System

Abstract

A system is provided.SOLUTION: A system comprising: input means for a user to input a desired action; analysis means for a server to receive and analyze the user's input; suggestion generation means for the server to refer to a database of a tie-up point service based on an analysis result and generate a suggestion to obtain points; transmission means for the server to transmit the generated suggestion to a user terminal; and confirmation means for the user to confirm the suggestion.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Today's consumers are overwhelmed with information and find it difficult to find the most efficient way to earn points for their desired actions. This can lead to consumers not knowing the optimal way to earn points, which can be detrimental. Furthermore, the lack of a way for consumers to automatically receive appropriate suggestions for their desired actions makes it difficult to efficiently earn points. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system that includes an input means for a user to input a desired action, an analysis means in which a server receives and analyzes the user's input, a proposal generation means in which the server references a database of affiliated point services based on the analysis results and generates proposals that can earn points, a transmission means for transmitting the proposals generated by the server to the user's terminal, and a confirmation means in which the user confirms the proposals.

[0006] In the system of the present invention, when a user simply inputs "I want to do XX," the analysis means analyzes the input, searches the database of affiliated point services, and suggests the optimal way to earn points. These suggestions are organized so that users can acquire points most efficiently, allowing users to easily and effectively participate in point activities. Therefore, the present invention makes users' point activities more efficient and enables consumers to quickly obtain the most appropriate information.

[0007] "Input means" refers to an interface for inputting a user's desired behavior or action.

[0008] "Analysis means" refers to a device or software that analyzes information input by a user and understands its content.

[0009] The "proposal generating means" refers to a device or software for referring to the database of the affiliated point service based on the information analyzed by the analyzing means, and proposing the most suitable point earning method to the user.

[0010] "Affiliated point service database" refers to a database that aggregates and manages information on multiple point services and promotions.

[0011] "Transmitting means" refers to a device or software for transmitting the generated proposal to the user's terminal.

[0012] "Verification means" refers to an interface for a user to verify a submitted proposal.

[0013] "Natural language processing technology" refers to artificial intelligence technology for analyzing human language and understanding its meaning.

[0014] "Suggestion" refers to information such as the specific method and location for earning points by performing a desired action by the user.

[0015] "User's device" refers to a device used by a user, such as a smartphone, tablet, or PC.

[0016] "User desired action" refers to a specific behavior or activity that the user wishes to perform. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The system of the present invention supports a series of operations from inputting a user's desired action to generating, sending, and confirming a proposal. Specific embodiments of the present invention will be described in detail below.

[0039] First, the user inputs an action into the terminal. For example, the user inputs "I want to go on a trip." At this time, the user's terminal provides an input interface to allow the user to input the desired action. After the input is complete, the terminal transmits the input data to the server.

[0040] The server then analyzes the input text data. Specifically, the server uses natural language processing technology to understand that the user entered "I want to go on a trip." In this analysis step, key words and phrases are extracted and it is determined that "travel" is the user's desired action.

[0041] The server then accesses a database of affiliated point services to search for travel-related reward point offers. This database contains information about various reward point services and campaigns, such as reward point offers for booking accommodations on a specific travel site.

[0042] From the search results, the server sorts through multiple suggestions and selects the most beneficial one for the user. For example, a suggestion may be found that if you book a hotel stay on a certain travel site, you will receive certain points. Based on this information, the server generates a suggestion message for the user.

[0043] This generated suggestion message is sent from the server to the user's device. The user's device receives the message and displays it on the screen. The user checks the suggestion message and decides what to do based on its content. For example, the user can choose to make a hotel reservation on a travel site.

[0044] As a concrete example, consider the case where a user inputs "I want to buy a new smartphone." The user's device sends the input to the server, which analyzes the input. The server then refers to a points service database and searches for offers that will earn points related to the new smartphone. For example, the server finds information that says that if you purchase a smartphone from a specific online shop, you will receive 1,000 points. The server sends this offer information to the user's device, and the user confirms it and then purchases the smartphone from the online shop.

[0045] In this way, the system of the present invention makes the user's point activities more efficient by automatically generating and providing optimal proposals that will earn points for the user's desired actions.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user inputs a desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal provides an interface to receive this input.

[0049] Step 2:

[0050] The terminal transmits the user's input to the server, specifically, using a communication protocol for transferring the input text data to the server.

[0051] Step 3:

[0052] The server receives input data from the terminal and prepares to analyze this data.

[0053] Step 4:

[0054] The server analyzes the user's input using natural language processing (NLP) technology. As a result of the analysis, the keyword "travel" is extracted and it is understood that the user's intention is "I want to go on a trip."

[0055] Step 5:

[0056] Based on the analysis results, the server accesses a database of affiliated point services and searches for information on point services and campaigns related to the keyword "travel."

[0057] Step 6:

[0058] The server extracts suggestions that can earn points from the search results. For example, it finds information such as "If you make a hotel reservation on a certain travel site, you will receive △△ points."

[0059] Step 7:

[0060] The server sorts through multiple offers and selects the most suitable offer for the user, taking into account factors such as point redemption rates and campaign periods.

[0061] Step 8:

[0062] The server generates a proposal message for the user based on the selected proposal. Specifically, it creates a message that says, "If you want to go on a trip, you can get △△ points by booking a hotel at XX travel site."

[0063] Step 9:

[0064] The server sends the generated proposal message to the user's terminal, and the data is transferred to the user's terminal using an appropriate communication protocol.

[0065] Step 10:

[0066] The terminal receives the proposal message from the server, and then displays the message on the screen.

[0067] Step 11:

[0068] The user checks the suggestion message, understands the content displayed on the device, and decides on an action based on the suggestion.

[0069] Step 12:

[0070] The user performs an action based on the suggestion, for example, booking a hotel room on a travel site and earning points.

[0071] Example 1

[0072] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0073] With today's diverse range of point services and campaigns, it is difficult for users to find the optimal point offer based on their desired actions. Users often have to compare multiple platforms to find the best deal. This not only requires a great deal of time and effort, but also increases the chances of missing the best offer. The present invention aims to solve these problems by automatically generating and efficiently providing the optimal point offer for the user's desired actions.

[0074] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0075] In this invention, the server includes input means for a user to input a desired action, transmission means by which the terminal transmits the user's input data to the server, analysis means by which the server receives and analyzes the input data, means by which the server analyzes the user's input using natural language processing technology, proposal generation means by which the server references a linkage points database based on the analysis results and generates proposals that can earn points, transmission means by which the server transmits the proposals generated by the server to the terminal, display means by which the terminal displays the proposals to the user, and confirmation means by which the user confirms the proposals. This enables the user to efficiently receive and reliably confirm optimal point proposals for their desired actions.

[0076] A "user" is an entity that utilizes the system to input specific actions and receive suggestions from the server based on those actions.

[0077] "Terminal" refers to a device that allows a user to input a desired action through an input means, transmits the input data to a server, and receives and displays suggested messages from the server.

[0078] The "server" is a central computer system that receives user input data, analyzes it, generates point proposals, and sends them to the terminals.

[0079] "Input means" refers to an interface that allows a user to input a specific action into a terminal, and includes a keyboard, voice input, and the like.

[0080] The "transmission means" is a communication means by which the terminal transmits the user's input data to the server and by which the server transmits the generated suggestions to the terminal.

[0081] The "analysis means" is a function in which the server receives input data from the user, analyzes the data using natural language processing technology, and identifies the action the user wants to take.

[0082] "Natural language processing technology" is a technology for analyzing input text data and understanding its content and meaning, and includes models such as BERT and GPT-3.

[0083] The "linked points database" is a database that contains information on various point services and campaigns, and is referenced by the server based on the analysis results.

[0084] The "proposal generation means" is a function that allows the server to generate optimal point proposals for the user based on the analysis results.

[0085] The "display means" is a function for visually displaying to the user the proposal message that the terminal receives from the server.

[0086] The "confirmation means" is a function that allows the user to confirm the proposal from the server on the terminal and decide on an action based on the proposal.

[0087] The system of the present invention streamlines users' point activities by generating and providing optimal point proposals based on the user's input of a desired action. Specifically, the user inputs a specific action using a terminal, and then a series of processes are carried out to generate and provide a proposal message to the user.

[0088] First, the user inputs an action into the terminal. For example, when the user inputs "I want to go on a trip," the terminal provides an input interface (keyboard, voice input, etc.) so that the user can input the desired action. Once input is complete, the terminal sends this input data to the server. When sending, the data is encrypted using HTTPS, a secure communication method.

[0089] The server uses natural language processing techniques (such as Google's BERT or OpenAI's GPT-3) to analyze the received input data. During the analysis, it understands that the user entered "I want to go on a trip" and extracts important keywords and phrases. This identifies "travel" as the user's desired action.

[0090] Next, the server accesses the linked point database based on the analysis results to search for relevant point offers. This database contains information on various point services and campaigns, such as information on points that can be earned by booking accommodation on a specific travel site. The server sorts through the search results and selects the most beneficial offer for the user.

[0091] The server generates a proposal message for the user based on the selected proposal. The proposal message is generated using text generation technology (e.g., OpenAI's GPT-3). The generated proposal message is then sent from the server to the device. This transmission is also done securely.

[0092] The device will display the received suggestion message on the screen. The user can then confirm the suggestion and decide on their next course of action based on the suggestion. For example, the user can confirm a suggestion such as "If you make a hotel reservation on a certain travel site, you will receive △△ points," and then actually make a hotel reservation on that travel site.

[0093] As a concrete example, consider the case where a user inputs "I want to buy a new smartphone." The user's device sends the text "I want to buy a new smartphone" to the server, which analyzes it. As a result of the analysis, the server searches for point offers related to smartphone purchases, and generates an offer such as "If you purchase a smartphone from a specific online shop, you will receive 1,000 points," and sends it to the user. The user can confirm this offer and purchase the smartphone from the online shop.

[0094] An example of a prompt statement would be:

[0095] Type: "I want to buy a new smartphone"

[0096] Parsing: The server uses an NLP model (GPT-3) to parse the input and extract the intent "I want to buy a smartphone."

[0097] Suggestion search: The server queries the database to find relevant point suggestions.

[0098] Proposal generation: "Buy a new smartphone at a specific online store and receive 1,000 points."

[0099] Ask: Show the user the suggestion and encourage them to take action.

[0100] In this way, the system of the present invention automatically generates optimal point suggestions for the user's desired actions and provides them efficiently and reliably. The hardware and software used include natural language processing models (BERT and GPT-3), HTTPS communication, and terminals such as smartphones and PCs.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] Step 1:

[0103] The user inputs the desired action into the device. The user inputs a specific action (e.g., "I want to go on a trip") using the device's input interface (keyboard, voice input, etc.). The input data is in text format.

[0104] Input: The user enters a specific action in text format.

[0105] Output: Text data is input to the terminal

[0106] Step 2:

[0107] The device sends the input data to the server. The device temporarily stores the input data and sends it to the server in an encrypted format using HTTPS.

[0108] Input: Text data entered by the user

[0109] Output: Text data is sent to the server

[0110] Step 3:

[0111] The server receives and analyzes the input text data. The received data is analyzed using an NLP model (e.g., GPT-3) to extract the intent and important keywords of the input text.

[0112] Input: Text data received from the device

[0113] Output: Parsed intent and keywords

[0114] Step 4:

[0115] The server references the federated points database based on the analysis results to search for relevant points offers, and executes a database query to search for points campaigns related to the user's desired action.

[0116] Input: Keywords extracted as analysis results

[0117] Output: A dataset of relevant point proposals

[0118] Step 5:

[0119] The server selects the most suitable point suggestions from the search results and generates a suggestion message. It uses text generation technology (e.g., GPT-3) to create a suggestion message that is useful to the user.

[0120] Input: Dataset of point proposals

[0121] Output: The generated proposal message

[0122] Step 6:

[0123] The server sends the generated proposal message to the terminal. The message is sent to the terminal using a secure communication method (HTTPS).

[0124] Input: The generated proposal message

[0125] Output: Proposal message sent to the terminal

[0126] Step 7:

[0127] The terminal displays the received proposal message to the user. The terminal displays the received message on the user interface so that the user can check it.

[0128] Input: Proposal message received from the server

[0129] Output: Proposal displayed on the device screen

[0130] Step 8:

[0131] The user checks the suggestion message and decides on an action based on it. The user selects the next action based on the suggested content (e.g., "If you make a hotel reservation on the XX website, you will receive XX points").

[0132] Input: The suggestion message displayed on the terminal

[0133] Output: User decision on action

[0134] (Application example 1)

[0135] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0136] Today's consumers need to quickly and efficiently obtain information to purchase products and services under the most favorable conditions amid the vast amount of online information and offers available. However, current systems require users to search for and compare offer information themselves, which takes a great deal of time and effort. Furthermore, because offer information is scattered, it is not easy for users to find the best offer.

[0137] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0138] In this invention, the server includes: an input means for inputting a user's desired behavior; an analysis means for receiving and analyzing the user's input; a proposal generation means for referencing a database of affiliated reward services based on the analysis results and generating proposals for obtaining rewards; a transmission means for transmitting the proposals generated by the server to the user's terminal; a confirmation means for the user to confirm the proposals; a means for the proposal generation means to automatically search for point rewards and campaign information related to the virtual store; and a means for the analysis means to create prompt sentences using a generative AI model for generating information related to the user's purchasing behavior. This enables the user to quickly and efficiently obtain reward information for purchasing products and services in the virtual store under the most favorable conditions.

[0139] "User" refers to an individual who utilizes the system to input desired actions and obtain information.

[0140] An "action" refers to a specific action desired by a user, such as a desire to purchase a product.

[0141] "Input means" refers to an interface for a user to input a desired action into a terminal.

[0142] "Server" refers to the computer system that receives and analyzes user input, and generates and transmits reward information.

[0143] "Analysis means" refers to the function of the server receiving user input and analyzing it using natural language processing technology, etc.

[0144] "Affiliate reward services" refers to a group of services included in a database accessed by the server that provide points or rewards for specific actions.

[0145] "Database" refers to a system with a data structure for storing and managing information regarding affiliated special services.

[0146] The "proposal generating means" refers to a function that generates a proposal for obtaining a benefit based on the analysis result and provides it to the user.

[0147] The "transmission means" refers to a function for transmitting the generated proposal to the user's terminal.

[0148] "Confirmation means" refers to a function that allows a user to confirm a submitted proposal.

[0149] A "virtual store" refers to an online shop or marketplace that exists on the Internet.

[0150] "Point benefits" refers to points or benefits that users can earn for specific actions.

[0151] "Campaign Information" refers to information about benefits offered during a specific period or under specific conditions.

[0152] "Generative AI model" refers to an artificial intelligence model used to generate information related to user behavior.

[0153] A "prompt sentence" refers to an input sentence that gives instructions to a generative AI model.

[0154] The system of the present invention comprises a terminal for inputting a user's desired action, a server, and a database of affiliated special service. The following describes in detail the embodiments of the present invention.

[0155] First, the user inputs a desired action using the input means of the device. For example, the user inputs "I want to buy a new smartphone." At this time, the device provides a user interface that allows the user to smoothly input the desired action.

[0156] Once the user completes the input on the device, the input data is sent to the server. The server then analyzes the received input data using an analysis method. Specifically, the server uses natural language processing technology to understand the user's input and identify their desire to purchase a new smartphone. In this analysis step, a generative AI model is used to generate prompt sentences and analyze the input in detail.

[0157] Next, the server refers to a database of affiliated reward services based on the analysis results and searches for reward information corresponding to the user's desired behavior. The database contains point rewards and campaign information from various virtual stores and online marketplaces, and the server selects and generates the most suitable proposal from among them.

[0158] The offer generated by the server is sent back to the user's terminal via the transmission means and displayed on the terminal screen for the user to review. The user reviews the provided offer information and decides on an action based on the content. For example, the user may purchase a new smartphone from a specific online shop based on the presented offer.

[0159] Hardware and software used

[0160] This system mainly uses the following hardware and software:

[0161] Hardware: Smartphone (user device)

[0162] software:

[0163] On the server side, you need analysis software using Python and a natural language processing library (e.g., NLTK or SpaCy).

[0164] In addition, generative AI models such as TensorFlow and PyTorch are used for learning and inference of AI models.

[0165] For database management, an SQL-based database (e.g., MySQL) is used.

[0166] Specific examples

[0167] As a concrete example of this system, consider the case where a user inputs "I want to go on a trip." After the user completes this input on their smartphone, it is sent to the server. The server uses a generative AI model to analyze the input "I want to go on a trip" and searches a database for travel-related reward information. As a result, the user's smartphone displays a suggestion such as "If you make a hotel reservation on a specific travel site, you will receive points."

[0168] Prompt Sentence Examples

[0169] The following sentences could be considered as prompts to be given to the generative AI model:

[0170] "Please let me know about the latest special offers related to popular products."

[0171] "Please tell me which products from the brand you recommend and what their benefits are."

[0172] The above is an embodiment of the present invention, and users can easily obtain special offer information and use products and services under the most advantageous conditions.

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] The user inputs the desired action. The user uses the input means of the terminal to input text such as "I want to buy a new smartphone." Once the input is complete, the terminal sends this data to the server. The input data contains detailed information about the user's desired action.

[0176] Step 2:

[0177] The server receives input data from the user. The received data is sent to the server as text information, where it is preprocessed. At this stage, unnecessary spaces and special characters are removed and the data is made easy to parse. This process prepares the input data for accurate parsing.

