user uses a terminal to input questions or requests to the system. For example, consider a user interested in fashion who inputs, "I want the latest trendy items that are perfect for spring." The terminal receives this input and sends it to the server via the network.

[0037] The server analyzes the user's input data using natural language processing techniques to identify the user's potential needs. Based on the analysis, the server uses a generative AI model to predict the most suitable products and services for the user. In this example, specific items such as "lightweight spring jacket" and "new sneakers" are considered.

[0038] Based on the anticipated needs, the server extracts relevant product information from the database and generates a list of recommended products. This list is optimized by taking into account the user's purchase history and market trends, providing the user with the most attractive options.

[0039] Next, the terminal interactively displays this recommendation list to the user. The user can browse the list on the screen and enter questions in a chat format. For example, if the user requests detailed specifications or reviews for a particular product, the server will provide that information immediately.

[0040] Finally, once the user decides to purchase, that decision is sent from the device to the server, which then processes the purchase. After the purchase is complete, the server provides after-sales support by sending shipping notifications and additional support information to the user. Throughout this entire process, the user can enjoy a seamless and satisfying purchasing experience.

[0041] As described above, the present invention significantly improves the convenience and efficiency of e-commerce by enabling product selection that meets the diverse needs of users and facilitating an interactive purchasing experience.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users input their needs and questions through their devices and send them to the system. For example, they might input, "I want lightweight outdoor gear suitable for summer."

[0045] Step 2:

[0046] The terminal receives user input and sends that data to the server.

[0047] Step 3:

[0048] The server analyzes the received input data using natural language processing techniques. This analysis extracts the user's potential needs and keywords.

[0049] Step 4:

[0050] Based on the analysis results, the server uses a generative model to infer the user's needs. Specifically, it identifies what attributes the input "outdoor gear" should have (e.g., lightweight, waterproof, durable).

[0051] Step 5:

[0052] The server searches its database for products and services that meet the inferred needs and generates a recommendation list. The user's past purchase history and market trends are also taken into consideration during this process.

[0053] Step 6:

[0054] The device presents the user with a generated list of recommendations and interactively displays detailed product information and reviews.

[0055] Step 7:

[0056] The user asks additional questions about the recommended products through the terminal, and the server provides detailed information in response to those questions.

[0057] Step 8:

[0058] Once a user decides to make a purchase, the terminal initiates the purchase process, which is then received and processed by the server.

[0059] Step 9:

[0060] After the purchase is complete, the server provides the user with shipping information and after-sales support information to support the purchase experience.

[0061] (Example 1)

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

[0063] In modern e-commerce, it is difficult for users to efficiently find the best-suited products from a vast array of options. Furthermore, there is a need for systems that accurately predict users' potential needs and quickly deliver products and services that match those needs. To overcome these challenges, it is essential to provide a seamless and interactive purchasing process that enhances the user experience.

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

[0065] In this invention, the server includes means for receiving and analyzing user input, means for using a generative model to infer the user's potential needs based on the analyzed information, and means for generating a list of recommended products or services that meet the inferred needs. This enables the user to quickly and accurately find products that meet their needs.

[0066] "Means of receiving and analyzing user input" refers to technologies that receive questions and requests from users to a system and process the data in order to understand their content.

[0067] A "generative model" is an algorithm or system used to infer a user's potential needs based on collected data.

[0068] "Means for generating recommendation lists" refers to a function that selects potential products and services that meet user requirements and prepares them for presentation to the user.

[0069] "Methods of presentation using interactive display functions" refer to technologies that provide information to users in a visually easy-to-understand manner and enable users to receive immediate responses to their questions and requests.

[0070] "Means for obtaining purchase intent and executing transaction procedures" refers to technology that confirms a user's intention to purchase and then carries out the actual payment processing and order placement procedures.

[0071] "Means of providing additional support information" refers to the process of providing users with additional information and support they need after purchasing a product or service, thereby improving user satisfaction.

[0072] The embodiment of the present invention is designed to enable users to efficiently find desired products and services on an e-commerce platform. The system consists of a server in a cloud environment, terminals for users to connect to, and a network infrastructure that mediates between them.

[0073] Server Role

[0074] The server receives input data from the user and analyzes it using natural language processing (NLP). This analysis utilizes NLP libraries such as spaCy and NLTK. Based on the analysis results, the server employs a generative AI model to infer the user's potential needs. Following these inferences, the server extracts a list of relevant products and services from its database and provides optimal recommendations. Furthermore, during the purchase process, it confirms the user's intent and processes the transaction. The server also plays a role in providing shipping notifications and after-sales support information to the user.

[0075] Terminal role

[0076] The terminal functions as the user interface, receiving user input and sending it to the server. The terminal also interactively displays recommendation lists from the server to the user, allowing them to access detailed information. This display is designed to make it easy for users to check product specifications and reviews. The terminal also plays a crucial role in ensuring that the user's purchase intention is reliably transmitted to the server.

[0077] User roles

[0078] Users input questions based on their needs and interests using a terminal. For example, if a user enters a prompt such as "I want the latest trendy items that are perfect for spring," the system will suggest related products based on that need. Users can then browse the suggested list, select the most suitable product, and make a purchase.

[0079] Thus, the embodiment of the invention allows users to enjoy a seamless and smooth purchasing experience. The entire system aims to improve user convenience and satisfaction.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user uses a terminal to input prompts based on their interests and requests. This input is received by the terminal as text data written in natural language. The terminal then sends this input data to a server over the network.

[0083] Step 2:

[0084] The server analyzes the received prompt message using natural language processing techniques. Specifically, it performs morphological analysis to identify the user's requests and topics of interest. This process generates structured data that includes the user's potential needs.

[0085] Step 3:

[0086] The server uses a generative AI model based on the analysis results to infer user needs. Here, it refers to a large dataset including historical data and market trends to predict the product categories and characteristics the user is likely looking for. This process generates a virtual product list.

[0087] Step 4:

[0088] The server extracts relevant information from its database to generate a list of recommended products based on the user's inferred needs. It filters product details, availability, and pricing information to create a list for the user. At this stage, the list is optimized by also considering the user's past purchase history and market trends.

[0089] Step 5:

[0090] The server sends the generated recommendation list to the terminal.

[0091] Step 6:

[0092] The device interactively displays the received recommendation list through a user interface. Here, the user can scroll through the products on the screen and click to view more details. This interface allows the user to view specifications and reviews of products that interest them.

[0093] Step 7:

[0094] When a user decides to make a purchase, they enter their intention on their device and send it to the server. This process includes the user entering details such as payment information and shipping address.

[0095] Step 8:

[0096] The server receives the user's purchase intention and proceeds with the purchase process. This includes processing payments and arranging for product shipment. Once the purchase is confirmed, the server provides after-sales support to the user, such as shipping confirmation and further helpful information.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] In today's e-commerce platforms, users often struggle to find the best products and services for them amidst a vast amount of information. In particular, accurately understanding users' latent needs and providing optimal recommendations accordingly is crucial. However, many currently available systems lack sufficient utilization of appropriate natural language processing techniques and generative AI models, resulting in a failure to provide users with a satisfying purchasing experience.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for receiving and analyzing input data from a user, means for using a generative model to infer the user's potential needs based on the analysis results, and means for generating a list of recommended products or services that meet the inferred needs. This enables the user to efficiently and effectively find products and services that meet their needs.

[0102] "User input data"

[0103] This refers to information provided by users in text format on e-commerce platforms, such as questions and requests regarding products and services.

[0104] "Means of analysis"

[0105] This refers to a technology that processes input data received from users, understands its content, and structures it.

[0106] "Generative Model"

[0107] This refers to a system that includes an algorithm that infers the user's potential needs based on analyzed data and recommends products that meet those needs.

[0108] "Natural language processing technology"

[0109] This refers to the technology used by computers to understand and analyze human language, and is utilized to precisely interpret text input from users.

[0110] "Recommendation List"

[0111] This refers to a list of optimal product and service options generated based on user needs, past purchase history, and current market trends.

[0112] "Detailed information in dialogue format"

[0113] This refers to information exchanged interactively between the user and the system, and is data provided in a way that complements what the user wants to know more about the product.

[0114] "Means of carrying out the purchase procedure"

[0115] This refers to a system that automatically handles the payment and shipping procedures for products and services that a user has decided to purchase.

[0116] "After-sales support information"

[0117] This refers to additional information provided after product purchase, such as usage instructions, warranty information, and additional support, aimed at improving user satisfaction.

[0118] The system implementing this invention consists of a server, a user terminal, and a network that mediates between them. The server receives input data from the user via the network and analyzes that data using natural language processing technology. Specifically, it utilizes a generative AI model, which is a machine learning model, to analyze text input and identify the user's potential needs.

[0119] The generative AI model processes prompts generated based on user input and creates a list of recommendations for relevant products and services. The server retrieves the user's past purchase history and current market trends from a database to create an optimized list, which is then sent to the user's terminal.

[0120] The user terminal displays this recommendation list interactively, and the user can re-enter information if they require more details. The server immediately provides additional information in response to the input. Furthermore, if the user decides to purchase, the server automatically handles the purchase process and manages everything from shipping to providing after-sales support information.

[0121] For example, if a user enters "I want new sports shoes," the server uses a generative AI model that takes into account currently popular models and the user's past preferences to make recommendations such as "These are our recommended sports shoes." This process proceeds in real time, significantly improving user convenience.

[0122] An example of a prompt message would be: "User input: I want new sports shoes. Please suggest related products."

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The user enters their requests regarding products and services through a terminal. The entered text data is sent to the server via the network. At this stage, the user's input is prepared as a prompt.

[0126] Step 2:

[0127] The server analyzes the user's input data using natural language processing techniques. The input is text data, and a generative AI model is used to understand its content. During this analysis process, the server identifies the user's potential needs and generates prompts based on them.

[0128] Step 3:

[0129] Based on the analysis results, the server references the database and generates a list of recommended products and services, taking into account the user's past purchase history and current market trends. This ensures that the products best suited to the user's needs are selected and compiled into a recommendation list.

[0130] Step 4:

[0131] The server sends a recommendation list to the terminal, which then displays it to the user in an interactive format. The user can then request further details about individual products. At this stage, the recommendation list serves as input, and the user interface is provided based on it.

[0132] Step 5:

[0133] When a user asks an additional question about a specific product, the device sends that question back to the server. The server analyzes the received question, collects detailed information and reviews about the relevant product, and immediately provides them to the user.

[0134] Step 6:

[0135] When a user decides to purchase, that decision is sent from the device to the server, which automatically initiates the purchase process. Payment is processed, and shipping instructions are relayed to the appropriate department.

[0136] Step 7:

[0137] After the purchase process is complete, the server provides after-sales support, such as shipping notifications and additional support information, to ensure a highly satisfying purchasing experience. All information generated during this process is accumulated as data that can be used to inform the user's future purchases.

[0138] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0139] This invention is a system that not only analyzes users' latent needs and individually recommends appropriate products and services, but also takes into account the user's emotional state to provide a more personalized purchasing experience. This system consists of a server operating in a cloud environment, a terminal operated by the user, and a network infrastructure connecting them.

[0140] The user uses a terminal to input information about the products or services they wish to purchase into the system. This information is expressed in natural language, including the user's needs and questions. For example, the user might input a request such as, "I want some comfortable loungewear to lift my spirits." The terminal receives this input and sends the data to the server.

[0141] The server first analyzes this input data using natural language processing techniques to identify the user's requests and potential needs. Next, it utilizes an emotion engine to recognize emotions from the user's input, extracting, for example, an emotional state such as "I want to relax."

[0142] Based on this, the server uses a generative AI model to predict the most suitable products and services for the user's latent needs and emotions. Based on the prediction results, the server retrieves relevant product information from the database and generates a recommendation list that prioritizes products that match the user's emotions. In this case, products such as "soft loungewear" and "soothing scented candles" might be selected.

[0143] The terminal visually displays a list of product recommendations based on this sentiment to the user, providing an interactive interface that allows the user to easily access information. Furthermore, if the user requests more detailed information, the server answers questions in a conversational format and presents detailed product information and ratings.

[0144] When a user decides to make a purchase, the device transmits their intention to the server and initiates the purchase process. The server completes the payment processing and continues to provide post-purchase support by delivering shipping information and after-sales support information to the user via the device.

[0145] Thus, the system of the present invention enables product recommendations that are tailored to the user's emotions, providing a comfortable and highly satisfying purchasing experience.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] Users access the system using their devices and input their purchase needs and preferences in natural language. For example, they might input, "I want some relaxation items to relieve stress."

