System

Abstract

A system is provided.SOLUTION: A system comprising: means for capturing and pre-processing data entered by a user; means for sending the pre-processed data to an artificial intelligence that generates suggestions; and means for presenting the generated suggestions to the user.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] There is a problem that store clerks are unable to provide appropriate suggestions or advice to customers due to lack of experience or expertise. This problem is particularly noticeable in new store clerks and stores with a shortage of staff, and it has a negative impact on customer satisfaction and sales. It is desirable to solve this problem. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for taking in data entered by a user and preprocessing it, a means for sending the preprocessed data to an artificial intelligence (AI) that generates the data, and a means for presenting the generated proposals to the user. The system generates the proposals based on past cases and related databases, and transmits and receives data using a secure communication protocol. The system also includes a means for visually displaying the proposals through a user interface, allowing users to easily view the proposals. This allows users, regardless of experience or expertise, to quickly and reliably provide highly specialized proposals to customers.

[0006] "User" refers to a person who operates the system and inputs the details of a customer's inquiry.

[0007] "Data" refers to the text information that users input into the system, particularly the customer's inquiries and requests.

[0008] "Preprocessing" refers to the process of normalizing and tokenizing input data into a form that is easier to analyze.

[0009] "Generative artificial intelligence (AI)" refers to the part of the system that has algorithms that analyze the data it receives and generate highly specialized recommendations.

[0010] "Suggestions" refer to highly specialized information or recommendations derived from data analyzed by the artificial intelligence (AI) that generates them.

[0011] "Presenting means" refers to an interface or device for visually displaying the generated suggestions to the user.

[0012] "Past cases" refers to previously collected data and consultation history, and is information that can be used as a reference when generating new proposals.

[0013] A "database" refers to a collection of information that can be referenced by AI, where related information and cases are stored.

[0014] A "secure communication protocol" refers to a communication protocol used to safely send and receive data. Specifically, it refers to a protocol that encrypts communication data.

[0015] "User interface" refers to the visual display and input devices that allow a user to interact with a system. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system in which a user (store clerk) inputs the customer's inquiry details and an artificial intelligence (AI) generates and provides optimal suggestions. This system involves taking in user input, sending the data, processing it with the AI, and displaying the suggestions.

[0038] Program processing flow

[0039] Capturing User Input

[0040] Terminal: A user interface (UI) is provided where the user inputs the customer's inquiry. For example, "I'm looking for a smartphone with a large battery and a high-performance camera."

[0041] Sending data

[0042] Terminal: The entered data is sent to the server using a secure communication protocol (e.g., HTTPS).

[0043] NPL pretreatment

[0044] Server: The server performs preprocessing such as tokenization and normalization on the received data, converting it into a form that can be easily analyzed by the generative AI.

[0045] Proposal generation by generative AI

[0046] Server: The generative AI analyzes the preprocessed data and extracts the input keywords and features. For example, "large capacity battery" and "high performance camera" are extracted. The generative AI then searches for products that meet these features based on a database and past cases, and generates optimal suggestions. The generative AI creates suggestions in natural language, including relevant information (e.g., battery capacity and camera performance).

[0047] Sending response data

[0048] Server: Converts the generated proposal into an appropriate format (e.g., JSON or XML) and sends it back to the device using a secure communication protocol.

[0049] What the user sees

[0050] Terminal: The terminal displays the proposal received from the server on the user interface. The user can interact with the customer based on this information and provide professional proposals, such as "Model X: 5000mAh battery, 48MP camera."

[0051] Specific examples

[0052] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life."

[0053] 1. Step 1: User Input

[0054] Terminal: The user (store clerk) inputs the customer's request into the terminal's input form. The request is for a "lightweight laptop with good battery life."

[0055] 2. Step 2: Sending data

[0056] Terminal: The entered data is sent to the server using a secure protocol.

[0057] 3. Step 3: Pretreatment

[0058] Server: Normalizes and tokenizes the received data into a format that is easy to analyze.

[0059] 4. Step 4: Proposal Generation

[0060] Server: The generation AI extracts the keywords "lightweight" and "long battery life" and searches the database for laptop models that match these keywords. The generation AI generates the following suggestions: "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours."

[0061] 5. Step 5: Sending response data

[0062] Server: Converts the generated proposal into JSON format and sends it to the device.

[0063] 6. Step 6: Suggestions

[0064] Terminal: Visually displays the received proposal to the user. The user interface displays "Model A: Weight 1.2 kg, Battery life 12 hours" and "Model B: Weight 1.1 kg, Battery life 10 hours."

[0065] 7. Step 7: User Actions

[0066] User: Based on the proposal, the salesperson (user) explains to the customer, "These models are laptops that meet your requirements."

[0067] This allows store clerks to quickly provide highly specialized information to customers without relying on their specialized knowledge or experience, which is expected to improve customer satisfaction and increase sales.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry details. The user enters the inquiry details in the text box and presses the send button.

[0071] Step 2:

[0072] Terminal: Sends user-entered data to the server using a secure protocol (e.g., HTTPS), properly encoding the data before sending it.

[0073] Step 3:

[0074] Server: The server receives the received data and performs preprocessing, which includes text normalization (removing unnecessary whitespace, standardizing characters, etc.) and tokenization (splitting into words).

[0075] Step 4:

[0076] Server: Passes the preprocessed data to the generative AI, which uses natural language processing (NLP) techniques to analyze the input data and extract keywords and important features.

[0077] Step 5:

[0078] Server: The generation AI searches the database for relevant information based on the extracted keywords and features. For example, it retrieves product information that matches keywords such as "large-capacity battery" and "high-performance camera."

[0079] Step 6:

[0080] Server: Based on the information retrieved from the database, the generative AI generates optimal suggestions. Suggestions are written in natural language and are provided in the form of, for example, "Model X: 5000mAh battery, 48MP camera."

[0081] Step 7:

[0082] Server: Converts the generated proposal into an appropriate format (e.g., JSON, XML) and sends it to the device using a secure protocol.

[0083] Step 8:

[0084] Terminal: The terminal analyzes the proposals received from the server and displays them on the user interface. Specifically, the proposed product models and their features are displayed in a list or tile format.

[0085] Step 9:

[0086] User: Based on the suggestions displayed in the user interface, the user (store clerk) provides expert advice to the customer, for example, explaining that "Model X and Model Y are products that meet your requirements."

[0087] In this way, the entire system operates in cooperation with one another, allowing users to make appropriate proposals to customers without relying on specialized knowledge.

[0088] Example 1

[0089] 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."

[0090] In conventional systems, store staff and users required advanced expertise to properly understand customer inquiries and provide optimal proposals. This resulted in problems such as the quality and consistency of proposals depending on the staff's skills, leading to inconsistencies in customer satisfaction. Furthermore, proposals were generated manually, resulting in a lack of speed. The present invention aims to solve these problems and provide high-quality proposals to customers quickly and consistently.