[0178] Step 3:

[0179] The server uses a generative AI model to generate prompts and analyze the input data. By utilizing the generative AI model, appropriate analysis processing is performed based on the content entered by the user. Using natural language processing technology, the server extracts key keywords and phrases from the input text and identifies elements such as "smartphone" and "purchase." This clarifies the user's intended action.

[0180] Step 4:

[0181] The server references a database of affiliated reward services based on the analysis results. The server sends the keyword information to the database as a query and searches for reward information. The database stores point rewards and campaign information from various virtual stores, and the server extracts information that matches the user's input. This process obtains the optimal reward information related to the user's desired behavior.

[0182] Step 5:

[0183] The server generates a proposal message based on the acquired benefit information. Using the proposal generation means, the server organizes multiple pieces of benefit information and selects the most useful information for the user. For example, it generates a proposal message that says, "Purchase a smartphone from a specific online shop and receive 1,000 points." This message also includes the necessary URL and detailed benefit information.

[0184] Step 6:

[0185] The server sends the proposal message to the user's terminal. The generated proposal message is delivered to the user's terminal using the sending means. The sent message is displayed to the user through the notification function of the terminal or the interface of the application.

[0186] Step 7:

[0187] The user checks the sent offer message. Using the terminal's confirmation means, the user displays the received offer information and checks its contents. At this stage, the user decides what to do based on the offered offer. For example, the user clicks on the notified URL to purchase a smartphone from a specific online shop.

[0188] Through the above processing steps, the user can quickly obtain optimal benefit information for the desired action, and can use products and services under advantageous conditions.

[0189] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0190] The present invention relates to a system that helps users efficiently perform desired actions and maximize points. In particular, the present invention is capable of generating more personalized suggestions by combining an emotion engine that recognizes the user's emotions.

[0191] First, the user inputs the desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal receives this input and sends it to the server via the network.

[0192] The server receives the input data from the terminal. It then uses natural language processing technology to analyze the text entered by the user. As a result of the analysis, the keyword "travel" is extracted, and it is understood that the user wants to "go on a trip."

[0193] Furthermore, the emotion engine analyzes the emotion from the user's input. For example, it is determined from the context and keywords of the user's input that the user has the emotion of "wanting to relieve stress." This emotion analysis allows the suggestion generation means to generate suggestions that match the user's emotion.

[0194] Next, the server accesses the database of the affiliated point service and searches for campaign information that allows users to earn points related to "travel." For example, it finds information that allows users to earn points by booking a hotel at a specific travel site.

[0195] The server then sorts through the search results and selects the best option for the user, taking into account the results of the user's sentiment analysis. For example, it prioritizes resorts and spas that are helpful for relieving stress.

[0196] Based on the selected proposal, the server generates a proposal message for the user. Specifically, it creates a message saying, "If you want to go on a trip, make a resort reservation on the XX travel site and get XX points."

[0197] The server sends the generated suggestion message to the user's device. The device receives the message and displays it to the user. The user checks the suggestion message and decides what to do based on its contents. For example, a user can make a resort reservation on a travel site and earn points.

[0198] As a concrete example, consider the case where a user types, "I want to buy a new smartphone." The user types the information into their device, and the input data is sent to the server. The server analyzes the information using natural language processing and an emotion engine to detect that the user is excited. It then references a database of affiliated point services and generates suggestions related to purchasing a smartphone. For example, it finds information that says, "If you purchase a smartphone from a specific online shop, you will receive 1,000 points." The server sends this suggestion to the user's device, where the user can review it and take action.

[0199] According to the present invention, a user can receive suggestions that are optimal for his or her emotional state, thereby enabling the user to engage in point activities with greater satisfaction.

[0200] The processing flow will be explained below.

[0201] Step 1:

[0202] The user inputs a desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal provides an interface for receiving this input.

[0203] Step 2:

[0204] The terminal transmits the input data to the server, specifically, using a network communication protocol to transfer the input text data to the server.

[0205] Step 3:

[0206] The server receives input data from the terminal and prepares to analyze this data.

[0207] Step 4:

[0208] The server analyzes the user's input using natural language processing (NLP) techniques. As a result of the analysis, the keyword "travel" is extracted, and it is understood that the user wants to "go on a trip."

[0209] Step 5:

[0210] The server uses an emotion engine to analyze the user's input to determine their emotions. For example, it can determine that the user is feeling stressed based on the user's context and keywords.

[0211] Step 6:

[0212] The server accesses the database of affiliated point services based on the analysis results and sentiment analysis results, and searches for point services and campaign information related to "travel."

[0213] Step 7:

[0214] The server extracts suggestions that can earn points from the search results. For example, it finds information such as "If you make a resort reservation on the XX travel site, you will receive XX points."

[0215] Step 8:

[0216] The server sorts through multiple suggestions and selects the best one for the user, taking into account the results of the user's sentiment analysis. For example, suggestions for resorts and spas that help relieve stress will be prioritized.

[0217] Step 9:

[0218] The server generates a proposal message for the user based on the selected proposal. Specifically, it creates a message such as, "If you want to go on a trip, you can get △△ points by booking a resort on XX travel site."

[0219] Step 10:

[0220] The server generates a proposal message and sends it to the user's terminal, which transfers the message to the terminal using an appropriate communication protocol.

[0221] Step 11:

[0222] The terminal receives the proposal message from the server, and then displays the message on the screen.

[0223] Step 12:

[0224] The user checks the suggestion message, understands the content displayed on the device, and decides on an action based on the suggestion.

[0225] Step 13:

[0226] The user performs an action based on the suggestion, for example, booking a resort on a travel site and earning points.

[0227] Example 2

[0228] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0229] Conventional suggestion systems only provide general suggestions for the user's desired actions and are unable to provide personalized suggestions that take into account the user's emotions and circumstances. As a result, it is difficult for users to receive suggestions that satisfy them, and the efficiency of point activities is low.

[0230] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an analysis means that analyzes a user's input using natural language processing technology, a sentiment analysis means that analyzes the user's sentiment based on the analyzed input data, and a proposal generation means that references a database of an affiliated point service and generates a proposal that allows points to be earned based on the sentiment analysis result. This makes it possible to make personalized proposals that take into account the user's sentiment and situation.

[0231] The "input means" is a means for a user to input a desired action into a terminal.

[0232] The "analysis means" is a means by which the server receives a user's input and analyzes the content of the input using natural language processing technology.

[0233] The "emotion analysis means" is a means for analyzing and identifying the user's emotions based on the analyzed input data.

[0234] The "proposal generating means" refers to a means for referencing the database of the affiliated point service and generating a proposal that will earn points based on the result of the sentiment analysis.

[0235] The "transmission means" is a means for transmitting the proposal generated by the server to the user's terminal.

[0236] The "confirmation means" is a means for the user to confirm the transmitted proposal displayed on the terminal and to decide on an action based on the proposal.

[0237] The present invention relates to a system that helps users efficiently perform desired actions and maximize points. In particular, the present invention is capable of generating more personalized suggestions by combining an emotion engine that recognizes the user's emotions.

[0238] The system includes the following main elements:

[0239] 1. An input method for the user to input the desired action

[0240] 2. Analysis means for the server to receive and analyze the user's input.

[0241] 3. Emotion analysis means for the server to analyze the user's emotions based on the analysis results

[0242] 4. A proposal generation means for the server to refer to a database of affiliated point services based on the emotion analysis results and generate proposals for earning points.

[0243] 5. A transmitting means for transmitting the proposal generated by the server to the user's terminal.

[0244] 6. A confirmation means for the user to confirm the proposal and decide on an action.

[0245] Hardware and software used

[0246] The system is implemented using the following hardware and software:

[0247] User device: smartphone, tablet, or computer

[0248] Server: Cloud-based server (e.g. AWS, Google Cloud)

[0249] Natural language processing techniques: Python's NLTK library, or a similar library

[0250] Emotion engine: IBM Watson's Tone Analyzer, or a similar system

[0251] Database: Database for affiliated points services (e.g., SQL database)

[0252] Specific processing explanation

[0253] The user inputs the desired action into the device. For example, the user inputs "I want to go on a trip." The device receives this input data and sends it to the server via API. The server analyzes the input data using natural language processing technology and extracts the keyword "travel." It then uses an emotion engine to analyze the user's emotions and identify, for example, the emotion "I want to relieve stress."

[0254] The server then accesses the database of affiliated point services and searches for campaign information related to "travel." For example, it finds information about reward points that can be earned by booking accommodation on a specific travel site. Based on the search results, the server generates optimal suggestions that take into account the user's emotions and situation. For example, it prioritizes suggestions for resorts and spas that will help relieve stress.

[0255] The generated suggestion message is sent to the user's device in a specific form, such as "If you want to go on a trip, make a resort reservation on the XX travel site and you will receive XX points." The user checks this suggestion and decides on an action based on its contents. Specifically, the user can make a resort reservation on the XX travel site and earn points.

[0256] Examples and prompts

[0257] As a concrete example, consider the case where a user types, "I want to buy a new smartphone." The user types this into their device, and the data is sent to the server. The server analyzes using natural language processing and an emotion engine to detect that the user is excited. It then references a database of affiliated point services and generates suggestions related to purchasing a smartphone. For example, it finds information that says, "If you purchase a smartphone from a specific online shop, you will receive 1,000 points." The server sends this suggestion to the user's device, where the user can review it and take action.

[0258] Example prompt sentence:

[0259] "If a user inputs that they want to buy a new smartphone, identify their excitement and generate a sentence suggesting the best rewards campaign based on that emotion."

[0260] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0261] Step 1:

[0262] The user inputs the desired action into the terminal.

[0263] Input: A user opens an app on their smartphone and types "I want to go on a trip" into the text box.

[0264] Specific behavior: The user completes the input and presses the submit button.

[0265] Output: The device receives the input data "I want to go on a trip."

[0266] Step 2:

[0267] The terminal sends the input data to the server.

[0268] Input: The input data acquired by the device is "I want to go on a trip."

[0269] Specific operation: The terminal generates an API request and sends a POST request to the server over the network.

[0270] Output: The server receives the input data.

[0271] Step 3:

[0272] The server analyzes the received data using natural language processing technology.

[0273] Input: The data received by the server is "I want to go on a trip."

[0274] Specific operation: The server extracts the keyword "travel" using Python's NLTK library.

[0275] Output: Extracted keyword "travel".

[0276] Step 4:

[0277] The server analyzes the user's emotions based on the analysis results.

[0278] Input: The extracted keyword "travel".

[0279] Specific operation: The server uses IBM Watson's Tone Analyzer to analyze the user's emotions and identify the emotion of "wanting to relieve stress."

[0280] Output: Identified emotion: "I want to relieve stress."

[0281] Step 5:

[0282] The server refers to the database of the affiliated point service and searches for related campaign information.

[0283] Input: Identified emotion "I want to relieve stress" and keyword "travel".

[0284] Specific operation: The server uses an SQL query to search the database of affiliated point services and finds campaign information that says, "If you make a hotel reservation on a specific travel site, you will receive points."

[0285] Output: Retrieved campaign information.

[0286] Step 6:

[0287] The server sorts through multiple proposals and selects the best one.

[0288] Input: Searched campaign information.

[0289] What it does: The server takes into account the results of the sentiment analysis and prioritizes suggestions for resorts and spas that will help relieve stress.

[0290] Output: Selected best proposal.

[0291] Step 7:

[0292] The server generates a proposal message based on the selected proposal.

[0293] Input: Best suggestion.

[0294] Specific operation: The server generates a message saying, "If you make a resort reservation on the XX travel site, you will receive XX points."

[0295] Output: The generated proposal message.

[0296] Step 8:

[0297] The server generates a message and sends it to the user's terminal.

[0298] Input: The generated proposal message.

[0299] Specific operation: The server calls the notification API and sends a message to the user's device.

[0300] Output: The terminal receives the proposal message.

[0301] Step 9:

[0302] The user checks the suggestion message and decides on an action.

[0303] Input: The proposal message received by the terminal.

[0304] What happens: The device displays a pop-up notification, and the user taps the notification to open the app and view the suggestions.

[0305] Output: The user decides to take action based on the suggestions, for example booking a resort on a travel site and earning points.

[0306] (Application example 2)

[0307] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] On modern online shopping sites, users have limited means to efficiently search for desired products and take appropriate actions. Furthermore, personalized suggestions based on users' emotions and interests are not provided, which can lead to reduced user satisfaction. A system that solves these problems and enables users to select and purchase optimal products and maximize points is needed.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0310] In this invention, the server includes an input means for inputting a desired action by a user, an emotion analysis means for extracting keywords from the user's input and analyzing emotions, an analysis means for receiving and analyzing the user's input and the emotion analysis results, a proposal generation means for referencing a database of an affiliated point service based on the analysis results and generating a proposal for earning points, a transmission means for transmitting the proposal generated by the server to the user's terminal, and a confirmation means for the user to confirm the proposal. This allows the user to receive proposals that are optimal for their emotional state, and enables them to efficiently select and purchase products and earn the maximum number of points.

[0311] "User" refers to a person who uses this system.

[0312] "Action" means a particular behavior or action that a user wishes to perform.

[0313] "Input means" refers to an interface for a user to input a desired action to the system.

[0314] "Emotion analysis means" refers to a technical element for analyzing emotions from user input and past behavior.

[0315] "Analysis means" refers to the mechanism by which the server receives user input and sentiment analysis results and analyzes the data.

[0316] The "proposal generating means" refers to a means by which the server generates a proposal that can earn points based on the analysis results.

[0317] "Affiliate point service" means a point program offered to users in cooperation with a specific service provider.

[0318] "Database" refers to a collection of information for storing point services and related information.

[0319] "Transmission means" is a mechanism by which the server transmits the generated proposals to the user's terminal.

[0320] "Terminal" refers to the device used by a User to receive and review Proposals.

[0321] "Verification means" refers to a function that allows a user to confirm or view the proposal sent from the server.

[0322] This invention is a system that helps users efficiently perform desired actions and maximize points. In particular, this system is equipped with an emotion analysis engine that analyzes emotions from user input and behavior to generate personalized suggestions.

[0323] The server receives data based on the actions entered by the user and analyzes the content using natural language processing technology. Specifically, it uses Python and the transformers library, also used by Happiness Capital, to extract keywords from the input text and perform sentiment analysis. Based on the results of this analysis, the server accesses the database of affiliated point services and generates optimal suggestions for the user. The generated suggestions are then sent to the user's device, where the user can confirm the suggestions.

[0324] The device provides an interface for the user to input their desired action. For example, if the user inputs "I want new sneakers," the input is sent to the server. The server analyzes the input text, identifies the sentiment, and generates product suggestions based on the database of affiliated point services, which are then sent to the user's device.

[0325] For example, if a user types "I want new sneakers," the server performs sentiment analysis and identifies the emotion as "excited." Then, it references a database of affiliated point services and generates a proposal for earning points by purchasing sneakers from a specific online shop. This proposal includes a wide selection of colorful new sneaker models, and is sent from the server to the user's device.

[0326] An example of a prompt is as follows:

[0327] User input: "I want new sneakers."

[0328] Expected output: "You're excited! Take advantage of this opportunity to try these products: [list of suggested products]"

[0329] Model used: 'sentiment-analysis' from Transformers

[0330] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0331] Step 1:

[0332] The user enters the desired action.

[0333] Input: A user types "I want new sneakers" into a terminal.

[0334] Operation: A terminal provides an input interface and receives user input.

[0335] Output: User input is recorded as text data on the terminal.

[0336] Step 2:

[0337] The terminal sends the user's input to the server.

[0338] Input: User input text: "I want new sneakers."

[0339] How it works: The terminal sends the input text to the server over the network.

[0340] Output: The input text is received by the server.

[0341] Step 3:

[0342] The server parses the user's input.

[0343] Input: The input text received by the server is "I want new sneakers."

[0344] How it works: The server uses Python's natural language processing techniques to extract keywords from text. Specifically, it uses TextBlob etc. to extract the keyword "sneakers".

[0345] Output: The extracted keyword "sneakers".

[0346] Step 4:

[0347] The server analyzes the emotions.

[0348] Input: The text "I want new sneakers" containing the keyword "sneakers" extracted by the server.

[0349] What it does: Analyzes the sentiment of text using Transformers sentiment-analysis models.

[0350] Output: Sentiment analysis result: "Excited".

[0351] Step 5:

[0352] The server accesses the database of the affiliated point service.

[0353] Input: keyword "sneakers" and sentiment analysis result "excited".

[0354] Operation: Accesses the affiliated points service database and retrieves relevant campaign information.

[0355] Output: Campaign information "Purchase sneakers from a specific online shop and receive 500 points."

[0356] Step 6:

[0357] The server generates offers that earn points.

[0358] Input: Campaign information "Purchase sneakers from a specific online shop and receive 500 points" and sentiment analysis result "I'm excited."

[0359] How it works: Generate personalized suggestions based on your emotional state, such as new colorful sneaker models with a wide selection.

[0360] Output: Suggestion "Choose some new colorful sneakers and get 500 points."

[0361] Step 7:

[0362] The server sends the generated proposal to the user's terminal.

[0363] Input: A server-generated proposal.

[0364] Operation: The server sends the generated proposal to the user's device over the network.

[0365] Output: The suggestion information is received on the user's device.

[0366] Step 8:

[0367] The user confirms the proposal.

[0368] Input: The proposal information received on the user's device.

