system

Abstract

We provide the system. [Solution] A means by which the user can access customer support via an input device, A server that receives inquiries from users and connects them to the appropriate support personnel, A system that includes means for support staff to respond to users and resolve problems.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the modern retail industry, there is a demand for customer support, personalized product recommendations, provision of product information and recipes, and efficient operation of automatic order processing. If these services are not provided appropriately, there is a risk of a decrease in customer satisfaction and a deterioration in business efficiency. However, in conventional systems, it is difficult to provide these functions collectively. In particular, there is a problem that there is no integrated system that allows customers to conveniently use these functions using a smartphone.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means: a system including means for a user to access customer support via an input device, means for a server to receive inquiries from users and connect them to appropriate support personnel, and means for support personnel to respond to users and resolve problems. The system also provides a server that generates individually personalized product lists based on user input conditions, means for displaying suggestions from the server on a user terminal, and means for the user to purchase the suggested products. Furthermore, the system provides means for enabling users to search for product information and recipes via an input device, means for a server to perform keyword searches and extract relevant information from a database, and means for displaying search results from the server on a user terminal. Finally, the system provides means for users to set up automatic orders via an input device, means for a server to store user setting information and manage order schedules, means for a server to periodically generate automatic orders and issue shipping instructions, and means for sending notifications to the user terminal when an order is processed. These means enable customers to efficiently receive customer support, receive product suggestions, search for product information and recipes, and place automatic orders using their smartphones.

[0006] "User" refers to an end user or customer who operates a smartphone or other input device to use the system.

[0007] An "input device" is a tool that a user uses to input information into a system, and examples include smartphones, tablets, and personal computers.

[0008] "Customer support" refers to the services and support activities provided to address user inquiries and resolve problems.

[0009] A "server" refers to a computer system that functions as a central processing unit, receiving and analyzing requests from users and providing appropriate information and services.

[0010] "Support staff" refers to individuals or automated response systems assigned to respond to user inquiries and provide support for problem resolution.

[0011] A "personalized product list" refers to a list of products that are customized based on a user's past history and current input conditions, and are suggested to a specific user.

[0012] "Product information" refers to detailed information about a product, including its characteristics, functions, usage instructions, and price.

[0013] A "recipe" refers to information that details the steps and ingredients for preparing food or dishes.

[0014] "Automatic ordering" refers to a purchasing process where, once set up by the user, products are automatically purchased and delivered on a regular basis.

[0015] "Order schedule" refers to the plan of orders generated based on the user's set automatic order cycle and date / time.

[0016] "Shipping instruction" refers to the process of instructing the logistics system to ship a product after the order has been placed. [Brief explanation of the drawing]

[0017] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0021] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] Modes for carrying out the invention

[0039] This invention relates to an integrated system for enabling users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system is configured as follows:

[0040] Strengthening customer support

[0041] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. The representative then assists the user via chat or voice and resolves the issue.

[0042] Specific example:

[0043] When a user asks a question about how to use the refrigerator, the app sends a query to the server. The server analyzes the question and connects the user to the refrigerator's product representative. The representative then provides the appropriate answer.

[0044] Personalized product suggestions

[0045] The user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. The generated list is displayed on the device, and the user can purchase the suggested products.

[0046] Specific example:

[0047] If a user is looking for diet foods, the server will recommend low-calorie foods and diet supplements based on their past purchase history, and will also provide discount coupons.

[0048] Product information and recipes

[0049] When a user enters keywords into the app's search bar, the device sends this information to the server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user.

[0050] Specific example:

[0051] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase the ingredients are also provided.

[0052] Automated order processing

[0053] The user selects "Automatic Order" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[0054] Specific example:

[0055] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a specified day each month and ships the product. An order confirmation notification is sent to the user's device.

[0056] The above describes the embodiments for carrying out the present invention. This system allows users to efficiently and effectively receive customer support, product suggestions, search for product information and recipes, and place automatic orders using their smartphones.

[0057] The following describes the processing flow.

[0058] Strengthening customer support

[0059] Processing steps:

[0060] Step 1:

[0061] The user taps the app icon on their smartphone's home screen to launch the app.

[0062] Step 2:

[0063] The user taps the "Customer Support" button within the app.

[0064] Step 3:

[0065] The device sends a support request to the server. This request includes the user ID, current screen information, and the content of the inquiry.

[0066] Step 4:

[0067] The server receives the request and analyzes the inquiry. If necessary, it forwards it to an automated response system or the appropriate person.

[0068] Step 5:

[0069] Based on the analysis results, the server connects to a chatbot or a human representative to generate a response.

[0070] Step 6:

[0071] The terminal displays the response information received from the server to the user. Support is provided in chat or voice format.

[0072] ---

[0073] Personalized product suggestions

[0074] Processing steps:

[0075] Step 1:

[0076] The user taps the "Product Suggestions" menu within the app.

[0077] Step 2:

[0078] Users input the product category and purpose they want suggested (e.g., diet, muscle training) via text or voice.

[0079] Step 3:

[0080] The terminal sends the user's input conditions to the server.

[0081] Step 4:

[0082] The server analyzes the database based on user input, past purchase history, and preference data.

[0083] Step 5:

[0084] The server generates a personalized product list based on the analysis results. This list includes relevant products, promotions, and discount information.

[0085] Step 6:

[0086] The terminal displays the product list received from the server to the user.

[0087] ---

[0088] Product information and recipes

[0089] Processing steps:

[0090] Step 1:

[0091] The user taps the search bar within the app.

[0092] Step 2:

[0093] Users enter keywords related to their interests, such as cooking methods, nutrients, and ingredients.

[0094] Step 3:

[0095] The device sends the keyword to the server.

[0096] Step 4:

[0097] The server performs a keyword search on the database and extracts relevant product information and recipes.

[0098] Step 5:

[0099] The server generates search results and formats them into a format for providing to the user.

[0100] Step 6:

[0101] The terminal displays the search results received from the server to the user.

[0102] ---

[0103] Automated order processing

[0104] Processing steps:

[0105] Step 1:

[0106] The user selects the "Automatic Ordering" setup screen within the app.

[0107] Step 2:

[0108] The user enters the product, quantity, purchase frequency (e.g., weekly, monthly), and payment method to be purchased.

[0109] Step 3:

[0110] The device sends configuration information to the server.

[0111] Step 4:

[0112] The server saves the automatic order settings to the database and registers the order schedule.

[0113] Step 5:

[0114] The server automatically generates orders based on the purchase cycle and verifies the user's payment information.

[0115] Step 6:

[0116] The server sends shipping instructions to the warehouse system and completes the order in conjunction with the logistics management system.

[0117] Step 7:

[0118] The terminal notifies the user that automatic ordering has been successfully set up, and the user receives a notification when each order is processed.

[0119] ---

[0120] The above details the specific operations at each processing step. This configuration allows users to easily access customer support, product suggestions, product information and recipes, and place automatic orders using their smartphones.

[0121] (Example 1)

[0122] 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."

[0123] Traditional customer support systems often failed to properly analyze user inquiries and quickly connect users to the appropriate support staff. Furthermore, personalized product recommendations frequently did not fully utilize past purchase history, making it difficult to generate optimal product lists for users. Additionally, product information and recipe search results often did not match user expectations, resulting in a poor user experience.

[0124] 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.

[0125] In this invention, the server includes means for receiving inquiries from users and analyzing the content of the inquiries using a natural language processing algorithm, means for selecting the most suitable support person based on the analysis results and connecting to the support person, and means for the support person to respond to the user and resolve the problem. This makes it possible to quickly analyze the content of user inquiries and connect to the most suitable support person.

[0126] Furthermore, the server includes means for generating individually personalized product lists using a generative AI model, taking into account past purchase history based on user input conditions; means for displaying suggestions from the server on the user's terminal; and means for the user to purchase the suggested products. This provides the user with the most suitable product suggestions and facilitates purchasing behavior.

[0127] Furthermore, the server includes means for performing keyword searches and extracting relevant information from the database, as well as means for displaying the extraction results on the user's terminal. This ensures that the information and recipes the user is looking for are provided quickly and accurately, improving the user experience.

[0128] A "user" is an individual or legal entity that uses the system to perform tasks such as customer support, product recommendations, and product information searches.

[0129] An "input device" is a device used by a user to input information, and examples include smartphones, tablets, and personal computers.

[0130] "Customer support" is a service in which support staff respond to inquiries and problems from users and attempt to resolve them.

[0131] A "server" is a device or system that processes information on a network, receives requests from users, and performs appropriate processing in response to those requests.

[0132] A "natural language processing algorithm" is a set of computational methods and techniques that enable computers to understand and analyze human language.

[0133] A "support staff member" is a staff member or agent who handles inquiries from users in customer support.

[0134] A "personalized product list" is a list of product suggestions that is generated individually based on the user's input criteria and past purchase history.

[0135] A "generative AI model" is a computational model that uses artificial intelligence to analyze data and generate new information or suggestions.

[0136] A "user terminal" is a device that a user directly operates to access the system, and includes smartphones, tablets, and personal computers.

[0137] "Keyword search" is the process by which a user enters a specific word or phrase and searches a database for information related to that word.

[0138] A "database" is a collection of data that is organized and stored so that it can be quickly and efficiently searched and retrieved when needed.

[0139] This invention relates to an integrated system for users to efficiently provide customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices. The system is designed to be easily operated by users using input devices such as smartphones, tablets, and personal computers.

[0140] Strengthening customer support

[0141] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server analyzes the received request using a natural language processing algorithm and selects the most suitable support representative. Once a representative is selected based on the analysis results, the server forwards the request to that representative, and the user can receive support via chat or voice.

[0142] Specific example:

[0143] When a user asks a question about how to use the refrigerator, the app sends a query to the server. The server analyzes the question and connects the user to the refrigerator's product representative. The representative then provides the appropriate answer.

[0144] Prompt example:

[0145] "I don't know how to use the refrigerator. How do I operate it?"

[0146] Personalized product suggestions

[0147] When a user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training), the device sends this information to the server. The server uses a generative AI model to analyze the database and generate a personalized product list. The generated list is displayed on the device, and the user can purchase the suggested products.

[0148] Specific example:

[0149] If a user is looking for diet foods, the server will recommend low-calorie foods and diet supplements based on their past purchase history, and will also provide discount coupons.

[0150] Prompt example:

[0151] "Based on my past purchase history, please recommend some diet foods."

[0152] Product information and recipes

[0153] When a user enters keywords into the app's search bar, the device sends this information to the server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user.

[0154] Specific example:

[0155] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase the ingredients are also provided.

[0156] Prompt example:

[0157] "Can you share a low-calorie smoothie recipe?"

[0158] Automated order processing

[0159] When a user selects "Automatic Ordering" within the app and enters the product and frequency, the device sends the settings information to the server. The server stores this information and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[0160] Specific example:

[0161] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a specified day each month and ships the product. An order confirmation notification is sent to the user's device.

[0162] This system allows users to efficiently and effectively receive customer support, product recommendations, search for product information and recipes, and place automatic orders using their smartphones.

[0163] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0164] Strengthening customer support

[0165] Step 1:

[0166] The user launches the app on their smartphone and taps the "Customer Support" button.

[0167] Operation: The user taps the app to launch it and selects the "Customer Support" button from the home screen. This initiates a customer support request.

[0168] Input: User action (button tap).

[0169] Output: The terminal generates support request data.

[0170] Step 2:

[0171] The terminal sends the generated support request data to the server.

[0172] Operation: The terminal encodes the data containing the support request and sends it to the server.

[0173] Input: Support request data.

[0174] Output: The request data reaches the server.

[0175] Step 3:

[0176] The server receives the request and parses it using a natural language processing algorithm.

[0177] Operation: The server parses the received request and applies NLP algorithms to understand the user's inquiry.

[0178] Input: Request data.

[0179] Output: Understanding the query content from the analysis results.

[0180] Step 4:

[0181] The server selects the most suitable support person based on the analysis results.

[0182] Operation: The server searches the database for the appropriate person in charge and selects the most suitable person.

[0183] Input: Analysis results.

[0184] Output: Information on the selected support staff member.

[0185] Step 5:

[0186] The server forwards the request to the person in charge and establishes the connection.

[0187] Operation: The server forwards the request to the designated contact person and establishes a chat or voice call connection.

[0188] Input: Person in charge information, request data.

[0189] Output: A connection notification is sent to the user's terminal.

[0190] Personalized product suggestions

[0191] Step 1:

[0192] The user selects the "Product Suggestions" menu in the app and enters their desired category and conditions.

[0193] Action: The user taps the "Product Suggestion" menu and enters their request in the form for entering conditions.

[0194] Input: Enter the user's category and conditions.

[0195] Output: Conditional data is generated.

[0196] Step 2:

[0197] The device sends this conditional data to the server.

[0198] Operation: The terminal encodes the input condition data and sends it to the server.

[0199] Input: Conditional data.

[0200] Output: The condition data reaches the server.

[0201] Step 3:

[0202] The server uses an AI model to analyze the database and generate individually personalized product lists.

[0203] Operation: The server searches the database and uses a generative AI model to generate a list of products that match the user's criteria.

[0204] Input: Conditional data, past purchase history.

[0205] Output: Personalized product list.

[0206] Step 4:

[0207] The server sends the generated product list to the terminal.

[0208] Operation: The server encodes the product list and sends it to the user's terminal.

[0209] Input: Product list.

[0210] Output: The product list arrives at the terminal.

[0211] Step 5:

[0212] The terminal displays the product list it received to the user.

[0213] Operation: The terminal decodes the product list and displays it in the user interface.

[0214] Input: Product list.

[0215] Output: The user is shown a list of products.

[0216] Product information and recipes

[0217] Step 1:

[0218] The user enters keywords into the search bar within the app.

[0219] Operation: The user taps the app's search bar and enters keywords for the information they are looking for.

[0220] Input: User keyword input.

[0221] Output: Keyword data is generated.

[0222] Step 2:

[0223] The device sends this keyword data to the server.

[0224] Operation: The terminal encodes the keyword data and sends it to the server.

[0225] Input: Keyword data.

[0226] Output: Keyword data reaches the server.

[0227] Step 3:

[0228] The server performs a keyword search and extracts relevant information from the database.

[0229] Operation: The server searches the database based on keywords and extracts relevant product information and recipes.

[0230] Input: Keyword data.

[0231] Output: Search results data.

[0232] Step 4:

[0233] The server sends the extracted search results to the terminal.

[0234] Operation: The server encodes the search results and sends them to the user's device.

[0235] Input: Search results data.

[0236] Output: Search results arrive at the terminal.

[0237] Step 5:

[0238] The search results received by the device are displayed to the user.

[0239] Operation: The device decodes the search results and displays them in the user interface.

[0240] Input: Search results data.

[0241] Output: The user is shown the search results.

[0242] Automated order processing

[0243] Step 1:

[0244] The user selects "Automatic Order" within the app and enters the product and delivery frequency.

[0245] Operation: The user taps the "Automatic Order" menu and enters the desired products and their frequency into the form.

[0246] Input: Enter the product and cycle.

[0247] Output: Automatic order setting data is generated.

[0248] Step 2:

[0249] The device sends this configuration data to the server.

[0250] Operation: The terminal encodes the automatic order setting data and sends it to the server.

[0251] Input: Automatic order setting data.

[0252] Output: Configuration data reaches the server.

[0253] Step 3:

[0254] The server stores configuration data and manages order schedules.

[0255] Operation: The server saves the received configuration data to the database and creates entries for managing order schedules.

[0256] Input: Automatic order setting data.

[0257] Output: Saved configuration data, schedule entries.

[0258] Step 4:

[0259] The server automatically generates orders based on the specified cycle.

[0260] Operation: The server automatically generates order data based on a cycle and prepares the data for order processing.

[0261] Input: Schedule entry.

[0262] Output: Order data.

[0263] Step 5:

[0264] After the server generates the order, it issues a shipping instruction.

[0265] Operation: The server uses the generated order data to create shipping instructions and sends them to the logistics system.

[0266] Input: Order data.

[0267] Output: Shipping instructions.

[0268] Step 6:

[0269] Once the order is processed, the server sends an order confirmation notification to the terminal.

[0270] Operation: After issuing a shipping instruction, the server encodes an order confirmation notification and sends it to the user's terminal.

[0271] Input: Shipping instructions.

[0272] Output: An order confirmation notification is sent to the terminal.

[0273] (Application Example 1)

[0274] 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."

[0275] Traditional customer support systems and online shopping sites struggle to efficiently provide personalized support, product recommendations, product information, and automated order management to users. In particular, there is a demand for automated and user-optimized product recommendations and automated order processing, but achieving this presents numerous technical challenges.

[0276] 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.

[0277] In this invention, the server includes means for the user to access customer support via an input device, means for the server to receive an inquiry from the user and connect it to an appropriate support staff, means for the support staff to respond to the user and solve the problem, means for the user to input setting information into the terminal and transmit the setting information to the server, means for the server to generate an automatic order based on the setting information and manage the order, and means for sending a notification to the terminal after the automatic order is processed. Thereby, the user can efficiently receive customer support, receive personalized product recommendations, search for product information and recipes, and place automatic orders.

[0278] The "input device" is hardware used by the user to input information, such as a smartphone or a tablet.

[0279] "Customer support" is a support service provided for the user to solve questions and problems related to products and services.

[0280] The "server" is a computer system that provides data and services to multiple clients via a network.

[0281] An "inquiry" is a question or request made by the user seeking support.

[0282] A "support staff" is a staff member assigned to provide customer support.

[0283] "Setting information" is information input by the user when using functions such as automatic orders, and includes product names, order cycles, etc.

[0284] "Automatic order" is a mechanism in which the server automatically places an order for a product based on conditions specified by the user.

[0285] A "notification" is a means of information transmission sent from the server to the user, including order confirmation, update information, etc.

[0286] A "personalized product recommendation" is a mechanism that individually recommends the most suitable products based on the user's history and preferences.

[0287] A "profile" is a collection of data recording the user's purchase history and interests.

[0288] A "shopping history" is a record of the products the user has purchased in the past.

[0289] A "discount coupon" is a coupon provided for the user to purchase a specific product at a lower price.

[0290] "Keyword search" is a means of searching a database based on keywords entered by the user and extracting relevant information.

[0291] "Product information" is detailed information about a specific product.

[0292] A "recipe" is information on the procedures and ingredients for making a specific dish.

[0293] A "link" is a URL that allows the user to access another web page or information by clicking on it.

[0294] This invention is an integrated system for the user to perform efficient customer support, personalized product recommendations, provision of product information and recipes, and automatic order processing via an input device such as a smartphone. This system is composed of the following elements.

[0295] Enhancement of customer support

[0296] The user launches the customer support application on their smartphone and taps the "Customer Support" button. The device then sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. This support representative responds to the user via chat or voice and resolves the issue.

[0297] Specific example:

[0298] When a user asks a question about how to use their refrigerator, the app sends a query to the server. The server analyzes the question, connects to the refrigerator's product representative, and the representative provides the appropriate answer. For example: "I bought a refrigerator. I don't know how to use it, so please tell me how."

[0299] Personalized product suggestions

[0300] The user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database to generate a personalized product list. This generated list is displayed on the device, and the user can purchase the suggested products. The server uses the user profile and shopping history to suggest the most suitable products and also provides discount coupons.

[0301] Specific example:

[0302] When a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history, and also provides discount coupons. "I'm looking for diet foods. Can you recommend some low-calorie options?"

[0303] Product information and recipes

[0304] When a user enters a keyword in the search bar within the app, the terminal sends this information to the server. The server conducts a keyword search, extracts relevant product information and recipes from the database, and displays them to the user. The search results include relevant product information and recipes, and further, links where materials can be purchased are also displayed.

[0305] Specific example:

[0306] When a user searches for a recipe for "low-calorie smoothie", the server provides relevant smoothie recipes and also displays the ingredients used and nutritional information. Additionally, links where the materials can be purchased are also displayed. "Please teach me a recipe for a low-calorie smoothie."

[0307] Automatic order processing

[0308] When a user selects "automatic order" within the app and enters a product and a cycle, the terminal sends the setting information to the server, and the server saves this and manages the order schedule. Based on the specified cycle, the server automatically generates an order and gives a shipping instruction. When the order is processed, a notification is sent to the terminal.

[0309] Specific example:

[0310] When a user sets to purchase protein powder every month, the server automatically generates an order on a specified day every month and ships the product. A notification of order confirmation is sent to the terminal. "Please set to automatically order protein powder every month."

[0311] This system is implemented using cloud-based servers such as AWS (registered trademark) and Google (registered trademark) Cloud Platform. Also, by using web frameworks such as Flask and Django, a REST API is built to perform data processing in response to user requests. MySQL (registered trademark) and PostgreSQL are used for the database.

[0312] By utilizing generative AI models and prompt messages, it is possible to provide the most optimal response immediately to user inquiries and search criteria. This allows users to efficiently receive support, product suggestions, obtain product information and recipes, and automatically order the necessary products.

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The user launches the customer support application on their smartphone and taps the "Customer Support" button.

[0316] Input: An action performed by the user, such as tapping a button.

[0317] Action: The terminal generates a support request.

[0318] Output: A support request is generated.

[0319] Step 2:

[0320] The device sends a support request to the server.

[0321] Input: The generated support request.

[0322] Operation: Sends data to the server using an HTTP POST request.

[0323] Output: A support request is sent to the server.

[0324] Step 3:

[0325] The server analyzes the support requests it receives.

[0326] Input: Submitted support request.

[0327] Operation: Uses database queries and natural language processing (NLP) techniques to parse the request content.