[0091] 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.

[0092] In this invention, the server includes a means for taking in data entered by a user and performing preprocessing, a means for sending the preprocessed data to a generative AI model and extracting keywords, and a means for searching a related database and generating optimal suggestions using the generative AI model, thereby enabling users to quickly make high-quality suggestions even if they do not have specialized knowledge.

[0093] "Means for capturing and preprocessing data entered by users" refers to a function that collects data such as consultation details entered by users via their terminals and processes them using natural language processing techniques such as tokenization and normalization.

[0094] "Means for sending preprocessed data to a generative AI model and extracting keywords" refers to the process of sending preprocessed data to a generative AI model and extracting important information and keywords from the input data.

[0095] "Means for searching relevant databases and generating optimal suggestions using a generative AI model" refers to a function that searches for similar or related information from a database based on keywords extracted by the generative AI model, and generates optimal suggestions based on that information.

[0096] "Means for transmitting the generated proposal to the user terminal using a secure communication protocol" refers to a process for securely transmitting the generated proposal content to the user terminal using a secure communication protocol (e.g., HTTPS).

[0097] The "means for displaying the generated proposal to the user" is a function for visually displaying the proposal content received on the user terminal so that the user can easily confirm it.

[0098] This invention is a system in which a user (store clerk) inputs the customer's inquiry details and the system generates artificial intelligence (AI) to provide optimal suggestions. This system uses a terminal, a server, a user interface (UI), a natural language processing (NLP) library, and a generative AI model.

[0099] System Overview

[0100] The system includes a means for taking in data entered by a user and performing preprocessing, a means for sending the preprocessed data to a generative AI model and extracting keywords, a means for searching a relevant database and generating optimal suggestions using the generative AI model, a means for sending the generated suggestions to a user terminal using a secure communication protocol, and a means for displaying the generated suggestions to a user.

[0101] Hardware and software used

[0102] Terminal: A device operated by a user (store clerk), including a PC, tablet, smartphone, etc. These terminals have a user interface that allows the user to input data.

[0103] Server: A remote server used for data processing and analysis of AI models. The database stores past cases and related information.

[0104] Natural language processing libraries: Software libraries used to preprocess data, including tokenization and normalization. Examples include NLP libraries in Python.

[0105] Generative AI model: An artificial intelligence model used to generate optimal recommendations based on user input. Examples include generative AI models such as OpenAI's GPT-3.

[0106] Secure communication protocol: A communication protocol used to protect the transmission and reception of data, such as HTTPS.

[0107] Specific examples

[0108] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with a long battery life." In this case, the process will proceed as follows:

[0109] User input: The store clerk enters "a lightweight laptop with good battery life" into the input form on the terminal and clicks the submit button.

[0110] Data transmission: The terminal encodes the input data into JSON format and sends it to the server using a secure communication protocol (HTTPS).

[0111] Preprocessing: The server receives the data and normalizes and tokenizes it using a natural language processing library. It breaks down "lightweight laptop with good battery life" into tokens such as "lightweight", "battery life", "good", and "laptop".

[0112] Proposal generation by generative AI: Preprocessed data is input into the generative AI model, and the keywords "lightweight" and "long battery life" are extracted. Based on this, information on laptops that fit the criteria is searched for in the database, and the generative AI generates the following proposals: "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours."

[0113] Sending response data: The generated proposal is converted into JSON format and sent to the terminal again using a secure communication protocol.

[0114] Proposal display: The device parses the received proposal and visually displays "Model A: weight 1.2 kg, battery 12 hours" and "Model B: weight 1.1 kg, battery 10 hours" on the user interface.

[0115] User action: The salesperson explains the proposal to the customer, saying, "These models of laptops meet your requirements."

[0116] Prompt Sentence Examples

[0117] Input prompt:

[0118] "I'm looking for a lightweight laptop with good battery life."

[0119] Output of the generative AI model:

[0120] "We recommend the following models: Model A (weight 1.2 kg, 12-hour battery life), Model B (weight 1.1 kg, 10-hour battery life)"

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: Capturing User Input

[0123] Terminal: Through the user interface (UI), the user (store clerk) inputs the customer's inquiry. At this point, the input is text data, such as "I'm looking for a lightweight laptop with good battery life." The terminal receives this input and prepares for the next processing step.

[0124] Step 2: Sending data

[0125] Terminal: Encodes input data into JSON format and sends it to the server using a secure communication protocol (e.g., HTTPS). This operation involves sending the data via an HTTP POST request. The input is the encoded JSON data, and the output is a status code indicating successful submission to the server.

[0126] Step 3: Preprocessing

[0127] Server: The received data is first normalized to remove unnecessary whitespace and special characters. Next, a natural language processing library (e.g., a Python NLP library) is used to tokenize the data and break it into words and phrases. For example, "lightweight laptop with good battery life" is broken down into "lightweight", "battery life", "good", and "laptop". The input is the received raw data, and the output is the tokenized data.

[0128] Step 4: Keyword extraction and suggestion generation

[0129] Server: A generative AI model is used to extract key keywords from the preprocessed data. After extracting the keywords "lightweight" and "long battery life," information on products related to these keywords is searched for in the database. Based on the search results, the generative AI model generates optimal suggestions in natural language. For example, suggestions such as "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours" are generated. The input is tokenized data, and the output is suggestions in natural language format.

[0130] Step 5: Sending response data

[0131] Server: Converts the generated proposal into JSON format and sends it back to the terminal using a secure communication protocol (HTTPS). The server sets the response header and returns an HTTP 200 status code. The input is the generated proposal, and the output is the encoded JSON data and the sending status code.

[0132] Step 6: Display suggestions

[0133] Terminal: Parses the JSON format proposal received from the server and displays it visually to the user. Specifically, proposals such as "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours" are displayed on the UI. The input is the JSON data received from the server, and the output is the visual information displayed on the user interface.

[0134] Step 7: User Actions

[0135] User: The salesperson (user) explains the proposal to the customer based on the content of the proposal. They introduce the laptop models that meet the customer's requirements and support the customer in making a selection. The input is the proposal information displayed on the terminal, and the output is the dialogue with the customer and the implementation of the proposal.

[0136] This allows users to quickly make high-quality proposals even without specialized knowledge.

[0137] (Application example 1)

[0138] 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."

[0139] Conventional systems have had difficulty quickly and accurately proposing products that meet the user's desired conditions. In particular, proposing products that meet multiple conditions requires the user to have specialized knowledge, which can be time-consuming. Furthermore, from a security standpoint, there is a risk of incomplete data transmission and customer information leaks. A system that solves these problems is needed.