[0369] Action: The device displays the suggestion information to the user, who then confirms the suggestion.

[0370] Output: User reviews the offer and makes a decision to take purchasing action.

[0371] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0372] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0373] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0374] [Second embodiment]

[0375] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0376] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0377] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0378] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0379] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0380] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0381] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0382] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0383] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0384] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0385] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0386] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0387] The system of the present invention supports a series of operations from inputting a user's desired action to generating, sending, and confirming a proposal. Specific embodiments of the present invention will be described in detail below.

[0388] First, the user inputs an action into the terminal. For example, the user inputs "I want to go on a trip." At this time, the user's terminal provides an input interface to allow the user to input the desired action. After the input is complete, the terminal transmits the input data to the server.

[0389] The server then analyzes the input text data. Specifically, the server uses natural language processing technology to understand that the user entered "I want to go on a trip." In this analysis step, key words and phrases are extracted and it is determined that "travel" is the user's desired action.

[0390] The server then accesses a database of affiliated point services to search for travel-related reward point offers. This database contains information about various reward point services and campaigns, such as reward point offers for booking accommodations on a specific travel site.

[0391] From the search results, the server sorts through multiple suggestions and selects the most beneficial one for the user. For example, a suggestion may be found that if you book a hotel stay on a certain travel site, you will receive certain points. Based on this information, the server generates a suggestion message for the user.

[0392] This generated suggestion message is sent from the server to the user's device. The user's device receives the message and displays it on the screen. The user checks the suggestion message and decides what to do based on its content. For example, the user can choose to make a hotel reservation on a travel site.

[0393] As a concrete example, consider the case where a user inputs "I want to buy a new smartphone." The user's device sends the input to the server, which analyzes the input. The server then refers to a points service database and searches for offers that will earn points related to the new smartphone. For example, the server finds information that says that if you purchase a smartphone from a specific online shop, you will receive 1,000 points. The server sends this offer information to the user's device, and the user confirms it and then purchases the smartphone from the online shop.

[0394] In this way, the system of the present invention makes the user's point activities more efficient by automatically generating and providing optimal proposals that will earn points for the user's desired actions.

[0395] The processing flow will be explained below.

[0396] Step 1:

[0397] The user inputs a desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal provides an interface to receive this input.

[0398] Step 2:

[0399] The terminal transmits the user's input to the server, specifically, using a communication protocol for transferring the input text data to the server.

[0400] Step 3:

[0401] The server receives input data from the terminal and prepares to analyze this data.

[0402] Step 4:

[0403] The server analyzes the user's input using natural language processing (NLP) technology. As a result of the analysis, the keyword "travel" is extracted and it is understood that the user's intention is "I want to go on a trip."

[0404] Step 5:

[0405] Based on the analysis results, the server accesses a database of affiliated point services and searches for information on point services and campaigns related to the keyword "travel."

[0406] Step 6:

[0407] The server extracts suggestions that can earn points from the search results. For example, it finds information such as "If you make a hotel reservation on a certain travel site, you will receive △△ points."

[0408] Step 7:

[0409] The server sorts through multiple offers and selects the most suitable offer for the user, taking into account factors such as point redemption rates and campaign periods.

[0410] Step 8:

[0411] The server generates a proposal message for the user based on the selected proposal. Specifically, it creates a message that says, "If you want to go on a trip, you can get △△ points by booking a hotel at XX travel site."

[0412] Step 9:

[0413] The server sends the generated proposal message to the user's terminal, and the data is transferred to the user's terminal using an appropriate communication protocol.

[0414] Step 10:

[0415] The terminal receives the proposal message from the server, and then displays the message on the screen.

[0416] Step 11:

[0417] The user checks the suggestion message, understands the content displayed on the device, and decides on an action based on the suggestion.

[0418] Step 12:

[0419] The user performs an action based on the suggestion, for example, booking a hotel room on a travel site and earning points.

[0420] Example 1

[0421] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0422] With today's diverse range of point services and campaigns, it is difficult for users to find the optimal point offer based on their desired actions. Users often have to compare multiple platforms to find the best deal. This not only requires a great deal of time and effort, but also increases the chances of missing the best offer. The present invention aims to solve these problems by automatically generating and efficiently providing the optimal point offer for the user's desired actions.

[0423] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0424] In this invention, the server includes input means for a user to input a desired action, transmission means by which the terminal transmits the user's input data to the server, analysis means by which the server receives and analyzes the input data, means by which the server analyzes the user's input using natural language processing technology, proposal generation means by which the server references a linkage points database based on the analysis results and generates proposals that can earn points, transmission means by which the server transmits the proposals generated by the server to the terminal, display means by which the terminal displays the proposals to the user, and confirmation means by which the user confirms the proposals. This enables the user to efficiently receive and reliably confirm optimal point proposals for their desired actions.

[0425] A "user" is an entity that utilizes the system to input specific actions and receive suggestions from the server based on those actions.

[0426] "Terminal" refers to a device that allows a user to input a desired action through an input means, transmits the input data to a server, and receives and displays suggested messages from the server.

[0427] The "server" is a central computer system that receives user input data, analyzes it, generates point proposals, and sends them to the terminals.

[0428] "Input means" refers to an interface that allows a user to input a specific action into a terminal, and includes a keyboard, voice input, and the like.

[0429] The "transmission means" is a communication means by which the terminal transmits the user's input data to the server and by which the server transmits the generated suggestions to the terminal.

[0430] The "analysis means" is a function in which the server receives input data from the user, analyzes the data using natural language processing technology, and identifies the action the user wants to take.

[0431] "Natural language processing technology" is a technology for analyzing input text data and understanding its content and meaning, and includes models such as BERT and GPT-3.

[0432] The "linked points database" is a database that contains information on various point services and campaigns, and is referenced by the server based on the analysis results.

[0433] The "proposal generation means" is a function that allows the server to generate optimal point proposals for the user based on the analysis results.

[0434] The "display means" is a function for visually displaying to the user the proposal message that the terminal receives from the server.

[0435] The "confirmation means" is a function that allows the user to confirm the proposal from the server on the terminal and decide on an action based on the proposal.

[0436] The system of the present invention streamlines users' point activities by generating and providing optimal point proposals based on the user's input of a desired action. Specifically, the user inputs a specific action using a terminal, and then a series of processes are carried out to generate and provide a proposal message to the user.

[0437] First, the user inputs an action into the terminal. For example, when the user inputs "I want to go on a trip," the terminal provides an input interface (keyboard, voice input, etc.) so that the user can input the desired action. Once input is complete, the terminal sends this input data to the server. When sending, the data is encrypted using HTTPS, a secure communication method.

[0438] The server uses natural language processing techniques (such as Google's BERT or OpenAI's GPT-3) to analyze the received input data. During the analysis, it understands that the user entered "I want to go on a trip" and extracts important keywords and phrases. This identifies "travel" as the user's desired action.

[0439] Next, the server accesses the linked point database based on the analysis results to search for relevant point offers. This database contains information on various point services and campaigns, such as information on points that can be earned by booking accommodation on a specific travel site. The server sorts through the search results and selects the most beneficial offer for the user.

[0440] The server generates a proposal message for the user based on the selected proposal. The proposal message is generated using text generation technology (e.g., OpenAI's GPT-3). The generated proposal message is then sent from the server to the device. This transmission is also done securely.

[0441] The device will display the received suggestion message on the screen. The user can then confirm the suggestion and decide on their next course of action based on the suggestion. For example, the user can confirm a suggestion such as "If you make a hotel reservation on a certain travel site, you will receive △△ points," and then actually make a hotel reservation on that travel site.

[0442] As a concrete example, consider the case where a user inputs "I want to buy a new smartphone." The user's device sends the text "I want to buy a new smartphone" to the server, which analyzes it. As a result of the analysis, the server searches for point offers related to smartphone purchases, and generates an offer such as "If you purchase a smartphone from a specific online shop, you will receive 1,000 points," and sends it to the user. The user can confirm this offer and purchase the smartphone from the online shop.

[0443] An example of a prompt statement would be:

[0444] Type: "I want to buy a new smartphone"

[0445] Parsing: The server uses an NLP model (GPT-3) to parse the input and extract the intent "I want to buy a smartphone."

[0446] Suggestion search: The server queries the database to find relevant point suggestions.

[0447] Proposal generation: "Buy a new smartphone at a specific online store and receive 1,000 points."

[0448] Ask: Show the user the suggestion and encourage them to take action.

[0449] In this way, the system of the present invention automatically generates optimal point suggestions for the user's desired actions and provides them efficiently and reliably. The hardware and software used include natural language processing models (BERT and GPT-3), HTTPS communication, and terminals such as smartphones and PCs.

[0450] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0451] Step 1:

[0452] The user inputs the desired action into the device. The user inputs a specific action (e.g., "I want to go on a trip") using the device's input interface (keyboard, voice input, etc.). The input data is in text format.

[0453] Input: The user enters a specific action in text format.

[0454] Output: Text data is input to the terminal

[0455] Step 2:

[0456] The device sends the input data to the server. The device temporarily stores the input data and sends it to the server in an encrypted format using HTTPS.

[0457] Input: Text data entered by the user

[0458] Output: Text data is sent to the server

[0459] Step 3:

[0460] The server receives and analyzes the input text data. The received data is analyzed using an NLP model (e.g., GPT-3) to extract the intent and important keywords of the input text.

[0461] Input: Text data received from the device

[0462] Output: Parsed intent and keywords

[0463] Step 4:

[0464] The server references the federated points database based on the analysis results to search for relevant points offers, and executes a database query to search for points campaigns related to the user's desired action.

[0465] Input: Keywords extracted as analysis results

[0466] Output: A dataset of relevant point proposals

[0467] Step 5:

[0468] The server selects the most suitable point suggestions from the search results and generates a suggestion message. It uses text generation technology (e.g., GPT-3) to create a suggestion message that is useful to the user.

[0469] Input: Dataset of point proposals

[0470] Output: The generated proposal message

[0471] Step 6:

[0472] The server sends the generated proposal message to the terminal. The message is sent to the terminal using a secure communication method (HTTPS).

[0473] Input: The generated proposal message

[0474] Output: Proposal message sent to the terminal

[0475] Step 7:

[0476] The terminal displays the received proposal message to the user. The terminal displays the received message on the user interface so that the user can check it.

[0477] Input: Proposal message received from the server

[0478] Output: Proposal displayed on the device screen

[0479] Step 8:

[0480] The user checks the suggestion message and decides on an action based on it. The user selects the next action based on the suggested content (e.g., "If you make a hotel reservation on the XX website, you will receive XX points").

[0481] Input: The suggestion message displayed on the terminal

[0482] Output: User decision on action

[0483] (Application example 1)

[0484] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0485] Today's consumers need to quickly and efficiently obtain information to purchase products and services under the most favorable conditions amid the vast amount of online information and offers available. However, current systems require users to search for and compare offer information themselves, which takes a great deal of time and effort. Furthermore, because offer information is scattered, it is not easy for users to find the best offer.

[0486] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0487] In this invention, the server includes: an input means for inputting a user's desired behavior; an analysis means for receiving and analyzing the user's input; a proposal generation means for referencing a database of affiliated reward services based on the analysis results and generating proposals for obtaining rewards; a transmission means for transmitting the proposals generated by the server to the user's terminal; a confirmation means for the user to confirm the proposals; a means for the proposal generation means to automatically search for point rewards and campaign information related to the virtual store; and a means for the analysis means to create prompt sentences using a generative AI model for generating information related to the user's purchasing behavior. This enables the user to quickly and efficiently obtain reward information for purchasing products and services in the virtual store under the most favorable conditions.

[0488] "User" refers to an individual who utilizes the system to input desired actions and obtain information.

[0489] An "action" refers to a specific action desired by a user, such as a desire to purchase a product.

[0490] "Input means" refers to an interface for a user to input a desired action into a terminal.

[0491] "Server" refers to the computer system that receives and analyzes user input, and generates and transmits reward information.

[0492] "Analysis means" refers to the function of the server receiving user input and analyzing it using natural language processing technology, etc.

[0493] "Affiliate reward services" refers to a group of services included in a database accessed by the server that provide points or rewards for specific actions.

[0494] "Database" refers to a system with a data structure for storing and managing information regarding affiliated special services.

[0495] The "proposal generating means" refers to a function that generates a proposal for obtaining a benefit based on the analysis result and provides it to the user.

[0496] The "transmission means" refers to a function for transmitting the generated proposal to the user's terminal.

[0497] "Confirmation means" refers to a function that allows a user to confirm a submitted proposal.

[0498] A "virtual store" refers to an online shop or marketplace that exists on the Internet.

[0499] "Point benefits" refers to points or benefits that users can earn for specific actions.

[0500] "Campaign Information" refers to information about benefits offered during a specific period or under specific conditions.

[0501] "Generative AI model" refers to an artificial intelligence model used to generate information related to user behavior.

[0502] A "prompt sentence" refers to an input sentence that gives instructions to a generative AI model.

[0503] The system of the present invention comprises a terminal for inputting a user's desired action, a server, and a database of affiliated special service. The following describes in detail the embodiments of the present invention.

[0504] First, the user inputs a desired action using the input means of the device. For example, the user inputs "I want to buy a new smartphone." At this time, the device provides a user interface that allows the user to smoothly input the desired action.

[0505] Once the user completes the input on the device, the input data is sent to the server. The server then analyzes the received input data using an analysis method. Specifically, the server uses natural language processing technology to understand the user's input and identify their desire to purchase a new smartphone. In this analysis step, a generative AI model is used to generate prompt sentences and analyze the input in detail.

[0506] Next, the server refers to a database of affiliated reward services based on the analysis results and searches for reward information corresponding to the user's desired behavior. The database contains point rewards and campaign information from various virtual stores and online marketplaces, and the server selects and generates the most suitable proposal from among them.

[0507] The offer generated by the server is sent back to the user's terminal via the transmission means and displayed on the terminal screen for the user to review. The user reviews the provided offer information and decides on an action based on the content. For example, the user may purchase a new smartphone from a specific online shop based on the presented offer.

[0508] Hardware and software used

[0509] This system mainly uses the following hardware and software:

[0510] Hardware: Smartphone (user device)

[0511] software:

[0512] On the server side, you need analysis software using Python and a natural language processing library (e.g., NLTK or SpaCy).

[0513] In addition, generative AI models such as TensorFlow and PyTorch are used for learning and inference of AI models.

[0514] For database management, an SQL-based database (e.g., MySQL) is used.

[0515] Specific examples

[0516] As a concrete example of this system, consider the case where a user inputs "I want to go on a trip." After the user completes this input on their smartphone, it is sent to the server. The server uses a generative AI model to analyze the input "I want to go on a trip" and searches a database for travel-related reward information. As a result, the user's smartphone displays a suggestion such as "If you make a hotel reservation on a specific travel site, you will receive points."

[0517] Prompt Sentence Examples

[0518] The following sentences could be considered as prompts to be given to the generative AI model:

[0519] "Please let me know about the latest special offers related to popular products."

[0520] "Please tell me which products from the brand you recommend and what their benefits are."

[0521] The above is an embodiment of the present invention, and users can easily obtain special offer information and use products and services under the most advantageous conditions.

[0522] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0523] Step 1:

[0524] The user inputs the desired action. The user uses the input means of the terminal to input text such as "I want to buy a new smartphone." Once the input is complete, the terminal sends this data to the server. The input data contains detailed information about the user's desired action.

[0525] Step 2:

[0526] The server receives input data from the user. The received data is sent to the server as text information, where it is preprocessed. At this stage, unnecessary spaces and special characters are removed and the data is made easy to parse. This process prepares the input data for accurate parsing.

[0527] Step 3:

[0528] The server uses a generative AI model to generate prompts and analyze the input data. By utilizing the generative AI model, appropriate analysis processing is performed based on the content entered by the user. Using natural language processing technology, the server extracts key keywords and phrases from the input text and identifies elements such as "smartphone" and "purchase." This clarifies the user's intended action.

[0529] Step 4:

[0530] The server references a database of affiliated reward services based on the analysis results. The server sends the keyword information to the database as a query and searches for reward information. The database stores point rewards and campaign information from various virtual stores, and the server extracts information that matches the user's input. This process obtains the optimal reward information related to the user's desired behavior.

[0531] Step 5:

[0532] The server generates a proposal message based on the acquired benefit information. Using the proposal generation means, the server organizes multiple pieces of benefit information and selects the most useful information for the user. For example, it generates a proposal message that says, "Purchase a smartphone from a specific online shop and receive 1,000 points." This message also includes the necessary URL and detailed benefit information.

[0533] Step 6:

[0534] The server sends the proposal message to the user's terminal. The generated proposal message is delivered to the user's terminal using the sending means. The sent message is displayed to the user through the notification function of the terminal or the interface of the application.

[0535] Step 7:

[0536] The user checks the sent offer message. Using the terminal's confirmation means, the user displays the received offer information and checks its contents. At this stage, the user decides what to do based on the offered offer. For example, the user clicks on the notified URL to purchase a smartphone from a specific online shop.

[0537] Through the above processing steps, the user can quickly obtain optimal benefit information for the desired action, and can use products and services under advantageous conditions.

[0538] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0539] The present invention relates to a system that helps users efficiently perform desired actions and maximize points. In particular, the present invention is capable of generating more personalized suggestions by combining an emotion engine that recognizes the user's emotions.

[0540] First, the user inputs the desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal receives this input and sends it to the server via the network.