[0149] Step 2:

[0150] The terminal sends the entered user data to the server.

[0151] Step 3:

[0152] The server performs natural language processing to analyze the incoming data and identify the user's requests and potential needs. In this case, keywords such as "healing" are extracted.

[0153] Step 4:

[0154] The server uses an emotion engine to recognize the user's emotional state from their input. For example, it might detect the emotion "I want to relieve stress."

[0155] Step 5:

[0156] The server uses a generative AI model to predict suitable products and services, taking into account the user's needs and emotional state.

[0157] Step 6:

[0158] Based on the prediction results, the server searches a database of related products and generates a list of recommended products that resonate with the user's emotions. For example, "aroma diffusers" and "music CDs good for stress relief" might be listed.

[0159] Step 7:

[0160] The device displays a generated list of recommendations to the user and provides detailed product information and reviews through an interactive interface.

[0161] Step 8:

[0162] Users can ask additional questions about the product from their device, and the server will continuously provide that information immediately.

[0163] Step 9:

[0164] When a user decides to make a purchase, that decision is sent from the device to the server, and the server completes the purchase process.

[0165] Step 10:

[0166] After the purchase is complete, the server provides shipping information and after-sales support information regarding the product, ensuring a satisfying purchasing experience for the user.

[0167] (Example 2)

[0168] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0169] Modern purchasing systems offer product recommendations based on simple user needs, but they lack personalized methods that take into account the user's emotional state. This makes it difficult for users to find products and services that resonate with their emotions, resulting in a challenging shopping experience.

[0170] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0171] In this invention, the server includes means for receiving and analyzing input data from the user, means for using a generative model to infer the user's potential needs and emotional state, and means for generating a list of recommended products or services that correspond to the inferred needs and emotions. This enables more personalized product recommendations that take the user's emotional state into account.

[0172] "Input data" refers to information that users enter into the system, including their purchasing needs and emotional state.

[0173] "Means of analysis" refers to a process or system that has the function of analyzing input data received from a user using natural language processing technology to identify needs and emotions.

[0174] A "generative model" is an AI-based model used to infer a user's potential needs and emotional state.

[0175] A "means of inference" refers to a system or process that utilizes generative models to infer needs and emotional states based on user input data.

[0176] A "recommendation list" is a list of products or services selected based on the user's potential needs and emotional state.

[0177] "Dialogue-based" refers to a format in which the user can actively ask questions of the system and receive answers.

[0178] "Purchase process" refers to the series of steps involved in completing payment and shipping procedures for the products or services selected by the user.

[0179] "After-sales support information" refers to additional support information and guidance regarding products and services provided to users after purchase.

[0180] This invention consists of a server operating in a cloud environment, a terminal operated by the user, and a communication network connecting them. The user uses the terminal to input information about the products or services they wish to purchase in natural language. For example, it is assumed that the user inputs a request such as "I would like some items that help me relax."

[0181] The terminal receives this user input and sends it to the server for analysis. The server uses natural language processing techniques, such as Python's NLTK library, to analyze the input data and identify the user's needs. Furthermore, it utilizes emotion recognition technologies, such as IBM Watson® Tone Analyzer, to evaluate the user's emotional state. This process may extract the emotion of "wanting to relax."

[0182] The server then uses a generative AI model and technologies such as OpenAI's GPT to predict products and services that match the user's latent needs and emotions. For example, it might send a prompt like, "What is the best product for this user?" to the generative AI model to make a prediction.

[0183] Based on the prediction results, the server retrieves relevant product information from a MySQL® database and other sources, and generates a recommendation list prioritizing products that best match the user's emotional state. In this case, products such as "soft loungewear" or "calming aroma candles" may be selected.

[0184] Next, the terminal visually displays the generated product recommendation list to the user. The interface is interactive and includes links that allow the user to easily view product information and access more detailed information. If the user requests more detailed product information, the server interactively presents additional information.

[0185] Finally, once the user decides to purchase, the device transmits its intention to the server. The server processes the payment via the Stripe API or similar, and provides shipping information and post-purchase support information to the user via the device. Through this system, users can enjoy a more personalized, emotion-based purchasing experience.

[0186] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0187] Step 1:

[0188] The user inputs information about the desired product and their emotional state into the terminal using natural language. At this point, the input text data does not clearly express the user's intent and therefore cannot be processed mechanically as is. The terminal receives this user input and prepares to send it to the server for analysis. The input is text data, and the output is formatted data.

[0189] Step 2:

[0190] The server receives input data sent from the terminal and begins data analysis using natural language processing (NLTK) technology. It performs grammatical and semantic analysis using software such as Python's NLTK library. Specifically, it identifies the part of speech of words and determines the intent of sentences. The input data is unstructured text, and after analysis, formalized data that identifies the user's needs is output.

[0191] Step 3:

[0192] Next, the server uses an emotion engine to recognize the user's emotional state from their input. Specifically, it uses technologies such as IBM Watson Tone Analyzer to obtain emotional indicators from the input text. The formalized data from the previous step is used as input, and the output is data representing emotional states such as "I want to relax" or "I'm stressed."

[0193] Step 4:

[0194] The server uses a generative AI model to predict products and services that match the user's potential needs and emotions. Utilizing OpenAI's GPT technology, it generates suggested products using the prompt "What is the best product for this user?". Emotional state data and user needs are used as input, and the output is a list of recommended products.

[0195] Step 5:

[0196] The server uses the prediction results to retrieve relevant product information from the database. It references databases such as MySQL and sorts products based on emotional states to create a recommendation list. Specifically, it retrieves detailed information on products such as soft loungewear and aromatherapy candles. The input is a list of recommended products, and the output is a list of detailed product information.

[0197] Step 6:

[0198] The terminal visually displays the final product recommendation list to the user. The interface is interactive and designed to allow the user to carefully review their options. The input data is a detailed product information list, which is presented to the user at this stage.

[0199] Step 7:

[0200] When a user indicates their intention to purchase a selected item, the terminal transmits this information to the server. The server completes the purchase process using a payment system such as the Stripe API. Specifically, it sends and verifies payment information and notifies the user that the payment was successful. The input data is the user's purchase intention, and the output is purchase approval information.

[0201] Step 8:

[0202] Once the purchase process is complete, the server provides the user with shipping information and after-sales support information via their device. Specific methods include email notifications and chatbot support. Input is purchase approval information, and output includes user notifications and support information.

[0203] (Application Example 2)

[0204] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0205] Modern information content delivery services face the challenge of providing content recommendations that take into account the individual emotional state of each user. Furthermore, they are required to respond quickly and accurately to users' diverse needs and desires based on their mood and circumstances. This necessitates increasing user satisfaction and providing a more personalized experience.

[0206] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0207] In this invention, the server includes means for receiving and analyzing input data from a user, means for using a generative model to infer the user's potential needs and emotional state based on the analysis results, and means for generating a list of recommended information content corresponding to the inferred needs and emotional state. This enables the recommendation of personalized information content that is attuned to the user's emotional state.

[0208] "User input data" refers to data, including natural language, that users use when they want to retrieve informational content.

[0209] "Means of analysis" refer to technologies and functions that receive input data from users and process it in order to understand its content.

[0210] "Latent needs" are requests or desires that users may have but do not explicitly express.

[0211] "Emotional state" refers to the psychological or emotional condition a user is in when providing input data.

[0212] A "generative model" is an artificial intelligence technique used to predict user needs and emotional states based on input data.

[0213] "Information content" refers to digital media such as movies, music, and videos that are delivered to users.

[0214] A "recommendation list" is a list of relevant informational content presented to the user based on analysis results and inferred information.

[0215] "Dialogue-based communication" is a method of communication between the user and the system, where they exchange information and interact with each other.

[0216] "Selective intent" refers to a user's intention to choose a specific item from among the recommended information content.

[0217] A "selection process" is the process by which a user, after indicating their choice, actually acquires or views informational content.

[0218] "After-sales support information" refers to support and additional information provided after selecting information content.

[0219] This invention provides a system that personalizes information content according to the user's emotional state. The system mainly consists of a server, a terminal operated by the user, and a network infrastructure between the two. The user inputs their emotions and requests in natural language through the terminal. For example, a specific request might be, "I'm feeling down today, so I want to watch a movie that will cheer me up." The terminal receives this input and transmits it to the server via the network.

[0220] The server first receives input data sent by the user and analyzes it using natural language processing. Text analysis software such as Google Cloud Natural Language API can be used for this process. The analysis identifies the user's potential needs and emotional state. For example, specific emotions such as "feeling cheerful" or "wanting to relax" are extracted. Based on this, the server utilizes a generative AI model to recommend informational content that matches the user's emotions and needs. OpenAI's GPT model, among others, can be applied to this technology.

[0221] As a result of the recommendations, a list of recommendations such as "lighthearted comedy movies" or "relaxing music videos" is generated and visually displayed on the user's device. The device provides an interactive user interface that receives requests for additional information and selections from the user. If the user selects a specific item from the recommended information content, they communicate their choice to the server and initiate the selection process. The server receives this and begins providing the selected content.

[0222] For example, if a user types, "It's cold today and I'm feeling down, so I want to watch some uplifting content," the server will interpret this as "providing positive energy" and present a list of recommendations such as "the latest heartwarming movies" or "upbeat music videos." An example of a prompt might look like this:

[0223] "Based on this emotional state, please recommend content that will help the user relax or feel happy."

[0224] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0225] Step 1:

[0226] The user inputs data into the terminal. This input includes natural language text expressing the user's emotions and requests. The terminal receives this data and sends it to the server via the network.

[0227] Step 2:

[0228] The server receives input data and analyzes it using natural language processing techniques. The input is natural language text, and the output is an analysis result that includes the user's emotional state and potential needs. Specifically, it uses tools such as the Google Cloud Natural Language API to analyze keywords and context within the text.

[0229] Step 3:

[0230] The server uses a generated AI model to recommend informational content best suited to the user's emotional state based on the analysis results. The input is the emotional state and potential needs, and the output is a list of recommended informational content. The system utilizes OpenAI's GPT model for content selection.

[0231] Step 4:

[0232] The server generates a list of recommendations and sends it to the terminal. The input is a list of recommended content, and the output is an interactive list that is visually displayed to the user. The terminal receives this and displays it in its user interface.

[0233] Step 5:

[0234] The user selects desired information content from a recommended list. The input is the user's selection, and the output is the selected content information. The terminal communicates this selection to the server.

[0235] Step 6:

[0236] The server initiates the process of providing the selected information content. The input is the user's selection, and the output is instructions regarding content provision. The server completes the selection process and provides the content.

[0237] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0238] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0239] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0240] [Second Embodiment]

[0241] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0242] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0243] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0244] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0245] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0246] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0247] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0248] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0249] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0250] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0251] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0252] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0253] The system of the present invention is designed to help users efficiently find the goods and services they desire on an e-commerce platform. This system consists of a server operating in a cloud environment, terminals accessed by users, and a network that mediates between them.

[0254] First, the user uses a terminal to input questions or requests to the system. For example, consider a user interested in fashion who inputs, "I want the latest trendy items that are perfect for spring." The terminal receives this input and sends it to the server via the network.

[0255] The server analyzes the user's input data using natural language processing techniques to identify the user's potential needs. Based on the analysis, the server uses a generative AI model to predict the most suitable products and services for the user. In this example, specific items such as "lightweight spring jacket" and "new sneakers" are considered.

[0256] Based on the anticipated needs, the server extracts relevant product information from the database and generates a list of recommended products. This list is optimized by taking into account the user's purchase history and market trends, providing the user with the most attractive options.

[0257] Next, the terminal interactively displays this recommendation list to the user. The user can browse the list on the screen and enter questions in a chat format. For example, if the user requests detailed specifications or reviews for a particular product, the server will provide that information immediately.

[0258] Finally, once the user decides to purchase, that decision is sent from the device to the server, which then processes the purchase. After the purchase is complete, the server provides after-sales support by sending shipping notifications and additional support information to the user. Throughout this entire process, the user can enjoy a seamless and satisfying purchasing experience.

[0259] As described above, the present invention significantly improves the convenience and efficiency of e-commerce by enabling product selection that meets the diverse needs of users and facilitating an interactive purchasing experience.

[0260] The following describes the processing flow.

[0261] Step 1:

[0262] Users input their needs and questions through their devices and send them to the system. For example, they might input, "I want lightweight outdoor gear suitable for summer."