[0140] 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.

[0141] In this invention, the server includes a means for receiving and preprocessing data entered by the user, a means for transmitting the preprocessed data to the AI ​​that generates the data, and a means for visually displaying the generated suggestions on a user interface. This allows the user to receive product suggestions quickly and accurately without having specialized knowledge, and furthermore, a secure communication protocol prevents the leakage of customer information.

[0142] A "user interface" is something that provides a visual or operational means for a user to interact with a system.

[0143] "Preprocessing" is the initial data processing that converts the data entered by the user into a form that is easy to analyze.

[0144] "Generative AI" is a system that uses specific algorithms to analyze input data and generate optimal suggestions.

[0145] A "secure communication protocol" is a communication method that ensures confidentiality, integrity, authenticity, etc. of information when sending and receiving data.

[0146] "Visually displaying" refers to a process of displaying the generated information on the screen in a format that is easy for the user to understand.

[0147] This invention is a system that integrates a user interface, data preprocessing, generative AI, and a secure communication protocol. This system uses AI to generate optimal suggestions based on data entered by the user and displays them to the user.

[0148] System Configuration

[0149] Hardware

[0150] Terminal: A device such as a smartphone or tablet on which a user interface is provided.

[0151] Server: The data center or cloud server where the generative AI runs.

[0152] software

[0153] Requests library: A library for sending HTTP requests.

[0154] Flask: A web framework used on the server side.

[0155] Generative AI: An artificial intelligence model that generates optimal suggestions based on input data.

[0156] Secure communication protocol: A protocol that ensures confidentiality, integrity, and authenticity of information when transmitting and receiving data (e.g., HTTPS).

[0157] Data processing flow

[0158] 1. User Interface

[0159] The user inputs the desired product specifications into the input form on the device. For example, the user may write in their inquiry about a "lightweight smartphone with good battery life."

[0160] 2. Data Preprocessing

[0161] The server tokenizes and normalizes the received user input, converting it into a format that is easy for the generative AI to analyze.

[0162] 3. Generation AI

[0163] Generative AI extracts relevant keywords and features from pre-processed data, and generates optimal product suggestions by referencing past cases and databases. The suggestions are expressed in natural language.

[0164] 4. Secure Data Communication

[0165] The proposed data is then sent back to the terminal using a secure communication protocol (e.g., HTTPS). By ensuring secure communication, the risk of customer information being leaked is reduced.

[0166] 5. Visual representation of suggestions

[0167] The device displays the received suggestions on the user interface, visually presenting information such as "Model A: weight 1.2 kg, battery life 12 hours."

[0168] Specific examples

[0169] For example, if a user inputs "I'm looking for a smartphone that's light and has a long battery life," the system operates as follows:

[0170] The user inputs the consultation content into the input form on the terminal.

[0171] The server tokenizes the input and extracts keywords such as "lightweight" and "good battery life."

[0172] The generative AI refers to a related past database and generates suggestions such as "Model X: weight 1.1 kg, battery life 15 hours" and "Model Y: weight 1.0 kg, battery life 14 hours."

[0173] The generated proposal is transmitted to the terminal using a secure communication protocol.

[0174] The proposed content is displayed on the user interface, and the user selects products based on this.

[0175] Prompt Sentence Examples

[0176] Based on the user's token "lightweight laptop with good battery life," prompt the user with products that meet the following criteria: 1. A lightweight, high-performance model. 2. A long battery life. 3. Multiple options are offered.

[0177] In this way, the technical scope of the invention is specifically indicated, and a system is provided that enables users to receive prompt and accurate product proposals even without specialized knowledge.

[0178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0179] Step 1:

[0180] The user inputs the desired product specifications into the input form on the device. For example, the user may write "I want a smartphone that is lightweight and has a long battery life." The input data is retrieved by the device's user interface.

[0181] (input)

[0182] Product specifications entered by the user (e.g., "A lightweight smartphone with good battery life")

[0183] (output)

[0184] Text data entered into the terminal

[0185] Step 2:

[0186] The terminal sends the entered text data to the server using a secure communication protocol (e.g., HTTPS), which ensures the confidentiality and integrity of the data.

[0187] (input)

[0188] Text data transmitted using a secure communication protocol

[0189] (output)

[0190] Text data that reaches the server

[0191] Step 3:

[0192] The server preprocesses the received text data, which includes tokenizing and normalizing the input (e.g., removing unnecessary whitespace and grammatical errors) to convert it into a format that is easy for the generative AI model to parse.

[0193] (input)

[0194] Text data that reaches the server

[0195] (output)

[0196] Tokenized and normalized data

[0197] Step 4:

[0198] The server sends the preprocessed data to a generative AI model, which references a database and past cases to extract relevant keywords and features (e.g., "lightweight" and "battery life") and then generates optimal suggestions based on those features.

[0199] (input)

[0200] Tokenized and normalized data

[0201] (output)

[0202] Generated product suggestions (e.g., "Model X: weight 1.1 kg, battery life 15 hours" "Model Y: weight 1.0 kg, battery life 14 hours")

[0203] Step 5:

[0204] The server converts the generated product proposals into an appropriate format (e.g., JSON) and sends them to the terminal, again using a secure communication protocol.

[0205] (input)

[0206] Generated product suggestion data

[0207] (output)

[0208] Proposal data in JSON format

[0209] Step 6:

[0210] The terminal analyzes the received product suggestion data and visually displays it on the user interface, such as product name, features, price, etc.

[0211] (input)

[0212] Proposal data in JSON format

[0213] (output)

[0214] Suggestions displayed in the user interface (e.g., "Model X: weight 1.1 kg, battery life 15 hours" or "Model Y: weight 1.0 kg, battery life 14 hours")

[0215] Step 7:

[0216] The user can then check the proposed content and select products as necessary. This allows the user to select products quickly and accurately, even if they do not have specialized knowledge.

[0217] (input)

[0218] Suggestions displayed in the user interface

[0219] (output)

[0220] User decision-making and product selection

[0221] In this way, a system is realized in which each processing step works together to provide the user with optimal suggestions.

[0222] 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.

[0223] This invention is a system that allows users (store clerks) to input customer inquiries and provides optimal suggestions using artificial intelligence (AI). The system also incorporates an emotion engine that recognizes the user's emotional state and adjusts the suggestions accordingly.

[0224] Program processing flow

[0225] Capturing User Input

[0226] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry. For example, the user might input, "I'm looking for a smartphone with a large battery and a high-performance camera."

[0227] Emotion recognition

[0228] Device: The device uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotional state. For example, the device uses a camera and microphone to determine whether the user is smiling or nervous.

[0229] Sending data

[0230] Terminal: The input data and the emotion information recognized by the emotion engine are sent to the server using a secure protocol (e.g., HTTPS).