[0541] The server receives the input data from the terminal. It then uses natural language processing technology to analyze the text entered by the user. As a result of the analysis, the keyword "travel" is extracted, and it is understood that the user wants to "go on a trip."

[0542] Furthermore, the emotion engine analyzes the emotion from the user's input. For example, it is determined from the context and keywords of the user's input that the user has the emotion of "wanting to relieve stress." This emotion analysis allows the suggestion generation means to generate suggestions that match the user's emotion.

[0543] Next, the server accesses the database of the affiliated point service and searches for campaign information that allows users to earn points related to "travel." For example, it finds information that allows users to earn points by booking a hotel at a specific travel site.

[0544] The server then sorts through the search results and selects the best option for the user, taking into account the results of the user's sentiment analysis. For example, it prioritizes resorts and spas that are helpful for relieving stress.

[0545] Based on the selected proposal, the server generates a proposal message for the user. Specifically, it creates a message saying, "If you want to go on a trip, make a resort reservation on the XX travel site and get XX points."

[0546] The server sends the generated suggestion message to the user's device. The device receives the message and displays it to the user. The user checks the suggestion message and decides what to do based on its contents. For example, a user can make a resort reservation on a travel site and earn points.

[0547] As a concrete example, consider the case where a user types, "I want to buy a new smartphone." The user types the information into their device, and the input data is sent to the server. The server analyzes the information using natural language processing and an emotion engine to detect that the user is excited. It then references a database of affiliated point services and generates suggestions related to purchasing a smartphone. For example, it finds information that says, "If you purchase a smartphone from a specific online shop, you will receive 1,000 points." The server sends this suggestion to the user's device, where the user can review it and take action.

[0548] According to the present invention, a user can receive suggestions that are optimal for his or her emotional state, thereby enabling the user to engage in point activities with greater satisfaction.

[0549] The processing flow will be explained below.

[0550] Step 1:

[0551] The user inputs a desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal provides an interface for receiving this input.

[0552] Step 2:

[0553] The terminal transmits the input data to the server, specifically, using a network communication protocol to transfer the input text data to the server.

[0554] Step 3:

[0555] The server receives input data from the terminal and prepares to analyze this data.

[0556] Step 4:

[0557] The server analyzes the user's input using natural language processing (NLP) techniques. As a result of the analysis, the keyword "travel" is extracted, and it is understood that the user wants to "go on a trip."

[0558] Step 5:

[0559] The server uses an emotion engine to analyze the user's input to determine their emotions. For example, it can determine that the user is feeling stressed based on the user's context and keywords.

[0560] Step 6:

[0561] The server accesses the database of affiliated point services based on the analysis results and sentiment analysis results, and searches for point services and campaign information related to "travel."

[0562] Step 7:

[0563] The server extracts suggestions that can earn points from the search results. For example, it finds information such as "If you make a resort reservation on the XX travel site, you will receive XX points."

[0564] Step 8:

[0565] The server sorts through multiple suggestions and selects the best one for the user, taking into account the results of the user's sentiment analysis. For example, suggestions for resorts and spas that help relieve stress will be prioritized.

[0566] Step 9:

[0567] The server generates a proposal message for the user based on the selected proposal. Specifically, it creates a message such as, "If you want to go on a trip, you can get △△ points by booking a resort on XX travel site."

[0568] Step 10:

[0569] The server generates a proposal message and sends it to the user's terminal, which transfers the message to the terminal using an appropriate communication protocol.

[0570] Step 11:

[0571] The terminal receives the proposal message from the server, and then displays the message on the screen.

[0572] Step 12:

[0573] The user checks the suggestion message, understands the content displayed on the device, and decides on an action based on the suggestion.

[0574] Step 13:

[0575] The user performs an action based on the suggestion, for example, booking a resort on a travel site and earning points.

[0576] Example 2

[0577] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0578] Conventional suggestion systems only provide general suggestions for the user's desired actions and are unable to provide personalized suggestions that take into account the user's emotions and circumstances. As a result, it is difficult for users to receive suggestions that satisfy them, and the efficiency of point activities is low.

[0579] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an analysis means that analyzes a user's input using natural language processing technology, a sentiment analysis means that analyzes the user's sentiment based on the analyzed input data, and a proposal generation means that references a database of an affiliated point service and generates a proposal that allows points to be earned based on the sentiment analysis result. This makes it possible to make personalized proposals that take into account the user's sentiment and situation.

[0580] The "input means" is a means for a user to input a desired action into a terminal.

[0581] The "analysis means" is a means by which the server receives a user's input and analyzes the content of the input using natural language processing technology.

[0582] The "emotion analysis means" is a means for analyzing and identifying the user's emotions based on the analyzed input data.

[0583] The "proposal generating means" refers to a means for referencing the database of the affiliated point service and generating a proposal that will earn points based on the result of the sentiment analysis.

[0584] The "transmission means" is a means for transmitting the proposal generated by the server to the user's terminal.

[0585] The "confirmation means" is a means for the user to confirm the transmitted proposal displayed on the terminal and to decide on an action based on the proposal.

[0586] The present invention relates to a system that helps users efficiently perform desired actions and maximize points. In particular, the present invention is capable of generating more personalized suggestions by combining an emotion engine that recognizes the user's emotions.

[0587] The system includes the following main elements:

[0588] 1. An input method for the user to input the desired action

[0589] 2. Analysis means for the server to receive and analyze the user's input.

[0590] 3. Emotion analysis means for the server to analyze the user's emotions based on the analysis results

[0591] 4. A proposal generation means for the server to refer to a database of affiliated point services based on the emotion analysis results and generate proposals for earning points.

[0592] 5. A transmitting means for transmitting the proposal generated by the server to the user's terminal.

[0593] 6. A confirmation means for the user to confirm the proposal and decide on an action.

[0594] Hardware and software used

[0595] The system is implemented using the following hardware and software:

[0596] User device: smartphone, tablet, or computer

[0597] Server: Cloud-based server (e.g. AWS, Google Cloud)

[0598] Natural language processing techniques: Python's NLTK library, or a similar library

[0599] Emotion engine: IBM Watson's Tone Analyzer, or a similar system

[0600] Database: Database for affiliated points services (e.g., SQL database)

[0601] Specific processing explanation

[0602] The user inputs the desired action into the device. For example, the user inputs "I want to go on a trip." The device receives this input data and sends it to the server via API. The server analyzes the input data using natural language processing technology and extracts the keyword "travel." It then uses an emotion engine to analyze the user's emotions and identify, for example, the emotion "I want to relieve stress."

[0603] The server then accesses the database of affiliated point services and searches for campaign information related to "travel." For example, it finds information about reward points that can be earned by booking accommodation on a specific travel site. Based on the search results, the server generates optimal suggestions that take into account the user's emotions and situation. For example, it prioritizes suggestions for resorts and spas that will help relieve stress.

[0604] The generated suggestion message is sent to the user's device in a specific form, such as "If you want to go on a trip, make a resort reservation on the XX travel site and you will receive XX points." The user checks this suggestion and decides on an action based on its contents. Specifically, the user can make a resort reservation on the XX travel site and earn points.

[0605] Examples and prompts

[0606] As a concrete example, consider the case where a user types, "I want to buy a new smartphone." The user types this into their device, and the data is sent to the server. The server analyzes using natural language processing and an emotion engine to detect that the user is excited. It then references a database of affiliated point services and generates suggestions related to purchasing a smartphone. For example, it finds information that says, "If you purchase a smartphone from a specific online shop, you will receive 1,000 points." The server sends this suggestion to the user's device, where the user can review it and take action.

[0607] Example prompt sentence:

[0608] "If a user inputs that they want to buy a new smartphone, identify their excitement and generate a sentence suggesting the best rewards campaign based on that emotion."

[0609] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0610] Step 1:

[0611] The user inputs the desired action into the terminal.

[0612] Input: A user opens an app on their smartphone and types "I want to go on a trip" into the text box.

[0613] Specific behavior: The user completes the input and presses the submit button.

[0614] Output: The device receives the input data "I want to go on a trip."

[0615] Step 2:

[0616] The terminal sends the input data to the server.

[0617] Input: The input data acquired by the device is "I want to go on a trip."

[0618] Specific operation: The terminal generates an API request and sends a POST request to the server over the network.

[0619] Output: The server receives the input data.

[0620] Step 3:

[0621] The server analyzes the received data using natural language processing technology.

[0622] Input: The data received by the server is "I want to go on a trip."

[0623] Specific operation: The server extracts the keyword "travel" using Python's NLTK library.

[0624] Output: Extracted keyword "travel".

[0625] Step 4:

[0626] The server analyzes the user's emotions based on the analysis results.

[0627] Input: The extracted keyword "travel".

[0628] Specific operation: The server uses IBM Watson's Tone Analyzer to analyze the user's emotions and identify the emotion of "wanting to relieve stress."

[0629] Output: Identified emotion: "I want to relieve stress."

[0630] Step 5:

[0631] The server refers to the database of the affiliated point service and searches for related campaign information.

[0632] Input: Identified emotion "I want to relieve stress" and keyword "travel".

[0633] Specific operation: The server uses an SQL query to search the database of affiliated point services and finds campaign information that says, "If you make a hotel reservation on a specific travel site, you will receive points."

[0634] Output: Retrieved campaign information.

[0635] Step 6:

[0636] The server sorts through multiple proposals and selects the best one.

[0637] Input: Searched campaign information.

[0638] What it does: The server takes into account the results of the sentiment analysis and prioritizes suggestions for resorts and spas that will help relieve stress.

[0639] Output: Selected best proposal.

[0640] Step 7:

[0641] The server generates a proposal message based on the selected proposal.

[0642] Input: Best suggestion.

[0643] Specific operation: The server generates a message saying, "If you make a resort reservation on the XX travel site, you will receive XX points."

[0644] Output: The generated proposal message.

[0645] Step 8:

[0646] The server generates a message and sends it to the user's terminal.

[0647] Input: The generated proposal message.

[0648] Specific operation: The server calls the notification API and sends a message to the user's device.

[0649] Output: The terminal receives the proposal message.

[0650] Step 9:

[0651] The user checks the suggestion message and decides on an action.

[0652] Input: The proposal message received by the terminal.

[0653] What happens: The device displays a pop-up notification, and the user taps the notification to open the app and view the suggestions.

[0654] Output: The user decides to take action based on the suggestions, for example booking a resort on a travel site and earning points.

[0655] (Application example 2)

[0656] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0657] On modern online shopping sites, users have limited means to efficiently search for desired products and take appropriate actions. Furthermore, personalized suggestions based on users' emotions and interests are not provided, which can lead to reduced user satisfaction. A system that solves these problems and enables users to select and purchase optimal products and maximize points is needed.

[0658] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0659] In this invention, the server includes an input means for inputting a desired action by a user, an emotion analysis means for extracting keywords from the user's input and analyzing emotions, an analysis means for receiving and analyzing the user's input and the emotion analysis results, a proposal generation means for referencing a database of an affiliated point service based on the analysis results and generating a proposal for earning points, a transmission means for transmitting the proposal generated by the server to the user's terminal, and a confirmation means for the user to confirm the proposal. This allows the user to receive proposals that are optimal for their emotional state, and enables them to efficiently select and purchase products and earn the maximum number of points.

[0660] "User" refers to a person who uses this system.

[0661] "Action" means a particular behavior or action that a user wishes to perform.

[0662] "Input means" refers to an interface for a user to input a desired action to the system.

[0663] "Emotion analysis means" refers to a technical element for analyzing emotions from user input and past behavior.

[0664] "Analysis means" refers to the mechanism by which the server receives user input and sentiment analysis results and analyzes the data.

[0665] The "proposal generating means" refers to a means by which the server generates a proposal that can earn points based on the analysis results.

[0666] "Affiliate point service" means a point program offered to users in cooperation with a specific service provider.

[0667] "Database" refers to a collection of information for storing point services and related information.

[0668] "Transmission means" is a mechanism by which the server transmits the generated proposals to the user's terminal.

[0669] "Terminal" refers to the device used by a User to receive and review Proposals.

[0670] "Verification means" refers to a function that allows a user to confirm or view the proposal sent from the server.

[0671] This invention is a system that helps users efficiently perform desired actions and maximize points. In particular, this system is equipped with an emotion analysis engine that analyzes emotions from user input and behavior to generate personalized suggestions.

[0672] The server receives data based on the actions entered by the user and analyzes the content using natural language processing technology. Specifically, it uses Python and the transformers library, also used by Happiness Capital, to extract keywords from the input text and perform sentiment analysis. Based on the results of this analysis, the server accesses the database of affiliated point services and generates optimal suggestions for the user. The generated suggestions are then sent to the user's device, where the user can confirm the suggestions.

[0673] The device provides an interface for the user to input their desired action. For example, if the user inputs "I want new sneakers," the input is sent to the server. The server analyzes the input text, identifies the sentiment, and generates product suggestions based on the database of affiliated point services, which are then sent to the user's device.

[0674] For example, if a user types "I want new sneakers," the server performs sentiment analysis and identifies the emotion as "excited." Then, it references a database of affiliated point services and generates a proposal for earning points by purchasing sneakers from a specific online shop. This proposal includes a wide selection of colorful new sneaker models, and is sent from the server to the user's device.

[0675] An example of a prompt is as follows:

[0676] User input: "I want new sneakers."

[0677] Expected output: "You're excited! Take advantage of this opportunity to try these products: [list of suggested products]"

[0678] Model used: 'sentiment-analysis' from Transformers

[0679] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0680] Step 1:

[0681] The user enters the desired action.

[0682] Input: A user types "I want new sneakers" into a terminal.

[0683] Operation: A terminal provides an input interface and receives user input.

[0684] Output: User input is recorded as text data on the terminal.

[0685] Step 2:

[0686] The terminal sends the user's input to the server.

[0687] Input: User input text: "I want new sneakers."

[0688] How it works: The terminal sends the input text to the server over the network.

[0689] Output: The input text is received by the server.

[0690] Step 3:

[0691] The server parses the user's input.

[0692] Input: The input text received by the server is "I want new sneakers."

[0693] How it works: The server uses Python's natural language processing techniques to extract keywords from text. Specifically, it uses TextBlob etc. to extract the keyword "sneakers".

[0694] Output: The extracted keyword "sneakers".

[0695] Step 4:

[0696] The server analyzes the emotions.

[0697] Input: The text "I want new sneakers" containing the keyword "sneakers" extracted by the server.

[0698] What it does: Analyzes the sentiment of text using Transformers sentiment-analysis models.

[0699] Output: Sentiment analysis result: "Excited".

[0700] Step 5:

[0701] The server accesses the database of the affiliated point service.

[0702] Input: keyword "sneakers" and sentiment analysis result "excited".

[0703] Operation: Accesses the affiliated points service database and retrieves relevant campaign information.

[0704] Output: Campaign information "Purchase sneakers from a specific online shop and receive 500 points."

[0705] Step 6:

[0706] The server generates offers that earn points.

[0707] Input: Campaign information "Purchase sneakers from a specific online shop and receive 500 points" and sentiment analysis result "I'm excited."

[0708] How it works: Generate personalized suggestions based on your emotional state, such as new colorful sneaker models with a wide selection.

[0709] Output: Suggestion "Choose some new colorful sneakers and get 500 points."

[0710] Step 7:

[0711] The server sends the generated proposal to the user's terminal.

[0712] Input: A server-generated proposal.

[0713] Operation: The server sends the generated proposal to the user's device over the network.

[0714] Output: The suggestion information is received on the user's device.

[0715] Step 8:

[0716] The user confirms the proposal.

[0717] Input: The proposal information received on the user's device.

[0718] Action: The device displays the suggestion information to the user, who then confirms the suggestion.

[0719] Output: User reviews the offer and makes a decision to take purchasing action.

[0720] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0721] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0722] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0723] [Third embodiment]

[0724] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0725] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0726] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0727] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0728] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0729] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0730] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0731] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0732] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0733] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0734] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0735] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0736] The system of the present invention supports a series of operations from inputting a user's desired action to generating, sending, and confirming a proposal. Specific embodiments of the present invention will be described in detail below.

[0737] First, the user inputs an action into the terminal. For example, the user inputs "I want to go on a trip." At this time, the user's terminal provides an input interface to allow the user to input the desired action. After the input is complete, the terminal transmits the input data to the server.

[0738] The server then analyzes the input text data. Specifically, the server uses natural language processing technology to understand that the user entered "I want to go on a trip." In this analysis step, key words and phrases are extracted and it is determined that "travel" is the user's desired action.

[0739] The server then accesses a database of affiliated point services to search for travel-related reward point offers. This database contains information about various reward point services and campaigns, such as reward point offers for booking accommodations on a specific travel site.

[0740] From the search results, the server sorts through multiple suggestions and selects the most beneficial one for the user. For example, a suggestion may be found that if you book a hotel stay on a certain travel site, you will receive certain points. Based on this information, the server generates a suggestion message for the user.

[0741] This generated suggestion message is sent from the server to the user's device. The user's device receives the message and displays it on the screen. The user checks the suggestion message and decides what to do based on its content. For example, the user can choose to make a hotel reservation on a travel site.

[0742] As a concrete example, consider the case where a user inputs "I want to buy a new smartphone." The user's device sends the input to the server, which analyzes the input. The server then refers to a points service database and searches for offers that will earn points related to the new smartphone. For example, the server finds information that says that if you purchase a smartphone from a specific online shop, you will receive 1,000 points. The server sends this offer information to the user's device, and the user confirms it and then purchases the smartphone from the online shop.