[0263] Step 2:

[0264] The terminal receives user input and sends that data to the server.

[0265] Step 3:

[0266] The server analyzes the received input data using natural language processing techniques. This analysis extracts the user's potential needs and keywords.

[0267] Step 4:

[0268] Based on the analysis results, the server uses a generative model to infer the user's needs. Specifically, it identifies what attributes the input "outdoor gear" should have (e.g., lightweight, waterproof, durable).

[0269] Step 5:

[0270] The server searches its database for products and services that meet the inferred needs and generates a recommendation list. The user's past purchase history and market trends are also taken into consideration during this process.

[0271] Step 6:

[0272] The device presents the user with a generated list of recommendations and interactively displays detailed product information and reviews.

[0273] Step 7:

[0274] The user asks additional questions about the recommended products through the terminal, and the server provides detailed information in response to those questions.

[0275] Step 8:

[0276] Once a user decides to make a purchase, the terminal initiates the purchase process, which is then received and processed by the server.

[0277] Step 9:

[0278] After the purchase is completed, the server provides the user with shipping information and support information as after - follow - up to support the purchase experience.

[0279] (Example 1)

[0280] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0281] In modern e - commerce, it is difficult for users to efficiently find the most suitable products for their needs from a vast number of products. Furthermore, there is a demand for a system that can accurately infer users' potential requirements and quickly provide products and services that meet those requirements. To overcome such problems, it is necessary to provide a seamless and interactive purchase process that improves the user experience.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0283] In this invention, the server includes means for receiving and analyzing an input from a user, means for using a generation model that infers the user's potential requirements based on the analyzed information, and means for generating a recommendation list of products or services according to the inferred requirements. As a result, the user can quickly and accurately find products that meet their needs.

[0284] The "means for receiving and analyzing an input from a user" is a technology for receiving questions and requests made by the user to the system and processing data to understand the content.

[0285] The "generation model" is an algorithm or system used to infer the user's potential requirements based on the collected data.

[0286] The "means for generating a recommendation list" is a function that selects candidates for products or services according to a user's request and prepares them for presentation to the user.

[0287] The "means for presenting using an interactive display function" is a technology that provides information to the user in a visually easy-to-understand manner and enables the user to obtain an immediate response to questions and requests.

[0288] The "means for obtaining a purchase intention and executing a transaction process" is a technology that, when the user decides to purchase, confirms that intention and performs actual payment processing and ordering procedures.

[0289] The "means for providing additional support information" is a process that provides additional information and support required by the user after purchasing a product or service, and improves the user's satisfaction.

[0290] The embodiment for implementing the present invention is designed so that a user can efficiently search for desired products or services on an e-commerce platform. The system is composed of a server in a cloud environment, a terminal for the user to connect, and a network infrastructure that mediates them.

[0291] Role of the server

[0292] The server receives input data sent from the user and analyzes the data using natural language processing (NLP). In this analysis, NLP libraries such as spaCy and NLTK are used, for example. The server utilizes a generated AI model based on the analysis results to infer the user's potential requirements. According to the inference results, the server extracts a recommendation list of relevant products or services from the database and makes an optimal recommendation. Furthermore, during the purchase procedure, the server confirms the user's intention and processes the transaction. In this process, the server also plays a role in providing shipping notifications and after-sales support information to the user.

[0293] Terminal role

[0294] The terminal functions as the user interface, receiving user input and sending it to the server. The terminal also interactively displays recommendation lists from the server to the user, allowing them to access detailed information. This display is designed to make it easy for users to check product specifications and reviews. The terminal also plays a crucial role in ensuring that the user's purchase intention is reliably transmitted to the server.

[0295] User roles

[0296] Users input questions based on their needs and interests using a terminal. For example, if a user enters a prompt such as "I want the latest trendy items that are perfect for spring," the system will suggest related products based on that need. Users can then browse the suggested list, select the most suitable product, and make a purchase.

[0297] Thus, the embodiment of the invention allows users to enjoy a seamless and smooth purchasing experience. The entire system aims to improve user convenience and satisfaction.

[0298] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0299] Step 1:

[0300] The user uses a terminal to input prompts based on their interests and requests. This input is received by the terminal as text data written in natural language. The terminal then sends this input data to a server over the network.

[0301] Step 2:

[0302] The server analyzes the received prompt text using natural language processing technology. Specifically, it performs morphological analysis to identify the user's requests and topics of interest. Through this process, structured data including the user's potential needs is generated.

[0303] Step 3:

[0304] Based on the analysis results, the server uses a generated AI model to infer the user's needs. Here, it refers to a large dataset including past data and market trends to infer the categories and features of products that the user is likely to require. Through this process, a virtual product list is generated.

[0305] Step 4:

[0306] The server extracts relevant information from its own database to generate a product recommendation list according to the inferred needs. It filters product details, inventory status, and price information to prepare a list for proposing to the user. At this stage, the user's past purchase history and market trends are also considered to optimize the list.

[0307] Step 5:

[0308] The server sends the generated recommendation list to the terminal.

[0309] Step 6:

[0310] The terminal interactively displays the received recommendation list through the user interface. Here, the user can scroll through the products on the screen and click to view more details. Through this interface, the user can view the specifications and reviews of the products of interest.

[0311] Step 7:

[0312] When a user decides to make a purchase, they enter their intention on their device and send it to the server. This process includes the user entering details such as payment information and shipping address.

[0313] Step 8:

[0314] The server receives the user's purchase intention and proceeds with the purchase process. This includes processing payments and arranging for product shipment. Once the purchase is confirmed, the server provides after-sales support to the user, such as shipping confirmation and further helpful information.

[0315] (Application Example 1)

[0316] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0317] In today's e-commerce platforms, users often struggle to find the best products and services for them amidst a vast amount of information. In particular, accurately understanding users' latent needs and providing optimal recommendations accordingly is crucial. However, many currently available systems lack sufficient utilization of appropriate natural language processing techniques and generative AI models, resulting in a failure to provide users with a satisfying purchasing experience.

[0318] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0319] In this invention, the server includes means for receiving and analyzing input data from a user, means for using a generative model to infer the user's potential needs based on the analysis results, and means for generating a list of recommended products or services that meet the inferred needs. This enables the user to efficiently and effectively find products and services that meet their needs.

[0320] "User input data"

[0321] This refers to information provided by users in text format on e-commerce platforms, such as questions and requests regarding products and services.

[0322] "Means of analysis"

[0323] This refers to a technology that processes input data received from users, understands its content, and structures it.

[0324] "Generative Model"

[0325] This refers to a system that includes an algorithm that infers the user's potential needs based on analyzed data and recommends products that meet those needs.

[0326] "Natural language processing technology"

[0327] This refers to the technology used by computers to understand and analyze human language, and is utilized to precisely interpret text input from users.

[0328] "Recommendation List"

[0329] This refers to a list of optimal product and service options generated based on user needs, past purchase history, and current market trends.

[0330] "Detailed information in dialogue format"

[0331] This refers to information exchanged interactively between the user and the system, and is data provided in a way that complements what the user wants to know more about the product.

[0332] "Means of carrying out the purchase procedure"

[0333] This refers to a system that automatically handles the payment and shipping procedures for products and services that a user has decided to purchase.

[0334] "After-sales support information"

[0335] This refers to additional information provided after product purchase, such as usage instructions, warranty information, and additional support, aimed at improving user satisfaction.

[0336] The system implementing this invention consists of a server, a user terminal, and a network that mediates between them. The server receives input data from the user via the network and analyzes that data using natural language processing technology. Specifically, it utilizes a generative AI model, which is a machine learning model, to analyze text input and identify the user's potential needs.

[0337] The generative AI model processes prompts generated based on user input and creates a list of recommendations for relevant products and services. The server retrieves the user's past purchase history and current market trends from a database to create an optimized list, which is then sent to the user's terminal.

[0338] The user terminal displays this recommendation list interactively, and the user can re-enter information if they require more details. The server immediately provides additional information in response to the input. Furthermore, if the user decides to purchase, the server automatically handles the purchase process and manages everything from shipping to providing after-sales support information.

[0339] For example, if a user enters "I want new sports shoes," the server uses a generative AI model that takes into account currently popular models and the user's past preferences to make recommendations such as "These are our recommended sports shoes." This process proceeds in real time, significantly improving user convenience.

[0340] An example of a prompt message would be: "User input: I want new sports shoes. Please suggest related products."

[0341] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0342] Step 1:

[0343] The user enters their requests regarding products and services through a terminal. The entered text data is sent to the server via the network. At this stage, the user's input is prepared as a prompt.

[0344] Step 2:

[0345] The server analyzes the user's input data using natural language processing techniques. The input is text data, and a generative AI model is used to understand its content. During this analysis process, the server identifies the user's potential needs and generates prompts based on them.

[0346] Step 3:

[0347] Based on the analysis results, the server references the database and generates a list of recommended products and services, taking into account the user's past purchase history and current market trends. This ensures that the products best suited to the user's needs are selected and compiled into a recommendation list.

[0348] Step 4:

[0349] The server sends a recommendation list to the terminal, which then displays it to the user in an interactive format. The user can then request further details about individual products. At this stage, the recommendation list serves as input, and the user interface is provided based on it.

[0350] Step 5:

[0351] When a user asks an additional question about a specific product, the device sends that question back to the server. The server analyzes the received question, collects detailed information and reviews about the relevant product, and immediately provides them to the user.

[0352] Step 6:

[0353] When a user decides to purchase, that decision is sent from the device to the server, which automatically initiates the purchase process. Payment is processed, and shipping instructions are relayed to the appropriate department.

[0354] Step 7:

[0355] After the purchase process is complete, the server provides after-sales support, such as shipping notifications and additional support information, to ensure a highly satisfying purchasing experience. All information generated during this process is accumulated as data that can be used to inform the user's future purchases.

[0356] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0357] This invention is a system that not only analyzes users' latent needs and individually recommends appropriate products and services, but also takes into account the user's emotional state to provide a more personalized purchasing experience. This system consists of a server operating in a cloud environment, a terminal operated by the user, and a network infrastructure connecting them.

[0358] The user uses a terminal to input information about the products or services they wish to purchase into the system. This information is expressed in natural language, including the user's needs and questions. For example, the user might input a request such as, "I want some comfortable loungewear to lift my spirits." The terminal receives this input and sends the data to the server.

[0359] The server first analyzes this input data using natural language processing techniques to identify the user's requests and potential needs. Next, it utilizes an emotion engine to recognize emotions from the user's input, extracting, for example, an emotional state such as "I want to relax."

[0360] Based on this, the server uses a generative AI model to predict the most suitable products and services for the user's latent needs and emotions. Based on the prediction results, the server retrieves relevant product information from the database and generates a recommendation list that prioritizes products that match the user's emotions. In this case, products such as "soft loungewear" and "soothing scented candles" might be selected.

[0361] The terminal visually displays a list of product recommendations based on this sentiment to the user, providing an interactive interface that allows the user to easily access information. Furthermore, if the user requests more detailed information, the server answers questions in a conversational format and presents detailed product information and ratings.

[0362] When a user decides to make a purchase, the device transmits their intention to the server and initiates the purchase process. The server completes the payment processing and continues to provide post-purchase support by delivering shipping information and after-sales support information to the user via the device.

[0363] Thus, the system of the present invention enables product recommendations that are tailored to the user's emotions, providing a comfortable and highly satisfying purchasing experience.

[0364] The following describes the processing flow.

[0365] Step 1:

[0366] Users access the system using their devices and input their purchase needs and preferences in natural language. For example, they might input, "I want some relaxation items to relieve stress."

[0367] Step 2:

[0368] The terminal sends the entered user data to the server.

[0369] Step 3:

[0370] The server performs natural language processing to analyze the incoming data and identify the user's requests and potential needs. In this case, keywords such as "healing" are extracted.

[0371] Step 4:

[0372] The server uses an emotion engine to recognize the user's emotional state from their input. For example, it might detect the emotion "I want to relieve stress."

[0373] Step 5:

[0374] The server uses a generative AI model to predict suitable products and services, taking into account the user's needs and emotional state.

[0375] Step 6:

[0376] Based on the prediction results, the server searches a database of related products and generates a list of recommended products that resonate with the user's emotions. For example, "aroma diffusers" and "music CDs good for stress relief" might be listed.

[0377] Step 7:

[0378] The device displays a generated list of recommendations to the user and provides detailed product information and reviews through an interactive interface.