[0328] Output: Identification of the appropriate support person.

[0329] Step 4:

[0330] The server connects to the most suitable support person.

[0331] Input: Identification information of the support staff member.

[0332] Action: Sends a connection request to the appropriate person.

[0333] Output: Connection to the person in charge is established.

[0334] Step 5:

[0335] Support staff respond to users and resolve issues.

[0336] Input: Connection to the person in charge and the user's inquiry.

[0337] Operation: A representative will respond to the user via chat or voice.

[0338] Output: The user's problem is resolved.

[0339] Step 6:

[0340] The user selects a product suggestion from the menu and enters their desired category and conditions.

[0341] Input: The user enters categories or conditions (e.g., diet, muscle training).

[0342] Operation: The terminal generates data to send input information to the server.

[0343] Output: Data is prepared according to the user's request.

[0344] Step 7:

[0345] The terminal sends the user's request data to the server.

[0346] Input: User's requested data.

[0347] Operation: Sends data to the server via an HTTP POST request.

[0348] Output: The server receives the user's request data.

[0349] Step 8:

[0350] The server analyzes user profiles and shopping history to generate personalized product recommendations.

[0351] Input: User request data and past purchase history.

[0352] Operation: Generates personalized product lists using database queries and machine learning models.

[0353] Output: Personalized product list.

[0354] Step 9:

[0355] The server sends the generated product list to the user's terminal.

[0356] Input: Personalized product list.

[0357] Operation: Sends a product list as an HTTP response.

[0358] Output: The product list is displayed on the user's terminal.

[0359] Step 10:

[0360] The user selects a suggested product and proceeds with the purchase.

[0361] Input: Product selection and purchase operation.

[0362] Operation: The device sends a purchase request to the server.

[0363] Output: Purchase confirmed.

[0364] Step 11:

[0365] The user enters their automatic order settings and saves them to the server.

[0366] Input: Product and cycle settings.

[0367] Operation: The terminal generates configuration information and sends it to the server.

[0368] Output: The server saves the configuration information to the database.

[0369] Step 12:

[0370] The server generates automatic orders at specified intervals based on the saved configuration information.

[0371] Input: Automatic order settings information.

[0372] Operation: The scheduler periodically generates automatic orders.

[0373] Output: An automated order is generated.

[0374] Step 13:

[0375] When an order is generated, the server sends a notification to the terminal.

[0376] Input: The result of the automated order.

[0377] Operation: Generates notification data and sends it to the device via an HTTP request.

[0378] Output: An order notification is displayed on the user's terminal.

[0379] This allows users to efficiently receive customer support, personalized product recommendations, access product information and recipes, and place automated orders. The system achieves a high level of customization and automation by combining cloud-based servers with data analytics technology.

[0380] 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.

[0381] Modes for carrying out the invention

[0382] This invention combines an emotion engine with an integrated system that enables users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system is configured as follows:

[0383] Strengthening customer support

[0384] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. When a user makes an inquiry using text or voice, the emotion engine analyzes the user's emotions and sends that information to the server. Based on the emotion data, the server provides appropriate information to the representative. The representative provides a response that takes the user's emotions into consideration and resolves the problem.

[0385] Specific example:

[0386] When a user asks a question about how to use the refrigerator, the app sends a query to the server. An emotion engine detects dissatisfaction or confusion from the user's voice and sends that data to the server. The server analyzes the emotion data and provides it to the refrigerator's product manager. The manager then provides an answer that resolves the user's confusion based on the appropriate information.

[0387] Personalized product suggestions

[0388] The user selects the "Product Suggestions" menu and enters their desired category and conditions (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. The server also takes the user's sentiment data into consideration when adjusting the product suggestions. The generated list is displayed on the device, and the user can purchase the suggested products.

[0389] Specific example:

[0390] When a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history. An emotion engine senses the user's happiness and satisfaction, and uses that data to increase the frequency of recommendations for specific products. Discount coupons are also provided.

[0391] Product information and recipes

[0392] When a user enters keywords into the app's search bar, the device sends this information to a server. The server performs a keyword search and extracts relevant product information and recipes from its database. An emotion engine analyzes the user's emotions and uses that data to refine the search results. The search results from the server are then displayed on the user's device.

[0393] Specific example:

[0394] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes. An emotion engine senses the user's excitement and expectations, and adjusts the display order of search results based on that data. Ingredients and nutritional information are also displayed, and links to purchase ingredients are provided.

[0395] Automated order processing

[0396] The user selects "Automatic Ordering" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. The server automatically generates orders based on the specified frequency and issues shipping instructions. An emotion engine analyzes the user's emotions and adds promotions or benefits if the user's satisfaction level is low. A notification is sent to the device when the order is processed.

[0397] Specific example:

[0398] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a designated day each month and ships the product. An emotion engine senses the user's purchasing intent and anxieties, and sends promotional emails based on that data. An order confirmation notification is sent to the user's device.

[0399] The above describes the embodiments for carrying out the present invention. This system allows users to efficiently receive customer support, product suggestions, search for product information and recipes, and place automatic orders using their smartphones, and these services are further personalized by the emotion engine.

[0400] The following describes the processing flow.

[0401] Strengthening customer support

[0402] Processing steps:

[0403] Step 1:

[0404] The user taps the app icon on their smartphone's home screen to launch the app.

[0405] Step 2:

[0406] The user taps the "Customer Support" button within the app.

[0407] Step 3:

[0408] The device sends a support request to the server. This request includes the user ID, current screen information, and the content of the inquiry.

[0409] Step 4:

[0410] The server receives the request and parses the query content.

[0411] Step 5:

[0412] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[0413] Step 6:

[0414] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling. This data includes information such as whether the user is irritated, confused, or satisfied.

[0415] Step 7:

[0416] The server connects to the most suitable support representative based on emotional data. The emotional data is also provided to the support representative.

[0417] Step 8:

[0418] Support staff use sentiment data as a reference to respond to users via chat or voice and resolve issues.

[0419] ---

[0420] Personalized product suggestions

[0421] Processing steps:

[0422] Step 1:

[0423] The user taps the "Product Suggestions" menu within the app.

[0424] Step 2:

[0425] Users input the product category and purpose they want suggested (e.g., diet, muscle training) via text or voice.

[0426] Step 3:

[0427] The terminal sends the user's input conditions to the server.

[0428] Step 4:

[0429] The server analyzes the database based on user input, past purchase history, and preference data.

[0430] Step 5:

[0431] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[0432] Step 6:

[0433] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling.

[0434] Step 7:

[0435] The server takes sentiment data into account to generate a personalized product list. This list includes relevant products, promotions, and discount information.

[0436] Step 8:

[0437] The terminal displays the product list received from the server to the user.

[0438] ---

[0439] Product information and recipes

[0440] Processing steps:

[0441] Step 1:

[0442] The user taps the search bar within the app.

[0443] Step 2:

[0444] Users enter keywords related to their interests, such as cooking methods, nutrients, and ingredients.

[0445] Step 3:

[0446] The device sends the keyword to the server.

[0447] Step 4:

[0448] The server performs a keyword search on the database and extracts relevant product information and recipes.

[0449] Step 5:

[0450] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[0451] Step 6:

[0452] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling.

[0453] Step 7:

[0454] The server takes emotional data into account when generating search results. For example, if a user expresses feelings of "excitement" or "anticipation," it will prioritize displaying recipes and product information that match those emotions.

[0455] Step 8:

[0456] The terminal displays the search results received from the server to the user.

[0457] ---

[0458] Automated order processing

[0459] Processing steps:

[0460] Step 1:

[0461] The user selects the "Automatic Ordering" setup screen within the app.

[0462] Step 2:

[0463] The user enters the product, quantity, purchase frequency (e.g., weekly, monthly), and payment method to be purchased.

[0464] Step 3:

[0465] The device sends configuration information to the server.

[0466] Step 4:

[0467] The server saves the automatic order settings to the database and registers the order schedule.

[0468] Step 5:

[0469] The server automatically generates orders based on the purchase cycle and verifies the user's payment information.

[0470] Step 6:

[0471] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[0472] Step 7:

[0473] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling.

[0474] Step 8:

[0475] The server takes emotional data into account and adds promotions or rewards if user satisfaction is low.

[0476] Step 9:

[0477] The server sends shipping instructions to the warehouse system and completes the order in conjunction with the logistics management system.

[0478] Step 10:

[0479] The terminal notifies the user that automatic ordering has been successfully set up, and the user receives a notification when each order is processed.

[0480] ---

[0481] The above details the specific operations at each processing step. This configuration allows users to use their smartphones to try out customer support with an emotion engine, product recommendations, product information and recipe searches, and automated ordering.

[0482] (Example 2)

[0483] 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".

[0484] Traditional customer support systems have limited potential for improving user satisfaction because they provide uniform responses without considering user emotions. Furthermore, personalized product suggestions and information provision fail to reflect individual user emotional states, making it difficult to provide optimal service. In addition, automated order processing also fails to consider user emotions and satisfaction, hindering long-term user engagement.

[0485] 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.

[0486] In this invention, the server includes means for an emotion engine to analyze the user's emotions and transmit that information to the server, means for the server to provide appropriate information to the person in charge based on the emotion data, and means for the person in charge to respond while taking the user's emotions into consideration. This makes it possible to analyze the user's emotions and provide optimal responses, suggestions, and services based on them, thereby improving user satisfaction.

[0487] A "user" is an individual or legal entity that uses the system to receive services.

[0488] An "input device" is a general term for hardware or software that users use to input information or instructions into a system, with examples including smartphones and PCs.

[0489] A "server" is a central processing unit that receives and analyzes requests from users and provides appropriate services.

[0490] "Inquiry" refers to the act of a user reporting a question or problem to customer support.

[0491] A "support staff member" is a specialist who responds to user inquiries and provides solutions.

[0492] An "emotion engine" is a software component that analyzes a user's emotions and utilizes that information within the system.

[0493] "Emotional data" refers to information about the user's emotional state, which is analyzed and generated by the emotion engine.

[0494] A "personalized product list" is a list of optimal products generated based on each user's individual preferences and criteria.

[0495] "Search results" refer to the information that the server extracts and displays based on the keywords entered by the user.

[0496] "Automatic ordering" is a function where the system automatically orders products based on a cycle set by the user.

[0497] This invention is an integrated system for users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices. By incorporating an emotion engine, this system can analyze user emotions and improve service quality. The details of this system are described below.

[0498] Strengthening customer support

[0499] Users access customer support using input devices such as smartphones or computers. When they launch the app and tap the "Customer Support" button, a support request is sent from their device to the server. The server receives and analyzes this request and connects them to the most suitable support representative. When a user makes an inquiry via text or voice, the emotion engine analyzes the user's emotions and sends that information to the server. Based on the emotion data, the server provides appropriate information to the representative, who then responds while considering the user's emotions.

[0500] Specific example: When a user asks a question about how to use a refrigerator, they send a query to the server via a smartphone app. An emotion engine detects dissatisfaction or confusion from the user's voice and sends the data to the server. The server analyzes the emotion data and provides it to the refrigerator product manager, who then provides an answer that resolves the user's confusion based on the appropriate information.

[0501] Personalized product suggestions

[0502] The user selects the "Product Suggestions" menu and enters their desired category and conditions (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. An emotion engine analyzes the user's emotions and adjusts the product suggestions accordingly. The generated list is displayed on the device, and the user can purchase the suggested products.

[0503] Specific example: If a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history. An emotion engine senses the user's happiness and satisfaction, and uses that data to increase the frequency of recommendations for specific products. Discount coupons are also provided.

[0504] Product information and recipes

[0505] When a user enters keywords into the app's search bar, the device sends this information to a server. The server performs a keyword search and extracts relevant product information and recipes from its database. An emotion engine analyzes the user's emotions and uses that data to refine the search results. The search results from the server are then displayed on the user's device.

[0506] Specific example: When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes. An emotion engine senses the user's excitement and expectations and adjusts the display order of search results based on that data. Ingredients and nutritional information are also displayed, and links to purchase ingredients are provided.

[0507] Automated order processing

[0508] The user selects the "Auto Order" function within the app and enters the product and frequency. The device sends this information to the server, which stores the settings and manages the order schedule. The server automatically generates orders based on the specified frequency and issues shipping instructions. An emotion engine analyzes the user's emotions and offers promotions or benefits if satisfaction is low. A notification is sent to the device when the order is processed.

[0509] Specific example: When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a designated day each month and ships the product. An emotion engine senses the user's purchasing intent and anxieties, sends promotional emails based on that data, and sends order confirmation notifications to the device.

[0510] This system allows users to efficiently receive customer support, product recommendations, search for product information and recipes, and place automatic orders using their smartphones. The emotion engine further personalizes these services, enhancing the user experience.

[0511] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0512] Strengthening customer support

[0513] Processing steps

[0514] Step 1:

[0515] The user taps the "Customer Support" button.

[0516] Input: User actions

[0517] Output: Generate a support request

[0518] Action: The user launches the smartphone app and taps the "Customer Support" button. This action causes the system to generate a support request.

[0519] Step 2:

[0520] The device sends a support request to the server.

[0521] Input: Support Request

[0522] Output: Request sent to the server

[0523] Operation: The terminal sends the generated support request to the server via the internet. The request includes the user ID and a summary of the inquiry.

[0524] Step 3:

[0525] The server parses the request.

[0526] Input: Request sent to the server

[0527] Output: Analysis results

[0528] Operation: The server analyzes received requests to determine their priority and content. This analysis is based on the user's past support history and current inquiry content.

[0529] Step 4:

[0530] The server selects the most suitable support person.

[0531] Input: Analysis results, support staff schedule information

[0532] Output: Instructions to connect to the person in charge

[0533] Operation: The server compares the analysis results with the support staff's schedule information to select the most suitable person. This selection takes into account the person's skill set and current workload.

[0534] Step 5:

[0535] The user submits an inquiry.

[0536] Input: User's inquiry (text or voice)

[0537] Output: Generation of query data

[0538] Operation: Users make inquiries via text or voice. In the case of voice inquiries, the system uses natural language processing (NLP) techniques to convert the speech to text.

[0539] Step 6:

[0540] The emotion engine analyzes the user's emotions.

[0541] Input: User inquiry data

[0542] Output: Sentiment data

[0543] Operation: The emotion engine analyzes query data to identify the user's emotional state (e.g., dissatisfaction, confusion, joy, etc.). This uses voice tone and text expression.

[0544] Step 7:

[0545] The server provides emotional data to the person in charge.

[0546] Input: Sentiment data

[0547] Output: Information package for the person in charge

[0548] Operation: The server sends emotional data to the person in charge, allowing them to understand the user's emotional state.

[0549] Step 8:

[0550] The person in charge will respond while taking the user's feelings into consideration.

[0551] Input: Information package for the person in charge

[0552] Output: Response to the user

[0553] Operation: Based on the received emotional data and analysis results, the staff member will provide thoughtful responses that take the user's emotions into consideration. For example, they will provide more helpful and clearer information to users who are feeling dissatisfied.

[0554] Personalized product suggestions

[0555] Processing steps

[0556] Step 1:

[0557] The user selects the "Product Suggestions" menu.

[0558] Input: User actions

[0559] Output: Generate product suggestion request

[0560] Operation: When the user selects the "Product Suggestions" menu within the app, the system generates a product suggestion request.

[0561] Step 2:

[0562] The user enters their desired category and conditions.

[0563] Input: User input information (category, conditions)

[0564] Output: Product proposal conditions

[0565] Operation: The user enters their desired category (e.g., diet, muscle training) and conditions. This information is registered in the system as product suggestion criteria.

[0566] Step 3:

[0567] The device sends information to the server.

[0568] Input: Product proposal conditions

[0569] Output: Product proposal conditions sent to the server

[0570] Operation: The terminal sends product suggestion conditions to the server. The information is communicated using a secure protocol.

[0571] Step 4:

[0572] The server analyzes the database.

[0573] Input: Product suggestion criteria, user history data

[0574] Output: Generation of personalized product lists

[0575] Operation: The server analyzes the database based on product suggestion criteria and the user's past purchase history to select the most suitable product.

[0576] Step 5:

[0577] The emotion engine analyzes the user's emotions and adjusts the product recommendations accordingly.

[0578] Input: Product list, user sentiment data

[0579] Output: Adjusted product list

[0580] Operation: The emotion engine analyzes the user's purchase history and current emotions to adjust the frequency and order of product suggestions in the product list.

[0581] Step 6:

[0582] The server provides the generated list to the terminal.

[0583] Input: Adjusted product list

[0584] Output: List display on the user's terminal

[0585] Operation: The server sends a refined product list to the user's terminal, and the terminal displays the list.

[0586] Step 7:

[0587] The user purchases the suggested product.

[0588] Input: User purchase operation

[0589] Output: Purchase data

[0590] Operation: The user selects the desired product from the suggested product list and proceeds with the purchase. Payment information is also included.

[0591] Product information and recipes

[0592] Processing steps

[0593] Step 1:

[0594] The user enters keywords into the search bar within the app.

[0595] Input: Search keywords

[0596] Output: Generating a search request

[0597] How it works: When the user enters keywords into the search bar within the app, the system generates a search request.

[0598] Step 2:

[0599] The device sends information to the server.

[0600] Input: Search Request

[0601] Output: Search request sent to the server

[0602] Operation: The terminal sends a search request to the server. This process uses an internet connection.

[0603] Step 3:

[0604] The server performs a keyword search.

[0605] Input: Keywords for your search request

[0606] Output: Search Results

[0607] Operation: The server performs a keyword search within the database and extracts relevant product information and recipes.

[0608] Step 4:

[0609] The emotion engine analyzes the user's emotions and adjusts the search results accordingly.

[0610] Input: Search results, user sentiment data

[0611] Output: Adjusted search results

[0612] How it works: The sentiment engine adjusts search results, optimizing the display order and content based on the user's emotions.

[0613] Step 5:

[0614] The server sends the results to the terminal.

[0615] Input: Adjusted search results

[0616] Output: Search results displayed on the user's terminal

[0617] Operation: The server sends the adjusted search results to the terminal, and the terminal displays them.

[0618] Step 6:

[0619] The user views the results.

[0620] Input: Adjusted search results

[0621] Output: User browsing data

[0622] Operation: The user views the displayed search results and clicks on relevant links as needed.

[0623] Automated order processing

[0624] Processing steps

[0625] Step 1:

[0626] The user selects the "automatic order" function.

[0627] Input: User actions

[0628] Output: Generation of automated order requests

[0629] Operation: When the user selects the "Auto Order" function within the app, the system generates an automatic order request.

[0630] Step 2:

[0631] The user enters the product and the cycle.

[0632] Input: Product information, order cycle

[0633] Output: Automatic Order Settings

[0634] Operation: The user enters the products they wish to purchase and their order frequency (daily, weekly, monthly, etc.). This configuration information is saved in the system.

[0635] Step 3:

[0636] The device sends configuration information to the server.

[0637] Input: Automatic Order Settings

[0638] Output: Configuration information sent to the server

[0639] Operation: The terminal sends automatic order setting information to the server.

[0640] Step 4:

[0641] The server stores information and manages order schedules.

[0642] Input: Configuration information

[0643] Output: Saved schedule information

[0644] Operation: The server stores the received configuration information and manages the order schedule.

[0645] Step 5:

[0646] The server automatically generates orders based on the specified interval.

[0647] Input: Schedule information, inventory data

[0648] Output: Automated order data

[0649] Operation: The server automatically generates orders based on a specified cycle and processes them by cross-referencing them with inventory data.

[0650] Step 6:

[0651] The emotion engine analyzes user emotions and provides promotions and benefits accordingly.

[0652] Input: User sentiment data

[0653] Output: Promotional and bonus data

[0654] Operation: The emotion engine analyzes the user's emotions and, if satisfaction is low, generates and provides promotions or rewards.

[0655] Step 7:

[0656] The server sends an automated order notification to the device.

[0657] Input: Auto-generated order data

[0658] Output: Notification to the device

[0659] Operation: The server sends information about the generated order to the terminal and notifies the user.

[0660] This processing flow improves the user experience and enables the provision of efficient and personalized support and services.

[0661] (Application Example 2)

[0662] 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".

[0663] Traditional customer support and product recommendation systems provided uniform responses without considering the user's emotional state, resulting in a lack of sufficient personalization of the user experience. This led to decreased user satisfaction and a high likelihood of customer churn. Furthermore, there was no system in place to sense the anxiety and satisfaction levels of occupants in autonomous vehicles in real time and provide appropriate feedback.

[0664] 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.

[0665] In this invention, the server includes the following means:

[0666] The system includes a sentiment analysis engine for collecting sentiment data from users accessing customer support, a server for analyzing the sentiment data and providing appropriate information to support staff, a system for staff to adjust their responses considering the user's sentiment information, a system for generating personalized product lists based on user input conditions and adjusting suggestions based on sentiment data, and a system for adjusting search results based on sentiment data when users search for product information or recipes. This makes it possible to personalize the user experience and significantly improve user satisfaction. It also enables appropriate feedback in autonomous vehicles that responds to the occupants' emotions.

[0667] An "input device" is an electronic device used by a user to input information and give instructions to a system.

[0668] "Customer support" refers to the activity of providing assistance and solutions to users regarding problems and questions they have about products and services.

[0669] A "server" is a computer system that operates on a network and provides functions such as data processing and storage.

[0670] An "appropriate support representative" is someone who can provide the most suitable response based on the user's inquiry and the support they require.

[0671] An "emotion analysis engine" is software that analyzes a user's emotional state from their voice or text and extracts that data.