[0743] In this way, the system of the present invention makes the user's point activities more efficient by automatically generating and providing optimal proposals that will earn points for the user's desired actions.

[0744] The processing flow will be explained below.

[0745] Step 1:

[0746] The user inputs a desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal provides an interface to receive this input.

[0747] Step 2:

[0748] The terminal transmits the user's input to the server, specifically, using a communication protocol for transferring the input text data to the server.

[0749] Step 3:

[0750] The server receives input data from the terminal and prepares to analyze this data.

[0751] Step 4:

[0752] The server analyzes the user's input using natural language processing (NLP) technology. As a result of the analysis, the keyword "travel" is extracted and it is understood that the user's intention is "I want to go on a trip."

[0753] Step 5:

[0754] Based on the analysis results, the server accesses a database of affiliated point services and searches for information on point services and campaigns related to the keyword "travel."

[0755] Step 6:

[0756] The server extracts suggestions that can earn points from the search results. For example, it finds information such as "If you make a hotel reservation on a certain travel site, you will receive △△ points."

[0757] Step 7:

[0758] The server sorts through multiple offers and selects the most suitable offer for the user, taking into account factors such as point redemption rates and campaign periods.

[0759] Step 8:

[0760] The server generates a proposal message for the user based on the selected proposal. Specifically, it creates a message that says, "If you want to go on a trip, you can get △△ points by booking a hotel at XX travel site."

[0761] Step 9:

[0762] The server sends the generated proposal message to the user's terminal, and the data is transferred to the user's terminal using an appropriate communication protocol.

[0763] Step 10:

[0764] The terminal receives the proposal message from the server, and then displays the message on the screen.

[0765] Step 11:

[0766] The user checks the suggestion message, understands the content displayed on the device, and decides on an action based on the suggestion.

[0767] Step 12:

[0768] The user performs an action based on the suggestion, for example, booking a hotel room on a travel site and earning points.

[0769] Example 1

[0770] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0771] With today's diverse range of point services and campaigns, it is difficult for users to find the optimal point offer based on their desired actions. Users often have to compare multiple platforms to find the best deal. This not only requires a great deal of time and effort, but also increases the chances of missing the best offer. The present invention aims to solve these problems by automatically generating and efficiently providing the optimal point offer for the user's desired actions.

[0772] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0773] In this invention, the server includes input means for a user to input a desired action, transmission means by which the terminal transmits the user's input data to the server, analysis means by which the server receives and analyzes the input data, means by which the server analyzes the user's input using natural language processing technology, proposal generation means by which the server references a linkage points database based on the analysis results and generates proposals that can earn points, transmission means by which the server transmits the proposals generated by the server to the terminal, display means by which the terminal displays the proposals to the user, and confirmation means by which the user confirms the proposals. This enables the user to efficiently receive and reliably confirm optimal point proposals for their desired actions.

[0774] A "user" is an entity that utilizes the system to input specific actions and receive suggestions from the server based on those actions.

[0775] "Terminal" refers to a device that allows a user to input a desired action through an input means, transmits the input data to a server, and receives and displays suggested messages from the server.

[0776] The "server" is a central computer system that receives user input data, analyzes it, generates point proposals, and sends them to the terminals.

[0777] "Input means" refers to an interface that allows a user to input a specific action into a terminal, and includes a keyboard, voice input, and the like.

[0778] The "transmission means" is a communication means by which the terminal transmits the user's input data to the server and by which the server transmits the generated suggestions to the terminal.

[0779] The "analysis means" is a function in which the server receives input data from the user, analyzes the data using natural language processing technology, and identifies the action the user wants to take.

[0780] "Natural language processing technology" is a technology for analyzing input text data and understanding its content and meaning, and includes models such as BERT and GPT-3.

[0781] The "linked points database" is a database that contains information on various point services and campaigns, and is referenced by the server based on the analysis results.

[0782] The "proposal generation means" is a function that allows the server to generate optimal point proposals for the user based on the analysis results.

[0783] The "display means" is a function for visually displaying to the user the proposal message that the terminal receives from the server.

[0784] The "confirmation means" is a function that allows the user to confirm the proposal from the server on the terminal and decide on an action based on the proposal.

[0785] The system of the present invention streamlines users' point activities by generating and providing optimal point proposals based on the user's input of a desired action. Specifically, the user inputs a specific action using a terminal, and then a series of processes are carried out to generate and provide a proposal message to the user.

[0786] First, the user inputs an action into the terminal. For example, when the user inputs "I want to go on a trip," the terminal provides an input interface (keyboard, voice input, etc.) so that the user can input the desired action. Once input is complete, the terminal sends this input data to the server. When sending, the data is encrypted using HTTPS, a secure communication method.

[0787] The server uses natural language processing techniques (such as Google's BERT or OpenAI's GPT-3) to analyze the received input data. During the analysis, it understands that the user entered "I want to go on a trip" and extracts important keywords and phrases. This identifies "travel" as the user's desired action.

[0788] Next, the server accesses the linked point database based on the analysis results to search for relevant point offers. This database contains information on various point services and campaigns, such as information on points that can be earned by booking accommodation on a specific travel site. The server sorts through the search results and selects the most beneficial offer for the user.

[0789] The server generates a proposal message for the user based on the selected proposal. The proposal message is generated using text generation technology (e.g., OpenAI's GPT-3). The generated proposal message is then sent from the server to the device. This transmission is also done securely.

[0790] The device will display the received suggestion message on the screen. The user can confirm the message and decide on the next course of action based on the suggestion. For example, the user can confirm the suggestion "If you make a hotel reservation on a certain travel site, you will receive △△ points," and then actually make a hotel reservation on that travel site.

[0791] As a concrete example, consider the case where a user inputs "I want to buy a new smartphone." The user's device sends the text "I want to buy a new smartphone" to the server, which analyzes it. As a result of the analysis, the server searches for point offers related to smartphone purchases, and generates an offer such as "If you purchase a smartphone from a specific online shop, you will receive 1,000 points," and sends it to the user. The user can confirm this offer and purchase the smartphone from the online shop.

[0792] An example of a prompt statement would be:

[0793] Type: "I want to buy a new smartphone"

[0794] Parsing: The server uses an NLP model (GPT-3) to parse the input and extract the intent "I want to buy a smartphone."

[0795] Suggestion search: The server queries the database to find relevant point suggestions.

[0796] Proposal generation: "Buy a new smartphone at a specific online store and receive 1,000 points."

[0797] Ask: Show the user the suggestion and encourage them to take action.

[0798] In this way, the system of the present invention automatically generates optimal point suggestions for the user's desired actions and provides them efficiently and reliably. The hardware and software used include natural language processing models (BERT and GPT-3), HTTPS communication, and terminals such as smartphones and PCs.

[0799] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0800] Step 1:

[0801] The user inputs the desired action into the device. The user inputs a specific action (e.g., "I want to go on a trip") using the device's input interface (keyboard, voice input, etc.). The input data is in text format.

[0802] Input: The user enters a specific action in text format.

[0803] Output: Text data is input to the terminal

[0804] Step 2:

[0805] The device sends the input data to the server. The device temporarily stores the input data and sends it to the server in an encrypted format using HTTPS.

[0806] Input: Text data entered by the user

[0807] Output: Text data is sent to the server

[0808] Step 3:

[0809] The server receives and analyzes the input text data. The received data is analyzed using an NLP model (e.g., GPT-3) to extract the intent and important keywords of the input text.

[0810] Input: Text data received from the device

[0811] Output: Parsed intent and keywords

[0812] Step 4:

[0813] The server references the federated points database based on the analysis results to search for relevant points offers, and executes a database query to search for points campaigns related to the user's desired action.

[0814] Input: Keywords extracted as analysis results

[0815] Output: A dataset of relevant point proposals

[0816] Step 5:

[0817] The server selects the most suitable point suggestions from the search results and generates a suggestion message. It uses text generation technology (e.g., GPT-3) to create a suggestion message that is useful to the user.

[0818] Input: Dataset of point proposals

[0819] Output: The generated proposal message

[0820] Step 6:

[0821] The server sends the generated proposal message to the device. The message is sent to the device using a secure communication method (HTTPS).

[0822] Input: The generated proposal message

[0823] Output: Proposal message sent to the terminal

[0824] Step 7:

[0825] The terminal displays the received proposal message to the user. The terminal displays the received message on the user interface so that the user can check it.

[0826] Input: Proposal message received from the server

[0827] Output: Proposal displayed on the device screen

[0828] Step 8:

[0829] The user checks the suggestion message and decides on an action based on it. The user selects the next action based on the suggested content (e.g., "If you make a hotel reservation on the XX website, you will receive XX points").

[0830] Input: The suggestion message displayed on the terminal

[0831] Output: User decision on action

[0832] (Application example 1)

[0833] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0834] Today's consumers need to quickly and efficiently obtain information to purchase products and services under the most favorable conditions amid the vast amount of online information and offers available. However, current systems require users to search for and compare offer information themselves, which takes a great deal of time and effort. Furthermore, because offer information is scattered, it is not easy for users to find the best offer.

[0835] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0836] In this invention, the server includes: an input means for inputting a user's desired behavior; an analysis means for receiving and analyzing the user's input; a proposal generation means for referencing a database of affiliated reward services based on the analysis results and generating proposals for obtaining rewards; a transmission means for transmitting the proposals generated by the server to the user's terminal; a confirmation means for the user to confirm the proposals; a means for the proposal generation means to automatically search for point rewards and campaign information related to the virtual store; and a means for the analysis means to create prompt sentences using a generative AI model for generating information related to the user's purchasing behavior. This enables the user to quickly and efficiently obtain reward information for purchasing products and services in the virtual store under the most favorable conditions.

[0837] "User" refers to an individual who utilizes the system to input desired actions and obtain information.

[0838] An "action" refers to a specific action desired by a user, such as a desire to purchase a product.

[0839] "Input means" refers to an interface for a user to input a desired action into a terminal.

[0840] "Server" refers to the computer system that receives and analyzes user input, and generates and transmits reward information.

[0841] "Analysis means" refers to the function of the server receiving user input and analyzing it using natural language processing technology, etc.

[0842] "Affiliate reward services" refers to a group of services included in a database accessed by the server that provide points or rewards for specific actions.

[0843] "Database" refers to a system with a data structure for storing and managing information regarding affiliated special services.

[0844] The "proposal generating means" refers to a function that generates a proposal for obtaining a benefit based on the analysis result and provides it to the user.

[0845] The "transmission means" refers to a function for transmitting the generated proposal to the user's terminal.

[0846] "Confirmation means" refers to a function that allows a user to confirm a submitted proposal.

[0847] A "virtual store" refers to an online shop or marketplace that exists on the Internet.

[0848] "Point benefits" refers to points or benefits that users can earn for specific actions.

[0849] "Campaign Information" refers to information about benefits offered during a specific period or under specific conditions.

[0850] "Generative AI model" refers to an artificial intelligence model used to generate information related to user behavior.

[0851] A "prompt sentence" refers to an input sentence that gives instructions to a generative AI model.

[0852] The system of the present invention comprises a terminal for inputting a user's desired action, a server, and a database of affiliated special service. The following describes in detail the embodiments of the present invention.

[0853] First, the user inputs a desired action using the input means of the device. For example, the user inputs "I want to buy a new smartphone." At this time, the device provides a user interface that allows the user to smoothly input the desired action.

[0854] Once the user completes the input on the device, the input data is sent to the server. The server then analyzes the received input data using an analysis method. Specifically, the server uses natural language processing technology to understand the user's input and identify their desire to purchase a new smartphone. In this analysis step, a generative AI model is used to generate prompt sentences and analyze the input in detail.

[0855] Next, the server refers to a database of affiliated reward services based on the analysis results and searches for reward information corresponding to the user's desired behavior. The database contains point rewards and campaign information from various virtual stores and online marketplaces, and the server selects and generates the most suitable proposal from among them.

[0856] The offer generated by the server is sent back to the user's terminal via the transmission means and displayed on the terminal screen for the user to review. The user reviews the provided offer information and decides on an action based on the content. For example, the user may purchase a new smartphone from a specific online shop based on the presented offer.

[0857] Hardware and software used

[0858] This system mainly uses the following hardware and software:

[0859] Hardware: Smartphone (user device)

[0860] software:

[0861] On the server side, you need analysis software using Python and a natural language processing library (e.g., NLTK or SpaCy).

[0862] In addition, generative AI models such as TensorFlow and PyTorch are used for learning and inference of AI models.

[0863] For database management, an SQL-based database (e.g., MySQL) is used.

[0864] Specific examples

[0865] As a concrete example of this system, consider the case where a user inputs "I want to go on a trip." After the user completes this input on their smartphone, it is sent to the server. The server uses a generative AI model to analyze the input "I want to go on a trip" and searches a database for travel-related reward information. As a result, the user's smartphone displays a suggestion such as "If you make a hotel reservation on a specific travel site, you will receive points."

[0866] Prompt Sentence Examples

[0867] The following sentences could be considered as prompts to be given to the generative AI model:

[0868] "Please let me know about the latest special offers related to popular products."

[0869] "Please tell me which products from the brand you recommend and what their benefits are."

[0870] The above is an embodiment of the present invention, and users can easily obtain special offer information and use products and services under the most advantageous conditions.

[0871] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0872] Step 1:

[0873] The user inputs the desired action. The user uses the input means of the terminal to input text such as "I want to buy a new smartphone." Once the input is complete, the terminal sends this data to the server. The input data contains detailed information about the user's desired action.

[0874] Step 2:

[0875] The server receives input data from the user. The received data is sent to the server as text information, where it is preprocessed. At this stage, unnecessary spaces and special characters are removed and the data is made easy to parse. This process prepares the input data for accurate parsing.

[0876] Step 3:

[0877] The server uses a generative AI model to generate prompts and analyze the input data. By utilizing the generative AI model, appropriate analysis processing is performed based on the content entered by the user. Using natural language processing technology, the server extracts key keywords and phrases from the input text and identifies elements such as "smartphone" and "purchase." This clarifies the user's intended action.

[0878] Step 4:

[0879] The server references a database of affiliated reward services based on the analysis results. The server sends the keyword information to the database as a query and searches for reward information. The database stores point rewards and campaign information from various virtual stores, and the server extracts information that matches the user's input. This process obtains the optimal reward information related to the user's desired behavior.

[0880] Step 5:

[0881] The server generates a proposal message based on the acquired benefit information. Using the proposal generation means, the server organizes multiple pieces of benefit information and selects the most useful information for the user. For example, it generates a proposal message that says, "Purchase a smartphone from a specific online shop and receive 1,000 points." This message also includes the necessary URL and detailed benefit information.

[0882] Step 6:

[0883] The server sends the proposal message to the user's terminal. The generated proposal message is delivered to the user's terminal using the sending means. The sent message is displayed to the user through the notification function of the terminal or the interface of the application.

[0884] Step 7:

[0885] The user checks the sent offer message. Using the terminal's confirmation means, the user displays the received offer information and checks its contents. At this stage, the user decides what to do based on the offered offer. For example, the user clicks on the notified URL to purchase a smartphone from a specific online shop.

[0886] Through the above processing steps, the user can quickly obtain optimal benefit information for the desired action, and can use products and services under advantageous conditions.

[0887] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0888] The present invention relates to a system that helps users efficiently perform desired actions and maximize points. In particular, the present invention is capable of generating more personalized suggestions by combining an emotion engine that recognizes the user's emotions.

[0889] First, the user inputs the desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal receives this input and sends it to the server via the network.

[0890] The server receives the input data from the terminal. It then uses natural language processing technology to analyze the text entered by the user. As a result of the analysis, the keyword "travel" is extracted, and it is understood that the user wants to "go on a trip."

[0891] Furthermore, the emotion engine analyzes the emotion from the user's input. For example, it is determined from the context and keywords of the user's input that the user has the emotion of "wanting to relieve stress." This emotion analysis allows the suggestion generation means to generate suggestions that match the user's emotion.

[0892] Next, the server accesses the database of the affiliated point service and searches for campaign information that allows users to earn points related to "travel." For example, it finds information that allows users to earn points by booking a hotel at a specific travel site.

[0893] The server then sorts through the search results and selects the best option for the user, taking into account the results of the user's sentiment analysis. For example, it prioritizes resorts and spas that are helpful for relieving stress.

[0894] Based on the selected proposal, the server generates a proposal message for the user. Specifically, it creates a message saying, "If you want to go on a trip, make a resort reservation on the XX travel site and get XX points."

[0895] The server sends the generated suggestion message to the user's device. The device receives the message and displays it to the user. The user checks the suggestion message and decides what to do based on its contents. For example, a user can make a resort reservation on a travel site and earn points.

[0896] As a concrete example, consider the case where a user types, "I want to buy a new smartphone." The user types the information into their device, and the input data is sent to the server. The server analyzes the information using natural language processing and an emotion engine to detect that the user is excited. It then references a database of affiliated point services and generates suggestions related to purchasing a smartphone. For example, it finds information that says, "If you purchase a smartphone from a specific online shop, you will receive 1,000 points." The server sends this suggestion to the user's device, where the user can review it and take action.

[0897] According to the present invention, a user can receive suggestions that are optimal for his or her emotional state, thereby enabling the user to engage in point activities with greater satisfaction.

[0898] The processing flow will be explained below.