[0231] Data Preprocessing

[0232] Server: The server takes the received user data and emotion information and performs preprocessing, which includes text normalization, tokenization, and emotion data formatting.

[0233] Generative AI analysis

[0234] Server: Passes the preprocessed data to the generation AI, which analyzes the input data and emotional information. The generation AI extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state.

[0235] Proposal Generation

[0236] Server: The AI ​​generator uses a database to generate suggestions that match the user's input and emotional state. For example, if the user is nervous, the generated suggestions will include reassuring expressions.

[0237] Submitting proposal data

[0238] Server: Converts the generated proposal into JSON or XML format and sends it to the device using a secure protocol.

[0239] What the user sees

[0240] Terminal: The proposal received from the server is visually displayed on the user interface. Specifically, the proposed product models and their features are displayed in a list or tile format. If the user is nervous, a concise and reassuring explanation is added to the proposal.

[0241] Specific examples

[0242] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life," and the device recognizes through its emotion engine that the user is a little nervous.

[0243] 1. User Input

[0244] Terminal: The user (store clerk) inputs the inquiry content, "a lightweight laptop with good battery life," into the input form on the terminal.

[0245] 2. Emotional Recognition

[0246] Device: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and recognizes whether the user is nervous.

[0247] 3. Data transmission

[0248] Terminal: The content of the consultation and the user's emotional information (state of tension) are sent to the server using a secure protocol.

[0249] 4. Data Preprocessing

[0250] Server: Normalizes the received data and formats the tokenized and sentiment data.

[0251] 5. Generative AI Analysis

[0252] Server: Analyzes data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's state of tension.

[0253] 6. Proposal Generation

[0254] Server: The generative AI searches the database for laptop models that match the keywords and emotional state. The generated suggestions include "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours," as well as reassuring language to ease the user's tension.

[0255] 7. Submitting Proposal Data

[0256] Server: Converts the generated proposal into JSON format and sends it to the device using a secure protocol.

[0257] 8. Display of Suggestions

[0258] Device: The received suggestions are displayed in the user interface. For example, "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[0259] 9. User Actions

[0260] User: Based on the displayed suggestions, the user (store clerk) explains to the customer, "These models are laptops that meet your requirements," and "Even beginners can use them with confidence."

[0261] In this way, by incorporating an emotion engine, it is possible to provide suggestions that are adapted to the user's emotional state, which is expected to lead to smoother communication with customers.

[0262] The processing flow will be explained below.

[0263] Step 1:

[0264] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry. For example, the user might enter "I want a lightweight laptop with good battery life" in the text box and press the send button.

[0265] Step 2:

[0266] Device: The device uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotional state. For example, the device uses a camera and microphone to determine whether the user is tense or relaxed.

[0267] Step 3:

[0268] Terminal: The input consultation content and recognized emotion information are sent to the server using a secure communication protocol (e.g., HTTPS). The data is properly encoded before being sent.

[0269] Step 4:

[0270] Server: The server takes in the received data and emotion information and performs preprocessing, which includes text normalization (removing unnecessary whitespace, standardizing characters, etc.), tokenization (splitting into words), and emotional data formatting.

[0271] Step 5:

[0272] Server: Passes the preprocessed data to the generation AI. The generation AI analyzes the input data using natural language processing (NLP) techniques and extracts keywords and important features. For example, "lightweight" and "long battery life" are extracted as features.

[0273] Step 6:

[0274] Server: The generation AI adjusts the analysis results by taking into account emotional information. For example, if the user is nervous, the generated suggestions will include explanations that will reassure the user.

[0275] Step 7:

[0276] Server: The generative AI searches the database for relevant information based on the extracted keywords and features. For example, it retrieves laptop models that match the criteria of "lightweight" and "long battery life."

[0277] Step 8:

[0278] Server: Based on information retrieved from the database, the generative AI generates optimal suggestions. The suggestions are written in natural language, such as "Model A: weight 1.2 kg, battery life 12 hours" or "Model B: weight 1.1 kg, battery life 10 hours." If the user is nervous, they can add an explanation such as "Even beginners can use it with confidence."

[0279] Step 9:

[0280] Server: Converts the generated proposal into an appropriate format (e.g., JSON, XML) and sends it to the device using a secure communication protocol.

[0281] Step 10:

[0282] Device: The device analyzes the suggestions received from the server and displays them on the user interface. For example, it might display "Model A: Weight 1.2 kg, 12-hour battery life. Easy to use even for beginners," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[0283] Step 11:

[0284] User: Based on the suggestions displayed on the user interface, the user (store clerk) explains to the customer, "These models are laptops that meet your requirements." If the user seems nervous, they can also provide additional information such as, "Even beginners can use it with confidence."

[0285] In this way, a system including an emotion engine can tailor its suggestions to the user's emotional state, providing more personalized information.

[0286] Example 2

[0287] 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."

[0288] Conventional recommendation systems make suggestions based solely on user input data, and therefore are unable to provide optimal suggestions that take into account the user's emotional state. Furthermore, the visual display of suggestions in the user interface is often insufficient, making the content of the suggestions difficult for users to understand. This leads to a poor user experience and makes smooth communication with customers difficult.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0290] In this invention, the server includes a means for taking in and preprocessing data entered by the user and the user's emotional state, a means for transmitting the preprocessed data and emotional information to an artificial intelligence that generates the data, and a means for visually presenting the generated suggestions to the user, thereby enabling optimal suggestions that take the user's emotional state into consideration.

[0291] "Data entered by the user" refers to the details of the consultation or request received by the user (store clerk) from the customer, and is information collected in the form of text input, voice input, or the like.

[0292] "Emotional state" refers to the user's psychological state and emotional movements, and is recognized by analyzing the user's facial expressions and tone of voice.

[0293] "Preprocessing" is the process of analyzing data entered by a user and performing operations such as normalization, tokenization, and formatting of emotion data.

[0294] "Generative AI" is a device that uses machine learning models and algorithms to analyze pre-processed data entered by users and generate optimal suggestions.

[0295] The "visual presentation means" is a method of displaying the generated proposal content on a user interface and providing information in a form that can be easily understood by the user.

[0296] A "secure communication protocol" is a communication protocol used to prevent information tampering or eavesdropping when sending and receiving data, and typical examples include HTTPS and TLS.

[0297] "Past cases" refers to historical data such as consultation details and proposal results previously made by users or customers that are stored in the system.

[0298] A "relevant database" is a collection of information that is referenced to generate recommendations, and is data storage that may include product information, user reviews, technical specifications, etc.

[0299] This invention is a system that allows users (store clerks) to input customer inquiries and provides optimal suggestions using artificial intelligence (AI). This system incorporates an emotion engine that recognizes the user's emotional state and adjusts the suggestions accordingly.