[0672] "Emotional data" refers to data that indicates a user's emotional state, extracted by an emotion analysis engine.

[0673] A "personalized product list" is a list of products that has been individually customized based on the user's input criteria and past browsing history.

[0674] "Search results" are a list of information that the server extracts from the database based on the keywords entered by the user.

[0675] This invention combines a sentiment analysis engine with an integrated system that allows users to perform efficient customer support, personalized product recommendations, provide product information and recipes, and process automated orders via an input device. The following describes specific embodiments of this system.

[0676] The system primarily consists of a smartphone, a server, and an emotion analysis engine. Users interact with the system using their smartphone as an input device. The server handles all data processing and logic, while the emotion analysis engine analyzes the user's emotional data.

[0677] Strengthening customer support

[0678] The user launches the smartphone app and taps the "Customer Support" button. This action sends a support request to the server. The server receives and analyzes the request and connects the user to the appropriate support representative. Furthermore, an emotion analysis engine analyzes the user's emotions from their voice and text and sends that information to the server. Based on the emotion data, the server provides appropriate information to the representative. The representative provides a response that takes the user's emotions into consideration and resolves the problem.

[0679] Specific example: When a user asks a question about how to use a refrigerator, the app sends a query to the server. An emotion analysis engine detects dissatisfaction or confusion from the user's voice and sends that data to the server. The server analyzes the emotion data and provides it to the refrigerator product manager. The manager then provides an answer that resolves the user's confusion based on the appropriate information.

[0680] Personalized product suggestions

[0681] The user selects the "Product Suggestions" menu and enters their desired categories and criteria. This information is sent from the device to the server, which analyzes the database to generate a personalized product list. The sentiment analysis engine also takes the user's sentiment data into consideration when adjusting the product suggestions. The generated list is displayed on the device, and the user can purchase the suggested products.

[0682] Specific example: If a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history. An emotion analysis engine senses the user's happiness and satisfaction, and uses that data to increase the frequency of recommendations for specific products. Discount coupons are also provided.

[0683] Product information and recipes

[0684] When a user enters keywords into the app's search bar, the device sends this information to a server. The server performs a keyword search and extracts relevant product information and recipes from its database. An emotion analysis engine analyzes the user's emotions and uses that data to refine the search results. The search results from the server are then displayed on the user's device.

[0685] Specific example: When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes. An emotion analysis engine senses the user's excitement and expectations, and adjusts the display order of search results based on that data. Ingredients and nutritional information are also displayed, and links to purchase ingredients are provided.

[0686] Emotional response in autonomous vehicles

[0687] The system for autonomous vehicles to analyze the emotional state of the occupants in real time and take appropriate action is similarly configured. An emotion analysis engine extracts emotional data from the occupants' voice and sends it to a server. The server analyzes the emotional data, generates optimal feedback, and provides it to the occupants.

[0688] Specific example: If a passenger says, "This road feels a little dangerous," the emotion analysis engine detects the anxiety and sends that data to the server. The server analyzes the current situation of the autonomous vehicle (e.g., road conditions and speed) and generates appropriate feedback, such as, "The current road conditions are safe. We will slow down a little and proceed cautiously," which is then provided to the passenger.

[0689] Example of a prompt:

[0690] The user stated, "This road feels a little dangerous." Analyze this anxiety as emotional data and, considering the current situation of the autonomous vehicle (current speed, surrounding safety, route), generate a response that will reassure the user. Example response: "The current road conditions are safe. We will slow down a little and proceed cautiously."

[0691] The implementation of this system will allow users to enjoy a more comfortable and personalized experience. Furthermore, it can improve safety and passenger satisfaction in autonomous vehicles.

[0692] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0693] Step 1:

[0694] Users access customer support via an input device (smartphone).

[0695] A support request is generated when a user launches the app and taps the "Customer Support" button. This request includes the user's voice and text data. Input consists of the user's voice instructions and text input, and output is a request sent to the server.

[0696] Step 2:

[0697] The device sends a support request to the server.

[0698] The terminal sends the generated request to the server over the network. In this process, the generated request data is the input, and the request is forwarded to the server as the output.

[0699] Step 3:

[0700] The server receives the user's inquiry and connects them to the appropriate support representative.

[0701] The server analyzes the received request and identifies the nature of the problem. Based on the analyzed problem, it selects the most suitable support person and connects the user to them. The input is the request data, and the output is instructions for connecting to the support person.

[0702] Step 4:

[0703] The emotion analysis engine extracts emotion data from the user's voice and text.

[0704] The server uses an emotion analysis engine to analyze the user's voice and text to identify their emotional state. Input is user voice and text data, and output is generated emotion data.

[0705] Step 5:

[0706] Emotional data is sent to a server, which then analyzes the data.

[0707] The extracted emotion data is sent to a server, which then analyzes the data. The input is emotion data, and the output is the analysis results.

[0708] Step 6:

[0709] The server provides appropriate information to the person in charge based on emotional data.

[0710] Based on the analyzed emotional data, the server provides the necessary information and advice to the person in charge. The input is the results of the emotional data analysis, and the output is the provision of information to the person in charge.

[0711] Step 7:

[0712] The person in charge adjusts their response, taking into account the user's emotional information.

[0713] Support staff provide appropriate responses to users based on the information and sentiment data provided. Inputs include information and sentiment data provided by the server, and output is the response to the user.

[0714] Step 8:

[0715] The user selects the "Product Suggestion" menu and enters their desired category and conditions.

[0716] The user selects the "Product Suggestions" menu within the app and enters specific conditions such as diet or muscle training. The input consists of the user's specified conditions, and the output is a request that is sent to the server.

[0717] Step 9:

[0718] The device sends this information to the server, which then analyzes the database to generate a personalized product list.

[0719] The terminal sends user-specified conditions to the server, which then analyzes the database based on those conditions. The input is the specified conditions, and the output is a product list.

[0720] Step 10:

[0721] The emotion analysis engine takes user emotion data into account and adjusts the product recommendations accordingly.

[0722] The sentiment analysis engine considers the user's sentiment data when analyzing the generated product list and adjusts the suggested products accordingly. The input consists of a product list and sentiment data, and the output is an adjusted product list.

[0723] Step 11:

[0724] The generated list is displayed on the device, and the user purchases the suggested items.

[0725] The adjusted product list is displayed on the terminal, allowing the user to view and purchase the suggested products. The input is the adjusted product list, and the output is the display to the user and the purchase process.

[0726] Step 12:

[0727] The user enters keywords into the search bar within the app.

[0728] Users enter keywords into the search bar to search for product information or recipes. The input is the search keywords, and the output is a search request.

[0729] Step 13:

[0730] The device sends this information to the server, which then performs a keyword search.

[0731] The terminal sends keyword information to the server, which then searches the database based on those keywords. The input is keywords, and the output is search results.

[0732] Step 14:

[0733] The emotion analysis engine analyzes the user's emotions and adjusts the search results accordingly.

[0734] The sentiment analysis engine considers the user's sentiment data and adjusts the search results accordingly. The input consists of the search results and sentiment data, and the output is the adjusted search results.

[0735] Step 15:

[0736] The search results from the server are displayed on the user's terminal.

[0737] The adjusted search results are sent to the device and displayed to the user. The adjusted search results are the input, and the output is what is displayed to the user.

[0738] Step 16:

[0739] While a user is riding in an "autonomous vehicle," an emotion analysis engine extracts emotional data from speech and text.

[0740] The emotion analysis engine analyzes the crew's voices and extracts emotion data. Voice data is the input, and emotion data is generated as the output.

[0741] Step 17:

[0742] The server analyzes emotional data and the vehicle's current status data to generate appropriate feedback.

[0743] The server analyzes emotional data and the vehicle's current status (speed, road conditions, etc.) to generate feedback. Emotional data and vehicle status data are inputs, and feedback is generated as output.

[0744] Step 18:

[0745] Provide the generated feedback to the crew.

[0746] The generated feedback is provided to the crew via a terminal. The generated feedback is the input, and the output is provided to the crew.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] [Second Embodiment]

[0751] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0752] 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.

[0753] 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).

[0754] 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.

[0755] 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.

[0756] 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).

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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".

[0763] Modes for carrying out the invention

[0764] This invention relates to an integrated system for enabling users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system is configured as follows:

[0765] Strengthening customer support

[0766] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. The representative then assists the user via chat or voice and resolves the issue.

[0767] Specific example:

[0768] When a user asks a question about how to use the refrigerator, the app sends a query to the server. The server analyzes the question and connects the user to the refrigerator's product representative. The representative then provides the appropriate answer.

[0769] Personalized product suggestions

[0770] The user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. The generated list is displayed on the device, and the user can purchase the suggested products.

[0771] Specific example:

[0772] If a user is looking for diet foods, the server will recommend low-calorie foods and diet supplements based on their past purchase history, and will also provide discount coupons.

[0773] Product information and recipes

[0774] When a user enters keywords into the app's search bar, the device sends this information to the server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user.

[0775] Specific example:

[0776] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase the ingredients are also provided.

[0777] Automated order processing

[0778] The user selects "Automatic Order" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[0779] Specific example:

[0780] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a specified day each month and ships the product. An order confirmation notification is sent to the user's device.

[0781] The above describes the embodiments for carrying out the present invention. This system allows users to efficiently and effectively receive customer support, product suggestions, search for product information and recipes, and place automatic orders using their smartphones.

[0782] The following describes the processing flow.

[0783] Strengthening customer support

[0784] Processing steps:

[0785] Step 1:

[0786] The user taps the app icon on their smartphone's home screen to launch the app.

[0787] Step 2:

[0788] The user taps the "Customer Support" button within the app.

[0789] Step 3:

[0790] The device sends a support request to the server. This request includes the user ID, current screen information, and the content of the inquiry.

[0791] Step 4:

[0792] The server receives the request and analyzes the inquiry. If necessary, it forwards it to an automated response system or the appropriate person.

[0793] Step 5:

[0794] Based on the analysis results, the server connects to a chatbot or a human representative to generate a response.

[0795] Step 6:

[0796] The terminal displays the response information received from the server to the user. Support is provided in chat or voice format.

[0797] ---

[0798] Personalized product suggestions

[0799] Processing steps:

[0800] Step 1:

[0801] The user taps the "Product Suggestions" menu within the app.

[0802] Step 2:

[0803] Users input the product category and purpose they want suggested (e.g., diet, muscle training) via text or voice.

[0804] Step 3:

[0805] The terminal sends the user's input conditions to the server.

[0806] Step 4:

[0807] The server analyzes the database based on user input, past purchase history, and preference data.

[0808] Step 5:

[0809] The server generates a personalized product list based on the analysis results. This list includes relevant products, promotions, and discount information.

[0810] Step 6:

[0811] The terminal displays the product list received from the server to the user.

[0812] ---

[0813] Product information and recipes

[0814] Processing steps:

[0815] Step 1:

[0816] The user taps the search bar within the app.

[0817] Step 2:

[0818] Users enter keywords related to their interests, such as cooking methods, nutrients, and ingredients.

[0819] Step 3:

[0820] The device sends the keyword to the server.

[0821] Step 4:

[0822] The server performs a keyword search on the database and extracts relevant product information and recipes.

[0823] Step 5:

[0824] The server generates search results and formats them into a format for providing to the user.

[0825] Step 6:

[0826] The terminal displays the search results received from the server to the user.

[0827] ---

[0828] Automated order processing

[0829] Processing steps:

[0830] Step 1:

[0831] The user selects the "Automatic Ordering" setup screen within the app.

[0832] Step 2:

[0833] The user enters the product, quantity, purchase frequency (e.g., weekly, monthly), and payment method to be purchased.

[0834] Step 3:

[0835] The device sends configuration information to the server.

[0836] Step 4:

[0837] The server saves the automatic order settings to the database and registers the order schedule.

[0838] Step 5:

[0839] The server automatically generates orders based on the purchase cycle and verifies the user's payment information.

[0840] Step 6:

[0841] The server sends shipping instructions to the warehouse system and completes the order in conjunction with the logistics management system.

[0842] Step 7:

[0843] The terminal notifies the user that automatic ordering has been successfully set up, and the user receives a notification when each order is processed.

[0844] ---

[0845] The above details the specific operations at each processing step. This configuration allows users to easily access customer support, product suggestions, product information and recipes, and place automatic orders using their smartphones.

[0846] (Example 1)

[0847] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0848] Traditional customer support systems often failed to properly analyze user inquiries and quickly connect users to the appropriate support staff. Furthermore, personalized product recommendations frequently did not fully utilize past purchase history, making it difficult to generate optimal product lists for users. Additionally, product information and recipe search results often did not match user expectations, resulting in a poor user experience.

[0849] 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.

[0850] In this invention, the server includes means for receiving inquiries from users and analyzing the content of the inquiries using a natural language processing algorithm, means for selecting the most suitable support person based on the analysis results and connecting to the support person, and means for the support person to respond to the user and resolve the problem. This makes it possible to quickly analyze the content of user inquiries and connect to the most suitable support person.

[0851] Furthermore, the server includes means for generating individually personalized product lists using a generative AI model, taking into account past purchase history based on user input conditions; means for displaying suggestions from the server on the user's terminal; and means for the user to purchase the suggested products. This provides the user with the most suitable product suggestions and facilitates purchasing behavior.

[0852] Furthermore, the server includes means for performing keyword searches and extracting relevant information from the database, as well as means for displaying the extraction results on the user's terminal. This ensures that the information and recipes the user is looking for are provided quickly and accurately, improving the user experience.

[0853] A "user" is an individual or legal entity that uses the system to perform tasks such as customer support, product recommendations, and product information searches.

[0854] An "input device" is a device used by a user to input information, and examples include smartphones, tablets, and personal computers.

[0855] "Customer support" is a service in which support staff respond to inquiries and problems from users and attempt to resolve them.

[0856] A "server" is a device or system that processes information on a network, receives requests from users, and performs appropriate processing in response to those requests.

[0857] A "natural language processing algorithm" is a set of computational methods and techniques that enable computers to understand and analyze human language.

[0858] A "support staff member" is a staff member or agent who handles inquiries from users in customer support.

[0859] A "personalized product list" is a list of product suggestions that is generated individually based on the user's input criteria and past purchase history.

[0860] A "generative AI model" is a computational model that uses artificial intelligence to analyze data and generate new information or suggestions.

[0861] A "user terminal" is a device that a user directly operates to access the system, and includes smartphones, tablets, and personal computers.

[0862] "Keyword search" is the process by which a user enters a specific word or phrase and searches a database for information related to that word.

[0863] A "database" is a collection of data that is organized and stored so that it can be quickly and efficiently searched and retrieved when needed.

[0864] This invention relates to an integrated system for users to efficiently provide customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices. The system is designed to be easily operated by users using input devices such as smartphones, tablets, and personal computers.

[0865] Strengthening customer support

[0866] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server analyzes the received request using a natural language processing algorithm and selects the most suitable support representative. Once a representative is selected based on the analysis results, the server forwards the request to that representative, and the user can receive support via chat or voice.

[0867] Specific example:

[0868] When a user asks a question about how to use the refrigerator, the app sends a query to the server. The server analyzes the question and connects the user to the refrigerator's product representative. The representative then provides the appropriate answer.

[0869] Prompt example:

[0870] "I don't know how to use the refrigerator. How do I operate it?"

[0871] Personalized product suggestions

[0872] When a user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training), the device sends this information to the server. The server uses a generative AI model to analyze the database and generate a personalized product list. The generated list is displayed on the device, and the user can purchase the suggested products.

[0873] Specific example:

[0874] If a user is looking for diet foods, the server will recommend low-calorie foods and diet supplements based on their past purchase history, and will also provide discount coupons.

[0875] Prompt example:

[0876] "Based on my past purchase history, please recommend some diet foods."

[0877] Product information and recipes

[0878] When a user enters keywords into the app's search bar, the device sends this information to the server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user.

[0879] Specific example:

[0880] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase the ingredients are also provided.

[0881] Prompt example:

[0882] "Can you share a low-calorie smoothie recipe?"

[0883] Automated order processing

[0884] When a user selects "Automatic Ordering" within the app and enters the product and frequency, the device sends the settings information to the server. The server stores this information and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[0885] Specific example:

[0886] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a specified day each month and ships the product. An order confirmation notification is sent to the user's device.

[0887] This system allows users to efficiently and effectively receive customer support, product recommendations, search for product information and recipes, and place automatic orders using their smartphones.

[0888] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0889] Strengthening customer support

[0890] Step 1:

[0891] The user launches the app on their smartphone and taps the "Customer Support" button.

[0892] Operation: The user taps the app to launch it and selects the "Customer Support" button from the home screen. This initiates a customer support request.

[0893] Input: User action (button tap).

[0894] Output: The terminal generates support request data.

[0895] Step 2:

[0896] The terminal sends the generated support request data to the server.

[0897] Operation: The terminal encodes the data containing the support request and sends it to the server.

[0898] Input: Support request data.

[0899] Output: The request data reaches the server.

[0900] Step 3:

[0901] The server receives the request and parses it using a natural language processing algorithm.

[0902] Operation: The server parses the received request and applies NLP algorithms to understand the user's inquiry.

[0903] Input: Request data.

[0904] Output: Understanding the query content from the analysis results.

[0905] Step 4:

[0906] The server selects the most suitable support person based on the analysis results.

[0907] Operation: The server searches the database for the appropriate person in charge and selects the most suitable person.

[0908] Input: Analysis results.

[0909] Output: Information on the selected support staff member.

[0910] Step 5:

[0911] The server forwards the request to the person in charge and establishes the connection.

[0912] Operation: The server forwards the request to the designated contact person and establishes a chat or voice call connection.

[0913] Input: Person in charge information, request data.

[0914] Output: A connection notification is sent to the user's terminal.

[0915] Personalized product suggestions

[0916] Step 1:

[0917] The user selects the "Product Suggestions" menu in the app and enters their desired category and conditions.

[0918] Action: The user taps the "Product Suggestion" menu and enters their request in the form for entering conditions.

[0919] Input: Enter the user's category and conditions.

[0920] Output: Conditional data is generated.

[0921] Step 2:

[0922] The device sends this conditional data to the server.

[0923] Operation: The terminal encodes the input condition data and sends it to the server.

[0924] Input: Conditional data.

[0925] Output: The condition data reaches the server.

[0926] Step 3:

[0927] The server uses an AI model to analyze the database and generate individually personalized product lists.

[0928] Operation: The server searches the database and uses a generative AI model to generate a list of products that match the user's criteria.

[0929] Input: Conditional data, past purchase history.

[0930] Output: Personalized product list.

[0931] Step 4:

[0932] The server sends the generated product list to the terminal.

[0933] Operation: The server encodes the product list and sends it to the user's terminal.

[0934] Input: Product list.

[0935] Output: The product list arrives at the terminal.

[0936] Step 5:

[0937] The terminal displays the product list it received to the user.

[0938] Operation: The terminal decodes the product list and displays it in the user interface.

[0939] Input: Product list.

[0940] Output: The user is shown a list of products.

[0941] Product information and recipes

[0942] Step 1:

[0943] The user enters keywords into the search bar within the app.

[0944] Operation: The user taps the app's search bar and enters keywords for the information they are looking for.

[0945] Input: User keyword input.

[0946] Output: Keyword data is generated.

[0947] Step 2:

[0948] The device sends this keyword data to the server.

[0949] Operation: The terminal encodes the keyword data and sends it to the server.

[0950] Input: Keyword data.

[0951] Output: Keyword data reaches the server.

[0952] Step 3:

[0953] The server performs a keyword search and extracts relevant information from the database.

[0954] Operation: The server searches the database based on keywords and extracts relevant product information and recipes.

[0955] Input: Keyword data.

[0956] Output: Search results data.

[0957] Step 4:

[0958] The server sends the extracted search results to the terminal.

[0959] Operation: The server encodes the search results and sends them to the user's device.

[0960] Input: Search results data.

[0961] Output: Search results arrive at the terminal.

[0962] Step 5:

[0963] The search results received by the device are displayed to the user.

[0964] Operation: The device decodes the search results and displays them in the user interface.

[0965] Input: Search results data.

[0966] Output: The user is shown the search results.

[0967] Automated order processing

[0968] Step 1:

[0969] The user selects "Automatic Order" within the app and enters the product and delivery frequency.

[0970] Operation: The user taps the "Automatic Order" menu and enters the desired products and their frequency into the form.

[0971] Input: Enter the product and cycle.

[0972] Output: Automatic order setting data is generated.

[0973] Step 2:

[0974] The device sends this configuration data to the server.

[0975] Operation: The terminal encodes the automatic order setting data and sends it to the server.

[0976] Input: Automatic order setting data.

[0977] Output: Configuration data reaches the server.

[0978] Step 3:

[0979] The server stores configuration data and manages order schedules.

[0980] Operation: The server saves the received configuration data to the database and creates entries for managing order schedules.

[0981] Input: Automatic order setting data.

[0982] Output: Saved configuration data, schedule entries.

[0983] Step 4:

[0984] The server automatically generates orders based on the specified cycle.

[0985] Operation: The server automatically generates order data based on a cycle and prepares the data for order processing.

[0986] Input: Schedule entry.

[0987] Output: Order data.

[0988] Step 5:

[0989] After the server generates the order, it issues a shipping instruction.

[0990] Operation: The server uses the generated order data to create shipping instructions and sends them to the logistics system.

[0991] Input: Order data.

[0992] Output: Shipping instructions.

[0993] Step 6:

[0994] Once the order is processed, the server sends an order confirmation notification to the terminal.