[0899] Step 1:

[0900] The user inputs a desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal provides an interface for receiving this input.

[0901] Step 2:

[0902] The terminal transmits the input data to the server, specifically, using a network communication protocol to transfer the input text data to the server.

[0903] Step 3:

[0904] The server receives input data from the terminal and prepares to analyze this data.

[0905] Step 4:

[0906] The server uses natural language processing (NLP) technology to analyze the user's input. As a result of the analysis, the keyword "travel" is extracted, and it is understood that the user wants to "go on a trip."

[0907] Step 5:

[0908] The server uses an emotion engine to analyze the user's input to determine their emotions. For example, it can determine that the user is feeling stressed based on the user's context and keywords.

[0909] Step 6:

[0910] The server accesses the database of affiliated point services based on the analysis results and sentiment analysis results, and searches for point services and campaign information related to "travel."

[0911] Step 7:

[0912] The server extracts suggestions that can earn points from the search results. For example, it finds information such as "If you make a resort reservation on the XX travel site, you will receive XX points."

[0913] Step 8:

[0914] The server sorts through multiple suggestions and selects the best one for the user, taking into account the results of the user's sentiment analysis. For example, suggestions for resorts and spas that help relieve stress will be prioritized.

[0915] Step 9:

[0916] The server generates a proposal message for the user based on the selected proposal. Specifically, it creates a message such as, "If you want to go on a trip, make a resort reservation on the XX travel site and you will receive XX points."

[0917] Step 10:

[0918] The server generates a proposal message and sends it to the user's terminal, which transfers the message to the terminal using an appropriate communication protocol.

[0919] Step 11:

[0920] The terminal receives the proposal message from the server, and then displays the message on the screen.

[0921] Step 12:

[0922] The user checks the suggestion message, understands the content displayed on the device, and decides on an action based on the suggestion.

[0923] Step 13:

[0924] The user performs an action based on the suggestion, for example, booking a resort on a travel site and earning points.

[0925] Example 2

[0926] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0927] Conventional suggestion systems only provide general suggestions for the user's desired actions and are unable to provide personalized suggestions that take into account the user's emotions and circumstances. As a result, it is difficult for users to receive suggestions that satisfy them, and the efficiency of point activities is low.

[0928] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an analysis means that analyzes a user's input using natural language processing technology, a sentiment analysis means that analyzes the user's sentiment based on the analyzed input data, and a proposal generation means that references a database of an affiliated point service and generates a proposal that allows points to be earned based on the sentiment analysis result. This makes it possible to make personalized proposals that take into account the user's sentiment and situation.

[0929] The "input means" is a means for a user to input a desired action into a terminal.

[0930] The "analysis means" is a means by which the server receives a user's input and analyzes the content of the input using natural language processing technology.

[0931] The "emotion analysis means" is a means for analyzing and identifying the user's emotions based on the analyzed input data.

[0932] The "proposal generating means" refers to a means for referencing the database of the affiliated point service and generating a proposal that will earn points based on the result of the sentiment analysis.

[0933] The "transmission means" is a means for transmitting the proposal generated by the server to the user's terminal.

[0934] The "confirmation means" is a means for the user to confirm the transmitted proposal displayed on the terminal and to decide on an action based on the proposal.

[0935] The present invention relates to a system that helps users efficiently perform desired actions and maximize points. In particular, the present invention is capable of generating more personalized suggestions by combining an emotion engine that recognizes the user's emotions.

[0936] The system includes the following main elements:

[0937] 1. An input method for the user to input the desired action

[0938] 2. Analysis means for the server to receive and analyze the user's input.

[0939] 3. Emotion analysis means for the server to analyze the user's emotions based on the analysis results

[0940] 4. A proposal generation means for the server to refer to a database of affiliated point services based on the emotion analysis results and generate proposals for earning points.

[0941] 5. A transmitting means for transmitting the proposal generated by the server to the user's terminal.

[0942] 6. A confirmation means for the user to confirm the proposal and decide on an action.

[0943] Hardware and software used

[0944] The system is implemented using the following hardware and software:

[0945] User device: smartphone, tablet, or computer

[0946] Server: Cloud-based server (e.g. AWS, Google Cloud)

[0947] Natural language processing techniques: Python's NLTK library, or a similar library

[0948] Emotion engine: IBM Watson's Tone Analyzer, or a similar system

[0949] Database: Database for affiliated points services (e.g., SQL database)

[0950] Specific processing explanation

[0951] The user inputs the desired action into the device. For example, the user inputs "I want to go on a trip." The device receives this input data and sends it to the server via API. The server analyzes the input data using natural language processing technology and extracts the keyword "travel." It then uses an emotion engine to analyze the user's emotions and identify, for example, the emotion "I want to relieve stress."

[0952] The server then accesses the database of affiliated point services and searches for campaign information related to "travel." For example, it finds information about reward points that can be earned by booking accommodation on a specific travel site. Based on the search results, the server generates optimal suggestions that take into account the user's emotions and situation. For example, it prioritizes suggestions for resorts and spas that will help relieve stress.

[0953] The generated suggestion message is sent to the user's device in a specific form, such as "If you want to go on a trip, make a resort reservation on the XX travel site and you will receive XX points." The user checks this suggestion and decides on an action based on its contents. Specifically, the user can make a resort reservation on the XX travel site and earn points.

[0954] Examples and prompts

[0955] As a concrete example, consider the case where a user types, "I want to buy a new smartphone." The user types this into their device, and the data is sent to the server. The server analyzes using natural language processing and an emotion engine to detect that the user is excited. It then references a database of affiliated point services and generates suggestions related to purchasing a smartphone. For example, it finds information that says, "If you purchase a smartphone from a specific online shop, you will receive 1,000 points." The server sends this suggestion to the user's device, where the user can review it and take action.

[0956] Example prompt sentence:

[0957] "If a user inputs that they want to buy a new smartphone, identify their excitement and generate a sentence suggesting the best rewards campaign based on that emotion."

[0958] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0959] Step 1:

[0960] The user inputs the desired action into the terminal.

[0961] Input: A user opens an app on their smartphone and types "I want to go on a trip" into the text box.

[0962] Specific behavior: The user completes the input and presses the submit button.

[0963] Output: The device receives the input data "I want to go on a trip."

[0964] Step 2:

[0965] The terminal sends the input data to the server.

[0966] Input: The input data acquired by the device is "I want to go on a trip."

[0967] Specific operation: The terminal generates an API request and sends a POST request to the server over the network.

[0968] Output: The server receives the input data.

[0969] Step 3:

[0970] The server analyzes the received data using natural language processing technology.

[0971] Input: The data received by the server is "I want to go on a trip."

[0972] Specific operation: The server extracts the keyword "travel" using Python's NLTK library.

[0973] Output: Extracted keyword "travel".

[0974] Step 4:

[0975] The server analyzes the user's emotions based on the analysis results.

[0976] Input: The extracted keyword "travel".

[0977] Specific operation: The server uses IBM Watson's Tone Analyzer to analyze the user's emotions and identify the emotion of "wanting to relieve stress."

[0978] Output: Identified emotion: "I want to relieve stress."

[0979] Step 5:

[0980] The server refers to the database of the affiliated point service and searches for related campaign information.

[0981] Input: Identified emotion "I want to relieve stress" and keyword "travel".

[0982] Specific operation: The server uses an SQL query to search the database of affiliated point services and finds campaign information that says, "If you make a hotel reservation on a specific travel site, you will receive points."

[0983] Output: Retrieved campaign information.

[0984] Step 6:

[0985] The server sorts through multiple proposals and selects the best one.

[0986] Input: Searched campaign information.

[0987] What it does: The server takes into account the results of the sentiment analysis and prioritizes suggestions for resorts and spas that will help relieve stress.

[0988] Output: Selected best proposal.

[0989] Step 7:

[0990] The server generates a proposal message based on the selected proposal.

[0991] Input: Best suggestion.

[0992] Specific operation: The server generates a message saying, "If you make a resort reservation on the XX travel site, you will receive XX points."

[0993] Output: The generated proposal message.

[0994] Step 8:

[0995] The server generates a message and sends it to the user's terminal.

[0996] Input: The generated proposal message.

[0997] Specific operation: The server calls the notification API and sends a message to the user's device.

[0998] Output: The terminal receives the proposal message.

[0999] Step 9:

[1000] The user checks the suggestion message and decides on an action.

[1001] Input: The proposal message received by the terminal.

[1002] What happens: The device displays a pop-up notification, and the user taps the notification to open the app and view the suggestions.

[1003] Output: The user decides to take action based on the suggestions, for example booking a resort on a travel site and earning points.

[1004] (Application example 2)

[1005] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1006] On modern online shopping sites, users have limited means to efficiently search for desired products and take appropriate actions. Furthermore, personalized suggestions based on users' emotions and interests are not provided, which can lead to reduced user satisfaction. A system that solves these problems and enables users to select and purchase optimal products and maximize points is needed.

[1007] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1008] In this invention, the server includes an input means for inputting a desired action by a user, an emotion analysis means for extracting keywords from the user's input and analyzing emotions, an analysis means for receiving and analyzing the user's input and the emotion analysis results, a proposal generation means for referencing a database of an affiliated point service based on the analysis results and generating a proposal for earning points, a transmission means for transmitting the proposal generated by the server to the user's terminal, and a confirmation means for the user to confirm the proposal. This allows the user to receive proposals that are optimal for their emotional state, and enables them to efficiently select and purchase products and earn the maximum number of points.

[1009] "User" refers to a person who uses this system.

[1010] "Action" means a particular behavior or action that a user wishes to perform.

[1011] "Input means" refers to an interface for a user to input a desired action to the system.

[1012] "Emotion analysis means" refers to a technical element for analyzing emotions from user input and past behavior.

[1013] "Analysis means" refers to the mechanism by which the server receives user input and sentiment analysis results and analyzes the data.

[1014] The "proposal generating means" refers to a means by which the server generates a proposal that can earn points based on the analysis results.

[1015] "Affiliate point service" means a point program offered to users in cooperation with a specific service provider.

[1016] "Database" refers to a collection of information for storing point services and related information.

[1017] "Transmitting means" is a mechanism by which the server transmits the generated proposals to the user's terminal.

[1018] "Terminal" refers to the device used by a User to receive and review Proposals.

[1019] "Confirmation means" refers to a function that allows a user to confirm or view the proposal sent from the server.

[1020] This invention is a system that helps users efficiently perform desired actions and maximize points. In particular, this system is equipped with an emotion analysis engine that analyzes emotions from user input and behavior to generate personalized suggestions.

[1021] The server receives data based on the actions entered by the user and analyzes the content using natural language processing technology. Specifically, it uses Python and the transformers library, also used by Happiness Capital, to extract keywords from the input text and perform sentiment analysis. Based on the results of this analysis, the server accesses the database of affiliated point services and generates optimal suggestions for the user. The generated suggestions are then sent to the user's device, where the user can confirm the suggestions.

[1022] The device provides an interface for the user to input their desired action. For example, if the user inputs "I want new sneakers," the input is sent to the server. The server analyzes the input text, identifies the sentiment, and generates product suggestions based on the database of affiliated point services, which are then sent to the user's device.

[1023] For example, if a user types "I want new sneakers," the server performs sentiment analysis and identifies the emotion as "excited." Then, it references a database of affiliated point services and generates a proposal for earning points by purchasing sneakers from a specific online shop. This proposal includes a wide selection of colorful new sneaker models, and is sent from the server to the user's device.

[1024] An example of a prompt is as follows:

[1025] User input: "I want new sneakers."

[1026] Expected output: "You're excited! Take advantage of this opportunity to try these products: [list of suggested products]"

[1027] Model used: 'sentiment-analysis' from Transformers

[1028] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1029] Step 1:

[1030] The user enters the desired action.

[1031] Input: A user types "I want new sneakers" into a terminal.

[1032] Operation: A terminal provides an input interface and receives user input.

[1033] Output: User input is recorded as text data on the terminal.

[1034] Step 2:

[1035] The terminal sends the user's input to the server.

[1036] Input: User input text: "I want new sneakers."

[1037] How it works: The terminal sends the input text to the server over the network.

[1038] Output: The input text is received by the server.

[1039] Step 3:

[1040] The server parses the user's input.

[1041] Input: The input text received by the server is "I want new sneakers."

[1042] How it works: The server uses Python's natural language processing techniques to extract keywords from text. Specifically, it uses TextBlob etc. to extract the keyword "sneakers".

[1043] Output: The extracted keyword "sneakers".

[1044] Step 4:

[1045] The server analyzes the emotions.

[1046] Input: The text "I want new sneakers" containing the keyword "sneakers" extracted by the server.

[1047] What it does: Analyzes the sentiment of text using Transformers sentiment-analysis models.

[1048] Output: Sentiment analysis result: "Excited".

[1049] Step 5:

[1050] The server accesses the database of the affiliated point service.

[1051] Input: keyword "sneakers" and sentiment analysis result "excited".

[1052] Operation: Accesses the affiliated points service database and retrieves relevant campaign information.

[1053] Output: Campaign information "Purchase sneakers from a specific online shop and receive 500 points."

[1054] Step 6:

[1055] The server generates offers that earn points.

[1056] Input: Campaign information "Purchase sneakers from a specific online shop and receive 500 points" and sentiment analysis result "I'm excited."

[1057] How it works: Generate personalized suggestions based on your emotional state, such as new colorful sneaker models with a wide selection.

[1058] Output: Suggestion "Choose some new colorful sneakers and get 500 points."

[1059] Step 7:

[1060] The server sends the generated proposal to the user's terminal.

[1061] Input: A server-generated proposal.

[1062] Operation: The server sends the generated proposal to the user's device over the network.

[1063] Output: The suggestion information is received on the user's device.

[1064] Step 8:

[1065] The user confirms the proposal.

[1066] Input: The proposal information received on the user's device.

[1067] Action: The device displays the suggestion information to the user, who then confirms the suggestion.

[1068] Output: User reviews the offer and makes a decision to take purchasing action.

[1069] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1070] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1071] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1072] [Fourth embodiment]

[1073] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1074] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1075] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1076] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1077] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1078] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1079] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1080] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1081] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1082] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1084] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1085] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1086] The system of the present invention supports a series of operations from inputting a user's desired action to generating, sending, and confirming a proposal. Specific embodiments of the present invention will be described in detail below.

[1087] First, the user inputs an action into the terminal. For example, the user inputs "I want to go on a trip." At this time, the user's terminal provides an input interface to allow the user to input the desired action. After the input is complete, the terminal transmits the input data to the server.

[1088] The server then analyzes the input text data. Specifically, the server uses natural language processing technology to understand that the user entered "I want to go on a trip." In this analysis step, key words and phrases are extracted and it is determined that "travel" is the user's desired action.

[1089] The server then accesses a database of affiliated point services to search for travel-related reward point offers. This database contains information about various reward point services and campaigns, such as reward point offers for booking accommodations on a specific travel site.

[1090] From the search results, the server sorts through multiple suggestions and selects the most beneficial one for the user. For example, a suggestion may be found that if you book a hotel stay on a certain travel site, you will receive certain points. Based on this information, the server generates a suggestion message for the user.

[1091] This generated suggestion message is sent from the server to the user's device. The user's device receives the message and displays it on the screen. The user checks the suggestion message and decides what to do based on its content. For example, the user can choose to make a hotel reservation on a travel site.

[1092] As a concrete example, consider the case where a user inputs "I want to buy a new smartphone." The user's device sends the input to the server, which analyzes the input. The server then refers to a points service database and searches for offers that will earn points related to the new smartphone. For example, the server finds information that says that if you purchase a smartphone from a specific online shop, you will receive 1,000 points. The server sends this offer information to the user's device, and the user confirms it and then purchases the smartphone from the online shop.

[1093] In this way, the system of the present invention makes the user's point activities more efficient by automatically generating and providing optimal proposals that will earn points for the user's desired actions.

[1094] The processing flow will be explained below.

[1095] Step 1:

[1096] The user inputs a desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal provides an interface to receive this input.

[1097] Step 2:

[1098] The terminal transmits the user's input to the server, specifically, using a communication protocol for transferring the input text data to the server.

[1099] Step 3:

[1100] The server receives input data from the terminal and prepares to analyze this data.

[1101] Step 4:

[1102] The server analyzes the user's input using natural language processing (NLP) technology. As a result of the analysis, the keyword "travel" is extracted and it is understood that the user's intention is "I want to go on a trip."

[1103] Step 5:

[1104] Based on the analysis results, the server accesses a database of affiliated point services and searches for information on point services and campaigns related to the keyword "travel."

[1105] Step 6:

[1106] The server extracts suggestions that can earn points from the search results. For example, it finds information such as "If you make a hotel reservation on a certain travel site, you will receive △△ points."

[1107] Step 7:

[1108] The server sorts through multiple offers and selects the most suitable offer for the user, taking into account factors such as point redemption rates and campaign periods.

[1109] Step 8:

[1110] The server generates a proposal message for the user based on the selected proposal. Specifically, it creates a message that says, "If you want to go on a trip, you can get △△ points by booking a hotel at XX travel site."

[1111] Step 9:

[1112] The server sends the generated proposal message to the user's terminal, and the data is transferred to the user's terminal using an appropriate communication protocol.

[1113] Step 10:

[1114] The terminal receives the proposal message from the server, and then displays the message on the screen.

[1115] Step 11:

[1116] The user checks the suggestion message, understands the content displayed on the device, and decides on an action based on the suggestion.