[0300] System configuration

[0301] This system is broadly divided into two components: a terminal and a server. Below, we will explain each component and its role.

[0302] Terminal

[0303] The device provides a user interface (UI) and has a means for users to input customer inquiries. It also uses a camera and microphone to analyze the user's facial expressions and tone of voice, and an emotion engine to recognize the user's emotional state.

[0304] Specifically, the user inputs text into an input form on the device or uses voice input. By inputting a prompt such as "a smartphone with good battery life," the user's needs are specified.

[0305] server

[0306] The server receives data sent from the device and performs preprocessing, which includes text normalization, tokenization, and emotional data formatting.

[0307] The preprocessed data is passed to a generative AI model, which analyzes the input data and emotional information. The generative AI model extracts important keywords and features and generates optimal suggestions based on the user's emotional state. The generated suggestions are also based on a database of past cases and related information.

[0308] The generated proposal is converted back into JSON format and sent to the device using a secure protocol (e.g., HTTPS).

[0309] Specific examples

[0310] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life," and the device recognizes through its emotion engine that the user is a little nervous.

[0311] 1. User Input

[0312] Terminal: The user (store clerk) inputs the inquiry content, "a lightweight laptop with good battery life," into the input form on the terminal.

[0313] 2. Emotional Recognition

[0314] Device: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and recognizes whether the user is nervous.

[0315] 3. Data transmission

[0316] Terminal: The content of the consultation and the user's emotional information (state of tension) are sent to the server using a secure protocol.

[0317] 4. Data Preprocessing

[0318] Server: Normalizes the received data and formats the tokenized and sentiment data.

[0319] 5. Generative AI Analysis

[0320] Server: Analyzes data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's state of tension.

[0321] 6. Proposal Generation

[0322] Server: The generative AI searches the database for laptop models that match the keywords and emotional state. The generated suggestions include "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours," as well as reassuring language to ease the user's tension.

[0323] 7. Submitting Proposal Data

[0324] Server: Converts the generated proposal into JSON format and sends it to the device using a secure protocol.

[0325] 8. Display of Suggestions

[0326] Device: The received suggestions are displayed in the user interface. For example, "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[0327] This system is expected to provide suggestions that take into account the user's emotional state, facilitating smoother communication with customers. Furthermore, the combination of a generative AI model and an emotion engine will improve the user experience.

[0328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0329] Program processing flow

[0330] Step 1:

[0331] The user inputs the consultation content.

[0332] Terminal: Using the text box or voice input function provided by the user interface (UI), the user (store clerk) inputs the customer's inquiry. For example, the user might input, "I'm looking for a smartphone with a large battery and a high-performance camera."

[0333] Input: User's consultation content (text or voice)

[0334] Output: Text data of consultation content

[0335] Step 2:

[0336] Recognize the user's emotional state.

[0337] Device: Uses a camera and microphone to analyze the user's facial expressions and tone of voice, and uses an emotion engine (e.g., emotion recognition software) to identify the user's emotional state. For example, it can determine whether the user is smiling, nervous, surprised, etc.

[0338] Input: User's facial expression data, voice data

[0339] Output: Emotional state (e.g., nervous, relieved, happy)

[0340] Step 3:

[0341] Send the data to the server.

[0342] Terminal: Converts the input consultation data and recognized emotion information into JSON format and sends it to the server using a secure protocol (HTTPS).

[0343] Input: Text data of consultation content, emotional state information

[0344] Output: Confirmation of successful transmission

[0345] Step 4:

[0346] Preprocessing of received data is performed.

[0347] Server: The server takes the data received from the device and performs preprocessing, which includes text normalization (e.g., converting all data to lowercase), tokenization (splitting data into words), and emotional data formatting (e.g., converting data into numerical data).

[0348] Input: Text data of consultation content, emotional state information

[0349] Output: Preprocessed data

[0350] Step 5:

[0351] The transmitted data is analyzed by the generation AI.

[0352] Server: Passes the preprocessed data to the generative AI model, which analyzes the input data and emotional information. The generative AI model extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state. For example, it extracts keywords such as "battery" and "camera" and adds elements that will give the user a sense of security if they are nervous.

[0353] Input: Preprocessed data

[0354] Output: Proposal

[0355] Step 6:

[0356] Retrieve suggestions from the database.

[0357] Server: Based on the keywords and emotion information extracted by the generative AI model, the server references a database to retrieve information on suitable products and services. For example, it searches the database for smartphones with "12-hour battery life" and "high-performance cameras."

[0358] Input: Extracted keywords, emotional information

[0359] Output: Specific product suggestions

[0360] Step 7:

[0361] The generated proposal data is transmitted to the terminal.

[0362] Server: The generated proposal is converted back to JSON format and sent to the device using a secure protocol, using an authentication token to prevent data tampering.

[0363] Input: Proposal content (product information)

[0364] Output: Confirmation of successful transmission

[0365] Step 8:

[0366] The suggestions are displayed in the user interface.

[0367] Device: The received proposal is visually displayed on the user interface. For example, it may be displayed as "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[0368] Input: Received proposal

[0369] Output: Visual suggestions

[0370] In this way, the user's input and emotional information are appropriately utilized at each step to generate and display optimal suggestions, thereby improving the user experience.

[0371] (Application example 2)

[0372] 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."

[0373] Conventional customer service systems were unable to consider the emotional state of individual customers when making proposals based on their wishes and consultation details. This made it difficult to respond to subtle changes in emotions, such as when a customer was nervous or when a particular expression made them feel anxious. As a result, proposals were sometimes not fully accepted by customers. To solve this problem, a system was needed that could recognize the customer's emotional state in real time and make optimal proposals based on that.

[0374] 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.

[0375] In this invention, the server includes means for taking in and preprocessing data and emotional state input by the user, means for transmitting the preprocessed data and emotional information to an artificial intelligence that generates the data, means for presenting the generated suggestions to the user and adjusting them based on the emotional state, and means for transmitting and receiving data using a secure communication protocol, thereby enabling optimal product suggestions that take the user's emotional state into account in real time.

[0376] "Data entered by the user" refers to information that the user provides to the system through the input interface, such as the customer's wishes and consultation details.

[0377] "Emotional state" refers to psychological and emotional information obtained from a user's or customer's facial expressions, tone of voice, etc.

[0378] "Preprocessing" refers to a series of steps that normalize, tokenize, and convert input data and emotional information into a format that is easier to analyze.

[0379] "Generative AI" refers to systems that include algorithms and models for generating optimal suggestions and responses based on input data and emotional information.

[0380] "Secure communications protocol" means a communications protocol (e.g., HTTPS) used to ensure the security of data communications.