[0995] Operation: After issuing a shipping instruction, the server encodes an order confirmation notification and sends it to the user's terminal.

[0996] Input: Shipping instructions.

[0997] Output: An order confirmation notification is sent to the terminal.

[0998] (Application Example 1)

[0999] 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."

[1000] Traditional customer support systems and online shopping sites struggle to efficiently provide personalized support, product recommendations, product information, and automated order management to users. In particular, there is a demand for automated and user-optimized product recommendations and automated order processing, but achieving this presents numerous technical challenges.

[1001] 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.

[1002] In this invention, the server includes means for the user to access customer support via an input device, means for the server to receive inquiries from the user and connect to the appropriate support person, means for the support person to respond to the user and resolve the problem, means for the user to input configuration information into a terminal and send the configuration information to the server, means for the server to generate an automated order based on the configuration information and manage the order, and means for sending a notification to the terminal after the automated order has been processed. This enables the user to efficiently receive customer support, receive personalized product suggestions, search for product information and recipes, and place automated orders.

[1003] An "input device" is hardware used by users to input information, and includes smartphones and tablets.

[1004] "Customer support" refers to assistance services provided to users to help them resolve questions and problems related to products and services.

[1005] A "server" is a computer system that provides data and services to multiple clients over a network.

[1006] An "inquiry" refers to a question or request made by a user seeking support.

[1007] A "support staff member" is a staff member assigned to provide customer support.

[1008] "Setting information" refers to the information that users enter when using features such as automatic ordering, and includes things like product names and order cycles.

[1009] "Automatic ordering" is a system where the server automatically orders products based on conditions specified by the user.

[1010] A "notification" is a means of communication sent from a server to a user, and includes order confirmations and update information.

[1011] "Personalized product recommendations" is a system that individually suggests the most suitable products based on the user's history and preferences.

[1012] A "profile" is a collection of data that records a user's purchase history and interests.

[1013] "Shopping history" is a record of products that a user has purchased in the past.

[1014] A "discount coupon" is a voucher offered to users to purchase specific products at a lower price.

[1015] "Keyword search" is a method of searching a database based on keywords entered by the user and extracting relevant information.

[1016] "Product information" refers to detailed information about a specific product.

[1017] A "recipe" is information about the steps and ingredients needed to make a specific dish.

[1018] A "link" is a URL that allows a user to access another webpage or piece of information by clicking on it.

[1019] This invention is an integrated system for providing efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system consists of the following elements:

[1020] Strengthening customer support

[1021] The user launches the customer support application on their smartphone and taps the "Customer Support" button. The device then sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. This support representative responds to the user via chat or voice and resolves the issue.

[1022] Specific example:

[1023] When a user asks a question about how to use their refrigerator, the app sends a query to the server. The server analyzes the question, connects to the refrigerator's product representative, and the representative provides the appropriate answer. For example: "I bought a refrigerator. I don't know how to use it, so please tell me how."

[1024] Personalized product suggestions

[1025] The user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database to generate a personalized product list. This generated list is displayed on the device, and the user can purchase the suggested products. The server uses the user profile and shopping history to suggest the most suitable products and also provides discount coupons.

[1026] Specific example:

[1027] When a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history, and also provides discount coupons. "I'm looking for diet foods. Can you recommend some low-calorie options?"

[1028] Product information and recipes

[1029] When a user enters keywords into the app's search bar, their device sends this information to a server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user. The search results include relevant product information and recipes, as well as links to purchase ingredients.

[1030] Specific example:

[1031] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase ingredients are also displayed. "Please share a low-calorie smoothie recipe."

[1032] Automated order processing

[1033] The user selects "Automatic Order" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[1034] Specific example:

[1035] When a user sets up a monthly subscription for protein powder, the server automatically generates an order on a specified day each month and ships the product. A notification confirming the order is sent to the user's device. The message reads: "Please set up automatic monthly orders for protein powder."

[1036] This system will be implemented using cloud-based servers such as AWS and Google Cloud Platform. Furthermore, web frameworks like Flask and Django will be used to build REST APIs and process data in response to user requests. MySQL and PostgreSQL will be used as the databases.

[1037] By utilizing generative AI models and prompt messages, it is possible to provide the most optimal response immediately to user inquiries and search criteria. This allows users to efficiently receive support, product suggestions, obtain product information and recipes, and automatically order the necessary products.

[1038] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1039] Step 1:

[1040] The user launches the customer support application on their smartphone and taps the "Customer Support" button.

[1041] Input: An action performed by the user, such as tapping a button.

[1042] Action: The terminal generates a support request.

[1043] Output: A support request is generated.

[1044] Step 2:

[1045] The device sends a support request to the server.

[1046] Input: The generated support request.

[1047] Operation: Sends data to the server using an HTTP POST request.

[1048] Output: A support request is sent to the server.

[1049] Step 3:

[1050] The server analyzes the support requests it receives.

[1051] Input: Submitted support request.

[1052] Operation: Uses database queries and natural language processing (NLP) techniques to parse the request content.

[1053] Output: Identification of the appropriate support person.

[1054] Step 4:

[1055] The server connects to the most suitable support person.

[1056] Input: Identification information of the support staff member.

[1057] Action: Sends a connection request to the appropriate person.

[1058] Output: Connection to the person in charge is established.

[1059] Step 5:

[1060] Support staff respond to users and resolve issues.

[1061] Input: Connection to the person in charge and the user's inquiry.

[1062] Operation: A representative will respond to the user via chat or voice.

[1063] Output: The user's problem is resolved.

[1064] Step 6:

[1065] The user selects a product suggestion from the menu and enters their desired category and conditions.

[1066] Input: The user enters categories or conditions (e.g., diet, muscle training).

[1067] Operation: The terminal generates data to send input information to the server.

[1068] Output: Data is prepared according to the user's request.

[1069] Step 7:

[1070] The terminal sends the user's request data to the server.

[1071] Input: User's requested data.

[1072] Operation: Sends data to the server via an HTTP POST request.

[1073] Output: The server receives the user's request data.

[1074] Step 8:

[1075] The server analyzes user profiles and shopping history to generate personalized product recommendations.

[1076] Input: User request data and past purchase history.

[1077] Operation: Generates personalized product lists using database queries and machine learning models.

[1078] Output: Personalized product list.

[1079] Step 9:

[1080] The server sends the generated product list to the user's terminal.

[1081] Input: Personalized product list.

[1082] Operation: Sends a product list as an HTTP response.

[1083] Output: The product list is displayed on the user's terminal.

[1084] Step 10:

[1085] The user selects a suggested product and proceeds with the purchase.

[1086] Input: Product selection and purchase operation.

[1087] Operation: The device sends a purchase request to the server.

[1088] Output: Purchase confirmed.

[1089] Step 11:

[1090] The user enters their automatic order settings and saves them to the server.

[1091] Input: Product and cycle settings.

[1092] Operation: The terminal generates configuration information and sends it to the server.

[1093] Output: The server saves the configuration information to the database.

[1094] Step 12:

[1095] The server generates automatic orders at specified intervals based on the saved configuration information.

[1096] Input: Automatic order settings information.

[1097] Operation: The scheduler periodically generates automatic orders.

[1098] Output: An automated order is generated.

[1099] Step 13:

[1100] When an order is generated, the server sends a notification to the terminal.

[1101] Input: The result of the automated order.

[1102] Operation: Generates notification data and sends it to the device via an HTTP request.

[1103] Output: An order notification is displayed on the user's terminal.

[1104] This allows users to efficiently receive customer support, personalized product recommendations, access product information and recipes, and place automated orders. The system achieves a high level of customization and automation by combining cloud-based servers with data analytics technology.

[1105] 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.

[1106] Modes for carrying out the invention

[1107] This invention combines an emotion engine with an integrated system that enables users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system is configured as follows:

[1108] Strengthening customer support

[1109] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. When a user makes an inquiry using text or voice, the emotion engine analyzes the user's emotions and sends that information to the server. Based on the emotion data, the server provides appropriate information to the representative. The representative provides a response that takes the user's emotions into consideration and resolves the problem.

[1110] Specific example:

[1111] When a user asks a question about how to use the refrigerator, the app sends a query to the server. An emotion engine detects dissatisfaction or confusion from the user's voice and sends that data to the server. The server analyzes the emotion data and provides it to the refrigerator's product manager. The manager then provides an answer that resolves the user's confusion based on the appropriate information.

[1112] Personalized product suggestions

[1113] The user selects the "Product Suggestions" menu and enters their desired category and conditions (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. The server also takes the user's sentiment data into consideration when adjusting the product suggestions. The generated list is displayed on the device, and the user can purchase the suggested products.

[1114] Specific example:

[1115] When a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history. An emotion engine senses the user's happiness and satisfaction, and uses that data to increase the frequency of recommendations for specific products. Discount coupons are also provided.

[1116] Product information and recipes

[1117] When a user enters keywords into the app's search bar, the device sends this information to a server. The server performs a keyword search and extracts relevant product information and recipes from its database. An emotion engine analyzes the user's emotions and uses that data to refine the search results. The search results from the server are then displayed on the user's device.

[1118] Specific example:

[1119] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes. An emotion engine senses the user's excitement and expectations, and adjusts the display order of search results based on that data. Ingredients and nutritional information are also displayed, and links to purchase ingredients are provided.

[1120] Automated order processing

[1121] The user selects "Automatic Ordering" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. The server automatically generates orders based on the specified frequency and issues shipping instructions. An emotion engine analyzes the user's emotions and adds promotions or benefits if the user's satisfaction level is low. A notification is sent to the device when the order is processed.

[1122] Specific example:

[1123] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a designated day each month and ships the product. An emotion engine senses the user's purchasing intent and anxieties, and sends promotional emails based on that data. An order confirmation notification is sent to the user's device.

[1124] The above describes the embodiments for carrying out the present invention. This system allows users to efficiently receive customer support, product suggestions, search for product information and recipes, and place automatic orders using their smartphones, and these services are further personalized by the emotion engine.

[1125] The following describes the processing flow.

[1126] Strengthening customer support

[1127] Processing steps:

[1128] Step 1:

[1129] The user taps the app icon on their smartphone's home screen to launch the app.

[1130] Step 2:

[1131] The user taps the "Customer Support" button within the app.

[1132] Step 3:

[1133] The device sends a support request to the server. This request includes the user ID, current screen information, and the content of the inquiry.

[1134] Step 4:

[1135] The server receives the request and parses the query content.

[1136] Step 5:

[1137] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[1138] Step 6:

[1139] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling. This data includes information such as whether the user is irritated, confused, or satisfied.

[1140] Step 7:

[1141] The server connects to the most suitable support representative based on emotional data. The emotional data is also provided to the support representative.

[1142] Step 8:

[1143] Support staff use sentiment data as a reference to respond to users via chat or voice and resolve issues.

[1144] ---

[1145] Personalized product suggestions

[1146] Processing steps:

[1147] Step 1:

[1148] The user taps the "Product Suggestions" menu within the app.

[1149] Step 2:

[1150] Users input the product category and purpose they want suggested (e.g., diet, muscle training) via text or voice.

[1151] Step 3:

[1152] The terminal sends the user's input conditions to the server.

[1153] Step 4:

[1154] The server analyzes the database based on user input, past purchase history, and preference data.

[1155] Step 5:

[1156] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[1157] Step 6:

[1158] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling.

[1159] Step 7:

[1160] The server takes sentiment data into account to generate a personalized product list. This list includes relevant products, promotions, and discount information.

[1161] Step 8:

[1162] The terminal displays the product list received from the server to the user.

[1163] ---

[1164] Product information and recipes

[1165] Processing steps:

[1166] Step 1:

[1167] The user taps the search bar within the app.

[1168] Step 2:

[1169] Users enter keywords related to their interests, such as cooking methods, nutrients, and ingredients.

[1170] Step 3:

[1171] The device sends the keyword to the server.

[1172] Step 4:

[1173] The server performs a keyword search on the database and extracts relevant product information and recipes.

[1174] Step 5:

[1175] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[1176] Step 6:

[1177] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling.

[1178] Step 7:

[1179] The server takes emotional data into account when generating search results. For example, if a user expresses feelings of "excitement" or "anticipation," it will prioritize displaying recipes and product information that match those emotions.

[1180] Step 8:

[1181] The terminal displays the search results received from the server to the user.

[1182] ---

[1183] Automated order processing

[1184] Processing steps:

[1185] Step 1:

[1186] The user selects the "Automatic Ordering" setup screen within the app.

[1187] Step 2:

[1188] The user enters the product, quantity, purchase frequency (e.g., weekly, monthly), and payment method to be purchased.

[1189] Step 3:

[1190] The device sends configuration information to the server.

[1191] Step 4:

[1192] The server saves the automatic order settings to the database and registers the order schedule.

[1193] Step 5:

[1194] The server automatically generates orders based on the purchase cycle and verifies the user's payment information.

[1195] Step 6:

[1196] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[1197] Step 7:

[1198] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling.

[1199] Step 8:

[1200] The server takes emotional data into account and adds promotions or rewards if user satisfaction is low.

[1201] Step 9:

[1202] The server sends shipping instructions to the warehouse system and completes the order in conjunction with the logistics management system.

[1203] Step 10:

[1204] The terminal notifies the user that automatic ordering has been successfully set up, and the user receives a notification when each order is processed.

[1205] ---

[1206] The above details the specific operations at each processing step. This configuration allows users to use their smartphones to try out customer support with an emotion engine, product recommendations, product information and recipe searches, and automated ordering.

[1207] (Example 2)

[1208] 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".

[1209] Traditional customer support systems have limited potential for improving user satisfaction because they provide uniform responses without considering user emotions. Furthermore, personalized product suggestions and information provision fail to reflect individual user emotional states, making it difficult to provide optimal service. In addition, automated order processing also fails to consider user emotions and satisfaction, hindering long-term user engagement.

[1210] 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.

[1211] In this invention, the server includes means for an emotion engine to analyze the user's emotions and transmit that information to the server, means for the server to provide appropriate information to the person in charge based on the emotion data, and means for the person in charge to respond while taking the user's emotions into consideration. This makes it possible to analyze the user's emotions and provide optimal responses, suggestions, and services based on them, thereby improving user satisfaction.

[1212] A "user" is an individual or legal entity that uses the system to receive services.

[1213] An "input device" is a general term for hardware or software that users use to input information or instructions into a system, with examples including smartphones and PCs.

[1214] A "server" is a central processing unit that receives and analyzes requests from users and provides appropriate services.

[1215] "Inquiry" refers to the act of a user reporting a question or problem to customer support.

[1216] A "support staff member" is a specialist who responds to user inquiries and provides solutions.

[1217] An "emotion engine" is a software component that analyzes a user's emotions and utilizes that information within the system.

[1218] "Emotional data" refers to information about the user's emotional state, which is analyzed and generated by the emotion engine.

[1219] A "personalized product list" is a list of optimal products generated based on each user's individual preferences and criteria.

[1220] "Search results" refer to the information that the server extracts and displays based on the keywords entered by the user.

[1221] "Automatic ordering" is a function where the system automatically orders products based on a cycle set by the user.

[1222] This invention is an integrated system for users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices. By incorporating an emotion engine, this system can analyze user emotions and improve service quality. The details of this system are described below.

[1223] Strengthening customer support

[1224] Users access customer support using input devices such as smartphones or computers. When they launch the app and tap the "Customer Support" button, a support request is sent from their device to the server. The server receives and analyzes this request and connects them to the most suitable support representative. When a user makes an inquiry via text or voice, the emotion engine analyzes the user's emotions and sends that information to the server. Based on the emotion data, the server provides appropriate information to the representative, who then responds while considering the user's emotions.

[1225] Specific example: When a user asks a question about how to use a refrigerator, they send a query to the server via a smartphone app. An emotion engine detects dissatisfaction or confusion from the user's voice and sends the data to the server. The server analyzes the emotion data and provides it to the refrigerator product manager, who then provides an answer that resolves the user's confusion based on the appropriate information.

[1226] Personalized product suggestions

[1227] The user selects the "Product Suggestions" menu and enters their desired category and conditions (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. An emotion engine analyzes the user's emotions and adjusts the product suggestions accordingly. The generated list is displayed on the device, and the user can purchase the suggested products.

[1228] Specific example: If a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history. An emotion engine senses the user's happiness and satisfaction, and uses that data to increase the frequency of recommendations for specific products. Discount coupons are also provided.

[1229] Product information and recipes

[1230] When a user enters keywords into the app's search bar, the device sends this information to a server. The server performs a keyword search and extracts relevant product information and recipes from its database. An emotion engine analyzes the user's emotions and uses that data to refine the search results. The search results from the server are then displayed on the user's device.

[1231] Specific example: When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes. An emotion engine senses the user's excitement and expectations and adjusts the display order of search results based on that data. Ingredients and nutritional information are also displayed, and links to purchase ingredients are provided.

[1232] Automated order processing

[1233] The user selects the "Auto Order" function within the app and enters the product and frequency. The device sends this information to the server, which stores the settings and manages the order schedule. The server automatically generates orders based on the specified frequency and issues shipping instructions. An emotion engine analyzes the user's emotions and offers promotions or benefits if satisfaction is low. A notification is sent to the device when the order is processed.

[1234] Specific example: When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a designated day each month and ships the product. An emotion engine senses the user's purchasing intent and anxieties, sends promotional emails based on that data, and sends order confirmation notifications to the device.

[1235] This system allows users to efficiently receive customer support, product recommendations, search for product information and recipes, and place automatic orders using their smartphones. The emotion engine further personalizes these services, enhancing the user experience.

[1236] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1237] Strengthening customer support

[1238] Processing steps

[1239] Step 1:

[1240] The user taps the "Customer Support" button.

[1241] Input: User actions

[1242] Output: Generate a support request

[1243] Action: The user launches the smartphone app and taps the "Customer Support" button. This action causes the system to generate a support request.

[1244] Step 2:

[1245] The device sends a support request to the server.

[1246] Input: Support Request

[1247] Output: Request sent to the server

[1248] Operation: The terminal sends the generated support request to the server via the internet. The request includes the user ID and a summary of the inquiry.

[1249] Step 3:

[1250] The server parses the request.

[1251] Input: Request sent to the server

[1252] Output: Analysis results

[1253] Operation: The server analyzes received requests to determine their priority and content. This analysis is based on the user's past support history and current inquiry content.

[1254] Step 4:

[1255] The server selects the most suitable support person.

[1256] Input: Analysis results, support staff schedule information

[1257] Output: Instructions to connect to the person in charge

[1258] Operation: The server compares the analysis results with the support staff's schedule information to select the most suitable person. This selection takes into account the person's skill set and current workload.

[1259] Step 5:

[1260] The user submits an inquiry.

[1261] Input: User's inquiry (text or voice)

[1262] Output: Generation of query data

[1263] Operation: Users make inquiries via text or voice. In the case of voice inquiries, the system uses natural language processing (NLP) techniques to convert the speech to text.

[1264] Step 6:

[1265] The emotion engine analyzes the user's emotions.

[1266] Input: User inquiry data

[1267] Output: Sentiment data

[1268] Operation: The emotion engine analyzes query data to identify the user's emotional state (e.g., dissatisfaction, confusion, joy, etc.). This uses voice tone and text expression.

[1269] Step 7:

[1270] The server provides emotional data to the person in charge.

[1271] Input: Sentiment data

[1272] Output: Information package for the person in charge

[1273] Operation: The server sends emotional data to the person in charge, allowing them to understand the user's emotional state.

[1274] Step 8:

[1275] The person in charge will respond while taking the user's feelings into consideration.

[1276] Input: Information package for the person in charge

[1277] Output: Response to the user

[1278] Operation: Based on the received emotional data and analysis results, the staff member will provide thoughtful responses that take the user's emotions into consideration. For example, they will provide more helpful and clearer information to users who are feeling dissatisfied.

[1279] Personalized product suggestions

[1280] Processing steps

[1281] Step 1:

[1282] The user selects the "Product Suggestions" menu.

[1283] Input: User actions

[1284] Output: Generate product suggestion request

[1285] Operation: When the user selects the "Product Suggestions" menu within the app, the system generates a product suggestion request.

[1286] Step 2:

[1287] The user enters their desired category and conditions.

[1288] Input: User input information (category, conditions)

[1289] Output: Product proposal conditions

[1290] Operation: The user enters their desired category (e.g., diet, muscle training) and conditions. This information is registered in the system as product suggestion criteria.

[1291] Step 3:

[1292] The device sends information to the server.

[1293] Input: Product proposal conditions

[1294] Output: Product proposal conditions sent to the server

[1295] Operation: The terminal sends product suggestion conditions to the server. The information is communicated using a secure protocol.

[1296] Step 4:

[1297] The server analyzes the database.

[1298] Input: Product suggestion criteria, user history data

[1299] Output: Generation of personalized product lists

[1300] Operation: The server analyzes the database based on product suggestion criteria and the user's past purchase history to select the most suitable product.

[1301] Step 5:

[1302] The emotion engine analyzes the user's emotions and adjusts the product recommendations accordingly.

[1303] Input: Product list, user sentiment data

[1304] Output: Adjusted product list

[1305] Operation: The emotion engine analyzes the user's purchase history and current emotions to adjust the frequency and order of product suggestions in the product list.

[1306] Step 6:

[1307] The server provides the generated list to the terminal.

[1308] Input: Adjusted product list

[1309] Output: List display on the user's terminal

[1310] Operation: The server sends a refined product list to the user's terminal, and the terminal displays the list.

[1311] Step 7:

[1312] The user purchases the suggested product.