[1117] Step 12:

[1118] The user performs an action based on the suggestion, for example, booking a hotel room on a travel site and earning points.

[1119] Example 1

[1120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1121] With today's diverse range of point services and campaigns, it is difficult for users to find the optimal point offer based on their desired actions. Users often have to compare multiple platforms to find the best deal. This not only requires a great deal of time and effort, but also increases the chances of missing the best offer. The present invention aims to solve these problems by automatically generating and efficiently providing the optimal point offer for the user's desired actions.

[1122] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1123] In this invention, the server includes input means for a user to input a desired action, transmission means by which the terminal transmits the user's input data to the server, analysis means by which the server receives and analyzes the input data, means by which the server analyzes the user's input using natural language processing technology, proposal generation means by which the server references a linkage points database based on the analysis results and generates proposals that can earn points, transmission means by which the server transmits the proposals generated by the server to the terminal, display means by which the terminal displays the proposals to the user, and confirmation means by which the user confirms the proposals. This enables the user to efficiently receive and reliably confirm optimal point proposals for their desired actions.

[1124] A "user" is an entity that utilizes the system to input specific actions and receive suggestions from the server based on those actions.

[1125] "Terminal" refers to a device that allows a user to input a desired action through an input means, transmits the input data to a server, and receives and displays suggested messages from the server.

[1126] The "server" is a central computer system that receives user input data, analyzes it, generates point proposals, and sends them to the terminals.

[1127] "Input means" refers to an interface that allows a user to input a specific action into a terminal, and includes a keyboard, voice input, and the like.

[1128] The "transmission means" is a communication means by which the terminal transmits the user's input data to the server and by which the server transmits the generated suggestions to the terminal.

[1129] The "analysis means" is a function in which the server receives input data from the user, analyzes the data using natural language processing technology, and identifies the action the user wants to take.

[1130] "Natural language processing technology" is a technology for analyzing input text data and understanding its content and meaning, and includes models such as BERT and GPT-3.

[1131] The "linked points database" is a database that contains information on various point services and campaigns, and is referenced by the server based on the analysis results.

[1132] The "proposal generation means" is a function that allows the server to generate optimal point proposals for the user based on the analysis results.

[1133] The "display means" is a function for visually displaying to the user the proposal message that the terminal receives from the server.

[1134] The "confirmation means" is a function that allows the user to confirm the proposal from the server on the terminal and decide on an action based on the proposal.

[1135] The system of the present invention streamlines users' point activities by generating and providing optimal point proposals based on the user's input of a desired action. Specifically, the user inputs a specific action using a terminal, and then a series of processes are carried out to generate and provide a proposal message to the user.

[1136] First, the user inputs an action into the terminal. For example, when the user inputs "I want to go on a trip," the terminal provides an input interface (keyboard, voice input, etc.) so that the user can input the desired action. Once input is complete, the terminal sends this input data to the server. When sending, the data is encrypted using HTTPS, a secure communication method.

[1137] The server uses natural language processing techniques (such as Google's BERT or OpenAI's GPT-3) to analyze the received input data. During the analysis, it understands that the user entered "I want to go on a trip" and extracts important keywords and phrases. This identifies "travel" as the user's desired action.

[1138] Next, the server accesses the linked point database based on the analysis results to search for relevant point offers. This database contains information on various point services and campaigns, such as information on points that can be earned by booking accommodation on a specific travel site. The server sorts through the search results and selects the most beneficial offer for the user.

[1139] The server generates a proposal message for the user based on the selected proposal. The proposal message is generated using text generation technology (e.g., OpenAI's GPT-3). The generated proposal message is then sent from the server to the device. This transmission is also done securely.

[1140] The device will display the received suggestion message on the screen. The user can confirm the message and decide on the next course of action based on the suggestion. For example, the user can confirm the suggestion "If you make a hotel reservation on a certain travel site, you will receive △△ points," and then actually make a hotel reservation on that travel site.

[1141] As a concrete example, consider the case where a user inputs "I want to buy a new smartphone." The user's device sends the text "I want to buy a new smartphone" to the server, which analyzes it. As a result of the analysis, the server searches for point offers related to smartphone purchases, and generates an offer such as "If you purchase a smartphone from a specific online shop, you will receive 1,000 points," and sends it to the user. The user can confirm this offer and purchase the smartphone from the online shop.

[1142] An example of a prompt statement would be:

[1143] Type: "I want to buy a new smartphone"

[1144] Parsing: The server uses an NLP model (GPT-3) to parse the input and extract the intent "I want to buy a smartphone."

[1145] Suggestion search: The server queries the database to find relevant point suggestions.

[1146] Proposal generation: "Buy a new smartphone at a specific online store and receive 1,000 points."

[1147] Ask: Show the user the suggestion and encourage them to take action.

[1148] In this way, the system of the present invention automatically generates optimal point suggestions for the user's desired actions and provides them efficiently and reliably. The hardware and software used include natural language processing models (BERT and GPT-3), HTTPS communication, and terminals such as smartphones and PCs.

[1149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1150] Step 1:

[1151] The user inputs the desired action into the device. The user inputs a specific action (e.g., "I want to go on a trip") using the device's input interface (keyboard, voice input, etc.). The input data is in text format.

[1152] Input: The user enters a specific action in text format.

[1153] Output: Text data is input to the terminal

[1154] Step 2:

[1155] The device sends the input data to the server. The device temporarily stores the input data and sends it to the server in an encrypted format using HTTPS.

[1156] Input: Text data entered by the user

[1157] Output: Text data is sent to the server

[1158] Step 3:

[1159] The server receives and analyzes the input text data. The received data is analyzed using an NLP model (e.g., GPT-3) to extract the intent and important keywords of the input text.

[1160] Input: Text data received from the device

[1161] Output: Parsed intent and keywords

[1162] Step 4:

[1163] The server references the federated points database based on the analysis results to search for relevant points offers, and executes a database query to search for points campaigns related to the user's desired action.

[1164] Input: Keywords extracted as analysis results

[1165] Output: A dataset of relevant point proposals

[1166] Step 5:

[1167] The server selects the most suitable point suggestions from the search results and generates a suggestion message. It uses text generation technology (e.g., GPT-3) to create a suggestion message that is useful to the user.

[1168] Input: Dataset of point proposals

[1169] Output: The generated proposal message

[1170] Step 6:

[1171] The server sends the generated proposal message to the device. The message is sent to the device using a secure communication method (HTTPS).

[1172] Input: The generated proposal message

[1173] Output: Proposal message sent to the terminal

[1174] Step 7:

[1175] The terminal displays the received proposal message to the user. The terminal displays the received message on the user interface so that the user can check it.

[1176] Input: Proposal message received from the server

[1177] Output: Proposal displayed on the device screen

[1178] Step 8:

[1179] The user checks the suggestion message and decides on an action based on it. The user selects the next action based on the suggested content (e.g., "If you make a hotel reservation on the XX website, you will receive XX points").

[1180] Input: The suggestion message displayed on the terminal

[1181] Output: User decision on action

[1182] (Application example 1)

[1183] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1184] Today's consumers need to quickly and efficiently obtain information to purchase products and services under the most favorable conditions amid the vast amount of online information and offers available. However, current systems require users to search for and compare offer information themselves, which takes a great deal of time and effort. Furthermore, because offer information is scattered, it is not easy for users to find the best offer.

[1185] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1186] In this invention, the server includes: an input means for inputting a user's desired behavior; an analysis means for receiving and analyzing the user's input; a proposal generation means for referencing a database of affiliated reward services based on the analysis results and generating proposals for obtaining rewards; a transmission means for transmitting the proposals generated by the server to the user's terminal; a confirmation means for the user to confirm the proposals; a means for the proposal generation means to automatically search for point rewards and campaign information related to the virtual store; and a means for the analysis means to create prompt sentences using a generative AI model for generating information related to the user's purchasing behavior. This enables the user to quickly and efficiently obtain reward information for purchasing products and services in the virtual store under the most favorable conditions.

[1187] "User" refers to an individual who utilizes the system to input desired actions and obtain information.

[1188] An "action" refers to a specific action desired by a user, such as a desire to purchase a product.

[1189] "Input means" refers to an interface for a user to input a desired action into a terminal.

[1190] "Server" refers to the computer system that receives and analyzes user input, and generates and transmits reward information.

[1191] "Analysis means" refers to the function of the server receiving user input and analyzing it using natural language processing technology, etc.

[1192] "Affiliate reward services" refers to a group of services included in a database accessed by the server that provide points or rewards for specific actions.

[1193] "Database" refers to a system with a data structure for storing and managing information regarding affiliated special services.

[1194] The "proposal generating means" refers to a function that generates a proposal for obtaining a benefit based on the analysis result and provides it to the user.

[1195] The "transmission means" refers to a function for transmitting the generated proposal to the user's terminal.

[1196] "Confirmation means" refers to a function that allows a user to confirm a submitted proposal.

[1197] A "virtual store" refers to an online shop or marketplace that exists on the Internet.

[1198] "Point benefits" refers to points or benefits that users can earn for specific actions.

[1199] "Campaign Information" refers to information about benefits offered during a specific period or under specific conditions.

[1200] "Generative AI model" refers to an artificial intelligence model used to generate information related to user behavior.

[1201] A "prompt sentence" refers to an input sentence that gives instructions to a generative AI model.

[1202] The system of the present invention comprises a terminal for inputting a user's desired action, a server, and a database of affiliated special service. The following describes in detail the embodiments of the present invention.

[1203] First, the user inputs a desired action using the input means of the device. For example, the user inputs "I want to buy a new smartphone." At this time, the device provides a user interface that allows the user to smoothly input the desired action.

[1204] Once the user completes the input on the device, the input data is sent to the server. The server then analyzes the received input data using an analysis method. Specifically, the server uses natural language processing technology to understand the user's input and identify their desire to purchase a new smartphone. In this analysis step, a generative AI model is used to generate prompt sentences and analyze the input in detail.

[1205] Next, the server refers to a database of affiliated reward services based on the analysis results and searches for reward information corresponding to the user's desired behavior. The database contains point rewards and campaign information from various virtual stores and online marketplaces, and the server selects and generates the most suitable proposal from among them.

[1206] The offer generated by the server is sent back to the user's terminal via the transmission means and displayed on the terminal screen for the user to review. The user reviews the provided offer information and decides on an action based on the content. For example, the user may purchase a new smartphone from a specific online shop based on the presented offer.

[1207] Hardware and software used

[1208] This system mainly uses the following hardware and software:

[1209] Hardware: Smartphone (user device)

[1210] software:

[1211] On the server side, you need analysis software using Python and a natural language processing library (e.g., NLTK or SpaCy).

[1212] In addition, generative AI models such as TensorFlow and PyTorch are used for learning and inference of AI models.

[1213] For database management, an SQL-based database (e.g., MySQL) is used.

[1214] Specific examples

[1215] As a concrete example of this system, consider the case where a user inputs "I want to go on a trip." After the user completes this input on their smartphone, it is sent to the server. The server uses a generative AI model to analyze the input "I want to go on a trip" and searches a database for travel-related reward information. As a result, the user's smartphone displays a suggestion such as "If you make a hotel reservation on a specific travel site, you will receive points."

[1216] Prompt Sentence Examples

[1217] The following sentences could be considered as prompts to be given to the generative AI model:

[1218] "Please let me know about the latest special offers related to popular products."

[1219] "Please tell me which products from the brand you recommend and what their benefits are."

[1220] The above is an embodiment of the present invention, and users can easily obtain special offer information and use products and services under the most advantageous conditions.

[1221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1222] Step 1:

[1223] The user inputs the desired action. The user uses the input means of the terminal to input text such as "I want to buy a new smartphone." Once the input is complete, the terminal sends this data to the server. The input data contains detailed information about the user's desired action.

[1224] Step 2:

[1225] The server receives input data from the user. The received data is sent to the server as text information, where it is preprocessed. At this stage, unnecessary spaces and special characters are removed and the data is made easy to parse. This process prepares the input data for accurate parsing.

[1226] Step 3:

[1227] The server uses a generative AI model to generate prompts and analyze the input data. By utilizing the generative AI model, appropriate analysis processing is performed based on the content entered by the user. Using natural language processing technology, the server extracts key keywords and phrases from the input text and identifies elements such as "smartphone" and "purchase." This clarifies the user's intended action.

[1228] Step 4:

[1229] The server references a database of affiliated reward services based on the analysis results. The server sends the keyword information to the database as a query and searches for reward information. The database stores point rewards and campaign information from various virtual stores, and the server extracts information that matches the user's input. This process obtains the optimal reward information related to the user's desired behavior.

[1230] Step 5:

[1231] The server generates a proposal message based on the acquired benefit information. Using the proposal generation means, the server organizes multiple pieces of benefit information and selects the most useful information for the user. For example, it generates a proposal message that says, "Purchase a smartphone from a specific online shop and receive 1,000 points." This message also includes the necessary URL and detailed benefit information.

[1232] Step 6:

[1233] The server sends the proposal message to the user's terminal. The generated proposal message is delivered to the user's terminal using the sending means. The sent message is displayed to the user through the notification function of the terminal or the interface of the application.

[1234] Step 7:

[1235] The user checks the sent offer message. Using the terminal's confirmation means, the user displays the received offer information and checks its contents. At this stage, the user decides what to do based on the offered offer. For example, the user clicks on the notified URL to purchase a smartphone from a specific online shop.

[1236] Through the above processing steps, the user can quickly obtain optimal benefit information for the desired action, and can use products and services under advantageous conditions.

[1237] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1238] The present invention relates to a system that helps users efficiently perform desired actions and maximize points. In particular, the present invention is capable of generating more personalized suggestions by combining an emotion engine that recognizes the user's emotions.

[1239] First, the user inputs the desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal receives this input and sends it to the server via the network.

[1240] The server receives the input data from the terminal. It then uses natural language processing technology to analyze the text entered by the user. As a result of the analysis, the keyword "travel" is extracted, and it is understood that the user wants to "go on a trip."

[1241] Furthermore, the emotion engine analyzes the emotion from the user's input. For example, it is determined from the context and keywords of the user's input that the user has the emotion of "wanting to relieve stress." This emotion analysis allows the suggestion generation means to generate suggestions that match the user's emotion.

[1242] Next, the server accesses the database of the affiliated point service and searches for campaign information that allows users to earn points related to "travel." For example, it finds information that allows users to earn points by booking a hotel at a specific travel site.

[1243] The server then sorts through the search results and selects the best option for the user, taking into account the results of the user's sentiment analysis. For example, it prioritizes resorts and spas that are helpful for relieving stress.

[1244] Based on the selected proposal, the server generates a proposal message for the user. Specifically, it creates a message saying, "If you want to go on a trip, make a resort reservation on the XX travel site and get XX points."

[1245] The server sends the generated suggestion message to the user's device. The device receives the message and displays it to the user. The user checks the suggestion message and decides what to do based on its contents. For example, a user can make a resort reservation on a travel site and earn points.

[1246] As a concrete example, consider the case where a user types, "I want to buy a new smartphone." The user types the information into their device, and the input data is sent to the server. The server analyzes the information using natural language processing and an emotion engine to detect that the user is excited. It then references a database of affiliated point services and generates suggestions related to purchasing a smartphone. For example, it finds information that says, "If you purchase a smartphone from a specific online shop, you will receive 1,000 points." The server sends this suggestion to the user's device, where the user can review it and take action.

[1247] According to the present invention, a user can receive suggestions that are optimal for his or her emotional state, thereby enabling the user to engage in point activities with greater satisfaction.

[1248] The processing flow will be explained below.

[1249] Step 1:

[1250] The user inputs a desired action into the terminal. For example, the user inputs "I want to go on a trip." The terminal provides an interface for receiving this input.

[1251] Step 2:

[1252] The terminal transmits the input data to the server, specifically, using a network communication protocol to transfer the input text data to the server.

[1253] Step 3:

[1254] The server receives input data from the terminal and prepares to analyze this data.

[1255] Step 4:

[1256] The server uses natural language processing (NLP) technology to analyze the user's input. As a result of the analysis, the keyword "travel" is extracted, and it is understood that the user wants to "go on a trip."

[1257] Step 5:

[1258] The server uses an emotion engine to analyze the user's input to determine their emotions. For example, it can determine that the user is feeling stressed based on the user's context and keywords.

[1259] Step 6:

[1260] The server accesses the database of affiliated point services based on the analysis results and sentiment analysis results, and searches for point services and campaign information related to "travel."

[1261] Step 7:

[1262] The server extracts suggestions that can earn points from the search results. For example, it finds information such as "If you make a resort reservation on the XX travel site, you will receive XX points."

[1263] Step 8:

[1264] The server sorts through multiple suggestions and selects the best one for the user, taking into account the results of the user's sentiment analysis. For example, suggestions for resorts and spas that help relieve stress will be prioritized.

[1265] Step 9:

[1266] The server generates a proposal message for the user based on the selected proposal. Specifically, it creates a message such as, "If you want to go on a trip, make a resort reservation on the XX travel site and you will receive XX points."

[1267] Step 10:

[1268] The server generates a proposal message and sends it to the user's terminal, which transfers the message to the terminal using an appropriate communication protocol.

[1269] Step 11:

[1270] The terminal receives the proposal message from the server, and then displays the message on the screen.

[1271] Step 12:

[1272] The user checks the suggestion message, understands the content displayed on the device, and decides on an action based on the suggestion.