[0379] Step 8:

[0380] Users can ask additional questions about the product from their device, and the server will continuously provide that information immediately.

[0381] Step 9:

[0382] When a user decides to make a purchase, that decision is sent from the device to the server, and the server completes the purchase process.

[0383] Step 10:

[0384] After the purchase is complete, the server provides shipping information and after-sales support information regarding the product, ensuring a satisfying purchasing experience for the user.

[0385] (Example 2)

[0386] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0387] Modern purchasing systems offer product recommendations based on simple user needs, but they lack personalized methods that take into account the user's emotional state. This makes it difficult for users to find products and services that resonate with their emotions, resulting in a challenging shopping experience.

[0388] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0389] In this invention, the server includes means for receiving and analyzing input data from the user, means for using a generative model to infer the user's potential needs and emotional state, and means for generating a list of recommended products or services that correspond to the inferred needs and emotions. This enables more personalized product recommendations that take the user's emotional state into account.

[0390] "Input data" refers to information that users enter into the system, including their purchasing needs and emotional state.

[0391] "Means of analysis" refers to a process or system that has the function of analyzing input data received from a user using natural language processing technology to identify needs and emotions.

[0392] A "generative model" is an AI-based model used to infer a user's potential needs and emotional state.

[0393] A "means of inference" refers to a system or process that utilizes generative models to infer needs and emotional states based on user input data.

[0394] A "recommendation list" is a list of products or services selected based on the user's potential needs and emotional state.

[0395] "Dialogue-based" refers to a format in which the user can actively ask questions of the system and receive answers.

[0396] "Purchase process" refers to the series of steps involved in completing payment and shipping procedures for the products or services selected by the user.

[0397] "After-sales support information" refers to additional support information and guidance regarding products and services provided to users after purchase.

[0398] This invention consists of a server operating in a cloud environment, a terminal operated by the user, and a communication network connecting them. The user uses the terminal to input information about the products or services they wish to purchase in natural language. For example, it is assumed that the user inputs a request such as "I would like some items that help me relax."

[0399] The terminal receives this user input and sends it to the server for analysis. The server uses natural language processing techniques, such as Python's NLTK library, to analyze the input data and identify the user's needs. Furthermore, it utilizes emotion recognition technologies, such as IBM Watson Tone Analyzer, to evaluate the user's emotional state. This process may extract the emotion of "wanting to relax."

[0400] The server then uses a generative AI model and technologies like OpenAI's GPT to predict products and services that match the user's latent needs and emotions. For example, it might send a prompt like, "What is the best product for this user?" to the generative AI model to make a prediction.

[0401] Based on the prediction results, the server retrieves relevant product information from a MySQL database, etc., and generates a recommendation list prioritizing products that best match the user's emotional state. In this case, products such as "soft loungewear" or "calming aroma candles" may be selected.

[0402] Next, the terminal visually displays the generated product recommendation list to the user. The interface is interactive and includes links that allow the user to easily view product information and access more detailed information. If the user requests more detailed product information, the server interactively presents additional information.

[0403] Finally, once the user decides to purchase, the device transmits its intention to the server. The server processes the payment via the Stripe API or similar, and provides shipping information and post-purchase support information to the user via the device. Through this system, users can enjoy a more personalized, emotion-based purchasing experience.

[0404] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0405] Step 1:

[0406] The user inputs information about the desired product and their emotional state into the terminal using natural language. At this point, the input text data does not clearly express the user's intent and therefore cannot be processed mechanically as is. The terminal receives this user input and prepares to send it to the server for analysis. The input is text data, and the output is formatted data.

[0407] Step 2:

[0408] The server receives input data sent from the terminal and begins data analysis using natural language processing (NLTK) technology. It performs grammatical and semantic analysis using software such as Python's NLTK library. Specifically, it identifies the part of speech of words and determines the intent of sentences. The input data is unstructured text, and after analysis, formalized data that identifies the user's needs is output.

[0409] Step 3:

[0410] Next, the server uses an emotion engine to recognize the user's emotional state from their input. Specifically, it uses technologies such as IBM Watson Tone Analyzer to obtain emotional indicators from the input text. The formalized data from the previous step is used as input, and the output is data representing emotional states such as "I want to relax" or "I'm stressed."

[0411] Step 4:

[0412] The server uses a generative AI model to predict products and services that match the user's potential needs and emotions. Utilizing OpenAI's GPT technology, it generates suggested products using the prompt "What is the best product for this user?". Emotional state data and user needs are used as input, and the output is a list of recommended products.

[0413] Step 5:

[0414] The server uses the prediction results to retrieve relevant product information from the database. It references databases such as MySQL and sorts products based on emotional states to create a recommendation list. Specifically, it retrieves detailed information on products such as soft loungewear and aromatherapy candles. The input is a list of recommended products, and the output is a list of detailed product information.

[0415] Step 6:

[0416] The terminal visually displays the final product recommendation list to the user. The interface is interactive and designed to allow the user to carefully review their options. The input data is a detailed product information list, which is presented to the user at this stage.

[0417] Step 7:

[0418] When a user indicates their intention to purchase a selected item, the terminal transmits this information to the server. The server completes the purchase process using a payment system such as the Stripe API. Specifically, it sends and verifies payment information and notifies the user that the payment was successful. The input data is the user's purchase intention, and the output is purchase approval information.

[0419] Step 8:

[0420] Once the purchase process is complete, the server provides the user with shipping information and after-sales support information via their device. Specific methods include email notifications and chatbot support. Input is purchase approval information, and output includes user notifications and support information.

[0421] (Application Example 2)

[0422] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0423] Modern information content delivery services face the challenge of providing content recommendations that take into account the individual emotional state of each user. Furthermore, they are required to respond quickly and accurately to users' diverse needs and desires based on their mood and circumstances. This necessitates increasing user satisfaction and providing a more personalized experience.

[0424] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0425] In this invention, the server includes means for receiving and analyzing input data from a user, means for using a generative model to infer the user's potential needs and emotional state based on the analysis results, and means for generating a list of recommended information content corresponding to the inferred needs and emotional state. This enables the recommendation of personalized information content that is attuned to the user's emotional state.

[0426] "User input data" refers to data, including natural language, that users use when they want to retrieve informational content.

[0427] "Means of analysis" refer to technologies and functions that receive input data from users and process it in order to understand its content.

[0428] "Latent needs" are requests or desires that users may have but do not explicitly express.

[0429] "Emotional state" refers to the psychological or emotional condition a user is in when providing input data.

[0430] A "generative model" is an artificial intelligence technique used to predict user needs and emotional states based on input data.

[0431] "Information content" refers to digital media such as movies, music, and videos that are delivered to users.

[0432] A "recommendation list" is a list of relevant informational content presented to the user based on analysis results and inferred information.

[0433] "Dialogue-based communication" is a method of communication between the user and the system, where they exchange information and interact with each other.

[0434] "Selective intent" refers to a user's intention to choose a specific item from among the recommended information content.

[0435] A "selection process" is the process by which a user, after indicating their choice, actually acquires or views informational content.

[0436] "After-sales support information" refers to support and additional information provided after selecting information content.

[0437] This invention provides a system that personalizes information content according to the user's emotional state. The system mainly consists of a server, a terminal operated by the user, and a network infrastructure between the two. The user inputs their emotions and requests in natural language through the terminal. For example, a specific request might be, "I'm feeling down today, so I want to watch a movie that will cheer me up." The terminal receives this input and transmits it to the server via the network.

[0438] The server first receives input data sent by the user and analyzes it using natural language processing. Text analysis software such as the Google Cloud Natural Language API can be used for this process. The analysis identifies the user's potential needs and emotional state. For example, it extracts specific emotions such as "feeling cheerful" or "wanting to relax." Based on this, the server utilizes a generative AI model to recommend informational content that matches the user's emotions and needs. OpenAI's GPT model, among others, can be applied to this technology.

[0439] As a result of the recommendations, a list of recommendations such as "lighthearted comedy movies" or "relaxing music videos" is generated and visually displayed on the user's device. The device provides an interactive user interface that receives requests for additional information and selections from the user. If the user selects a specific item from the recommended information content, they communicate their choice to the server and initiate the selection process. The server receives this and begins providing the selected content.

[0440] For example, if a user types, "It's cold today and I'm feeling down, so I want to watch some uplifting content," the server will interpret this as "providing positive energy" and present a list of recommendations such as "the latest heartwarming movies" or "upbeat music videos." An example of a prompt might look like this:

[0441] "Based on this emotional state, please recommend content that will help the user relax or feel happy."

[0442] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0443] Step 1:

[0444] The user inputs data into the terminal. This input includes natural language text expressing the user's emotions and requests. The terminal receives this data and sends it to the server via the network.

[0445] Step 2:

[0446] The server receives input data and analyzes it using natural language processing techniques. The input is natural language text, and the output is an analysis result that includes the user's emotional state and potential needs. Specifically, it uses tools such as the Google Cloud Natural Language API to analyze keywords and context within the text.

[0447] Step 3:

[0448] The server uses a generated AI model to recommend informational content best suited to the user's emotional state based on the analysis results. The input is the emotional state and potential needs, and the output is a list of recommended informational content. The system utilizes OpenAI's GPT model for content selection.

[0449] Step 4:

[0450] The server generates a list of recommendations and sends it to the terminal. The input is a list of recommended content, and the output is an interactive list that is visually displayed to the user. The terminal receives this and displays it in its user interface.

[0451] Step 5:

[0452] The user selects desired information content from a recommended list. The input is the user's selection, and the output is the selected content information. The terminal communicates this selection to the server.

[0453] Step 6:

[0454] The server initiates the process of providing the selected information content. The input is the user's selection, and the output is instructions regarding content provision. The server completes the selection process and provides the content.

[0455] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0456] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0457] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0458] [Third Embodiment]

[0459] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0460] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0461] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0462] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0463] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0464] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0465] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0466] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0467] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0468] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0469] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0470] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0471] The system of the present invention is designed to help users efficiently find the goods and services they desire on an e-commerce platform. This system consists of a server operating in a cloud environment, terminals accessed by users, and a network that mediates between them.

[0472] First, the user uses a terminal to input questions or requests to the system. For example, consider a user interested in fashion who inputs, "I want the latest trendy items that are perfect for spring." The terminal receives this input and sends it to the server via the network.

[0473] The server analyzes the user's input data using natural language processing techniques to identify the user's potential needs. Based on the analysis, the server uses a generative AI model to predict the most suitable products and services for the user. In this example, specific items such as "lightweight spring jacket" and "new sneakers" are considered.

[0474] Based on the anticipated needs, the server extracts relevant product information from the database and generates a list of recommended products. This list is optimized by taking into account the user's purchase history and market trends, providing the user with the most attractive options.

[0475] Next, the terminal interactively displays this recommendation list to the user. The user can browse the list on the screen and enter questions in a chat format. For example, if the user requests detailed specifications or reviews for a particular product, the server will provide that information immediately.

[0476] Finally, once the user decides to purchase, that decision is sent from the device to the server, which then processes the purchase. After the purchase is complete, the server provides after-sales support by sending shipping notifications and additional support information to the user. Throughout this entire process, the user can enjoy a seamless and satisfying purchasing experience.

[0477] As described above, the present invention significantly improves the convenience and efficiency of e-commerce by enabling product selection that meets the diverse needs of users and facilitating an interactive purchasing experience.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] Users input their needs and questions through their devices and send them to the system. For example, they might input, "I want lightweight outdoor gear suitable for summer."

[0481] Step 2:

[0482] The terminal receives user input and sends that data to the server.

[0483] Step 3:

[0484] The server analyzes the received input data using natural language processing techniques. This analysis extracts the user's potential needs and keywords.

[0485] Step 4:

[0486] Based on the analysis results, the server uses a generative model to infer the user's needs. Specifically, it identifies what attributes the input "outdoor gear" should have (e.g., lightweight, waterproof, durable).

[0487] Step 5:

[0488] The server searches its database for products and services that meet the inferred needs and generates a recommendation list. The user's past purchase history and market trends are also taken into consideration during this process.

[0489] Step 6:

[0490] The device presents the user with a generated list of recommendations and interactively displays detailed product information and reviews.

[0491] Step 7:

[0492] The user asks additional questions about the recommended products through the terminal, and the server provides detailed information in response to those questions.

[0493] Step 8:

[0494] Once a user decides to make a purchase, the terminal initiates the purchase process, which is then received and processed by the server.