[1313] Input: User purchase operation

[1314] Output: Purchase data

[1315] Operation: The user selects the desired product from the suggested product list and proceeds with the purchase. Payment information is also included.

[1316] Product information and recipes

[1317] Processing steps

[1318] Step 1:

[1319] The user enters keywords into the search bar within the app.

[1320] Input: Search keywords

[1321] Output: Generating a search request

[1322] How it works: When the user enters keywords into the search bar within the app, the system generates a search request.

[1323] Step 2:

[1324] The device sends information to the server.

[1325] Input: Search Request

[1326] Output: Search request sent to the server

[1327] Operation: The terminal sends a search request to the server. This process uses an internet connection.

[1328] Step 3:

[1329] The server performs a keyword search.

[1330] Input: Keywords for your search request

[1331] Output: Search Results

[1332] Operation: The server performs a keyword search within the database and extracts relevant product information and recipes.

[1333] Step 4:

[1334] The emotion engine analyzes the user's emotions and adjusts the search results accordingly.

[1335] Input: Search results, user sentiment data

[1336] Output: Adjusted search results

[1337] How it works: The sentiment engine adjusts search results, optimizing the display order and content based on the user's emotions.

[1338] Step 5:

[1339] The server sends the results to the terminal.

[1340] Input: Adjusted search results

[1341] Output: Search results displayed on the user's terminal

[1342] Operation: The server sends the adjusted search results to the terminal, and the terminal displays them.

[1343] Step 6:

[1344] The user views the results.

[1345] Input: Adjusted search results

[1346] Output: User browsing data

[1347] Operation: The user views the displayed search results and clicks on relevant links as needed.

[1348] Automated order processing

[1349] Processing steps

[1350] Step 1:

[1351] The user selects the "automatic order" function.

[1352] Input: User actions

[1353] Output: Generation of automated order requests

[1354] Operation: When the user selects the "Auto Order" function within the app, the system generates an automatic order request.

[1355] Step 2:

[1356] The user enters the product and the cycle.

[1357] Input: Product information, order cycle

[1358] Output: Automatic Order Settings

[1359] Operation: The user enters the products they wish to purchase and their order frequency (daily, weekly, monthly, etc.). This configuration information is saved in the system.

[1360] Step 3:

[1361] The device sends configuration information to the server.

[1362] Input: Automatic Order Settings

[1363] Output: Configuration information sent to the server

[1364] Operation: The terminal sends automatic order setting information to the server.

[1365] Step 4:

[1366] The server stores information and manages order schedules.

[1367] Input: Configuration information

[1368] Output: Saved schedule information

[1369] Operation: The server stores the received configuration information and manages the order schedule.

[1370] Step 5:

[1371] The server automatically generates orders based on the specified interval.

[1372] Input: Schedule information, inventory data

[1373] Output: Automated order data

[1374] Operation: The server automatically generates orders based on a specified cycle and processes them by cross-referencing them with inventory data.

[1375] Step 6:

[1376] The emotion engine analyzes user emotions and provides promotions and benefits accordingly.

[1377] Input: User sentiment data

[1378] Output: Promotional and bonus data

[1379] Operation: The emotion engine analyzes the user's emotions and, if satisfaction is low, generates and provides promotions or rewards.

[1380] Step 7:

[1381] The server sends an automated order notification to the device.

[1382] Input: Auto-generated order data

[1383] Output: Notification to the device

[1384] Operation: The server sends information about the generated order to the terminal and notifies the user.

[1385] This processing flow improves the user experience and enables the provision of efficient and personalized support and services.

[1386] (Application Example 2)

[1387] 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."

[1388] Traditional customer support and product recommendation systems provided uniform responses without considering the user's emotional state, resulting in a lack of sufficient personalization of the user experience. This led to decreased user satisfaction and a high likelihood of customer churn. Furthermore, there was no system in place to sense the anxiety and satisfaction levels of occupants in autonomous vehicles in real time and provide appropriate feedback.

[1389] 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.

[1390] In this invention, the server includes the following means:

[1391] The system includes a sentiment analysis engine for collecting sentiment data from users accessing customer support, a server for analyzing the sentiment data and providing appropriate information to support staff, a system for staff to adjust their responses considering the user's sentiment information, a system for generating personalized product lists based on user input conditions and adjusting suggestions based on sentiment data, and a system for adjusting search results based on sentiment data when users search for product information or recipes. This makes it possible to personalize the user experience and significantly improve user satisfaction. It also enables appropriate feedback in autonomous vehicles that responds to the occupants' emotions.

[1392] An "input device" is an electronic device used by a user to input information and give instructions to a system.

[1393] "Customer support" refers to the activity of providing assistance and solutions to users regarding problems and questions they have about products and services.

[1394] A "server" is a computer system that operates on a network and provides functions such as data processing and storage.

[1395] An "appropriate support representative" is someone who can provide the most suitable response based on the user's inquiry and the support they require.

[1396] An "emotion analysis engine" is software that analyzes a user's emotional state from their voice or text and extracts that data.

[1397] "Emotional data" refers to data that indicates a user's emotional state, extracted by an emotion analysis engine.

[1398] A "personalized product list" is a list of products that has been individually customized based on the user's input criteria and past browsing history.

[1399] "Search results" are a list of information that the server extracts from the database based on the keywords entered by the user.

[1400] This invention combines a sentiment analysis engine with an integrated system that allows users to perform efficient customer support, personalized product recommendations, provide product information and recipes, and process automated orders via an input device. The following describes specific embodiments of this system.

[1401] The system primarily consists of a smartphone, a server, and an emotion analysis engine. Users interact with the system using their smartphone as an input device. The server handles all data processing and logic, while the emotion analysis engine analyzes the user's emotional data.

[1402] Strengthening customer support

[1403] The user launches the smartphone app and taps the "Customer Support" button. This action sends a support request to the server. The server receives and analyzes the request and connects the user to the appropriate support representative. Furthermore, an emotion analysis engine analyzes the user's emotions from their voice and text and sends that information to the server. Based on the emotion data, the server provides appropriate information to the representative. The representative provides a response that takes the user's emotions into consideration and resolves the problem.

[1404] Specific example: When a user asks a question about how to use a refrigerator, the app sends a query to the server. An emotion analysis engine detects dissatisfaction or confusion from the user's voice and sends that data to the server. The server analyzes the emotion data and provides it to the refrigerator product manager. The manager then provides an answer that resolves the user's confusion based on the appropriate information.

[1405] Personalized product suggestions

[1406] The user selects the "Product Suggestions" menu and enters their desired categories and criteria. This information is sent from the device to the server, which analyzes the database to generate a personalized product list. The sentiment analysis engine also takes the user's sentiment data into consideration when adjusting the product suggestions. The generated list is displayed on the device, and the user can purchase the suggested products.

[1407] Specific example: If a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history. An emotion analysis engine senses the user's happiness and satisfaction, and uses that data to increase the frequency of recommendations for specific products. Discount coupons are also provided.

[1408] Product information and recipes

[1409] When a user enters keywords into the app's search bar, the device sends this information to a server. The server performs a keyword search and extracts relevant product information and recipes from its database. An emotion analysis engine analyzes the user's emotions and uses that data to refine the search results. The search results from the server are then displayed on the user's device.

[1410] Specific example: When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes. An emotion analysis engine senses the user's excitement and expectations, and adjusts the display order of search results based on that data. Ingredients and nutritional information are also displayed, and links to purchase ingredients are provided.

[1411] Emotional response in autonomous vehicles

[1412] The system for autonomous vehicles to analyze the emotional state of the occupants in real time and take appropriate action is similarly configured. An emotion analysis engine extracts emotional data from the occupants' voice and sends it to a server. The server analyzes the emotional data, generates optimal feedback, and provides it to the occupants.

[1413] Specific example: If a passenger says, "This road feels a little dangerous," the emotion analysis engine detects the anxiety and sends that data to the server. The server analyzes the current situation of the autonomous vehicle (e.g., road conditions and speed) and generates appropriate feedback, such as, "The current road conditions are safe. We will slow down a little and proceed cautiously," which is then provided to the passenger.

[1414] Example of a prompt:

[1415] The user stated, "This road feels a little dangerous." Analyze this anxiety as emotional data and, considering the current situation of the autonomous vehicle (current speed, surrounding safety, route), generate a response that will reassure the user. Example response: "The current road conditions are safe. We will slow down a little and proceed cautiously."

[1416] The implementation of this system will allow users to enjoy a more comfortable and personalized experience. Furthermore, it can improve safety and passenger satisfaction in autonomous vehicles.

[1417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1418] Step 1:

[1419] Users access customer support via an input device (smartphone).

[1420] A support request is generated when a user launches the app and taps the "Customer Support" button. This request includes the user's voice and text data. Input consists of the user's voice instructions and text input, and output is a request sent to the server.

[1421] Step 2:

[1422] The device sends a support request to the server.

[1423] The terminal sends the generated request to the server over the network. In this process, the generated request data is the input, and the request is forwarded to the server as the output.

[1424] Step 3:

[1425] The server receives the user's inquiry and connects them to the appropriate support representative.

[1426] The server analyzes the received request and identifies the nature of the problem. Based on the analyzed problem, it selects the most suitable support person and connects the user to them. The input is the request data, and the output is instructions for connecting to the support person.

[1427] Step 4:

[1428] The emotion analysis engine extracts emotion data from the user's voice and text.

[1429] The server uses an emotion analysis engine to analyze the user's voice and text to identify their emotional state. Input is user voice and text data, and output is generated emotion data.

[1430] Step 5:

[1431] Emotional data is sent to a server, which then analyzes the data.

[1432] The extracted emotion data is sent to a server, which then analyzes the data. The input is emotion data, and the output is the analysis results.

[1433] Step 6:

[1434] The server provides appropriate information to the person in charge based on emotional data.

[1435] Based on the analyzed emotional data, the server provides the necessary information and advice to the person in charge. The input is the results of the emotional data analysis, and the output is the provision of information to the person in charge.

[1436] Step 7:

[1437] The person in charge adjusts their response, taking into account the user's emotional information.

[1438] Support staff provide appropriate responses to users based on the information and sentiment data provided. Inputs include information and sentiment data provided by the server, and output is the response to the user.

[1439] Step 8:

[1440] The user selects the "Product Suggestion" menu and enters their desired category and conditions.

[1441] The user selects the "Product Suggestions" menu within the app and enters specific conditions such as diet or muscle training. The input consists of the user's specified conditions, and the output is a request that is sent to the server.

[1442] Step 9:

[1443] The device sends this information to the server, which then analyzes the database to generate a personalized product list.

[1444] The terminal sends user-specified conditions to the server, which then analyzes the database based on those conditions. The input is the specified conditions, and the output is a product list.

[1445] Step 10:

[1446] The emotion analysis engine takes user emotion data into account and adjusts the product recommendations accordingly.

[1447] The sentiment analysis engine considers the user's sentiment data when analyzing the generated product list and adjusts the suggested products accordingly. The input consists of a product list and sentiment data, and the output is an adjusted product list.

[1448] Step 11:

[1449] The generated list is displayed on the device, and the user purchases the suggested items.

[1450] The adjusted product list is displayed on the terminal, allowing the user to view and purchase the suggested products. The input is the adjusted product list, and the output is the display to the user and the purchase process.

[1451] Step 12:

[1452] The user enters keywords into the search bar within the app.

[1453] Users enter keywords into the search bar to search for product information or recipes. The input is the search keywords, and the output is a search request.

[1454] Step 13:

[1455] The device sends this information to the server, which then performs a keyword search.

[1456] The terminal sends keyword information to the server, which then searches the database based on those keywords. The input is keywords, and the output is search results.

[1457] Step 14:

[1458] The emotion analysis engine analyzes the user's emotions and adjusts the search results accordingly.

[1459] The sentiment analysis engine considers the user's sentiment data and adjusts the search results accordingly. The input consists of the search results and sentiment data, and the output is the adjusted search results.

[1460] Step 15:

[1461] The search results from the server are displayed on the user's terminal.

[1462] The adjusted search results are sent to the device and displayed to the user. The adjusted search results are the input, and the output is what is displayed to the user.

[1463] Step 16:

[1464] While a user is riding in an "autonomous vehicle," an emotion analysis engine extracts emotional data from speech and text.

[1465] The emotion analysis engine analyzes the crew's voices and extracts emotion data. Voice data is the input, and emotion data is generated as the output.

[1466] Step 17:

[1467] The server analyzes emotional data and the vehicle's current status data to generate appropriate feedback.

[1468] The server analyzes emotional data and the vehicle's current status (speed, road conditions, etc.) to generate feedback. Emotional data and vehicle status data are inputs, and feedback is generated as output.

[1469] Step 18:

[1470] Provide the generated feedback to the crew.

[1471] The generated feedback is provided to the crew via a terminal. The generated feedback is the input, and the output is provided to the crew.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] [Third Embodiment]

[1476] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1477] 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.

[1478] 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).

[1479] 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.

[1480] 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.

[1481] 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).

[1482] 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.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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".

[1488] Modes for carrying out the invention

[1489] This invention relates to an integrated system for enabling users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system is configured as follows:

[1490] Strengthening customer support

[1491] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. The representative then assists the user via chat or voice and resolves the issue.

[1492] Specific example:

[1493] When a user asks a question about how to use the refrigerator, the app sends a query to the server. The server analyzes the question and connects the user to the refrigerator's product representative. The representative then provides the appropriate answer.

[1494] Personalized product suggestions

[1495] The user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. The generated list is displayed on the device, and the user can purchase the suggested products.

[1496] Specific example:

[1497] If a user is looking for diet foods, the server will recommend low-calorie foods and diet supplements based on their past purchase history, and will also provide discount coupons.

[1498] Product information and recipes

[1499] When a user enters keywords into the app's search bar, the device sends this information to the server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user.

[1500] Specific example:

[1501] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase the ingredients are also provided.

[1502] Automated order processing

[1503] The user selects "Automatic Order" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[1504] Specific example:

[1505] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a specified day each month and ships the product. An order confirmation notification is sent to the user's device.

[1506] The above describes the embodiments for carrying out the present invention. This system allows users to efficiently and effectively receive customer support, product suggestions, search for product information and recipes, and place automatic orders using their smartphones.

[1507] The following describes the processing flow.

[1508] Strengthening customer support

[1509] Processing steps:

[1510] Step 1:

[1511] The user taps the app icon on their smartphone's home screen to launch the app.

[1512] Step 2:

[1513] The user taps the "Customer Support" button within the app.

[1514] Step 3:

[1515] The device sends a support request to the server. This request includes the user ID, current screen information, and the content of the inquiry.

[1516] Step 4:

[1517] The server receives the request and analyzes the inquiry. If necessary, it forwards it to an automated response system or the appropriate person.

[1518] Step 5:

[1519] Based on the analysis results, the server connects to a chatbot or a human representative to generate a response.

[1520] Step 6:

[1521] The terminal displays the response information received from the server to the user. Support is provided in chat or voice format.

[1522] ---

[1523] Personalized product suggestions

[1524] Processing steps:

[1525] Step 1:

[1526] The user taps the "Product Suggestions" menu within the app.

[1527] Step 2:

[1528] Users input the product category and purpose they want suggested (e.g., diet, muscle training) via text or voice.

[1529] Step 3:

[1530] The terminal sends the user's input conditions to the server.

[1531] Step 4:

[1532] The server analyzes the database based on user input, past purchase history, and preference data.

[1533] Step 5:

[1534] The server generates a personalized product list based on the analysis results. This list includes relevant products, promotions, and discount information.

[1535] Step 6:

[1536] The terminal displays the product list received from the server to the user.

[1537] ---

[1538] Product information and recipes

[1539] Processing steps:

[1540] Step 1:

[1541] The user taps the search bar within the app.

[1542] Step 2:

[1543] Users enter keywords related to their interests, such as cooking methods, nutrients, and ingredients.

[1544] Step 3:

[1545] The device sends the keyword to the server.

[1546] Step 4:

[1547] The server performs a keyword search on the database and extracts relevant product information and recipes.

[1548] Step 5:

[1549] The server generates search results and formats them into a format for providing to the user.

[1550] Step 6:

[1551] The terminal displays the search results received from the server to the user.

[1552] ---

[1553] Automated order processing

[1554] Processing steps:

[1555] Step 1:

[1556] The user selects the "Automatic Ordering" setup screen within the app.

[1557] Step 2:

[1558] The user enters the product, quantity, purchase frequency (e.g., weekly, monthly), and payment method to be purchased.

[1559] Step 3:

[1560] The device sends configuration information to the server.

[1561] Step 4:

[1562] The server saves the automatic order settings to the database and registers the order schedule.

[1563] Step 5:

[1564] The server automatically generates orders based on the purchase cycle and verifies the user's payment information.

[1565] Step 6:

[1566] The server sends shipping instructions to the warehouse system and completes the order in conjunction with the logistics management system.

[1567] Step 7:

[1568] The terminal notifies the user that automatic ordering has been successfully set up, and the user receives a notification when each order is processed.

[1569] ---

[1570] The above details the specific operations at each processing step. This configuration allows users to easily access customer support, product suggestions, product information and recipes, and place automatic orders using their smartphones.

[1571] (Example 1)

[1572] 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."

[1573] Traditional customer support systems often failed to properly analyze user inquiries and quickly connect users to the appropriate support staff. Furthermore, personalized product recommendations frequently did not fully utilize past purchase history, making it difficult to generate optimal product lists for users. Additionally, product information and recipe search results often did not match user expectations, resulting in a poor user experience.

[1574] 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.

[1575] In this invention, the server includes means for receiving inquiries from users and analyzing the content of the inquiries using a natural language processing algorithm, means for selecting the most suitable support person based on the analysis results and connecting to the support person, and means for the support person to respond to the user and resolve the problem. This makes it possible to quickly analyze the content of user inquiries and connect to the most suitable support person.

[1576] Furthermore, the server includes means for generating individually personalized product lists using a generative AI model, taking into account past purchase history based on user input conditions; means for displaying suggestions from the server on the user's terminal; and means for the user to purchase the suggested products. This provides the user with the most suitable product suggestions and facilitates purchasing behavior.

[1577] Furthermore, the server includes means for performing keyword searches and extracting relevant information from the database, as well as means for displaying the extraction results on the user's terminal. This ensures that the information and recipes the user is looking for are provided quickly and accurately, improving the user experience.

[1578] A "user" is an individual or legal entity that uses the system to perform tasks such as customer support, product recommendations, and product information searches.

[1579] An "input device" is a device used by a user to input information, and examples include smartphones, tablets, and personal computers.

[1580] "Customer support" is a service in which support staff respond to inquiries and problems from users and attempt to resolve them.

[1581] A "server" is a device or system that processes information on a network, receives requests from users, and performs appropriate processing in response to those requests.

[1582] A "natural language processing algorithm" is a set of computational methods and techniques that enable computers to understand and analyze human language.

[1583] A "support staff member" is a staff member or agent who handles inquiries from users in customer support.

[1584] A "personalized product list" is a list of product suggestions that is generated individually based on the user's input criteria and past purchase history.

[1585] A "generative AI model" is a computational model that uses artificial intelligence to analyze data and generate new information or suggestions.

[1586] A "user terminal" is a device that a user directly operates to access the system, and includes smartphones, tablets, and personal computers.

[1587] "Keyword search" is the process by which a user enters a specific word or phrase and searches a database for information related to that word.

[1588] A "database" is a collection of data that is organized and stored so that it can be quickly and efficiently searched and retrieved when needed.

[1589] This invention relates to an integrated system for users to efficiently provide customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices. The system is designed to be easily operated by users using input devices such as smartphones, tablets, and personal computers.

[1590] Strengthening customer support

[1591] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server analyzes the received request using a natural language processing algorithm and selects the most suitable support representative. Once a representative is selected based on the analysis results, the server forwards the request to that representative, and the user can receive support via chat or voice.

[1592] Specific example:

[1593] When a user asks a question about how to use the refrigerator, the app sends a query to the server. The server analyzes the question and connects the user to the refrigerator's product representative. The representative then provides the appropriate answer.

[1594] Prompt example:

[1595] "I don't know how to use the refrigerator. How do I operate it?"

[1596] Personalized product suggestions

[1597] When a user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training), the device sends this information to the server. The server uses a generative AI model to analyze the database and generate a personalized product list. The generated list is displayed on the device, and the user can purchase the suggested products.

[1598] Specific example:

[1599] If a user is looking for diet foods, the server will recommend low-calorie foods and diet supplements based on their past purchase history, and will also provide discount coupons.

[1600] Prompt example:

[1601] "Based on my past purchase history, please recommend some diet foods."

[1602] Product information and recipes

[1603] When a user enters keywords into the app's search bar, the device sends this information to the server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user.

[1604] Specific example:

[1605] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase the ingredients are also provided.

[1606] Prompt example:

[1607] "Can you share a low-calorie smoothie recipe?"

[1608] Automated order processing

[1609] When a user selects "Automatic Ordering" within the app and enters the product and frequency, the device sends the settings information to the server. The server stores this information and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[1610] Specific example:

[1611] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a specified day each month and ships the product. An order confirmation notification is sent to the user's device.

[1612] This system allows users to efficiently and effectively receive customer support, product recommendations, search for product information and recipes, and place automatic orders using their smartphones.

[1613] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1614] Strengthening customer support

[1615] Step 1:

[1616] The user launches the app on their smartphone and taps the "Customer Support" button.

[1617] Operation: The user taps the app to launch it and selects the "Customer Support" button from the home screen. This initiates a customer support request.

[1618] Input: User action (button tap).