[1273] Step 13:

[1274] The user performs an action based on the suggestion, for example, booking a resort on a travel site and earning points.

[1275] Example 2

[1276] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1277] Conventional suggestion systems only provide general suggestions for the user's desired actions and are unable to provide personalized suggestions that take into account the user's emotions and circumstances. As a result, it is difficult for users to receive suggestions that satisfy them, and the efficiency of point activities is low.

[1278] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an analysis means that analyzes a user's input using natural language processing technology, a sentiment analysis means that analyzes the user's sentiment based on the analyzed input data, and a proposal generation means that references a database of an affiliated point service and generates a proposal that allows points to be earned based on the sentiment analysis result. This makes it possible to make personalized proposals that take into account the user's sentiment and situation.

[1279] The "input means" is a means for a user to input a desired action into a terminal.

[1280] The "analysis means" is a means by which the server receives a user's input and analyzes the content of the input using natural language processing technology.

[1281] The "emotion analysis means" is a means for analyzing and identifying the user's emotions based on the analyzed input data.

[1282] The "proposal generating means" refers to a means for referencing the database of the affiliated point service and generating a proposal that will earn points based on the result of the sentiment analysis.

[1283] The "transmission means" is a means for transmitting the proposal generated by the server to the user's terminal.

[1284] The "confirmation means" is a means for the user to confirm the transmitted proposal displayed on the terminal and to decide on an action based on the proposal.

[1285] The present invention relates to a system that helps users efficiently perform desired actions and maximize points. In particular, the present invention is capable of generating more personalized suggestions by combining an emotion engine that recognizes the user's emotions.

[1286] The system includes the following main elements:

[1287] 1. An input method for the user to input the desired action

[1288] 2. Analysis means for the server to receive and analyze the user's input.

[1289] 3. Emotion analysis means for the server to analyze the user's emotions based on the analysis results

[1290] 4. A proposal generation means for the server to refer to a database of affiliated point services based on the emotion analysis results and generate proposals for earning points.

[1291] 5. A transmitting means for transmitting the proposal generated by the server to the user's terminal.

[1292] 6. A confirmation means for the user to confirm the proposal and decide on an action.

[1293] Hardware and software used

[1294] The system is implemented using the following hardware and software:

[1295] User device: smartphone, tablet, or computer

[1296] Server: Cloud-based server (e.g. AWS, Google Cloud)

[1297] Natural language processing techniques: Python's NLTK library, or a similar library

[1298] Emotion engine: IBM Watson's Tone Analyzer, or a similar system

[1299] Database: Database for affiliated points services (e.g., SQL database)

[1300] Specific processing explanation

[1301] The user inputs the desired action into the device. For example, the user inputs "I want to go on a trip." The device receives this input data and sends it to the server via API. The server analyzes the input data using natural language processing technology and extracts the keyword "travel." It then uses an emotion engine to analyze the user's emotions and identify, for example, the emotion "I want to relieve stress."

[1302] The server then accesses the database of affiliated point services and searches for campaign information related to "travel." For example, it finds information about reward points that can be earned by booking accommodation on a specific travel site. Based on the search results, the server generates optimal suggestions that take into account the user's emotions and situation. For example, it prioritizes suggestions for resorts and spas that will help relieve stress.

[1303] The generated suggestion message is sent to the user's device in a specific form, such as "If you want to go on a trip, make a resort reservation on the XX travel site and you will receive XX points." The user checks this suggestion and decides on an action based on its contents. Specifically, the user can make a resort reservation on the XX travel site and earn points.

[1304] Examples and prompts

[1305] As a concrete example, consider the case where a user types, "I want to buy a new smartphone." The user types this into their device, and the data is sent to the server. The server analyzes using natural language processing and an emotion engine to detect that the user is excited. It then references a database of affiliated point services and generates suggestions related to purchasing a smartphone. For example, it finds information that says, "If you purchase a smartphone from a specific online shop, you will receive 1,000 points." The server sends this suggestion to the user's device, where the user can review it and take action.

[1306] Example prompt sentence:

[1307] "If a user inputs that they want to buy a new smartphone, identify their excitement and generate a sentence suggesting the best rewards campaign based on that emotion."

[1308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1309] Step 1:

[1310] The user inputs the desired action into the terminal.

[1311] Input: A user opens an app on their smartphone and types "I want to go on a trip" into the text box.

[1312] Specific behavior: The user completes the input and presses the submit button.

[1313] Output: The device receives the input data "I want to go on a trip."

[1314] Step 2:

[1315] The terminal sends the input data to the server.

[1316] Input: The input data acquired by the device is "I want to go on a trip."

[1317] Specific operation: The terminal generates an API request and sends a POST request to the server over the network.

[1318] Output: The server receives the input data.

[1319] Step 3:

[1320] The server analyzes the received data using natural language processing technology.

[1321] Input: The data received by the server is "I want to go on a trip."

[1322] Specific operation: The server extracts the keyword "travel" using Python's NLTK library.

[1323] Output: Extracted keyword "travel".

[1324] Step 4:

[1325] The server analyzes the user's emotions based on the analysis results.

[1326] Input: The extracted keyword "travel".

[1327] Specific operation: The server uses IBM Watson's Tone Analyzer to analyze the user's emotions and identify the emotion of "wanting to relieve stress."

[1328] Output: Identified emotion: "I want to relieve stress."

[1329] Step 5:

[1330] The server refers to the database of the affiliated point service and searches for related campaign information.

[1331] Input: Identified emotion "I want to relieve stress" and keyword "travel".

[1332] Specific operation: The server uses an SQL query to search the database of affiliated point services and finds campaign information that says, "If you make a hotel reservation on a specific travel site, you will receive points."

[1333] Output: Retrieved campaign information.

[1334] Step 6:

[1335] The server sorts through multiple proposals and selects the best one.

[1336] Input: Searched campaign information.

[1337] What it does: The server takes into account the results of the sentiment analysis and prioritizes suggestions for resorts and spas that will help relieve stress.

[1338] Output: Selected best proposal.

[1339] Step 7:

[1340] The server generates a proposal message based on the selected proposal.

[1341] Input: Best suggestion.

[1342] Specific operation: The server generates a message saying, "If you make a resort reservation on the XX travel site, you will receive XX points."

[1343] Output: The generated proposal message.

[1344] Step 8:

[1345] The server generates a message and sends it to the user's terminal.

[1346] Input: The generated proposal message.

[1347] Specific operation: The server calls the notification API and sends a message to the user's device.

[1348] Output: The terminal receives the proposal message.

[1349] Step 9:

[1350] The user checks the suggestion message and decides on an action.

[1351] Input: The proposal message received by the terminal.

[1352] What happens: The device displays a pop-up notification, and the user taps the notification to open the app and view the suggestions.

[1353] Output: The user decides to take action based on the suggestions, for example booking a resort on a travel site and earning points.

[1354] (Application example 2)

[1355] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1356] On modern online shopping sites, users have limited means to efficiently search for desired products and take appropriate actions. Furthermore, personalized suggestions based on users' emotions and interests are not provided, which can lead to reduced user satisfaction. A system that solves these problems and enables users to select and purchase optimal products and maximize points is needed.

[1357] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1358] In this invention, the server includes an input means for inputting a desired action by a user, an emotion analysis means for extracting keywords from the user's input and analyzing emotions, an analysis means for receiving and analyzing the user's input and the emotion analysis results, a proposal generation means for referencing a database of an affiliated point service based on the analysis results and generating a proposal for earning points, a transmission means for transmitting the proposal generated by the server to the user's terminal, and a confirmation means for the user to confirm the proposal. This allows the user to receive proposals that are optimal for their emotional state, and enables them to efficiently select and purchase products and earn the maximum number of points.

[1359] "User" refers to a person who uses this system.

[1360] "Action" means a particular behavior or action that a user wishes to perform.

[1361] "Input means" refers to an interface for a user to input a desired action to the system.

[1362] "Emotion analysis means" refers to a technical element for analyzing emotions from user input and past behavior.

[1363] "Analysis means" refers to the mechanism by which the server receives user input and sentiment analysis results and analyzes the data.

[1364] The "proposal generating means" refers to a means by which the server generates a proposal that can earn points based on the analysis results.

[1365] "Affiliate point service" means a point program offered to users in cooperation with a specific service provider.

[1366] "Database" refers to a collection of information for storing point services and related information.

[1367] "Transmitting means" is a mechanism by which the server transmits the generated proposals to the user's terminal.

[1368] "Terminal" refers to the device used by a User to receive and review Proposals.

[1369] "Confirmation means" refers to a function that allows a user to confirm or view the proposal sent from the server.

[1370] This invention is a system that helps users efficiently perform desired actions and maximize points. In particular, this system is equipped with an emotion analysis engine that analyzes emotions from user input and behavior to generate personalized suggestions.

[1371] The server receives data based on the actions entered by the user and analyzes the content using natural language processing technology. Specifically, it uses Python and the transformers library, also used by Happiness Capital, to extract keywords from the input text and perform sentiment analysis. Based on the results of this analysis, the server accesses the database of affiliated point services and generates optimal suggestions for the user. The generated suggestions are then sent to the user's device, where the user can confirm the suggestions.

[1372] The device provides an interface for the user to input their desired action. For example, if the user inputs "I want new sneakers," the input is sent to the server. The server analyzes the input text, identifies the sentiment, and generates product suggestions based on the database of affiliated point services, which are then sent to the user's device.

[1373] For example, if a user types "I want new sneakers," the server performs sentiment analysis and identifies the emotion as "excited." Then, it references a database of affiliated point services and generates a proposal for earning points by purchasing sneakers from a specific online shop. This proposal includes a wide selection of colorful new sneaker models, and is sent from the server to the user's device.

[1374] An example of a prompt is as follows:

[1375] User input: "I want new sneakers."

[1376] Expected output: "You're excited! Take advantage of this opportunity to try these products: [list of suggested products]"

[1377] Model used: 'sentiment-analysis' from Transformers

[1378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1379] Step 1:

[1380] The user enters the desired action.

[1381] Input: A user types "I want new sneakers" into a terminal.

[1382] Operation: A terminal provides an input interface and receives user input.

[1383] Output: User input is recorded as text data on the terminal.

[1384] Step 2:

[1385] The terminal sends the user's input to the server.

[1386] Input: User input text: "I want new sneakers."

[1387] How it works: The terminal sends the input text to the server over the network.

[1388] Output: The input text is received by the server.

[1389] Step 3:

[1390] The server parses the user's input.

[1391] Input: The input text received by the server is "I want new sneakers."

[1392] How it works: The server uses Python's natural language processing techniques to extract keywords from text. Specifically, it uses TextBlob etc. to extract the keyword "sneakers".

[1393] Output: The extracted keyword "sneakers".

[1394] Step 4:

[1395] The server analyzes the emotions.

[1396] Input: The text "I want new sneakers" containing the keyword "sneakers" extracted by the server.

[1397] What it does: Analyzes the sentiment of text using Transformers sentiment-analysis models.

[1398] Output: Sentiment analysis result: "Excited".

[1399] Step 5:

[1400] The server accesses the database of the affiliated point service.

[1401] Input: keyword "sneakers" and sentiment analysis result "excited".

[1402] Operation: Accesses the affiliated points service database and retrieves relevant campaign information.

[1403] Output: Campaign information "Purchase sneakers from a specific online shop and receive 500 points."

[1404] Step 6:

[1405] The server generates offers that earn points.

[1406] Input: Campaign information "Purchase sneakers from a specific online shop and receive 500 points" and sentiment analysis result "I'm excited."

[1407] How it works: Generate personalized suggestions based on your emotional state, such as new colorful sneaker models with a wide selection.

[1408] Output: Suggestion "Choose some new colorful sneakers and get 500 points."

[1409] Step 7:

[1410] The server sends the generated proposal to the user's terminal.

[1411] Input: A server-generated proposal.

[1412] Operation: The server sends the generated proposal to the user's device over the network.

[1413] Output: The suggestion information is received on the user's device.

[1414] Step 8:

[1415] The user confirms the proposal.

[1416] Input: The proposal information received on the user's device.

[1417] Action: The device displays the suggestion information to the user, who then confirms the suggestion.

[1418] Output: User reviews the offer and makes a decision to take purchasing action.

[1419] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1420] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1421] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1422] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1423] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1424] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1425] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1426] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1427] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1428] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1429] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1430] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1431] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1432] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1433] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1434] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1435] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1436] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1437] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1438] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1439] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1440] The following is further disclosed regarding the above embodiment.

[1441] (Claim 1)

[1442] an input means for inputting a desired action by a user;

[1443] an analysis means for receiving and analyzing the user's input in the server;

[1444] a proposal generating means for generating a proposal for obtaining points by referring to a database of affiliated point services based on the analysis result;

[1445] a transmitting means for transmitting the proposal generated by the server to a user's terminal;

[1446] confirmation means for allowing the user to confirm the proposal;

[1447] A system including:

[1448] (Claim 2)

[1449] 2. The system according to claim 1, wherein the proposal generating means includes means for sorting a plurality of proposals and selecting an optimal proposal.

[1450] (Claim 3)

[1451] 10. The system of claim 1, wherein the analyzing means includes means for analyzing the user's input using natural language processing techniques.

[1452] "Example 1"

[1453] (Claim 1)

[1454] an input means for inputting a desired action by a user;

[1455] a transmitting means for transmitting the user input data to a server from the terminal;

[1456] an analysis means for receiving and analyzing the input data in the server;

[1457] means for the server to analyze the user's input using natural language processing techniques;

[1458] a proposal generating means for generating a proposal for obtaining points by referring to a collaboration point database based on the analysis result;

[1459] a transmitting means for transmitting the proposal generated by the server to a terminal;

[1460] a display means for displaying the proposal to the user on the terminal;

[1461] confirmation means for allowing the user to confirm the proposal;

[1462] A system including:

[1463] (Claim 2)

[1464] 2. The system of claim 1, wherein the suggestion generating means includes means for sorting through a plurality of suggestions and selecting a most beneficial suggestion.

[1465] (Claim 3)

[1466] 10. The system of claim 1, wherein the parsing means includes means for parsing the user's input using natural language processing techniques.

[1467] "Application Example 1"

[1468] (Claim 1)

[1469] an input means for inputting a desired action by a user;

[1470] an analysis means for receiving and analyzing the user's input in the server;

[1471] a proposal generating means for generating a proposal for obtaining a benefit by referring to a database of affiliated benefit services based on the analysis result;

[1472] a transmitting means for transmitting the proposal generated by the server to a user's terminal;

[1473] confirmation means for allowing the user to confirm the proposal;

[1474] The proposal generating means includes a means for automatically finding point benefits and campaign information related to the virtual store;

[1475] The analysis means generates a prompt sentence using a generative AI model for generating information related to the user's purchasing behavior;

[1476] A system including:

[1477] (Claim 2)

[1478] 2. The system according to claim 1, wherein the proposal generating means includes means for sorting a plurality of proposals and selecting an optimal proposal.

[1479] (Claim 3)

[1480] 10. The system of claim 1, wherein the analyzing means includes means for analyzing the user's input using natural language processing techniques.

[1481] "Example 2: Combining Emotion Engines"

[1482] (Claim 1)

[1483] an input means for inputting a desired action by a user;

[1484] an analysis means for receiving the user's input and analyzing it using natural language processing technology;

[1485] emotion analysis means for analyzing the emotion of a user based on the input data analyzed by the analysis means;

[1486] a proposal generation means for generating proposals for obtaining points based on the emotion analysis results by referring to a database of affiliated point services;

[1487] a transmitting means for transmitting the proposal generated by the server to a user's terminal;

[1488] A confirmation means for allowing the user to confirm the proposal and decide on an action;

[1489] A system including:

[1490] (Claim 2)

[1491] 2. The system according to claim 1, wherein the proposal generating means includes means for sorting a plurality of proposals and selecting an optimal proposal.

[1492] (Claim 3)

[1493] 2. The system of claim 1, wherein the emotion analysis means includes means for identifying an emotion based on user input data.

[1494] "Application example 2 when combining emotion engines"

[1495] (Claim 1)

[1496] an input means for inputting a desired action by a user;

[1497] emotion analysis means for extracting keywords from the user's input and analyzing emotions;

[1498] an analysis means for receiving and analyzing the user's input and emotion analysis results in a server;

[1499] a proposal generating means for generating a proposal for obtaining points by referring to a database of affiliated point services based on the analysis result;

[1500] a transmitting means for transmitting the proposal generated by the server to a user's terminal;

[1501] confirmation means for allowing the user to confirm the proposal;

[1502] A system including:

[1503] (Claim 2)

[1504] 2. The system according to claim 1, wherein the proposal generating means includes means for sorting a plurality of proposals and selecting an optimal proposal.

[1505] (Claim 3)

[1506] 10. The system of claim 1, wherein the analyzing means includes means for analyzing the user's input using natural language processing techniques. [Explanation of symbols]

[1507] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting a desired action by a user; an analysis means for receiving and analyzing the user's input in the server; a proposal generating means for generating a proposal for obtaining points by referring to a database of affiliated point services based on the analysis result; a transmitting means for transmitting the proposal generated by the server to a user's terminal; confirmation means for allowing the user to confirm the proposal; A system including:

2. 2. The system according to claim 1, wherein the proposal generating means includes means for sorting a plurality of proposals and selecting an optimal proposal.

3. 2. The system of claim 1, wherein the analyzing means includes means for analyzing the user's input using natural language processing techniques.

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