[0495] Step 9:

[0496] After the purchase is complete, the server provides the user with shipping information and after-sales support information to support the purchase experience.

[0497] (Example 1)

[0498] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0499] In modern e-commerce, it is difficult for users to efficiently find the best-suited products from a vast array of options. Furthermore, there is a need for systems that accurately predict users' potential needs and quickly deliver products and services that match those needs. To overcome these challenges, it is essential to provide a seamless and interactive purchasing process that enhances the user experience.

[0500] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0501] In this invention, the server includes means for receiving and analyzing user input, means for using a generative model to infer the user's potential needs based on the analyzed information, and means for generating a list of recommended products or services that meet the inferred needs. This enables the user to quickly and accurately find products that meet their needs.

[0502] "Means of receiving and analyzing user input" refers to technologies that receive questions and requests from users to a system and process the data in order to understand their content.

[0503] A "generative model" is an algorithm or system used to infer a user's potential needs based on collected data.

[0504] "Means for generating recommendation lists" refers to a function that selects potential products and services that meet user requirements and prepares them for presentation to the user.

[0505] "Methods of presentation using interactive display functions" refer to technologies that provide information to users in a visually easy-to-understand manner and enable users to receive immediate responses to their questions and requests.

[0506] "Means for obtaining purchase intent and executing transaction procedures" refers to technology that confirms a user's intention to purchase and then carries out the actual payment processing and order placement procedures.

[0507] "Means of providing additional support information" refers to the process of providing users with additional information and support they need after purchasing a product or service, thereby improving user satisfaction.

[0508] The embodiment of the present invention is designed to enable users to efficiently find desired products and services on an e-commerce platform. The system consists of a server in a cloud environment, terminals for users to connect to, and a network infrastructure that mediates between them.

[0509] Server Role

[0510] The server receives input data from the user and analyzes it using natural language processing (NLP). This analysis utilizes NLP libraries such as spaCy and NLTK. Based on the analysis results, the server employs a generative AI model to infer the user's potential needs. Following these inferences, the server extracts a list of relevant products and services from its database and provides optimal recommendations. Furthermore, during the purchase process, it confirms the user's intent and processes the transaction. The server also plays a role in providing shipping notifications and after-sales support information to the user.

[0511] Terminal role

[0512] The terminal functions as the user interface, receiving user input and sending it to the server. The terminal also interactively displays recommendation lists from the server to the user, allowing them to access detailed information. This display is designed to make it easy for users to check product specifications and reviews. The terminal also plays a crucial role in ensuring that the user's purchase intention is reliably transmitted to the server.

[0513] User roles

[0514] Users input questions based on their needs and interests using a terminal. For example, if a user enters a prompt such as "I want the latest trendy items that are perfect for spring," the system will suggest related products based on that need. Users can then browse the suggested list, select the most suitable product, and make a purchase.

[0515] Thus, the embodiment of the invention allows users to enjoy a seamless and smooth purchasing experience. The entire system aims to improve user convenience and satisfaction.

[0516] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0517] Step 1:

[0518] The user uses a terminal to input prompts based on their interests and requests. This input is received by the terminal as text data written in natural language. The terminal then sends this input data to a server over the network.

[0519] Step 2:

[0520] The server analyzes the received prompt message using natural language processing techniques. Specifically, it performs morphological analysis to identify the user's requests and topics of interest. This process generates structured data that includes the user's potential needs.

[0521] Step 3:

[0522] The server uses a generative AI model based on the analysis results to infer user needs. Here, it refers to a large dataset including historical data and market trends to predict the product categories and characteristics the user is likely looking for. This process generates a virtual product list.

[0523] Step 4:

[0524] The server extracts relevant information from its database to generate a list of recommended products based on the user's inferred needs. It filters product details, availability, and pricing information to create a list for the user. At this stage, the list is optimized by also considering the user's past purchase history and market trends.

[0525] Step 5:

[0526] The server sends the generated recommendation list to the terminal.

[0527] Step 6:

[0528] The device interactively displays the received recommendation list through a user interface. Here, the user can scroll through the products on the screen and click to view more details. This interface allows the user to view specifications and reviews of products that interest them.

[0529] Step 7:

[0530] When a user decides to make a purchase, they enter their intention on their device and send it to the server. This process includes the user entering details such as payment information and shipping address.

[0531] Step 8:

[0532] The server receives the user's purchase intention and proceeds with the purchase process. This includes processing payments and arranging for product shipment. Once the purchase is confirmed, the server provides after-sales support to the user, such as shipping confirmation and further helpful information.

[0533] (Application Example 1)

[0534] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0535] In today's e-commerce platforms, users often struggle to find the best products and services for them amidst a vast amount of information. In particular, accurately understanding users' latent needs and providing optimal recommendations accordingly is crucial. However, many currently available systems lack sufficient utilization of appropriate natural language processing techniques and generative AI models, resulting in a failure to provide users with a satisfying purchasing experience.

[0536] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0537] In this invention, the server includes means for receiving and analyzing input data from a user, means for using a generative model to infer the user's potential needs based on the analysis results, and means for generating a list of recommended products or services that meet the inferred needs. This enables the user to efficiently and effectively find products and services that meet their needs.

[0538] "User input data"

[0539] This refers to information provided by users in text format on e-commerce platforms, such as questions and requests regarding products and services.

[0540] "Means of analysis"

[0541] This refers to a technology that processes input data received from users, understands its content, and structures it.

[0542] "Generative Model"

[0543] This refers to a system that includes an algorithm that infers the user's potential needs based on analyzed data and recommends products that meet those needs.

[0544] "Natural language processing technology"

[0545] This refers to the technology used by computers to understand and analyze human language, and is utilized to precisely interpret text input from users.

[0546] "Recommendation List"

[0547] This refers to a list of optimal product and service options generated based on user needs, past purchase history, and current market trends.

[0548] "Detailed information in dialogue format"

[0549] This refers to information exchanged interactively between the user and the system, and is data provided in a way that complements what the user wants to know more about the product.

[0550] "Means of carrying out the purchase procedure"

[0551] This refers to a system that automatically handles the payment and shipping procedures for products and services that a user has decided to purchase.

[0552] "After-sales support information"

[0553] This refers to additional information provided after product purchase, such as usage instructions, warranty information, and additional support, aimed at improving user satisfaction.

[0554] The system implementing this invention consists of a server, a user terminal, and a network that mediates between them. The server receives input data from the user via the network and analyzes that data using natural language processing technology. Specifically, it utilizes a generative AI model, which is a machine learning model, to analyze text input and identify the user's potential needs.

[0555] The generative AI model processes prompts generated based on user input and creates a list of recommendations for relevant products and services. The server retrieves the user's past purchase history and current market trends from a database to create an optimized list, which is then sent to the user's terminal.

[0556] The user terminal displays this recommendation list interactively, and the user can re-enter information if they require more details. The server immediately provides additional information in response to the input. Furthermore, if the user decides to purchase, the server automatically handles the purchase process and manages everything from shipping to providing after-sales support information.

[0557] For example, if a user enters "I want new sports shoes," the server uses a generative AI model that takes into account currently popular models and the user's past preferences to make recommendations such as "These are our recommended sports shoes." This process proceeds in real time, significantly improving user convenience.

[0558] An example of a prompt message would be: "User input: I want new sports shoes. Please suggest related products."

[0559] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0560] Step 1:

[0561] The user enters their requests regarding products and services through a terminal. The entered text data is sent to the server via the network. At this stage, the user's input is prepared as a prompt.

[0562] Step 2:

[0563] The server analyzes the user's input data using natural language processing techniques. The input is text data, and a generative AI model is used to understand its content. During this analysis process, the server identifies the user's potential needs and generates prompts based on them.

[0564] Step 3:

[0565] Based on the analysis results, the server references the database and generates a list of recommended products and services, taking into account the user's past purchase history and current market trends. This ensures that the products best suited to the user's needs are selected and compiled into a recommendation list.

[0566] Step 4:

[0567] The server sends a recommendation list to the terminal, which then displays it to the user in an interactive format. The user can then request further details about individual products. At this stage, the recommendation list serves as input, and the user interface is provided based on it.

[0568] Step 5:

[0569] When a user asks an additional question about a specific product, the device sends that question back to the server. The server analyzes the received question, collects detailed information and reviews about the relevant product, and immediately provides them to the user.

[0570] Step 6:

[0571] When a user decides to purchase, that decision is sent from the device to the server, which automatically initiates the purchase process. Payment is processed, and shipping instructions are relayed to the appropriate department.

[0572] Step 7:

[0573] After the purchase process is complete, the server provides after-sales support, such as shipping notifications and additional support information, to ensure a highly satisfying purchasing experience. All information generated during this process is accumulated as data that can be used to inform the user's future purchases.

[0574] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0575] This invention is a system that not only analyzes users' latent needs and individually recommends appropriate products and services, but also takes into account the user's emotional state to provide a more personalized purchasing experience. This system consists of a server operating in a cloud environment, a terminal operated by the user, and a network infrastructure connecting them.

[0576] The user uses a terminal to input information about the products or services they wish to purchase into the system. This information is expressed in natural language, including the user's needs and questions. For example, the user might input a request such as, "I want some comfortable loungewear to lift my spirits." The terminal receives this input and sends the data to the server.

[0577] The server first analyzes this input data using natural language processing techniques to identify the user's requests and potential needs. Next, it utilizes an emotion engine to recognize emotions from the user's input, extracting, for example, an emotional state such as "I want to relax."

[0578] Based on this, the server uses a generative AI model to predict the most suitable products and services for the user's latent needs and emotions. Based on the prediction results, the server retrieves relevant product information from the database and generates a recommendation list that prioritizes products that match the user's emotions. In this case, products such as "soft loungewear" and "soothing scented candles" might be selected.

[0579] The terminal visually displays a list of product recommendations based on this sentiment to the user, providing an interactive interface that allows the user to easily access information. Furthermore, if the user requests more detailed information, the server answers questions in a conversational format and presents detailed product information and ratings.

[0580] When a user decides to make a purchase, the device transmits their intention to the server and initiates the purchase process. The server completes the payment processing and continues to provide post-purchase support by delivering shipping information and after-sales support information to the user via the device.

[0581] Thus, the system of the present invention enables product recommendations that are tailored to the user's emotions, providing a comfortable and highly satisfying purchasing experience.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] Users access the system using their devices and input their purchase needs and preferences in natural language. For example, they might input, "I want some relaxation items to relieve stress."

[0585] Step 2:

[0586] The terminal sends the entered user data to the server.

[0587] Step 3:

[0588] The server performs natural language processing to analyze the incoming data and identify the user's requests and potential needs. In this case, keywords such as "healing" are extracted.

[0589] Step 4:

[0590] The server uses an emotion engine to recognize the user's emotional state from their input. For example, it might detect the emotion "I want to relieve stress."

[0591] Step 5:

[0592] The server uses a generative AI model to predict suitable products and services, taking into account the user's needs and emotional state.

[0593] Step 6:

[0594] Based on the prediction results, the server searches a database of related products and generates a list of recommended products that resonate with the user's emotions. For example, "aroma diffusers" and "music CDs good for stress relief" might be listed.

[0595] Step 7:

[0596] The device displays a generated list of recommendations to the user and provides detailed product information and reviews through an interactive interface.

[0597] Step 8:

[0598] Users can ask additional questions about the product from their device, and the server will continuously provide that information immediately.

[0599] Step 9:

[0600] When a user decides to make a purchase, that decision is sent from the device to the server, and the server completes the purchase process.

[0601] Step 10:

[0602] After the purchase is complete, the server provides shipping information and after-sales support information regarding the product, ensuring a satisfying purchasing experience for the user.

[0603] (Example 2)

[0604] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0605] Modern purchasing systems offer product recommendations based on simple user needs, but they lack personalized methods that take into account the user's emotional state. This makes it difficult for users to find products and services that resonate with their emotions, resulting in a challenging shopping experience.

[0606] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0607] In this invention, the server includes means for receiving and analyzing input data from the user, means for using a generative model to infer the user's potential needs and emotional state, and means for generating a list of recommended products or services that correspond to the inferred needs and emotions. This enables more personalized product recommendations that take the user's emotional state into account.

[0608] "Input data" refers to information that users enter into the system, including their purchasing needs and emotional state.

[0609] "Means of analysis" refers to a process or system that has the function of analyzing input data received from a user using natural language processing technology to identify needs and emotions.