[1619] Output: The terminal generates support request data.

[1620] Step 2:

[1621] The terminal sends the generated support request data to the server.

[1622] Operation: The terminal encodes the data containing the support request and sends it to the server.

[1623] Input: Support request data.

[1624] Output: The request data reaches the server.

[1625] Step 3:

[1626] The server receives the request and parses it using a natural language processing algorithm.

[1627] Operation: The server parses the received request and applies NLP algorithms to understand the user's inquiry.

[1628] Input: Request data.

[1629] Output: Understanding the query content from the analysis results.

[1630] Step 4:

[1631] The server selects the most suitable support person based on the analysis results.

[1632] Operation: The server searches the database for the appropriate person in charge and selects the most suitable person.

[1633] Input: Analysis results.

[1634] Output: Information on the selected support staff member.

[1635] Step 5:

[1636] The server forwards the request to the person in charge and establishes the connection.

[1637] Operation: The server forwards the request to the designated contact person and establishes a chat or voice call connection.

[1638] Input: Person in charge information, request data.

[1639] Output: A connection notification is sent to the user's terminal.

[1640] Personalized product suggestions

[1641] Step 1:

[1642] The user selects the "Product Suggestions" menu in the app and enters their desired category and conditions.

[1643] Action: The user taps the "Product Suggestion" menu and enters their request in the form for entering conditions.

[1644] Input: Enter the user's category and conditions.

[1645] Output: Conditional data is generated.

[1646] Step 2:

[1647] The device sends this conditional data to the server.

[1648] Operation: The terminal encodes the input condition data and sends it to the server.

[1649] Input: Conditional data.

[1650] Output: The condition data reaches the server.

[1651] Step 3:

[1652] The server uses an AI model to analyze the database and generate individually personalized product lists.

[1653] Operation: The server searches the database and uses a generative AI model to generate a list of products that match the user's criteria.

[1654] Input: Conditional data, past purchase history.

[1655] Output: Personalized product list.

[1656] Step 4:

[1657] The server sends the generated product list to the terminal.

[1658] Operation: The server encodes the product list and sends it to the user's terminal.

[1659] Input: Product list.

[1660] Output: The product list arrives at the terminal.

[1661] Step 5:

[1662] The terminal displays the product list it received to the user.

[1663] Operation: The terminal decodes the product list and displays it in the user interface.

[1664] Input: Product list.

[1665] Output: The user is shown a list of products.

[1666] Product information and recipes

[1667] Step 1:

[1668] The user enters keywords into the search bar within the app.

[1669] Operation: The user taps the app's search bar and enters keywords for the information they are looking for.

[1670] Input: User keyword input.

[1671] Output: Keyword data is generated.

[1672] Step 2:

[1673] The device sends this keyword data to the server.

[1674] Operation: The terminal encodes the keyword data and sends it to the server.

[1675] Input: Keyword data.

[1676] Output: Keyword data reaches the server.

[1677] Step 3:

[1678] The server performs a keyword search and extracts relevant information from the database.

[1679] Operation: The server searches the database based on keywords and extracts relevant product information and recipes.

[1680] Input: Keyword data.

[1681] Output: Search results data.

[1682] Step 4:

[1683] The server sends the extracted search results to the terminal.

[1684] Operation: The server encodes the search results and sends them to the user's device.

[1685] Input: Search results data.

[1686] Output: Search results arrive at the terminal.

[1687] Step 5:

[1688] The search results received by the device are displayed to the user.

[1689] Operation: The device decodes the search results and displays them in the user interface.

[1690] Input: Search results data.

[1691] Output: The user is shown the search results.

[1692] Automated order processing

[1693] Step 1:

[1694] The user selects "Automatic Order" within the app and enters the product and delivery frequency.

[1695] Operation: The user taps the "Automatic Order" menu and enters the desired products and their frequency into the form.

[1696] Input: Enter the product and cycle.

[1697] Output: Automatic order setting data is generated.

[1698] Step 2:

[1699] The device sends this configuration data to the server.

[1700] Operation: The terminal encodes the automatic order setting data and sends it to the server.

[1701] Input: Automatic order setting data.

[1702] Output: Configuration data reaches the server.

[1703] Step 3:

[1704] The server stores configuration data and manages order schedules.

[1705] Operation: The server saves the received configuration data to the database and creates entries for managing order schedules.

[1706] Input: Automatic order setting data.

[1707] Output: Saved configuration data, schedule entries.

[1708] Step 4:

[1709] The server automatically generates orders based on the specified cycle.

[1710] Operation: The server automatically generates order data based on a cycle and prepares the data for order processing.

[1711] Input: Schedule entry.

[1712] Output: Order data.

[1713] Step 5:

[1714] After the server generates the order, it issues a shipping instruction.

[1715] Operation: The server uses the generated order data to create shipping instructions and sends them to the logistics system.

[1716] Input: Order data.

[1717] Output: Shipping instructions.

[1718] Step 6:

[1719] Once the order is processed, the server sends an order confirmation notification to the terminal.

[1720] Operation: After issuing a shipping instruction, the server encodes an order confirmation notification and sends it to the user's terminal.

[1721] Input: Shipping instructions.

[1722] Output: An order confirmation notification is sent to the terminal.

[1723] (Application Example 1)

[1724] 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."

[1725] Traditional customer support systems and online shopping sites struggle to efficiently provide personalized support, product recommendations, product information, and automated order management to users. In particular, there is a demand for automated and user-optimized product recommendations and automated order processing, but achieving this presents numerous technical challenges.

[1726] 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.

[1727] In this invention, the server includes means for the user to access customer support via an input device, means for the server to receive inquiries from the user and connect to the appropriate support person, means for the support person to respond to the user and resolve the problem, means for the user to input configuration information into a terminal and send the configuration information to the server, means for the server to generate an automated order based on the configuration information and manage the order, and means for sending a notification to the terminal after the automated order has been processed. This enables the user to efficiently receive customer support, receive personalized product suggestions, search for product information and recipes, and place automated orders.

[1728] An "input device" is hardware used by users to input information, and includes smartphones and tablets.

[1729] "Customer support" refers to assistance services provided to users to help them resolve questions and problems related to products and services.

[1730] A "server" is a computer system that provides data and services to multiple clients over a network.

[1731] An "inquiry" refers to a question or request made by a user seeking support.

[1732] A "support staff member" is a staff member assigned to provide customer support.

[1733] "Setting information" refers to the information that users enter when using features such as automatic ordering, and includes things like product names and order cycles.

[1734] "Automatic ordering" is a system where the server automatically orders products based on conditions specified by the user.

[1735] A "notification" is a means of communication sent from a server to a user, and includes order confirmations and update information.

[1736] "Personalized product recommendations" is a system that individually suggests the most suitable products based on the user's history and preferences.

[1737] A "profile" is a collection of data that records a user's purchase history and interests.

[1738] "Shopping history" is a record of products that a user has purchased in the past.

[1739] A "discount coupon" is a voucher offered to users to purchase specific products at a lower price.

[1740] "Keyword search" is a method of searching a database based on keywords entered by the user and extracting relevant information.

[1741] "Product information" refers to detailed information about a specific product.

[1742] A "recipe" is information about the steps and ingredients needed to make a specific dish.

[1743] A "link" is a URL that allows a user to access another webpage or piece of information by clicking on it.

[1744] This invention is an integrated system for providing efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system consists of the following elements:

[1745] Strengthening customer support

[1746] The user launches the customer support application on their smartphone and taps the "Customer Support" button. The device then sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. This support representative responds to the user via chat or voice and resolves the issue.

[1747] Specific example:

[1748] When a user asks a question about how to use their refrigerator, the app sends a query to the server. The server analyzes the question, connects to the refrigerator's product representative, and the representative provides the appropriate answer. For example: "I bought a refrigerator. I don't know how to use it, so please tell me how."

[1749] Personalized product suggestions

[1750] The user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database to generate a personalized product list. This generated list is displayed on the device, and the user can purchase the suggested products. The server uses the user profile and shopping history to suggest the most suitable products and also provides discount coupons.

[1751] Specific example:

[1752] When a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history, and also provides discount coupons. "I'm looking for diet foods. Can you recommend some low-calorie options?"

[1753] Product information and recipes

[1754] When a user enters keywords into the app's search bar, their device sends this information to a server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user. The search results include relevant product information and recipes, as well as links to purchase ingredients.

[1755] Specific example:

[1756] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase ingredients are also displayed. "Please share a low-calorie smoothie recipe."

[1757] Automated order processing

[1758] The user selects "Automatic Order" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[1759] Specific example:

[1760] When a user sets up a monthly subscription for protein powder, the server automatically generates an order on a specified day each month and ships the product. A notification confirming the order is sent to the user's device. The message reads: "Please set up automatic monthly orders for protein powder."

[1761] This system will be implemented using cloud-based servers such as AWS and Google Cloud Platform. Furthermore, web frameworks like Flask and Django will be used to build REST APIs and process data in response to user requests. MySQL and PostgreSQL will be used as the databases.

[1762] By utilizing generative AI models and prompt messages, it is possible to provide the most optimal response immediately to user inquiries and search criteria. This allows users to efficiently receive support, product suggestions, obtain product information and recipes, and automatically order the necessary products.

[1763] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1764] Step 1:

[1765] The user launches the customer support application on their smartphone and taps the "Customer Support" button.

[1766] Input: An action performed by the user, such as tapping a button.

[1767] Action: The terminal generates a support request.

[1768] Output: A support request is generated.

[1769] Step 2:

[1770] The device sends a support request to the server.

[1771] Input: The generated support request.

[1772] Operation: Sends data to the server using an HTTP POST request.

[1773] Output: A support request is sent to the server.

[1774] Step 3:

[1775] The server analyzes the support requests it receives.

[1776] Input: Submitted support request.

[1777] Operation: Uses database queries and natural language processing (NLP) techniques to parse the request content.

[1778] Output: Identification of the appropriate support person.

[1779] Step 4:

[1780] The server connects to the most suitable support person.

[1781] Input: Identification information of the support staff member.

[1782] Action: Sends a connection request to the appropriate person.

[1783] Output: Connection to the person in charge is established.

[1784] Step 5:

[1785] Support staff respond to users and resolve issues.

[1786] Input: Connection to the person in charge and the user's inquiry.

[1787] Operation: A representative will respond to the user via chat or voice.

[1788] Output: The user's problem is resolved.

[1789] Step 6:

[1790] The user selects a product suggestion from the menu and enters their desired category and conditions.

[1791] Input: The user enters categories or conditions (e.g., diet, muscle training).

[1792] Operation: The terminal generates data to send input information to the server.

[1793] Output: Data is prepared according to the user's request.

[1794] Step 7:

[1795] The terminal sends the user's request data to the server.

[1796] Input: User's requested data.

[1797] Operation: Sends data to the server via an HTTP POST request.

[1798] Output: The server receives the user's request data.

[1799] Step 8:

[1800] The server analyzes user profiles and shopping history to generate personalized product recommendations.

[1801] Input: User request data and past purchase history.

[1802] Operation: Generates personalized product lists using database queries and machine learning models.

[1803] Output: Personalized product list.

[1804] Step 9:

[1805] The server sends the generated product list to the user's terminal.

[1806] Input: Personalized product list.

[1807] Operation: Sends a product list as an HTTP response.

[1808] Output: The product list is displayed on the user's terminal.

[1809] Step 10:

[1810] The user selects a suggested product and proceeds with the purchase.

[1811] Input: Product selection and purchase operation.

[1812] Operation: The device sends a purchase request to the server.

[1813] Output: Purchase confirmed.

[1814] Step 11:

[1815] The user enters their automatic order settings and saves them to the server.

[1816] Input: Product and cycle settings.

[1817] Operation: The terminal generates configuration information and sends it to the server.

[1818] Output: The server saves the configuration information to the database.

[1819] Step 12:

[1820] The server generates automatic orders at specified intervals based on the saved configuration information.

[1821] Input: Automatic order settings information.

[1822] Operation: The scheduler periodically generates automatic orders.

[1823] Output: An automated order is generated.

[1824] Step 13:

[1825] When an order is generated, the server sends a notification to the terminal.

[1826] Input: The result of the automated order.

[1827] Operation: Generates notification data and sends it to the device via an HTTP request.

[1828] Output: An order notification is displayed on the user's terminal.

[1829] This allows users to efficiently receive customer support, personalized product recommendations, access product information and recipes, and place automated orders. The system achieves a high level of customization and automation by combining cloud-based servers with data analytics technology.

[1830] 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.

[1831] Modes for carrying out the invention

[1832] This invention combines an emotion engine with an integrated system that enables users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system is configured as follows:

[1833] Strengthening customer support

[1834] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. When a user makes an inquiry using text or voice, the emotion engine analyzes the user's emotions and sends that information to the server. Based on the emotion data, the server provides appropriate information to the representative. The representative provides a response that takes the user's emotions into consideration and resolves the problem.

[1835] Specific example:

[1836] When a user asks a question about how to use the refrigerator, the app sends a query to the server. An emotion engine detects dissatisfaction or confusion from the user's voice and sends that data to the server. The server analyzes the emotion data and provides it to the refrigerator's product manager. The manager then provides an answer that resolves the user's confusion based on the appropriate information.

[1837] Personalized product suggestions

[1838] The user selects the "Product Suggestions" menu and enters their desired category and conditions (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. The server also takes the user's sentiment data into consideration when adjusting the product suggestions. The generated list is displayed on the device, and the user can purchase the suggested products.

[1839] Specific example:

[1840] When a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history. An emotion engine senses the user's happiness and satisfaction, and uses that data to increase the frequency of recommendations for specific products. Discount coupons are also provided.

[1841] Product information and recipes

[1842] When a user enters keywords into the app's search bar, the device sends this information to a server. The server performs a keyword search and extracts relevant product information and recipes from its database. An emotion engine analyzes the user's emotions and uses that data to refine the search results. The search results from the server are then displayed on the user's device.

[1843] Specific example:

[1844] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes. An emotion engine senses the user's excitement and expectations, and adjusts the display order of search results based on that data. Ingredients and nutritional information are also displayed, and links to purchase ingredients are provided.

[1845] Automated order processing

[1846] The user selects "Automatic Ordering" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. The server automatically generates orders based on the specified frequency and issues shipping instructions. An emotion engine analyzes the user's emotions and adds promotions or benefits if the user's satisfaction level is low. A notification is sent to the device when the order is processed.

[1847] Specific example:

[1848] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a designated day each month and ships the product. An emotion engine senses the user's purchasing intent and anxieties, and sends promotional emails based on that data. An order confirmation notification is sent to the user's device.

[1849] The above describes the embodiments for carrying out the present invention. This system allows users to efficiently receive customer support, product suggestions, search for product information and recipes, and place automatic orders using their smartphones, and these services are further personalized by the emotion engine.

[1850] The following describes the processing flow.

[1851] Strengthening customer support

[1852] Processing steps:

[1853] Step 1:

[1854] The user taps the app icon on their smartphone's home screen to launch the app.

[1855] Step 2:

[1856] The user taps the "Customer Support" button within the app.

[1857] Step 3:

[1858] The device sends a support request to the server. This request includes the user ID, current screen information, and the content of the inquiry.

[1859] Step 4:

[1860] The server receives the request and parses the query content.

[1861] Step 5:

[1862] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[1863] Step 6:

[1864] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling. This data includes information such as whether the user is irritated, confused, or satisfied.

[1865] Step 7:

[1866] The server connects to the most suitable support representative based on emotional data. The emotional data is also provided to the support representative.

[1867] Step 8:

[1868] Support staff use sentiment data as a reference to respond to users via chat or voice and resolve issues.

[1869] ---

[1870] Personalized product suggestions

[1871] Processing steps:

[1872] Step 1:

[1873] The user taps the "Product Suggestions" menu within the app.

[1874] Step 2:

[1875] Users input the product category and purpose they want suggested (e.g., diet, muscle training) via text or voice.

[1876] Step 3:

[1877] The terminal sends the user's input conditions to the server.

[1878] Step 4:

[1879] The server analyzes the database based on user input, past purchase history, and preference data.

[1880] Step 5:

[1881] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[1882] Step 6:

[1883] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling.

[1884] Step 7:

[1885] The server takes sentiment data into account to generate a personalized product list. This list includes relevant products, promotions, and discount information.

[1886] Step 8:

[1887] The terminal displays the product list received from the server to the user.

[1888] ---

[1889] Product information and recipes

[1890] Processing steps:

[1891] Step 1:

[1892] The user taps the search bar within the app.

[1893] Step 2:

[1894] Users enter keywords related to their interests, such as cooking methods, nutrients, and ingredients.

[1895] Step 3:

[1896] The device sends the keyword to the server.

[1897] Step 4:

[1898] The server performs a keyword search on the database and extracts relevant product information and recipes.

[1899] Step 5:

[1900] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[1901] Step 6:

[1902] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling.

[1903] Step 7:

[1904] The server takes emotional data into account when generating search results. For example, if a user expresses feelings of "excitement" or "anticipation," it will prioritize displaying recipes and product information that match those emotions.

[1905] Step 8:

[1906] The terminal displays the search results received from the server to the user.

[1907] ---

[1908] Automated order processing

[1909] Processing steps:

[1910] Step 1:

[1911] The user selects the "Automatic Ordering" setup screen within the app.

[1912] Step 2:

[1913] The user enters the product, quantity, purchase frequency (e.g., weekly, monthly), and payment method to be purchased.

[1914] Step 3:

[1915] The device sends configuration information to the server.

[1916] Step 4:

[1917] The server saves the automatic order settings to the database and registers the order schedule.

[1918] Step 5:

[1919] The server automatically generates orders based on the purchase cycle and verifies the user's payment information.

[1920] Step 6:

[1921] The device sends the user's voice and text to the emotion engine to acquire emotion data.

[1922] Step 7:

[1923] The emotion engine analyzes voice and text data to recognize the emotions the user is feeling.

[1924] Step 8:

[1925] The server takes emotional data into account and adds promotions or rewards if user satisfaction is low.

[1926] Step 9:

[1927] The server sends shipping instructions to the warehouse system and completes the order in conjunction with the logistics management system.

[1928] Step 10:

[1929] The terminal notifies the user that automatic ordering has been successfully set up, and the user receives a notification when each order is processed.

[1930] ---

[1931] The above details the specific operations at each processing step. This configuration allows users to use their smartphones to try out customer support with an emotion engine, product recommendations, product information and recipe searches, and automated ordering.

[1932] (Example 2)

[1933] 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."

[1934] Traditional customer support systems have limited potential for improving user satisfaction because they provide uniform responses without considering user emotions. Furthermore, personalized product suggestions and information provision fail to reflect individual user emotional states, making it difficult to provide optimal service. In addition, automated order processing also fails to consider user emotions and satisfaction, hindering long-term user engagement.

[1935] 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.

[1936] In this invention, the server includes means for an emotion engine to analyze the user's emotions and transmit that information to the server, means for the server to provide appropriate information to the person in charge based on the emotion data, and means for the person in charge to respond while taking the user's emotions into consideration. This makes it possible to analyze the user's emotions and provide optimal responses, suggestions, and services based on them, thereby improving user satisfaction.

[1937] A "user" is an individual or legal entity that uses the system to receive services.

[1938] An "input device" is a general term for hardware or software that users use to input information or instructions into a system, with examples including smartphones and PCs.

[1939] A "server" is a central processing unit that receives and analyzes requests from users and provides appropriate services.

[1940] "Inquiry" refers to the act of a user reporting a question or problem to customer support.

[1941] A "support staff member" is a specialist who responds to user inquiries and provides solutions.

[1942] An "emotion engine" is a software component that analyzes a user's emotions and utilizes that information within the system.

[1943] "Emotional data" refers to information about the user's emotional state, which is analyzed and generated by the emotion engine.

[1944] A "personalized product list" is a list of optimal products generated based on each user's individual preferences and criteria.

[1945] "Search results" refer to the information that the server extracts and displays based on the keywords entered by the user.

[1946] "Automatic ordering" is a function where the system automatically orders products based on a cycle set by the user.

[1947] This invention is an integrated system for users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices. By incorporating an emotion engine, this system can analyze user emotions and improve service quality. The details of this system are described below.

[1948] Strengthening customer support

[1949] Users access customer support using input devices such as smartphones or computers. When they launch the app and tap the "Customer Support" button, a support request is sent from their device to the server. The server receives and analyzes this request and connects them to the most suitable support representative. When a user makes an inquiry via text or voice, the emotion engine analyzes the user's emotions and sends that information to the server. Based on the emotion data, the server provides appropriate information to the representative, who then responds while considering the user's emotions.

[1950] Specific example: When a user asks a question about how to use a refrigerator, they send a query to the server via a smartphone app. An emotion engine detects dissatisfaction or confusion from the user's voice and sends the data to the server. The server analyzes the emotion data and provides it to the refrigerator product manager, who then provides an answer that resolves the user's confusion based on the appropriate information.

[1951] Personalized product suggestions

[1952] The user selects the "Product Suggestions" menu and enters their desired category and conditions (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. An emotion engine analyzes the user's emotions and adjusts the product suggestions accordingly. The generated list is displayed on the device, and the user can purchase the suggested products.

[1953] Specific example: If a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history. An emotion engine senses the user's happiness and satisfaction, and uses that data to increase the frequency of recommendations for specific products. Discount coupons are also provided.

[1954] Product information and recipes

[1955] When a user enters keywords into the app's search bar, the device sends this information to a server. The server performs a keyword search and extracts relevant product information and recipes from its database. An emotion engine analyzes the user's emotions and uses that data to refine the search results. The search results from the server are then displayed on the user's device.