[0381] The "means for presenting suggestions to the user and adjusting them based on the user's emotional state" refers to a mechanism for displaying generated suggestions to the user and appropriately adjusting the content and expression of the suggestions, taking into account the user's emotional state.

[0382] "Optimal product proposals" refer to proposals for products or services that best suit the customer's wishes, consultation content, and emotional state.

[0383] The present invention is a system that takes in data and emotional state input by a user and makes optimal product recommendations based on that data. This system operates by having a salesperson input the customer's wishes and inquiries using a device such as a smartphone, tablet, or smart glasses when the customer is searching for a product in a physical store, and then using an emotion engine to recognize the customer's emotional state.

[0384] The system consists of hardware including a smartphone, tablet, and smart glasses (e.g., general-purpose smart glasses), and software including a user interface (UI) library, an emotion recognition engine (e.g., a general-purpose emotion recognition API), a secure communication protocol (HTTPS), and a generative AI model (e.g., a general-purpose generative model).

[0385] Program processing

[0386] User Input and Emotion Recognition

[0387] The terminal first receives the customer's requests and inquiries from the user (store clerk) through an input interface. For example, if a customer says, "I want a large-screen tablet that I can share with my friends," the store clerk enters this information into the terminal.

[0388] The device then uses an emotion engine to recognize the customer's emotional state. For example, it uses a camera and microphone to analyze the customer's facial expressions and tone of voice to determine whether they are feeling a little nervous. This emotional information is then processed along with the input data.

[0389] Data submission and preprocessing

[0390] The device sends the input data and emotion information to the server using a secure communication protocol (HTTPS). The server takes the received data and performs preprocessing such as text normalization, tokenization, and emotion data formatting.

[0391] Proposal generation using generative AI models

[0392] The server passes the preprocessed data to a generative AI model, which analyzes the input data and emotional information. This generative AI model extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state. For example, based on keywords such as "large screen," "shared," and "tablet," it compares them with past cases and database information to make product suggestions. If the customer is nervous, the suggestions will be generated to include expressions that will give them a sense of security.

[0393] Viewing Proposals

[0394] The generated proposal is converted into JSON format or similar and sent to the device again using a secure communication protocol. The device visually displays the received proposal content on the user interface. For example, the proposal content may include a reassuring description such as "Model A: 10 inches, easy to use even for beginners."

[0395] Specific examples

[0396] For example, suppose a customer asks, "I'm looking for a lightweight laptop with good battery life," and the device's emotion engine recognizes that the customer is a little nervous.

[0397] 1. User Input:

[0398] The terminal receives a request from the user (store clerk) via an input interface for a "lightweight laptop with good battery life."

[0399] 2. Emotion recognition:

[0400] The device uses a camera and microphone to analyze the customer's facial expressions and tone of voice, and recognizes if they are a little nervous.

[0401] 3. Data submission and preprocessing:

[0402] The device transmits the consultation content and emotional information (tension state) to the server using a secure protocol, and the server normalizes, tokenizes, and formats the emotional data.

[0403] 4. Generative AI analysis:

[0404] The server analyzes the data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's level of tension.

[0405] 5. Proposal generation:

[0406] The generative AI searches the database to find laptop models that match the keywords and emotional state. For example, generated suggestions might include "Model A: weight 1.2 kg, battery life 12 hours" or "Model B: weight 1.1 kg, battery life 10 hours," and include reassuring language to ease the user's tension.

[0407] 6. Suggestion display:

[0408] The proposal is converted into JSON format and sent to the device using a secure protocol. The device then displays the received proposal in its user interface, for example, "Model A: Weight 1.2 kg, Battery life 12 hours. Safe for beginners to use," or "Model B: Weight 1.1 kg, Battery life 10 hours. Lightweight and ideal for carrying around."

[0409] Prompt Sentence Examples

[0410] The following text could be fed into a generative AI model as a prompt:

[0411] text

[0412] Customer's request: "A large-screen tablet that can be shared with colleagues."

[0413] Customer sentiment: "I'm a little nervous"

[0414] Generate a proposal for a tablet with a large screen that's easy to share, including its features, beginner-friendly features, and a reassuring description.

[0415] As described above, the present invention provides a system that grasps the emotional state of a user and makes appropriate product suggestions in real time, thereby realizing smooth communication with customers.

[0416] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0417] Step 1:

[0418] The terminal accepts user input. The user (store clerk) inputs the customer's wishes or inquiries into the input interface. For example, the user might input, "I'm looking for a lightweight laptop with good battery life." This becomes the input data.

[0419] Step 2:

[0420] The device performs emotion recognition. It uses a camera and microphone to analyze the user's (store clerk's) facial expressions and tone of voice, and uses an emotion engine to identify the customer's emotional state. Emotional information is obtained in this step.

[0421] Step 3:

[0422] The device preprocesses the data, integrating the input data with sentiment information, normalizing and tokenizing the text, and formatting the sentiment data. This preprocessed data is the input for the next step.

[0423] Step 4:

[0424] The device sends the preprocessed data to the server. The preprocessed data is transferred to the server using a secure communication protocol (HTTPS). The server receives this data and begins processing it as new input data.

[0425] Step 5:

[0426] The server analyzes the data using a generative AI model. The server passes the received input data and emotional information to a generative AI model (general-purpose generative model) to extract important keywords and features. For example, keywords such as "lightweight" and "long battery life" are extracted, and analysis is performed taking into account the emotional state.

[0427] Step 6:

[0428] The server generates optimal suggestions. The generative AI model references past cases and databases to generate suggestions that best fit the user's requirements and emotional state. The generated suggestions include specific product information and reassuring descriptions.

[0429] Step 7:

[0430] The server sends the generated proposal to the device. The server converts the generated proposal into JSON format or similar and sends it to the device using a secure communication protocol. The device receives this data.

[0431] Step 8:

[0432] The device presents the suggestions to the user. The received suggestions are visually displayed on the user interface. For example, it may display "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[0433] Step 9:

[0434] The user (store clerk) responds to the customer. Based on the generated proposal, the user (store clerk) provides an appropriate product explanation to the customer. The user explains the proposed product model and its features, and makes a proposal that best suits the customer's needs.

[0435] 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.

[0436] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0437] 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.

[0438] [Second embodiment]

[0439] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] In the smart glasses 214, 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.

[0450] 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."

[0451] This invention is a system in which a user (store clerk) inputs the customer's inquiry details and an artificial intelligence (AI) generates and provides optimal suggestions. This system involves taking in user input, sending the data, processing it with the AI, and displaying the suggestions.

[0452] Program processing flow

[0453] Capturing User Input

[0454] Terminal: A user interface (UI) is provided where the user inputs the customer's inquiry. For example, "I'm looking for a smartphone with a large battery and a high-performance camera."