[0610] A "generative model" is an AI-based model used to infer a user's potential needs and emotional state.

[0611] A "means of inference" refers to a system or process that utilizes generative models to infer needs and emotional states based on user input data.

[0612] A "recommendation list" is a list of products or services selected based on the user's potential needs and emotional state.

[0613] "Dialogue-based" refers to a format in which the user can actively ask questions of the system and receive answers.

[0614] "Purchase process" refers to the series of steps involved in completing payment and shipping procedures for the products or services selected by the user.

[0615] "After-sales support information" refers to additional support information and guidance regarding products and services provided to users after purchase.

[0616] This invention consists of a server operating in a cloud environment, a terminal operated by the user, and a communication network connecting them. The user uses the terminal to input information about the products or services they wish to purchase in natural language. For example, it is assumed that the user inputs a request such as "I would like some items that help me relax."

[0617] The terminal receives this user input and sends it to the server for analysis. The server uses natural language processing techniques, such as Python's NLTK library, to analyze the input data and identify the user's needs. Furthermore, it utilizes emotion recognition technologies, such as IBM Watson Tone Analyzer, to evaluate the user's emotional state. This process may extract the emotion of "wanting to relax."

[0618] The server then uses a generative AI model and technologies like OpenAI's GPT to predict products and services that match the user's latent needs and emotions. For example, it might send a prompt like, "What is the best product for this user?" to the generative AI model to make a prediction.

[0619] Based on the prediction results, the server retrieves relevant product information from a MySQL database, etc., and generates a recommendation list prioritizing products that best match the user's emotional state. In this case, products such as "soft loungewear" or "calming aroma candles" may be selected.

[0620] Next, the terminal visually displays the generated product recommendation list to the user. The interface is interactive and includes links that allow the user to easily view product information and access more detailed information. If the user requests more detailed product information, the server interactively presents additional information.

[0621] Finally, once the user decides to purchase, the device transmits its intention to the server. The server processes the payment via the Stripe API or similar, and provides shipping information and post-purchase support information to the user via the device. Through this system, users can enjoy a more personalized, emotion-based purchasing experience.

[0622] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0623] Step 1:

[0624] The user inputs information about the desired product and their emotional state into the terminal using natural language. At this point, the input text data does not clearly express the user's intent and therefore cannot be processed mechanically as is. The terminal receives this user input and prepares to send it to the server for analysis. The input is text data, and the output is formatted data.

[0625] Step 2:

[0626] The server receives input data sent from the terminal and begins data analysis using natural language processing (NLTK) technology. It performs grammatical and semantic analysis using software such as Python's NLTK library. Specifically, it identifies the part of speech of words and determines the intent of sentences. The input data is unstructured text, and after analysis, formalized data that identifies the user's needs is output.

[0627] Step 3:

[0628] Next, the server uses an emotion engine to recognize the user's emotional state from their input. Specifically, it uses technologies such as IBM Watson Tone Analyzer to obtain emotional indicators from the input text. The formalized data from the previous step is used as input, and the output is data representing emotional states such as "I want to relax" or "I'm stressed."

[0629] Step 4:

[0630] The server uses a generative AI model to predict products and services that match the user's potential needs and emotions. Utilizing OpenAI's GPT technology, it generates suggested products using the prompt "What is the best product for this user?". Emotional state data and user needs are used as input, and the output is a list of recommended products.

[0631] Step 5:

[0632] The server uses the prediction results to retrieve relevant product information from the database. It references databases such as MySQL and sorts products based on emotional states to create a recommendation list. Specifically, it retrieves detailed information on products such as soft loungewear and aromatherapy candles. The input is a list of recommended products, and the output is a list of detailed product information.

[0633] Step 6:

[0634] The terminal visually displays the final product recommendation list to the user. The interface is interactive and designed to allow the user to carefully review their options. The input data is a detailed product information list, which is presented to the user at this stage.

[0635] Step 7:

[0636] When a user indicates their intention to purchase a selected item, the terminal transmits this information to the server. The server completes the purchase process using a payment system such as the Stripe API. Specifically, it sends and verifies payment information and notifies the user that the payment was successful. The input data is the user's purchase intention, and the output is purchase approval information.

[0637] Step 8:

[0638] Once the purchase process is complete, the server provides the user with shipping information and after-sales support information via their device. Specific methods include email notifications and chatbot support. Input is purchase approval information, and output includes user notifications and support information.

[0639] (Application Example 2)

[0640] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0641] Modern information content delivery services face the challenge of providing content recommendations that take into account the individual emotional state of each user. Furthermore, they are required to respond quickly and accurately to users' diverse needs and desires based on their mood and circumstances. This necessitates increasing user satisfaction and providing a more personalized experience.

[0642] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0643] In this invention, the server includes means for receiving and analyzing input data from a user, means for using a generative model to infer the user's potential needs and emotional state based on the analysis results, and means for generating a list of recommended information content corresponding to the inferred needs and emotional state. This enables the recommendation of personalized information content that is attuned to the user's emotional state.

[0644] "User input data" refers to data, including natural language, that users use when they want to retrieve informational content.

[0645] "Means of analysis" refer to technologies and functions that receive input data from users and process it in order to understand its content.

[0646] "Latent needs" are requests or desires that users may have but do not explicitly express.

[0647] "Emotional state" refers to the psychological or emotional condition a user is in when providing input data.

[0648] A "generative model" is an artificial intelligence technique used to predict user needs and emotional states based on input data.

[0649] "Information content" refers to digital media such as movies, music, and videos that are delivered to users.

[0650] A "recommendation list" is a list of relevant informational content presented to the user based on analysis results and inferred information.

[0651] "Dialogue-based communication" is a method of communication between the user and the system, where they exchange information and interact with each other.

[0652] "Selective intent" refers to a user's intention to choose a specific item from among the recommended information content.

[0653] A "selection process" is the process by which a user, after indicating their choice, actually acquires or views informational content.

[0654] "After-sales support information" refers to support and additional information provided after selecting information content.

[0655] This invention provides a system that personalizes information content according to the user's emotional state. The system mainly consists of a server, a terminal operated by the user, and a network infrastructure between the two. The user inputs their emotions and requests in natural language through the terminal. For example, a specific request might be, "I'm feeling down today, so I want to watch a movie that will cheer me up." The terminal receives this input and transmits it to the server via the network.

[0656] The server first receives input data sent by the user and analyzes it using natural language processing. Text analysis software such as the Google Cloud Natural Language API can be used for this process. The analysis identifies the user's potential needs and emotional state. For example, it extracts specific emotions such as "feeling cheerful" or "wanting to relax." Based on this, the server utilizes a generative AI model to recommend informational content that matches the user's emotions and needs. OpenAI's GPT model, among others, can be applied to this technology.

[0657] As a result of the recommendations, a list of recommendations such as "lighthearted comedy movies" or "relaxing music videos" is generated and visually displayed on the user's device. The device provides an interactive user interface that receives requests for additional information and selections from the user. If the user selects a specific item from the recommended information content, they communicate their choice to the server and initiate the selection process. The server receives this and begins providing the selected content.

[0658] For example, if a user types, "It's cold today and I'm feeling down, so I want to watch some uplifting content," the server will interpret this as "providing positive energy" and present a list of recommendations such as "the latest heartwarming movies" or "upbeat music videos." An example of a prompt might look like this:

[0659] "Based on this emotional state, please recommend content that will help the user relax or feel happy."

[0660] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0661] Step 1:

[0662] The user inputs data into the terminal. This input includes natural language text expressing the user's emotions and requests. The terminal receives this data and sends it to the server via the network.

[0663] Step 2:

[0664] The server receives input data and analyzes it using natural language processing techniques. The input is natural language text, and the output is an analysis result that includes the user's emotional state and potential needs. Specifically, it uses tools such as the Google Cloud Natural Language API to analyze keywords and context within the text.

[0665] Step 3:

[0666] The server uses a generated AI model to recommend informational content best suited to the user's emotional state based on the analysis results. The input is the emotional state and potential needs, and the output is a list of recommended informational content. The system utilizes OpenAI's GPT model for content selection.

[0667] Step 4:

[0668] The server generates a list of recommendations and sends it to the terminal. The input is a list of recommended content, and the output is an interactive list that is visually displayed to the user. The terminal receives this and displays it in its user interface.

[0669] Step 5:

[0670] The user selects desired information content from a recommended list. The input is the user's selection, and the output is the selected content information. The terminal communicates this selection to the server.

[0671] Step 6:

[0672] The server initiates the process of providing the selected information content. The input is the user's selection, and the output is instructions regarding content provision. The server completes the selection process and provides the content.

[0673] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0674] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0675] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0676] [Fourth Embodiment]

[0677] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0678] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0679] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0680] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0681] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0682] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0683] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0684] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0685] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0686] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0687] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0688] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0689] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0690] The system of the present invention is designed to help users efficiently find the goods and services they desire on an e-commerce platform. This system consists of a server operating in a cloud environment, terminals accessed by users, and a network that mediates between them.

[0691] First, the user uses a terminal to input questions or requests to the system. For example, consider a user interested in fashion who inputs, "I want the latest trendy items that are perfect for spring." The terminal receives this input and sends it to the server via the network.

[0692] The server analyzes the user's input data using natural language processing techniques to identify the user's potential needs. Based on the analysis, the server uses a generative AI model to predict the most suitable products and services for the user. In this example, specific items such as "lightweight spring jacket" and "new sneakers" are considered.

[0693] Based on the anticipated needs, the server extracts relevant product information from the database and generates a list of recommended products. This list is optimized by taking into account the user's purchase history and market trends, providing the user with the most attractive options.

[0694] Next, the terminal interactively displays this recommendation list to the user. The user can browse the list on the screen and enter questions in a chat format. For example, if the user requests detailed specifications or reviews for a particular product, the server will provide that information immediately.

[0695] Finally, once the user decides to purchase, that decision is sent from the device to the server, which then processes the purchase. After the purchase is complete, the server provides after-sales support by sending shipping notifications and additional support information to the user. Throughout this entire process, the user can enjoy a seamless and satisfying purchasing experience.

[0696] As described above, the present invention significantly improves the convenience and efficiency of e-commerce by enabling product selection that meets the diverse needs of users and facilitating an interactive purchasing experience.

[0697] The following describes the processing flow.

[0698] Step 1:

[0699] Users input their needs and questions through their devices and send them to the system. For example, they might input, "I want lightweight outdoor gear suitable for summer."

[0700] Step 2:

[0701] The terminal receives user input and sends that data to the server.

[0702] Step 3:

[0703] The server analyzes the received input data using natural language processing techniques. This analysis extracts the user's potential needs and keywords.

[0704] Step 4:

[0705] Based on the analysis results, the server uses a generative model to infer the user's needs. Specifically, it identifies what attributes the input "outdoor gear" should have (e.g., lightweight, waterproof, durable).

[0706] Step 5:

[0707] The server searches its database for products and services that meet the inferred needs and generates a recommendation list. The user's past purchase history and market trends are also taken into consideration during this process.

[0708] Step 6:

[0709] The device presents the user with a generated list of recommendations and interactively displays detailed product information and reviews.

[0710] Step 7:

[0711] The user asks additional questions about the recommended products through the terminal, and the server provides detailed information in response to those questions.

[0712] Step 8:

[0713] Once a user decides to make a purchase, the terminal initiates the purchase process, which is then received and processed by the server.

[0714] Step 9:

[0715] After the purchase is complete, the server provides the user with shipping information and after-sales support information to support the purchase experience.

[0716] (Example 1)

[0717] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0718] In modern e-commerce, it is difficult for users to efficiently find the best-suited products from a vast array of options. Furthermore, there is a need for systems that accurately predict users' potential needs and quickly deliver products and services that match those needs. To overcome these challenges, it is essential to provide a seamless and interactive purchasing process that enhances the user experience.

[0719] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0720] In this invention, the server includes means for receiving and analyzing user input, means for using a generative model to infer the user's potential needs based on the analyzed information, and means for generating a list of recommended products or services that meet the inferred needs. This enables the user to quickly and accurately find products that meet their needs.

[0721] "Means of receiving and analyzing user input" refers to technologies that receive questions and requests from users to a system and process the data in order to understand their content.

[0722] A "generative model" is an algorithm or system used to infer a user's potential needs based on collected data.

[0723] "Means for generating recommendation lists" refers to a function that selects potential products and services that meet user requirements and prepares them for presentation to the user.