[1956] Specific example: When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes. An emotion engine senses the user's excitement and expectations and adjusts the display order of search results based on that data. Ingredients and nutritional information are also displayed, and links to purchase ingredients are provided.

[1957] Automated order processing

[1958] The user selects the "Auto Order" function within the app and enters the product and frequency. The device sends this information to the server, which stores the settings and manages the order schedule. The server automatically generates orders based on the specified frequency and issues shipping instructions. An emotion engine analyzes the user's emotions and offers promotions or benefits if satisfaction is low. A notification is sent to the device when the order is processed.

[1959] Specific example: When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a designated day each month and ships the product. An emotion engine senses the user's purchasing intent and anxieties, sends promotional emails based on that data, and sends order confirmation notifications to the device.

[1960] This system allows users to efficiently receive customer support, product recommendations, search for product information and recipes, and place automatic orders using their smartphones. The emotion engine further personalizes these services, enhancing the user experience.

[1961] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1962] Strengthening customer support

[1963] Processing steps

[1964] Step 1:

[1965] The user taps the "Customer Support" button.

[1966] Input: User actions

[1967] Output: Generate a support request

[1968] Action: The user launches the smartphone app and taps the "Customer Support" button. This action causes the system to generate a support request.

[1969] Step 2:

[1970] The device sends a support request to the server.

[1971] Input: Support Request

[1972] Output: Request sent to the server

[1973] Operation: The terminal sends the generated support request to the server via the internet. The request includes the user ID and a summary of the inquiry.

[1974] Step 3:

[1975] The server parses the request.

[1976] Input: Request sent to the server

[1977] Output: Analysis results

[1978] Operation: The server analyzes received requests to determine their priority and content. This analysis is based on the user's past support history and current inquiry content.

[1979] Step 4:

[1980] The server selects the most suitable support person.

[1981] Input: Analysis results, support staff schedule information

[1982] Output: Instructions to connect to the person in charge

[1983] Operation: The server compares the analysis results with the support staff's schedule information to select the most suitable person. This selection takes into account the person's skill set and current workload.

[1984] Step 5:

[1985] The user submits an inquiry.

[1986] Input: User's inquiry (text or voice)

[1987] Output: Generation of query data

[1988] Operation: Users make inquiries via text or voice. In the case of voice inquiries, the system uses natural language processing (NLP) techniques to convert the speech to text.

[1989] Step 6:

[1990] The emotion engine analyzes the user's emotions.

[1991] Input: User inquiry data

[1992] Output: Sentiment data

[1993] Operation: The emotion engine analyzes query data to identify the user's emotional state (e.g., dissatisfaction, confusion, joy, etc.). This uses voice tone and text expression.

[1994] Step 7:

[1995] The server provides emotional data to the person in charge.

[1996] Input: Sentiment data

[1997] Output: Information package for the person in charge

[1998] Operation: The server sends emotional data to the person in charge, allowing them to understand the user's emotional state.

[1999] Step 8:

[2000] The person in charge will respond while taking the user's feelings into consideration.

[2001] Input: Information package for the person in charge

[2002] Output: Response to the user

[2003] Operation: Based on the received emotional data and analysis results, the staff member will provide thoughtful responses that take the user's emotions into consideration. For example, they will provide more helpful and clearer information to users who are feeling dissatisfied.

[2004] Personalized product suggestions

[2005] Processing steps

[2006] Step 1:

[2007] The user selects the "Product Suggestions" menu.

[2008] Input: User actions

[2009] Output: Generate product suggestion request

[2010] Operation: When the user selects the "Product Suggestions" menu within the app, the system generates a product suggestion request.

[2011] Step 2:

[2012] The user enters their desired category and conditions.

[2013] Input: User input information (category, conditions)

[2014] Output: Product proposal conditions

[2015] Operation: The user enters their desired category (e.g., diet, muscle training) and conditions. This information is registered in the system as product suggestion criteria.

[2016] Step 3:

[2017] The device sends information to the server.

[2018] Input: Product proposal conditions

[2019] Output: Product proposal conditions sent to the server

[2020] Operation: The terminal sends product suggestion conditions to the server. The information is communicated using a secure protocol.

[2021] Step 4:

[2022] The server analyzes the database.

[2023] Input: Product suggestion criteria, user history data

[2024] Output: Generation of personalized product lists

[2025] Operation: The server analyzes the database based on product suggestion criteria and the user's past purchase history to select the most suitable product.

[2026] Step 5:

[2027] The emotion engine analyzes the user's emotions and adjusts the product recommendations accordingly.

[2028] Input: Product list, user sentiment data

[2029] Output: Adjusted product list

[2030] Operation: The emotion engine analyzes the user's purchase history and current emotions to adjust the frequency and order of product suggestions in the product list.

[2031] Step 6:

[2032] The server provides the generated list to the terminal.

[2033] Input: Adjusted product list

[2034] Output: List display on the user's terminal

[2035] Operation: The server sends a refined product list to the user's terminal, and the terminal displays the list.

[2036] Step 7:

[2037] The user purchases the suggested product.

[2038] Input: User purchase operation

[2039] Output: Purchase data

[2040] Operation: The user selects the desired product from the suggested product list and proceeds with the purchase. Payment information is also included.

[2041] Product information and recipes

[2042] Processing steps

[2043] Step 1:

[2044] The user enters keywords into the search bar within the app.

[2045] Input: Search keywords

[2046] Output: Generating a search request

[2047] How it works: When the user enters keywords into the search bar within the app, the system generates a search request.

[2048] Step 2:

[2049] The device sends information to the server.

[2050] Input: Search Request

[2051] Output: Search request sent to the server

[2052] Operation: The terminal sends a search request to the server. This process uses an internet connection.

[2053] Step 3:

[2054] The server performs a keyword search.

[2055] Input: Keywords for your search request

[2056] Output: Search Results

[2057] Operation: The server performs a keyword search within the database and extracts relevant product information and recipes.

[2058] Step 4:

[2059] The emotion engine analyzes the user's emotions and adjusts the search results accordingly.

[2060] Input: Search results, user sentiment data

[2061] Output: Adjusted search results

[2062] How it works: The sentiment engine adjusts search results, optimizing the display order and content based on the user's emotions.

[2063] Step 5:

[2064] The server sends the results to the terminal.

[2065] Input: Adjusted search results

[2066] Output: Search results displayed on the user's terminal

[2067] Operation: The server sends the adjusted search results to the terminal, and the terminal displays them.

[2068] Step 6:

[2069] The user views the results.

[2070] Input: Adjusted search results

[2071] Output: User browsing data

[2072] Operation: The user views the displayed search results and clicks on relevant links as needed.

[2073] Automated order processing

[2074] Processing steps

[2075] Step 1:

[2076] The user selects the "automatic order" function.

[2077] Input: User actions

[2078] Output: Generation of automated order requests

[2079] Operation: When the user selects the "Auto Order" function within the app, the system generates an automatic order request.

[2080] Step 2:

[2081] The user enters the product and the cycle.

[2082] Input: Product information, order cycle

[2083] Output: Automatic Order Settings

[2084] Operation: The user enters the products they wish to purchase and their order frequency (daily, weekly, monthly, etc.). This configuration information is saved in the system.

[2085] Step 3:

[2086] The device sends configuration information to the server.

[2087] Input: Automatic Order Settings

[2088] Output: Configuration information sent to the server

[2089] Operation: The terminal sends automatic order setting information to the server.

[2090] Step 4:

[2091] The server stores information and manages order schedules.

[2092] Input: Configuration information

[2093] Output: Saved schedule information

[2094] Operation: The server stores the received configuration information and manages the order schedule.

[2095] Step 5:

[2096] The server automatically generates orders based on the specified interval.

[2097] Input: Schedule information, inventory data

[2098] Output: Automated order data

[2099] Operation: The server automatically generates orders based on a specified cycle and processes them by cross-referencing them with inventory data.

[2100] Step 6:

[2101] The emotion engine analyzes user emotions and provides promotions and benefits accordingly.

[2102] Input: User sentiment data

[2103] Output: Promotional and bonus data

[2104] Operation: The emotion engine analyzes the user's emotions and, if satisfaction is low, generates and provides promotions or rewards.

[2105] Step 7:

[2106] The server sends an automated order notification to the device.

[2107] Input: Auto-generated order data

[2108] Output: Notification to the device

[2109] Operation: The server sends information about the generated order to the terminal and notifies the user.

[2110] This processing flow improves the user experience and enables the provision of efficient and personalized support and services.

[2111] (Application Example 2)

[2112] 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."

[2113] Traditional customer support and product recommendation systems provided uniform responses without considering the user's emotional state, resulting in a lack of sufficient personalization of the user experience. This led to decreased user satisfaction and a high likelihood of customer churn. Furthermore, there was no system in place to sense the anxiety and satisfaction levels of occupants in autonomous vehicles in real time and provide appropriate feedback.

[2114] 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.

[2115] In this invention, the server includes the following means:

[2116] The system includes a sentiment analysis engine for collecting sentiment data from users accessing customer support, a server for analyzing the sentiment data and providing appropriate information to support staff, a system for staff to adjust their responses considering the user's sentiment information, a system for generating personalized product lists based on user input conditions and adjusting suggestions based on sentiment data, and a system for adjusting search results based on sentiment data when users search for product information or recipes. This makes it possible to personalize the user experience and significantly improve user satisfaction. It also enables appropriate feedback in autonomous vehicles that responds to the occupants' emotions.

[2117] An "input device" is an electronic device used by a user to input information and give instructions to a system.

[2118] "Customer support" refers to the activity of providing assistance and solutions to users regarding problems and questions they have about products and services.

[2119] A "server" is a computer system that operates on a network and provides functions such as data processing and storage.

[2120] An "appropriate support representative" is someone who can provide the most suitable response based on the user's inquiry and the support they require.

[2121] An "emotion analysis engine" is software that analyzes a user's emotional state from their voice or text and extracts that data.

[2122] "Emotional data" refers to data that indicates a user's emotional state, extracted by an emotion analysis engine.

[2123] A "personalized product list" is a list of products that has been individually customized based on the user's input criteria and past browsing history.

[2124] "Search results" are a list of information that the server extracts from the database based on the keywords entered by the user.

[2125] This invention combines a sentiment analysis engine with an integrated system that allows users to perform efficient customer support, personalized product recommendations, provide product information and recipes, and process automated orders via an input device. The following describes specific embodiments of this system.

[2126] The system primarily consists of a smartphone, a server, and an emotion analysis engine. Users interact with the system using their smartphone as an input device. The server handles all data processing and logic, while the emotion analysis engine analyzes the user's emotional data.

[2127] Strengthening customer support

[2128] The user launches the smartphone app and taps the "Customer Support" button. This action sends a support request to the server. The server receives and analyzes the request and connects the user to the appropriate support representative. Furthermore, an emotion analysis engine analyzes the user's emotions from their voice and text and sends that information to the server. Based on the emotion data, the server provides appropriate information to the representative. The representative provides a response that takes the user's emotions into consideration and resolves the problem.

[2129] Specific example: When a user asks a question about how to use a refrigerator, the app sends a query to the server. An emotion analysis engine detects dissatisfaction or confusion from the user's voice and sends that data to the server. The server analyzes the emotion data and provides it to the refrigerator product manager. The manager then provides an answer that resolves the user's confusion based on the appropriate information.

[2130] Personalized product suggestions

[2131] The user selects the "Product Suggestions" menu and enters their desired categories and criteria. This information is sent from the device to the server, which analyzes the database to generate a personalized product list. The sentiment analysis engine also takes the user's sentiment data into consideration when adjusting the product suggestions. The generated list is displayed on the device, and the user can purchase the suggested products.

[2132] Specific example: If a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history. An emotion analysis engine senses the user's happiness and satisfaction, and uses that data to increase the frequency of recommendations for specific products. Discount coupons are also provided.

[2133] Product information and recipes

[2134] When a user enters keywords into the app's search bar, the device sends this information to a server. The server performs a keyword search and extracts relevant product information and recipes from its database. An emotion analysis engine analyzes the user's emotions and uses that data to refine the search results. The search results from the server are then displayed on the user's device.

[2135] Specific example: When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes. An emotion analysis engine senses the user's excitement and expectations, and adjusts the display order of search results based on that data. Ingredients and nutritional information are also displayed, and links to purchase ingredients are provided.

[2136] Emotional response in autonomous vehicles

[2137] The system for autonomous vehicles to analyze the emotional state of the occupants in real time and take appropriate action is similarly configured. An emotion analysis engine extracts emotional data from the occupants' voice and sends it to a server. The server analyzes the emotional data, generates optimal feedback, and provides it to the occupants.

[2138] Specific example: If a passenger says, "This road feels a little dangerous," the emotion analysis engine detects the anxiety and sends that data to the server. The server analyzes the current situation of the autonomous vehicle (e.g., road conditions and speed) and generates appropriate feedback, such as, "The current road conditions are safe. We will slow down a little and proceed cautiously," which is then provided to the passenger.

[2139] Example of a prompt:

[2140] The user stated, "This road feels a little dangerous." Analyze this anxiety as emotional data and, considering the current situation of the autonomous vehicle (current speed, surrounding safety, route), generate a response that will reassure the user. Example response: "The current road conditions are safe. We will slow down a little and proceed cautiously."

[2141] The implementation of this system will allow users to enjoy a more comfortable and personalized experience. Furthermore, it can improve safety and passenger satisfaction in autonomous vehicles.

[2142] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2143] Step 1:

[2144] Users access customer support via an input device (smartphone).

[2145] A support request is generated when a user launches the app and taps the "Customer Support" button. This request includes the user's voice and text data. Input consists of the user's voice instructions and text input, and output is a request sent to the server.

[2146] Step 2:

[2147] The device sends a support request to the server.

[2148] The terminal sends the generated request to the server over the network. In this process, the generated request data is the input, and the request is forwarded to the server as the output.

[2149] Step 3:

[2150] The server receives the user's inquiry and connects them to the appropriate support representative.

[2151] The server analyzes the received request and identifies the nature of the problem. Based on the analyzed problem, it selects the most suitable support person and connects the user to them. The input is the request data, and the output is instructions for connecting to the support person.

[2152] Step 4:

[2153] The emotion analysis engine extracts emotion data from the user's voice and text.

[2154] The server uses an emotion analysis engine to analyze the user's voice and text to identify their emotional state. Input is user voice and text data, and output is generated emotion data.

[2155] Step 5:

[2156] Emotional data is sent to a server, which then analyzes the data.

[2157] The extracted emotion data is sent to a server, which then analyzes the data. The input is emotion data, and the output is the analysis results.

[2158] Step 6:

[2159] The server provides appropriate information to the person in charge based on emotional data.

[2160] Based on the analyzed emotional data, the server provides the necessary information and advice to the person in charge. The input is the results of the emotional data analysis, and the output is the provision of information to the person in charge.

[2161] Step 7:

[2162] The person in charge adjusts their response, taking into account the user's emotional information.

[2163] Support staff provide appropriate responses to users based on the information and sentiment data provided. Inputs include information and sentiment data provided by the server, and output is the response to the user.

[2164] Step 8:

[2165] The user selects the "Product Suggestion" menu and enters their desired category and conditions.

[2166] The user selects the "Product Suggestions" menu within the app and enters specific conditions such as diet or muscle training. The input consists of the user's specified conditions, and the output is a request that is sent to the server.

[2167] Step 9:

[2168] The device sends this information to the server, which then analyzes the database to generate a personalized product list.

[2169] The terminal sends user-specified conditions to the server, which then analyzes the database based on those conditions. The input is the specified conditions, and the output is a product list.

[2170] Step 10:

[2171] The emotion analysis engine takes user emotion data into account and adjusts the product recommendations accordingly.

[2172] The sentiment analysis engine considers the user's sentiment data when analyzing the generated product list and adjusts the suggested products accordingly. The input consists of a product list and sentiment data, and the output is an adjusted product list.

[2173] Step 11:

[2174] The generated list is displayed on the device, and the user purchases the suggested items.

[2175] The adjusted product list is displayed on the terminal, allowing the user to view and purchase the suggested products. The input is the adjusted product list, and the output is the display to the user and the purchase process.

[2176] Step 12:

[2177] The user enters keywords into the search bar within the app.

[2178] Users enter keywords into the search bar to search for product information or recipes. The input is the search keywords, and the output is a search request.

[2179] Step 13:

[2180] The device sends this information to the server, which then performs a keyword search.

[2181] The terminal sends keyword information to the server, which then searches the database based on those keywords. The input is keywords, and the output is search results.

[2182] Step 14:

[2183] The emotion analysis engine analyzes the user's emotions and adjusts the search results accordingly.

[2184] The sentiment analysis engine considers the user's sentiment data and adjusts the search results accordingly. The input consists of the search results and sentiment data, and the output is the adjusted search results.

[2185] Step 15:

[2186] The search results from the server are displayed on the user's terminal.

[2187] The adjusted search results are sent to the device and displayed to the user. The adjusted search results are the input, and the output is what is displayed to the user.

[2188] Step 16:

[2189] While a user is riding in an "autonomous vehicle," an emotion analysis engine extracts emotional data from speech and text.

[2190] The emotion analysis engine analyzes the crew's voices and extracts emotion data. Voice data is the input, and emotion data is generated as the output.

[2191] Step 17:

[2192] The server analyzes emotional data and the vehicle's current status data to generate appropriate feedback.

[2193] The server analyzes emotional data and the vehicle's current status (speed, road conditions, etc.) to generate feedback. Emotional data and vehicle status data are inputs, and feedback is generated as output.

[2194] Step 18:

[2195] Provide the generated feedback to the crew.

[2196] The generated feedback is provided to the crew via a terminal. The generated feedback is the input, and the output is provided to the crew.

[2197] 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.

[2198] 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.

[2199] 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.

[2200] [Fourth Embodiment]

[2201] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[2202] 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.

[2203] 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).

[2204] 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.

[2205] 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.

[2206] 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).

[2207] 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.

[2208] 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.

[2209] 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.

[2210] 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.

[2211] 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.

[2212] 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.

[2213] 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".

[2214] Modes for carrying out the invention

[2215] This invention relates to an integrated system for enabling users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system is configured as follows:

[2216] Strengthening customer support

[2217] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. The representative then assists the user via chat or voice and resolves the issue.

[2218] Specific example:

[2219] When a user asks a question about how to use the refrigerator, the app sends a query to the server. The server analyzes the question and connects the user to the refrigerator's product representative. The representative then provides the appropriate answer.

[2220] Personalized product suggestions

[2221] The user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. The generated list is displayed on the device, and the user can purchase the suggested products.

[2222] Specific example:

[2223] If a user is looking for diet foods, the server will recommend low-calorie foods and diet supplements based on their past purchase history, and will also provide discount coupons.

[2224] Product information and recipes

[2225] When a user enters keywords into the app's search bar, the device sends this information to the server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user.

[2226] Specific example:

[2227] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase the ingredients are also provided.

[2228] Automated order processing

[2229] The user selects "Automatic Order" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[2230] Specific example:

[2231] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a specified day each month and ships the product. An order confirmation notification is sent to the user's device.

[2232] The above describes the embodiments for carrying out the present invention. This system allows users to efficiently and effectively receive customer support, product suggestions, search for product information and recipes, and place automatic orders using their smartphones.

[2233] The following describes the processing flow.

[2234] Strengthening customer support

[2235] Processing steps:

[2236] Step 1:

[2237] The user taps the app icon on their smartphone's home screen to launch the app.

[2238] Step 2:

[2239] The user taps the "Customer Support" button within the app.

[2240] Step 3:

[2241] The device sends a support request to the server. This request includes the user ID, current screen information, and the content of the inquiry.

[2242] Step 4:

[2243] The server receives the request and analyzes the inquiry. If necessary, it forwards it to an automated response system or the appropriate person.

[2244] Step 5:

[2245] Based on the analysis results, the server connects to a chatbot or a human representative to generate a response.

[2246] Step 6:

[2247] The terminal displays the response information received from the server to the user. Support is provided in chat or voice format.

[2248] ---

[2249] Personalized product suggestions

[2250] Processing steps:

[2251] Step 1:

[2252] The user taps the "Product Suggestions" menu within the app.

[2253] Step 2:

[2254] Users input the product category and purpose they want suggested (e.g., diet, muscle training) via text or voice.

[2255] Step 3:

[2256] The terminal sends the user's input conditions to the server.

[2257] Step 4:

[2258] The server analyzes the database based on user input, past purchase history, and preference data.

[2259] Step 5:

[2260] The server generates a personalized product list based on the analysis results. This list includes relevant products, promotions, and discount information.

[2261] Step 6:

[2262] The terminal displays the product list received from the server to the user.

[2263] ---

[2264] Product information and recipes

[2265] Processing steps:

[2266] Step 1:

[2267] The user taps the search bar within the app.

[2268] Step 2:

[2269] Users enter keywords related to their interests, such as cooking methods, nutrients, and ingredients.

[2270] Step 3:

[2271] The device sends the keyword to the server.

[2272] Step 4:

[2273] The server performs a keyword search on the database and extracts relevant product information and recipes.

[2274] Step 5:

[2275] The server generates search results and formats them into a format for providing to the user.

[2276] Step 6:

[2277] The terminal displays the search results received from the server to the user.

[2278] ---

[2279] Automated order processing

[2280] Processing steps:

[2281] Step 1:

[2282] The user selects the "Automatic Ordering" setup screen within the app.

[2283] Step 2:

[2284] The user enters the product, quantity, purchase frequency (e.g., weekly, monthly), and payment method to be purchased.