[0455] Sending data

[0456] Terminal: The entered data is sent to the server using a secure communication protocol (e.g., HTTPS).

[0457] NPL pretreatment

[0458] Server: The server performs preprocessing such as tokenization and normalization on the received data, converting it into a form that can be easily analyzed by the generative AI.

[0459] Proposal generation by generative AI

[0460] Server: The generative AI analyzes the preprocessed data and extracts the input keywords and features. For example, "large capacity battery" and "high performance camera" are extracted. The generative AI then searches for products that meet these features based on a database and past cases, and generates optimal suggestions. The generative AI creates suggestions in natural language, including relevant information (e.g., battery capacity and camera performance).

[0461] Sending response data

[0462] Server: Converts the generated proposal into an appropriate format (e.g., JSON or XML) and sends it back to the device using a secure communication protocol.

[0463] What the user sees

[0464] Terminal: The terminal displays the proposal received from the server on the user interface. The user can interact with the customer based on this information and provide professional proposals, such as "Model X: 5000mAh battery, 48MP camera."

[0465] Specific examples

[0466] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life."

[0467] 1. Step 1: User Input

[0468] Terminal: The user (store clerk) inputs the customer's request into the terminal's input form. The request is for a "lightweight laptop with good battery life."

[0469] 2. Step 2: Sending data

[0470] Terminal: The entered data is sent to the server using a secure protocol.

[0471] 3. Step 3: Pretreatment

[0472] Server: Normalizes and tokenizes the received data into a format that is easy to analyze.

[0473] 4. Step 4: Proposal Generation

[0474] Server: The generation AI extracts the keywords "lightweight" and "long battery life" and searches the database for laptop models that match these keywords. The generation AI generates the following suggestions: "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours."

[0475] 5. Step 5: Sending response data

[0476] Server: Converts the generated proposal into JSON format and sends it to the device.

[0477] 6. Step 6: Suggestions

[0478] Terminal: Visually displays the received proposal to the user. The user interface displays "Model A: Weight 1.2 kg, Battery life 12 hours" and "Model B: Weight 1.1 kg, Battery life 10 hours."

[0479] 7. Step 7: User Actions

[0480] User: Based on the proposal, the salesperson (user) explains to the customer, "These models are laptops that meet your requirements."

[0481] This allows store clerks to quickly provide highly specialized information to customers without relying on their specialized knowledge or experience, which is expected to improve customer satisfaction and increase sales.

[0482] The processing flow will be explained below.

[0483] Step 1:

[0484] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry details. The user enters the inquiry details in the text box and presses the send button.

[0485] Step 2:

[0486] Terminal: Sends user-entered data to the server using a secure protocol (e.g., HTTPS), properly encoding the data before sending it.

[0487] Step 3:

[0488] Server: The server receives the received data and performs preprocessing, which includes text normalization (removing unnecessary whitespace, standardizing characters, etc.) and tokenization (splitting into words).

[0489] Step 4:

[0490] Server: Passes the preprocessed data to the generative AI, which uses natural language processing (NLP) techniques to analyze the input data and extract keywords and important features.

[0491] Step 5:

[0492] Server: The generation AI searches the database for relevant information based on the extracted keywords and features. For example, it retrieves product information that matches keywords such as "large-capacity battery" and "high-performance camera."

[0493] Step 6:

[0494] Server: Based on the information retrieved from the database, the generative AI generates optimal suggestions. Suggestions are written in natural language and are provided in the form of, for example, "Model X: 5000mAh battery, 48MP camera."

[0495] Step 7:

[0496] Server: Converts the generated proposal into an appropriate format (e.g., JSON, XML) and sends it to the device using a secure protocol.

[0497] Step 8:

[0498] Terminal: The terminal analyzes the proposals received from the server and displays them on the user interface. Specifically, the proposed product models and their features are displayed in a list or tile format.

[0499] Step 9:

[0500] User: Based on the suggestions displayed in the user interface, the user (store clerk) provides expert advice to the customer, for example, explaining that "Model X and Model Y are products that meet your requirements."

[0501] In this way, the entire system operates in cooperation with one another, allowing users to make appropriate proposals to customers without relying on specialized knowledge.

[0502] Example 1

[0503] 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."

[0504] In conventional systems, store staff and users required advanced expertise to properly understand customer inquiries and provide optimal proposals. This resulted in problems such as the quality and consistency of proposals depending on the staff's skills, leading to inconsistencies in customer satisfaction. Furthermore, proposals were generated manually, resulting in a lack of speed. The present invention aims to solve these problems and provide high-quality proposals to customers quickly and consistently.

[0505] 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.

[0506] In this invention, the server includes a means for taking in data entered by a user and performing preprocessing, a means for sending the preprocessed data to a generative AI model and extracting keywords, and a means for searching a related database and generating optimal suggestions using the generative AI model, thereby enabling users to quickly make high-quality suggestions even if they do not have specialized knowledge.

[0507] "Means for capturing and preprocessing data entered by users" refers to a function that collects data such as consultation details entered by users via their terminals and processes them using natural language processing techniques such as tokenization and normalization.

[0508] "Means for sending preprocessed data to a generative AI model and extracting keywords" refers to the process of sending preprocessed data to a generative AI model and extracting important information and keywords from the input data.

[0509] "Means for searching relevant databases and generating optimal suggestions using a generative AI model" refers to a function that searches for similar or related information from a database based on keywords extracted by the generative AI model, and generates optimal suggestions based on that information.

[0510] "Means for transmitting the generated proposal to the user terminal using a secure communication protocol" refers to a process for securely transmitting the generated proposal content to the user terminal using a secure communication protocol (e.g., HTTPS).

[0511] The "means for displaying the generated proposal to the user" is a function for visually displaying the proposal content received on the user terminal so that the user can easily confirm it.

[0512] This invention is a system in which a user (store clerk) inputs the customer's inquiry details and the system generates artificial intelligence (AI) to provide optimal suggestions. This system uses a terminal, a server, a user interface (UI), a natural language processing (NLP) library, and a generative AI model.

[0513] System Overview

[0514] The system includes a means for taking in data entered by a user and performing preprocessing, a means for sending the preprocessed data to a generative AI model and extracting keywords, a means for searching a relevant database and generating optimal suggestions using the generative AI model, a means for sending the generated suggestions to a user terminal using a secure communication protocol, and a means for displaying the generated suggestions to a user.

[0515] Hardware and software used

[0516] Terminal: A device operated by a user (store clerk), including a PC, tablet, smartphone, etc. These terminals have a user interface that allows the user to input data.

[0517] Server: A remote server used for data processing and analysis of AI models. The database stores past cases and related information.