[0724] "Methods of presentation using interactive display functions" refer to technologies that provide information to users in a visually easy-to-understand manner and enable users to receive immediate responses to their questions and requests.

[0725] "Means for obtaining purchase intent and executing transaction procedures" refers to technology that confirms a user's intention to purchase and then carries out the actual payment processing and order placement procedures.

[0726] "Means of providing additional support information" refers to the process of providing users with additional information and support they need after purchasing a product or service, thereby improving user satisfaction.

[0727] The embodiment of the present invention is designed to enable users to efficiently find desired products and services on an e-commerce platform. The system consists of a server in a cloud environment, terminals for users to connect to, and a network infrastructure that mediates between them.

[0728] Server Role

[0729] The server receives input data from the user and analyzes it using natural language processing (NLP). This analysis utilizes NLP libraries such as spaCy and NLTK. Based on the analysis results, the server employs a generative AI model to infer the user's potential needs. Following these inferences, the server extracts a list of relevant products and services from its database and provides optimal recommendations. Furthermore, during the purchase process, it confirms the user's intent and processes the transaction. The server also plays a role in providing shipping notifications and after-sales support information to the user.

[0730] Terminal role

[0731] The terminal functions as the user interface, receiving user input and sending it to the server. The terminal also interactively displays recommendation lists from the server to the user, allowing them to access detailed information. This display is designed to make it easy for users to check product specifications and reviews. The terminal also plays a crucial role in ensuring that the user's purchase intention is reliably transmitted to the server.

[0732] User roles

[0733] Users input questions based on their needs and interests using a terminal. For example, if a user enters a prompt such as "I want the latest trendy items that are perfect for spring," the system will suggest related products based on that need. Users can then browse the suggested list, select the most suitable product, and make a purchase.

[0734] Thus, the embodiment of the invention allows users to enjoy a seamless and smooth purchasing experience. The entire system aims to improve user convenience and satisfaction.

[0735] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0736] Step 1:

[0737] The user uses a terminal to input prompts based on their interests and requests. This input is received by the terminal as text data written in natural language. The terminal then sends this input data to a server over the network.

[0738] Step 2:

[0739] The server analyzes the received prompt message using natural language processing techniques. Specifically, it performs morphological analysis to identify the user's requests and topics of interest. This process generates structured data that includes the user's potential needs.

[0740] Step 3:

[0741] The server uses a generative AI model based on the analysis results to infer user needs. Here, it refers to a large dataset including historical data and market trends to predict the product categories and characteristics the user is likely looking for. This process generates a virtual product list.

[0742] Step 4:

[0743] The server extracts relevant information from its database to generate a list of recommended products based on the user's inferred needs. It filters product details, availability, and pricing information to create a list for the user. At this stage, the list is optimized by also considering the user's past purchase history and market trends.

[0744] Step 5:

[0745] The server sends the generated recommendation list to the terminal.

[0746] Step 6:

[0747] The device interactively displays the received recommendation list through a user interface. Here, the user can scroll through the products on the screen and click to view more details. This interface allows the user to view specifications and reviews of products that interest them.

[0748] Step 7:

[0749] When a user decides to make a purchase, they enter their intention on their device and send it to the server. This process includes the user entering details such as payment information and shipping address.

[0750] Step 8:

[0751] The server receives the user's purchase intention and proceeds with the purchase process. This includes processing payments and arranging for product shipment. Once the purchase is confirmed, the server provides after-sales support to the user, such as shipping confirmation and further helpful information.

[0752] (Application Example 1)

[0753] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0754] In today's e-commerce platforms, users often struggle to find the best products and services for them amidst a vast amount of information. In particular, accurately understanding users' latent needs and providing optimal recommendations accordingly is crucial. However, many currently available systems lack sufficient utilization of appropriate natural language processing techniques and generative AI models, resulting in a failure to provide users with a satisfying purchasing experience.

[0755] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0756] In this invention, the server includes means for receiving and analyzing input data from a user, means for using a generative model to infer the user's potential needs based on the analysis results, and means for generating a list of recommended products or services that meet the inferred needs. This enables the user to efficiently and effectively find products and services that meet their needs.

[0757] "User input data"

[0758] This refers to information provided by users in text format on e-commerce platforms, such as questions and requests regarding products and services.

[0759] "Means of analysis"

[0760] This refers to a technology that processes input data received from users, understands its content, and structures it.

[0761] "Generative Model"

[0762] This refers to a system that includes an algorithm that infers the user's potential needs based on analyzed data and recommends products that meet those needs.

[0763] "Natural language processing technology"

[0764] This refers to the technology used by computers to understand and analyze human language, and is utilized to precisely interpret text input from users.

[0765] "Recommendation List"

[0766] This refers to a list of optimal product and service options generated based on user needs, past purchase history, and current market trends.

[0767] "Detailed information in dialogue format"

[0768] This refers to information exchanged interactively between the user and the system, and is data provided in a way that complements what the user wants to know more about the product.

[0769] "Means of carrying out the purchase procedure"

[0770] This refers to a system that automatically handles the payment and shipping procedures for products and services that a user has decided to purchase.

[0771] "After-sales support information"

[0772] This refers to additional information provided after product purchase, such as usage instructions, warranty information, and additional support, aimed at improving user satisfaction.

[0773] The system implementing this invention consists of a server, a user terminal, and a network that mediates between them. The server receives input data from the user via the network and analyzes that data using natural language processing technology. Specifically, it utilizes a generative AI model, which is a machine learning model, to analyze text input and identify the user's potential needs.

[0774] The generative AI model processes prompts generated based on user input and creates a list of recommendations for relevant products and services. The server retrieves the user's past purchase history and current market trends from a database to create an optimized list, which is then sent to the user's terminal.

[0775] The user terminal displays this recommendation list interactively, and the user can re-enter information if they require more details. The server immediately provides additional information in response to the input. Furthermore, if the user decides to purchase, the server automatically handles the purchase process and manages everything from shipping to providing after-sales support information.

[0776] For example, if a user enters "I want new sports shoes," the server uses a generative AI model that takes into account currently popular models and the user's past preferences to make recommendations such as "These are our recommended sports shoes." This process proceeds in real time, significantly improving user convenience.

[0777] An example of a prompt message would be: "User input: I want new sports shoes. Please suggest related products."

[0778] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0779] Step 1:

[0780] The user enters their requests regarding products and services through a terminal. The entered text data is sent to the server via the network. At this stage, the user's input is prepared as a prompt.

[0781] Step 2:

[0782] The server analyzes the user's input data using natural language processing techniques. The input is text data, and a generative AI model is used to understand its content. During this analysis process, the server identifies the user's potential needs and generates prompts based on them.

[0783] Step 3:

[0784] Based on the analysis results, the server references the database and generates a list of recommended products and services, taking into account the user's past purchase history and current market trends. This ensures that the products best suited to the user's needs are selected and compiled into a recommendation list.

[0785] Step 4:

[0786] The server sends a recommendation list to the terminal, which then displays it to the user in an interactive format. The user can then request further details about individual products. At this stage, the recommendation list serves as input, and the user interface is provided based on it.

[0787] Step 5:

[0788] When a user asks an additional question about a specific product, the device sends that question back to the server. The server analyzes the received question, collects detailed information and reviews about the relevant product, and immediately provides them to the user.

[0789] Step 6:

[0790] When a user decides to purchase, that decision is sent from the device to the server, which automatically initiates the purchase process. Payment is processed, and shipping instructions are relayed to the appropriate department.

[0791] Step 7:

[0792] After the purchase process is complete, the server provides after-sales support, such as shipping notifications and additional support information, to ensure a highly satisfying purchasing experience. All information generated during this process is accumulated as data that can be used to inform the user's future purchases.

[0793] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0794] This invention is a system that not only analyzes users' latent needs and individually recommends appropriate products and services, but also takes into account the user's emotional state to provide a more personalized purchasing experience. This system consists of a server operating in a cloud environment, a terminal operated by the user, and a network infrastructure connecting them.

[0795] The user uses a terminal to input information about the products or services they wish to purchase into the system. This information is expressed in natural language, including the user's needs and questions. For example, the user might input a request such as, "I want some comfortable loungewear to lift my spirits." The terminal receives this input and sends the data to the server.

[0796] The server first analyzes this input data using natural language processing techniques to identify the user's requests and potential needs. Next, it utilizes an emotion engine to recognize emotions from the user's input, extracting, for example, an emotional state such as "I want to relax."

[0797] Based on this, the server uses a generative AI model to predict the most suitable products and services for the user's latent needs and emotions. Based on the prediction results, the server retrieves relevant product information from the database and generates a recommendation list that prioritizes products that match the user's emotions. In this case, products such as "soft loungewear" and "soothing scented candles" might be selected.

[0798] The terminal visually displays a list of product recommendations based on this sentiment to the user, providing an interactive interface that allows the user to easily access information. Furthermore, if the user requests more detailed information, the server answers questions in a conversational format and presents detailed product information and ratings.

[0799] When a user decides to make a purchase, the device transmits their intention to the server and initiates the purchase process. The server completes the payment processing and continues to provide post-purchase support by delivering shipping information and after-sales support information to the user via the device.

[0800] Thus, the system of the present invention enables product recommendations that are tailored to the user's emotions, providing a comfortable and highly satisfying purchasing experience.

[0801] The following describes the processing flow.

[0802] Step 1:

[0803] Users access the system using their devices and input their purchase needs and preferences in natural language. For example, they might input, "I want some relaxation items to relieve stress."

[0804] Step 2:

[0805] The terminal sends the entered user data to the server.

[0806] Step 3:

[0807] The server performs natural language processing to analyze the incoming data and identify the user's requests and potential needs. In this case, keywords such as "healing" are extracted.

[0808] Step 4:

[0809] The server uses an emotion engine to recognize the user's emotional state from their input. For example, it might detect the emotion "I want to relieve stress."

[0810] Step 5:

[0811] The server uses a generative AI model to predict suitable products and services, taking into account the user's needs and emotional state.

[0812] Step 6:

[0813] Based on the prediction results, the server searches a database of related products and generates a list of recommended products that resonate with the user's emotions. For example, "aroma diffusers" and "music CDs good for stress relief" might be listed.

[0814] Step 7:

[0815] The device displays a generated list of recommendations to the user and provides detailed product information and reviews through an interactive interface.

[0816] Step 8:

[0817] Users can ask additional questions about the product from their device, and the server will continuously provide that information immediately.

[0818] Step 9:

[0819] When a user decides to make a purchase, that decision is sent from the device to the server, and the server completes the purchase process.

[0820] Step 10:

[0821] After the purchase is complete, the server provides shipping information and after-sales support information regarding the product, ensuring a satisfying purchasing experience for the user.

[0822] (Example 2)

[0823] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0824] Modern purchasing systems offer product recommendations based on simple user needs, but they lack personalized methods that take into account the user's emotional state. This makes it difficult for users to find products and services that resonate with their emotions, resulting in a challenging shopping experience.

[0825] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0826] In this invention, the server includes means for receiving and analyzing input data from the user, means for using a generative model to infer the user's potential needs and emotional state, and means for generating a list of recommended products or services that correspond to the inferred needs and emotions. This enables more personalized product recommendations that take the user's emotional state into account.

[0827] "Input data" refers to information that users enter into the system, including their purchasing needs and emotional state.

[0828] "Means of analysis" refers to a process or system that has the function of analyzing input data received from a user using natural language processing technology to identify needs and emotions.

[0829] A "generative model" is an AI-based model used to infer a user's potential needs and emotional state.

[0830] A "means of inference" refers to a system or process that utilizes generative models to infer needs and emotional states based on user input data.

[0831] A "recommendation list" is a list of products or services selected based on the user's potential needs and emotional state.

[0832] "Dialogue-based" refers to a format in which the user can actively ask questions of the system and receive answers.

[0833] "Purchase process" refers to the series of steps involved in completing payment and shipping procedures for the products or services selected by the user.

[0834] "After-sales support information" refers to additional support information and guidance regarding products and services provided to users after purchase.

[0835] This invention consists of a server operating in a cloud environment, a terminal operated by the user, and a communication network connecting them. The user uses the terminal to input information about the products or services they wish to purchase in natural language. For example, it is assumed that the user inputs a request such as "I would like some items that help me relax."

[0836] The terminal receives this user input and sends it to the server for analysis. The server uses natural language processing techniques, such as Python's NLTK library, to analyze the input data and identify the user's needs. Furthermore, it utilizes emotion recognition technologies, such as IBM Watson Tone Analyzer, to evaluate the user's emotional state. This process may extract the emotion of "wanting to relax."