[2285] Step 3:

[2286] The device sends configuration information to the server.

[2287] Step 4:

[2288] The server saves the automatic order settings to the database and registers the order schedule.

[2289] Step 5:

[2290] The server automatically generates orders based on the purchase cycle and verifies the user's payment information.

[2291] Step 6:

[2292] The server sends shipping instructions to the warehouse system and completes the order in conjunction with the logistics management system.

[2293] Step 7:

[2294] The terminal notifies the user that automatic ordering has been successfully set up, and the user receives a notification when each order is processed.

[2295] ---

[2296] The above details the specific operations at each processing step. This configuration allows users to easily access customer support, product suggestions, product information and recipes, and place automatic orders using their smartphones.

[2297] (Example 1)

[2298] 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".

[2299] Traditional customer support systems often failed to properly analyze user inquiries and quickly connect users to the appropriate support staff. Furthermore, personalized product recommendations frequently did not fully utilize past purchase history, making it difficult to generate optimal product lists for users. Additionally, product information and recipe search results often did not match user expectations, resulting in a poor user experience.

[2300] 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.

[2301] In this invention, the server includes means for receiving inquiries from users and analyzing the content of the inquiries using a natural language processing algorithm, means for selecting the most suitable support person based on the analysis results and connecting to the support person, and means for the support person to respond to the user and resolve the problem. This makes it possible to quickly analyze the content of user inquiries and connect to the most suitable support person.

[2302] Furthermore, the server includes means for generating individually personalized product lists using a generative AI model, taking into account past purchase history based on user input conditions; means for displaying suggestions from the server on the user's terminal; and means for the user to purchase the suggested products. This provides the user with the most suitable product suggestions and facilitates purchasing behavior.

[2303] Furthermore, the server includes means for performing keyword searches and extracting relevant information from the database, as well as means for displaying the extraction results on the user's terminal. This ensures that the information and recipes the user is looking for are provided quickly and accurately, improving the user experience.

[2304] A "user" is an individual or legal entity that uses the system to perform tasks such as customer support, product recommendations, and product information searches.

[2305] An "input device" is a device used by a user to input information, and examples include smartphones, tablets, and personal computers.

[2306] "Customer support" is a service in which support staff respond to inquiries and problems from users and attempt to resolve them.

[2307] A "server" is a device or system that processes information on a network, receives requests from users, and performs appropriate processing in response to those requests.

[2308] A "natural language processing algorithm" is a set of computational methods and techniques that enable computers to understand and analyze human language.

[2309] A "support staff member" is a staff member or agent who handles inquiries from users in customer support.

[2310] A "personalized product list" is a list of product suggestions that is generated individually based on the user's input criteria and past purchase history.

[2311] A "generative AI model" is a computational model that uses artificial intelligence to analyze data and generate new information or suggestions.

[2312] A "user terminal" is a device that a user directly operates to access the system, and includes smartphones, tablets, and personal computers.

[2313] "Keyword search" is the process by which a user enters a specific word or phrase and searches a database for information related to that word.

[2314] A "database" is a collection of data that is organized and stored so that it can be quickly and efficiently searched and retrieved when needed.

[2315] This invention relates to an integrated system for users to efficiently provide customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices. The system is designed to be easily operated by users using input devices such as smartphones, tablets, and personal computers.

[2316] Strengthening customer support

[2317] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server analyzes the received request using a natural language processing algorithm and selects the most suitable support representative. Once a representative is selected based on the analysis results, the server forwards the request to that representative, and the user can receive support via chat or voice.

[2318] Specific example:

[2319] When a user asks a question about how to use the refrigerator, the app sends a query to the server. The server analyzes the question and connects the user to the refrigerator's product representative. The representative then provides the appropriate answer.

[2320] Prompt example:

[2321] "I don't know how to use the refrigerator. How do I operate it?"

[2322] Personalized product suggestions

[2323] When a user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training), the device sends this information to the server. The server uses a generative AI model to analyze the database and generate a personalized product list. The generated list is displayed on the device, and the user can purchase the suggested products.

[2324] Specific example:

[2325] If a user is looking for diet foods, the server will recommend low-calorie foods and diet supplements based on their past purchase history, and will also provide discount coupons.

[2326] Prompt example:

[2327] "Based on my past purchase history, please recommend some diet foods."

[2328] Product information and recipes

[2329] When a user enters keywords into the app's search bar, the device sends this information to the server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user.

[2330] Specific example:

[2331] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase the ingredients are also provided.

[2332] Prompt example:

[2333] "Can you share a low-calorie smoothie recipe?"

[2334] Automated order processing

[2335] When a user selects "Automatic Ordering" within the app and enters the product and frequency, the device sends the settings information to the server. The server stores this information and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[2336] Specific example:

[2337] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a specified day each month and ships the product. An order confirmation notification is sent to the user's device.

[2338] This system allows users to efficiently and effectively receive customer support, product recommendations, search for product information and recipes, and place automatic orders using their smartphones.

[2339] The flow of the specific processing in Example 1 will be explained using Figure 11.

[2340] Strengthening customer support

[2341] Step 1:

[2342] The user launches the app on their smartphone and taps the "Customer Support" button.

[2343] Operation: The user taps the app to launch it and selects the "Customer Support" button from the home screen. This initiates a customer support request.

[2344] Input: User action (button tap).

[2345] Output: The terminal generates support request data.

[2346] Step 2:

[2347] The terminal sends the generated support request data to the server.

[2348] Operation: The terminal encodes the data containing the support request and sends it to the server.

[2349] Input: Support request data.

[2350] Output: The request data reaches the server.

[2351] Step 3:

[2352] The server receives the request and parses it using a natural language processing algorithm.

[2353] Operation: The server parses the received request and applies NLP algorithms to understand the user's inquiry.

[2354] Input: Request data.

[2355] Output: Understanding the query content from the analysis results.

[2356] Step 4:

[2357] The server selects the most suitable support person based on the analysis results.

[2358] Operation: The server searches the database for the appropriate person in charge and selects the most suitable person.

[2359] Input: Analysis results.

[2360] Output: Information on the selected support staff member.

[2361] Step 5:

[2362] The server forwards the request to the person in charge and establishes the connection.

[2363] Operation: The server forwards the request to the designated contact person and establishes a chat or voice call connection.

[2364] Input: Person in charge information, request data.

[2365] Output: A connection notification is sent to the user's terminal.

[2366] Personalized product suggestions

[2367] Step 1:

[2368] The user selects the "Product Suggestions" menu in the app and enters their desired category and conditions.

[2369] Action: The user taps the "Product Suggestion" menu and enters their request in the form for entering conditions.

[2370] Input: Enter the user's category and conditions.

[2371] Output: Conditional data is generated.

[2372] Step 2:

[2373] The device sends this conditional data to the server.

[2374] Operation: The terminal encodes the input condition data and sends it to the server.

[2375] Input: Conditional data.

[2376] Output: The condition data reaches the server.

[2377] Step 3:

[2378] The server uses an AI model to analyze the database and generate individually personalized product lists.

[2379] Operation: The server searches the database and uses a generative AI model to generate a list of products that match the user's criteria.

[2380] Input: Conditional data, past purchase history.

[2381] Output: Personalized product list.

[2382] Step 4:

[2383] The server sends the generated product list to the terminal.

[2384] Operation: The server encodes the product list and sends it to the user's terminal.

[2385] Input: Product list.

[2386] Output: The product list arrives at the terminal.

[2387] Step 5:

[2388] The terminal displays the product list it received to the user.

[2389] Operation: The terminal decodes the product list and displays it in the user interface.

[2390] Input: Product list.

[2391] Output: The user is shown a list of products.

[2392] Product information and recipes

[2393] Step 1:

[2394] The user enters keywords into the search bar within the app.

[2395] Operation: The user taps the app's search bar and enters keywords for the information they are looking for.

[2396] Input: User keyword input.

[2397] Output: Keyword data is generated.

[2398] Step 2:

[2399] The device sends this keyword data to the server.

[2400] Operation: The terminal encodes the keyword data and sends it to the server.

[2401] Input: Keyword data.

[2402] Output: Keyword data reaches the server.

[2403] Step 3:

[2404] The server performs a keyword search and extracts relevant information from the database.

[2405] Operation: The server searches the database based on keywords and extracts relevant product information and recipes.

[2406] Input: Keyword data.

[2407] Output: Search results data.

[2408] Step 4:

[2409] The server sends the extracted search results to the terminal.

[2410] Operation: The server encodes the search results and sends them to the user's device.

[2411] Input: Search results data.

[2412] Output: Search results arrive at the terminal.

[2413] Step 5:

[2414] The search results received by the device are displayed to the user.

[2415] Operation: The device decodes the search results and displays them in the user interface.

[2416] Input: Search results data.

[2417] Output: The user is shown the search results.

[2418] Automated order processing

[2419] Step 1:

[2420] The user selects "Automatic Order" within the app and enters the product and delivery frequency.

[2421] Operation: The user taps the "Automatic Order" menu and enters the desired products and their frequency into the form.

[2422] Input: Enter the product and cycle.

[2423] Output: Automatic order setting data is generated.

[2424] Step 2:

[2425] The device sends this configuration data to the server.

[2426] Operation: The terminal encodes the automatic order setting data and sends it to the server.

[2427] Input: Automatic order setting data.

[2428] Output: Configuration data reaches the server.

[2429] Step 3:

[2430] The server stores configuration data and manages order schedules.

[2431] Operation: The server saves the received configuration data to the database and creates entries for managing order schedules.

[2432] Input: Automatic order setting data.

[2433] Output: Saved configuration data, schedule entries.

[2434] Step 4:

[2435] The server automatically generates orders based on the specified cycle.

[2436] Operation: The server automatically generates order data based on a cycle and prepares the data for order processing.

[2437] Input: Schedule entry.

[2438] Output: Order data.

[2439] Step 5:

[2440] After the server generates the order, it issues a shipping instruction.

[2441] Operation: The server uses the generated order data to create shipping instructions and sends them to the logistics system.

[2442] Input: Order data.

[2443] Output: Shipping instructions.

[2444] Step 6:

[2445] Once the order is processed, the server sends an order confirmation notification to the terminal.

[2446] Operation: After issuing a shipping instruction, the server encodes an order confirmation notification and sends it to the user's terminal.

[2447] Input: Shipping instructions.

[2448] Output: An order confirmation notification is sent to the terminal.

[2449] (Application Example 1)

[2450] 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".

[2451] Traditional customer support systems and online shopping sites struggle to efficiently provide personalized support, product recommendations, product information, and automated order management to users. In particular, there is a demand for automated and user-optimized product recommendations and automated order processing, but achieving this presents numerous technical challenges.

[2452] 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.

[2453] In this invention, the server includes means for the user to access customer support via an input device, means for the server to receive inquiries from the user and connect to the appropriate support person, means for the support person to respond to the user and resolve the problem, means for the user to input configuration information into a terminal and send the configuration information to the server, means for the server to generate an automated order based on the configuration information and manage the order, and means for sending a notification to the terminal after the automated order has been processed. This enables the user to efficiently receive customer support, receive personalized product suggestions, search for product information and recipes, and place automated orders.

[2454] An "input device" is hardware used by users to input information, and includes smartphones and tablets.

[2455] "Customer support" refers to assistance services provided to users to help them resolve questions and problems related to products and services.

[2456] A "server" is a computer system that provides data and services to multiple clients over a network.

[2457] An "inquiry" refers to a question or request made by a user seeking support.

[2458] A "support staff member" is a staff member assigned to provide customer support.

[2459] "Setting information" refers to the information that users enter when using features such as automatic ordering, and includes things like product names and order cycles.

[2460] "Automatic ordering" is a system where the server automatically orders products based on conditions specified by the user.

[2461] A "notification" is a means of communication sent from a server to a user, and includes order confirmations and update information.

[2462] "Personalized product recommendations" is a system that individually suggests the most suitable products based on the user's history and preferences.

[2463] A "profile" is a collection of data that records a user's purchase history and interests.

[2464] "Shopping history" is a record of products that a user has purchased in the past.

[2465] A "discount coupon" is a voucher offered to users to purchase specific products at a lower price.

[2466] "Keyword search" is a method of searching a database based on keywords entered by the user and extracting relevant information.

[2467] "Product information" refers to detailed information about a specific product.

[2468] A "recipe" is information about the steps and ingredients needed to make a specific dish.

[2469] A "link" is a URL that allows a user to access another webpage or piece of information by clicking on it.

[2470] This invention is an integrated system for providing efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system consists of the following elements:

[2471] Strengthening customer support

[2472] The user launches the customer support application on their smartphone and taps the "Customer Support" button. The device then sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. This support representative responds to the user via chat or voice and resolves the issue.

[2473] Specific example:

[2474] When a user asks a question about how to use their refrigerator, the app sends a query to the server. The server analyzes the question, connects to the refrigerator's product representative, and the representative provides the appropriate answer. For example: "I bought a refrigerator. I don't know how to use it, so please tell me how."

[2475] Personalized product suggestions

[2476] The user selects the "Product Suggestions" menu and enters their desired category and criteria (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database to generate a personalized product list. This generated list is displayed on the device, and the user can purchase the suggested products. The server uses the user profile and shopping history to suggest the most suitable products and also provides discount coupons.

[2477] Specific example:

[2478] When a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history, and also provides discount coupons. "I'm looking for diet foods. Can you recommend some low-calorie options?"

[2479] Product information and recipes

[2480] When a user enters keywords into the app's search bar, their device sends this information to a server. The server performs a keyword search, extracts relevant product information and recipes from its database, and displays them to the user. The search results include relevant product information and recipes, as well as links to purchase ingredients.

[2481] Specific example:

[2482] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes, displaying ingredients and nutritional information. Links to purchase ingredients are also displayed. "Please share a low-calorie smoothie recipe."

[2483] Automated order processing

[2484] The user selects "Automatic Order" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. Based on the specified frequency, the server automatically generates orders and issues shipping instructions. A notification is sent to the device when the order is processed.

[2485] Specific example:

[2486] When a user sets up a monthly subscription for protein powder, the server automatically generates an order on a specified day each month and ships the product. A notification confirming the order is sent to the user's device. The message reads: "Please set up automatic monthly orders for protein powder."

[2487] This system will be implemented using cloud-based servers such as AWS and Google Cloud Platform. Furthermore, web frameworks like Flask and Django will be used to build REST APIs and process data in response to user requests. MySQL and PostgreSQL will be used as the databases.

[2488] By utilizing generative AI models and prompt messages, it is possible to provide the most optimal response immediately to user inquiries and search criteria. This allows users to efficiently receive support, product suggestions, obtain product information and recipes, and automatically order the necessary products.

[2489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[2490] Step 1:

[2491] The user launches the customer support application on their smartphone and taps the "Customer Support" button.

[2492] Input: An action performed by the user, such as tapping a button.

[2493] Action: The terminal generates a support request.

[2494] Output: A support request is generated.

[2495] Step 2:

[2496] The device sends a support request to the server.

[2497] Input: The generated support request.

[2498] Operation: Sends data to the server using an HTTP POST request.

[2499] Output: A support request is sent to the server.

[2500] Step 3:

[2501] The server analyzes the support requests it receives.

[2502] Input: Submitted support request.

[2503] Operation: Uses database queries and natural language processing (NLP) techniques to parse the request content.

[2504] Output: Identification of the appropriate support person.

[2505] Step 4:

[2506] The server connects to the most suitable support person.

[2507] Input: Identification information of the support staff member.

[2508] Action: Sends a connection request to the appropriate person.

[2509] Output: Connection to the person in charge is established.

[2510] Step 5:

[2511] Support staff respond to users and resolve issues.

[2512] Input: Connection to the person in charge and the user's inquiry.

[2513] Operation: A representative will respond to the user via chat or voice.

[2514] Output: The user's problem is resolved.

[2515] Step 6:

[2516] The user selects a product suggestion from the menu and enters their desired category and conditions.

[2517] Input: The user enters categories or conditions (e.g., diet, muscle training).

[2518] Operation: The terminal generates data to send input information to the server.

[2519] Output: Data is prepared according to the user's request.

[2520] Step 7:

[2521] The terminal sends the user's request data to the server.

[2522] Input: User's requested data.

[2523] Operation: Sends data to the server via an HTTP POST request.

[2524] Output: The server receives the user's request data.

[2525] Step 8:

[2526] The server analyzes user profiles and shopping history to generate personalized product recommendations.

[2527] Input: User request data and past purchase history.

[2528] Operation: Generates personalized product lists using database queries and machine learning models.

[2529] Output: Personalized product list.

[2530] Step 9:

[2531] The server sends the generated product list to the user's terminal.

[2532] Input: Personalized product list.

[2533] Operation: Sends a product list as an HTTP response.

[2534] Output: The product list is displayed on the user's terminal.

[2535] Step 10:

[2536] The user selects a suggested product and proceeds with the purchase.

[2537] Input: Product selection and purchase operation.

[2538] Operation: The device sends a purchase request to the server.

[2539] Output: Purchase confirmed.

[2540] Step 11:

[2541] The user enters their automatic order settings and saves them to the server.

[2542] Input: Product and cycle settings.

[2543] Operation: The terminal generates configuration information and sends it to the server.

[2544] Output: The server saves the configuration information to the database.

[2545] Step 12:

[2546] The server generates automatic orders at specified intervals based on the saved configuration information.

[2547] Input: Automatic order settings information.

[2548] Operation: The scheduler periodically generates automatic orders.

[2549] Output: An automated order is generated.

[2550] Step 13:

[2551] When an order is generated, the server sends a notification to the terminal.

[2552] Input: The result of the automated order.

[2553] Operation: Generates notification data and sends it to the device via an HTTP request.

[2554] Output: An order notification is displayed on the user's terminal.

[2555] This allows users to efficiently receive customer support, personalized product recommendations, access product information and recipes, and place automated orders. The system achieves a high level of customization and automation by combining cloud-based servers with data analytics technology.

[2556] 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.

[2557] Modes for carrying out the invention

[2558] This invention combines an emotion engine with an integrated system that enables users to receive efficient customer support, personalized product recommendations, product information and recipes, and automated order processing via input devices such as smartphones. The system is configured as follows:

[2559] Strengthening customer support

[2560] When a user launches the smartphone app and taps the "Customer Support" button, the device sends a support request to the server. The server receives and analyzes the request and connects the user to the most suitable support representative. When a user makes an inquiry using text or voice, the emotion engine analyzes the user's emotions and sends that information to the server. Based on the emotion data, the server provides appropriate information to the representative. The representative provides a response that takes the user's emotions into consideration and resolves the problem.

[2561] Specific example:

[2562] When a user asks a question about how to use the refrigerator, the app sends a query to the server. An emotion engine detects dissatisfaction or confusion from the user's voice and sends that data to the server. The server analyzes the emotion data and provides it to the refrigerator's product manager. The manager then provides an answer that resolves the user's confusion based on the appropriate information.

[2563] Personalized product suggestions

[2564] The user selects the "Product Suggestions" menu and enters their desired category and conditions (e.g., diet, muscle training). The device sends this information to the server, which analyzes the database and generates a personalized product list. The server also takes the user's sentiment data into consideration when adjusting the product suggestions. The generated list is displayed on the device, and the user can purchase the suggested products.

[2565] Specific example:

[2566] When a user is looking for diet foods, the server recommends low-calorie foods and diet supplements based on their past purchase history. An emotion engine senses the user's happiness and satisfaction, and uses that data to increase the frequency of recommendations for specific products. Discount coupons are also provided.

[2567] Product information and recipes

[2568] When a user enters keywords into the app's search bar, the device sends this information to a server. The server performs a keyword search and extracts relevant product information and recipes from its database. An emotion engine analyzes the user's emotions and uses that data to refine the search results. The search results from the server are then displayed on the user's device.

[2569] Specific example:

[2570] When a user searches for a "low-calorie smoothie" recipe, the server provides relevant smoothie recipes. An emotion engine senses the user's excitement and expectations, and adjusts the display order of search results based on that data. Ingredients and nutritional information are also displayed, and links to purchase ingredients are provided.

[2571] Automated order processing

[2572] The user selects "Automatic Ordering" within the app and enters the product and frequency. The device sends the settings information to the server, which stores it and manages the order schedule. The server automatically generates orders based on the specified frequency and issues shipping instructions. An emotion engine analyzes the user's emotions and adds promotions or benefits if the user's satisfaction level is low. A notification is sent to the device when the order is processed.

[2573] Specific example:

[2574] When a user sets up a monthly subscription to purchase protein powder, the server automatically generates an order on a designated day each month and ships the product. An emotion engine senses the user's purchasing intent and anxieties, and sends promotional emails based on that data. An order confirmation notification is sent to the user's device.

[2575] The above describes the embodiments for carrying out the present invention....

Claims

1. Means by which users can access customer support via input devices, A server that receives inquiries from users and connects them to the appropriate support personnel, A system that includes means for support staff to respond to users and resolve problems.

2. A server means that generates individually personalized product lists based on user input conditions, A means of displaying suggestions from the server on the user terminal, The system according to claim 1, comprising means for a user to purchase a suggested product.

3. A means to enable users to search for product information and recipes via input devices, A server performs a keyword search and extracts relevant information from the database, The system according to claim 1, comprising means for displaying search results from a server on a user terminal.

4. A means for users to set up automatic orders via an input device, The server has a means of saving user configuration information and managing order schedules, A means by which the server periodically generates automatic orders and issues shipping instructions, The system according to claim 1, comprising means for sending a notification to a user terminal when an order has been processed.

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