[0837] The server then uses a generative AI model and technologies like OpenAI's GPT to predict products and services that match the user's latent needs and emotions. For example, it might send a prompt like, "What is the best product for this user?" to the generative AI model to make a prediction.

[0838] Based on the prediction results, the server retrieves relevant product information from a MySQL database, etc., and generates a recommendation list prioritizing products that best match the user's emotional state. In this case, products such as "soft loungewear" or "calming aroma candles" may be selected.

[0839] Next, the terminal visually displays the generated product recommendation list to the user. The interface is interactive and includes links that allow the user to easily view product information and access more detailed information. If the user requests more detailed product information, the server interactively presents additional information.

[0840] Finally, once the user decides to purchase, the device transmits its intention to the server. The server processes the payment via the Stripe API or similar, and provides shipping information and post-purchase support information to the user via the device. Through this system, users can enjoy a more personalized, emotion-based purchasing experience.

[0841] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0842] Step 1:

[0843] The user inputs information about the desired product and their emotional state into the terminal using natural language. At this point, the input text data does not clearly express the user's intent and therefore cannot be processed mechanically as is. The terminal receives this user input and prepares to send it to the server for analysis. The input is text data, and the output is formatted data.

[0844] Step 2:

[0845] The server receives input data sent from the terminal and begins data analysis using natural language processing (NLTK) technology. It performs grammatical and semantic analysis using software such as Python's NLTK library. Specifically, it identifies the part of speech of words and determines the intent of sentences. The input data is unstructured text, and after analysis, formalized data that identifies the user's needs is output.

[0846] Step 3:

[0847] Next, the server uses an emotion engine to recognize the user's emotional state from their input. Specifically, it uses technologies such as IBM Watson Tone Analyzer to obtain emotional indicators from the input text. The formalized data from the previous step is used as input, and the output is data representing emotional states such as "I want to relax" or "I'm stressed."

[0848] Step 4:

[0849] The server uses a generative AI model to predict products and services that match the user's potential needs and emotions. Utilizing OpenAI's GPT technology, it generates suggested products using the prompt "What is the best product for this user?". Emotional state data and user needs are used as input, and the output is a list of recommended products.

[0850] Step 5:

[0851] The server uses the prediction results to retrieve relevant product information from the database. It references databases such as MySQL and sorts products based on emotional states to create a recommendation list. Specifically, it retrieves detailed information on products such as soft loungewear and aromatherapy candles. The input is a list of recommended products, and the output is a list of detailed product information.

[0852] Step 6:

[0853] The terminal visually displays the final product recommendation list to the user. The interface is interactive and designed to allow the user to carefully review their options. The input data is a detailed product information list, which is presented to the user at this stage.

[0854] Step 7:

[0855] When a user indicates their intention to purchase a selected item, the terminal transmits this information to the server. The server completes the purchase process using a payment system such as the Stripe API. Specifically, it sends and verifies payment information and notifies the user that the payment was successful. The input data is the user's purchase intention, and the output is purchase approval information.

[0856] Step 8:

[0857] Once the purchase process is complete, the server provides the user with shipping information and after-sales support information via their device. Specific methods include email notifications and chatbot support. Input is purchase approval information, and output includes user notifications and support information.

[0858] (Application Example 2)

[0859] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0860] Modern information content delivery services face the challenge of providing content recommendations that take into account the individual emotional state of each user. Furthermore, they are required to respond quickly and accurately to users' diverse needs and desires based on their mood and circumstances. This necessitates increasing user satisfaction and providing a more personalized experience.

[0861] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0862] In this invention, the server includes means for receiving and analyzing input data from a user, means for using a generative model to infer the user's potential needs and emotional state based on the analysis results, and means for generating a list of recommended information content corresponding to the inferred needs and emotional state. This enables the recommendation of personalized information content that is attuned to the user's emotional state.

[0863] "User input data" refers to data, including natural language, that users use when they want to retrieve informational content.

[0864] "Means of analysis" refer to technologies and functions that receive input data from users and process it in order to understand its content.

[0865] "Latent needs" are requests or desires that users may have but do not explicitly express.

[0866] "Emotional state" refers to the psychological or emotional condition a user is in when providing input data.

[0867] A "generative model" is an artificial intelligence technique used to predict user needs and emotional states based on input data.

[0868] "Information content" refers to digital media such as movies, music, and videos that are delivered to users.

[0869] A "recommendation list" is a list of relevant informational content presented to the user based on analysis results and inferred information.

[0870] "Dialogue-based communication" is a method of communication between the user and the system, where they exchange information and interact with each other.

[0871] "Selective intent" refers to a user's intention to choose a specific item from among the recommended information content.

[0872] A "selection process" is the process by which a user, after indicating their choice, actually acquires or views informational content.

[0873] "After-sales support information" refers to support and additional information provided after selecting information content.

[0874] This invention provides a system that personalizes information content according to the user's emotional state. The system mainly consists of a server, a terminal operated by the user, and a network infrastructure between the two. The user inputs their emotions and requests in natural language through the terminal. For example, a specific request might be, "I'm feeling down today, so I want to watch a movie that will cheer me up." The terminal receives this input and transmits it to the server via the network.

[0875] The server first receives input data sent by the user and analyzes it using natural language processing. Text analysis software such as the Google Cloud Natural Language API can be used for this process. The analysis identifies the user's potential needs and emotional state. For example, it extracts specific emotions such as "feeling cheerful" or "wanting to relax." Based on this, the server utilizes a generative AI model to recommend informational content that matches the user's emotions and needs. OpenAI's GPT model, among others, can be applied to this technology.

[0876] As a result of the recommendations, a list of recommendations such as "lighthearted comedy movies" or "relaxing music videos" is generated and visually displayed on the user's device. The device provides an interactive user interface that receives requests for additional information and selections from the user. If the user selects a specific item from the recommended information content, they communicate their choice to the server and initiate the selection process. The server receives this and begins providing the selected content.

[0877] For example, if a user types, "It's cold today and I'm feeling down, so I want to watch some uplifting content," the server will interpret this as "providing positive energy" and present a list of recommendations such as "the latest heartwarming movies" or "upbeat music videos." An example of a prompt might look like this:

[0878] "Based on this emotional state, please recommend content that will help the user relax or feel happy."

[0879] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0880] Step 1:

[0881] The user inputs data into the terminal. This input includes natural language text expressing the user's emotions and requests. The terminal receives this data and sends it to the server via the network.

[0882] Step 2:

[0883] The server receives input data and analyzes it using natural language processing techniques. The input is natural language text, and the output is an analysis result that includes the user's emotional state and potential needs. Specifically, it uses tools such as the Google Cloud Natural Language API to analyze keywords and context within the text.

[0884] Step 3:

[0885] The server uses a generated AI model to recommend informational content best suited to the user's emotional state based on the analysis results. The input is the emotional state and potential needs, and the output is a list of recommended informational content. The system utilizes OpenAI's GPT model for content selection.

[0886] Step 4:

[0887] The server generates a list of recommendations and sends it to the terminal. The input is a list of recommended content, and the output is an interactive list that is visually displayed to the user. The terminal receives this and displays it in its user interface.

[0888] Step 5:

[0889] The user selects desired information content from a recommended list. The input is the user's selection, and the output is the selected content information. The terminal communicates this selection to the server.

[0890] Step 6:

[0891] The server initiates the process of providing the selected information content. The input is the user's selection, and the output is instructions regarding content provision. The server completes the selection process and provides the content.

[0892] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0893] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0894] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0895] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0896] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0897] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0898] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0899] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0900] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0901] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0902] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0903] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0904] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0905] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0906] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0907] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0908] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0909] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0910] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0911] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0912] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0913] The following is further disclosed regarding the embodiments described above.

[0914] (Claim 1)

[0915] A means of receiving and analyzing user input data,

[0916] A means of using a generative model to infer the user's potential needs based on the aforementioned analysis results,

[0917] A means for generating a list of recommended products or services that meet the aforementioned inferred needs,

[0918] A means of presenting the aforementioned recommendation list to the user and providing detailed information in an interactive format,

[0919] A means of receiving the user's intention to purchase and carrying out the purchase procedure,

[0920] A means of providing after-sales support information after purchase,

[0921] A system that includes this.

[0922] (Claim 2)

[0923] The system according to claim 1, wherein the generative model analyzes user input data using natural language processing technology.

[0924] (Claim 3)

[0925] The system according to claim 1, wherein the recommendation list is optimized based on the user's past purchase history and current market trends.

[0926] "Example 1"

[0927] (Claim 1)

[0928] A means of receiving and analyzing user input,

[0929] Means for using a generative model that infers the user's potential needs based on the analyzed information,

[0930] means for generating a list of recommended products or services that meet the aforementioned inferred requirements,

[0931] A means of presenting the aforementioned recommendation list to the user using an interactive display function and providing detailed information,

[0932] A means of obtaining the user's intention to purchase and executing the transaction procedure,

[0933] A means of providing additional support information after purchase,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, wherein the generative model analyzes user input data using natural language processing techniques.

[0937] (Claim 3)

[0938] The system according to claim 1, wherein the recommendation list is optimized based on the user's past transaction history and current market trends.

[0939] "Application Example 1"

[0940] (Claim 1)

[0941] A means of receiving and analyzing user input data,

[0942] A means of using a generative model to infer the user's potential needs based on the aforementioned analysis results,

[0943] A means for generating a list of recommended products or services that meet the aforementioned inferred needs,

[0944] A means of presenting the aforementioned recommendation list to the user and providing detailed information in an interactive format,

[0945] A means of analyzing user input using natural language processing technology and presenting the optimal choice from the generated recommendation list,

[0946] A means of receiving the user's intention to purchase and carrying out the purchase procedure,

[0947] A means of providing after-sales support information after purchase,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, wherein the generative model analyzes user input data using natural language processing technology and recommends products along with relevant information.

[0951] (Claim 3)

[0952] The system according to claim 1, wherein the recommendation list is optimized based on the user's past purchase history and current market trends and is displayed through an interactive user interface.

[0953] "Example 2 of combining an emotion engine"

[0954] (Claim 1)

[0955] A means of receiving and analyzing user input data,

[0956] A means of using a generative model that infers the user's potential needs and emotional state based on the aforementioned analysis results,

[0957] A means for generating a list of recommended products or services that correspond to the aforementioned inferred needs and emotions,

[0958] A means of presenting the aforementioned recommendation list to the user and providing detailed information in an interactive format,

[0959] A means of receiving the user's intention to purchase and carrying out the purchase procedure,

[0960] A means of providing after-sales support information after purchase,

[0961] A system that includes this.

[0962] (Claim 2)

[0963] The system according to claim 1, wherein the generative model analyzes user input data using natural language processing technology and emotion recognition technology.

[0964] (Claim 3)

[0965] The system according to claim 1, wherein the recommendation list is optimized based on the user's past purchase history, current market trends, and estimated emotional state.

[0966] "Application example 2 when combining with an emotional engine"

[0967] (Claim 1)

[0968] A means of receiving and analyzing user input data,

[0969] A means for using a generative model that infers the user's potential needs and emotional state based on the aforementioned analysis results,

[0970] A means for generating a list of recommended information content corresponding to the aforementioned inferred needs and emotional states,

[0971] A means of presenting the aforementioned recommendation list to the user and providing detailed information in an interactive format,

[0972] A means for receiving the user's choice and carrying out the selection procedure,

[0973] A means of providing after-sales follow-up information after selection,

[0974] A system that includes this.

[0975] (Claim 2)

[0976] The system according to claim 1, wherein the generative model analyzes user input data using natural language processing technology.

[0977] (Claim 3)

[0978] The system according to claim 1, wherein the recommendation list is optimized based on the user's past selection history and current market trends. [Explanation of symbols]

[0979] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving and analyzing user input data, A means of using a generative model to infer the user's potential needs based on the aforementioned analysis results, A means for generating a list of recommended products or services that meet the aforementioned inferred needs, A means of presenting the aforementioned recommendation list to the user and providing detailed information in an interactive format, A means of receiving the user's intention to purchase and carrying out the purchase procedure, A means of providing after-sales support information after purchase, A system that includes this.

2. The system according to claim 1, wherein the generative model analyzes user input data using natural language processing technology.

3. The system according to claim 1, wherein the recommendation list is optimized based on the user's past purchase history and current market trends.

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