[0518] Natural language processing libraries: Software libraries used to preprocess data, including tokenization and normalization. Examples include NLP libraries in Python.

[0519] Generative AI model: An artificial intelligence model used to generate optimal recommendations based on user input. Examples include generative AI models such as OpenAI's GPT-3.

[0520] Secure communication protocol: A communication protocol used to protect the transmission and reception of data, such as HTTPS.

[0521] Specific examples

[0522] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with a long battery life." In this case, the process will proceed as follows:

[0523] User input: The store clerk enters "a lightweight laptop with good battery life" into the input form on the terminal and clicks the submit button.

[0524] Data transmission: The terminal encodes the input data into JSON format and sends it to the server using a secure communication protocol (HTTPS).

[0525] Preprocessing: The server receives the data and normalizes and tokenizes it using a natural language processing library. It breaks down "lightweight laptop with good battery life" into tokens such as "lightweight", "battery life", "good", and "laptop".

[0526] Proposal generation by generative AI: Preprocessed data is input into the generative AI model, and the keywords "lightweight" and "long battery life" are extracted. Based on this, information on laptops that fit the criteria is searched for in the database, and the generative AI generates the following proposals: "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours."

[0527] Sending response data: The generated proposal is converted into JSON format and sent to the terminal again using a secure communication protocol.

[0528] Proposal display: The device parses the received proposal and visually displays "Model A: weight 1.2 kg, battery 12 hours" and "Model B: weight 1.1 kg, battery 10 hours" on the user interface.

[0529] User action: The salesperson explains the proposal to the customer, saying, "These models of laptops meet your requirements."

[0530] Prompt Sentence Examples

[0531] Input prompt:

[0532] "I'm looking for a lightweight laptop with good battery life."

[0533] Output of the generative AI model:

[0534] "We recommend the following models: Model A (weight 1.2 kg, 12-hour battery life), Model B (weight 1.1 kg, 10-hour battery life)"

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] Step 1: Capturing User Input

[0537] Terminal: Through the user interface (UI), the user (store clerk) inputs the customer's inquiry. At this point, the input is text data, such as "I'm looking for a lightweight laptop with good battery life." The terminal receives this input and prepares for the next processing step.

[0538] Step 2: Sending data

[0539] Terminal: Encodes input data into JSON format and sends it to the server using a secure communication protocol (e.g., HTTPS). This operation involves sending the data via an HTTP POST request. The input is the encoded JSON data, and the output is a status code indicating successful submission to the server.

[0540] Step 3: Preprocessing

[0541] Server: The received data is first normalized to remove unnecessary whitespace and special characters. Next, a natural language processing library (e.g., a Python NLP library) is used to tokenize the data and break it into words and phrases. For example, "lightweight laptop with good battery life" is broken down into "lightweight", "battery life", "good", and "laptop". The input is the received raw data, and the output is the tokenized data.

[0542] Step 4: Keyword extraction and suggestion generation

[0543] Server: A generative AI model is used to extract key keywords from the preprocessed data. After extracting the keywords "lightweight" and "long battery life," information on products related to these keywords is searched for in the database. Based on the search results, the generative AI model generates optimal suggestions in natural language. For example, suggestions such as "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours" are generated. The input is tokenized data, and the output is suggestions in natural language format.

[0544] Step 5: Sending response data

[0545] Server: Converts the generated proposal into JSON format and sends it back to the terminal using a secure communication protocol (HTTPS). The server sets the response header and returns an HTTP 200 status code. The input is the generated proposal, and the output is the encoded JSON data and the sending status code.

[0546] Step 6: Display suggestions

[0547] Terminal: Parses the JSON format proposal received from the server and displays it visually to the user. Specifically, proposals such as "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours" are displayed on the UI. The input is the JSON data received from the server, and the output is the visual information displayed on the user interface.

[0548] Step 7: User Actions

[0549] User: The salesperson (user) explains the proposal to the customer based on the content of the proposal. They introduce the laptop models that meet the customer's requirements and support the customer in making a selection. The input is the proposal information displayed on the terminal, and the output is the dialogue with the customer and the implementation of the proposal.

[0550] This allows users to quickly make high-quality proposals even without specialized knowledge.

[0551] (Application example 1)

[0552] 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."

[0553] Conventional systems have had difficulty quickly and accurately proposing products that meet the user's desired conditions. In particular, proposing products that meet multiple conditions requires the user to have specialized knowledge, which can be time-consuming. Furthermore, from a security standpoint, there is a risk of incomplete data transmission and customer information leaks. A system that solves these problems is needed.

[0554] 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.

[0555] In this invention, the server includes a means for receiving and preprocessing data entered by the user, a means for transmitting the preprocessed data to the AI ​​that generates the data, and a means for visually displaying the generated suggestions on a user interface. This allows the user to receive product suggestions quickly and accurately without having specialized knowledge, and furthermore, a secure communication protocol prevents the leakage of customer information.

[0556] A "user interface" is something that provides a visual or operational means for a user to interact with a system.

[0557] "Preprocessing" is the initial data processing that converts the data entered by the user into a form that is easy to analyze.

[0558] "Generative AI" is a system that uses specific algorithms to analyze input data and generate optimal suggestions.

[0559] A "secure communication protocol" is a communication method that ensures confidentiality, integrity, authenticity, etc. of information when sending and receiving data.

[0560] "Visually displaying" refers to a process of displaying the generated information on the screen in a format that is easy for the user to understand.

[0561] This invention is a system that integrates a user interface, data preprocessing, generative AI, and a secure communication protocol. This system uses AI to generate optimal suggestions based on data entered by the user and displays them to the user.

[0562] System Configuration

[0563] Hardware

[0564] Terminal: A device such as a smartphone or tablet on which a user interface is provided.

[0565] Server: The data center or cloud server where the generative AI runs.

[0566] software

[0567] Requests library: A library for sending HTTP requests.

[0568] Flask: A web framework used on the server side.

[0569] Generative AI: An artificial intelligence model that generates optimal suggestions based on input data.

[0570] Secure communication protocol: A protocol that ensures confidentiality, integrity, and authenticity of information when transmitting and receiving data (e.g., HTTPS).

[0571] Data processing flow

[0572] 1. User Interface

[0573] The user inputs the desired product specifications into the input form on the device. For example, the user may write in their inquiry about a "lightweight smartphone with good battery life."

[0574] 2. Data Preprocessing

[0575] The server tokenizes and normalizes the received user input, converting it into a format that is easy for the generative AI to analyze.

[0576] 3. Generation AI

[0577] Generative AI extracts relevant keywords and features from pre-processed data, and generates optimal product suggestions by referencing past cases and databases. The suggestions are expressed in natural language.

[0578] 4. Secure Data Communication

[0579] The proposed data is then sent back to the terminal using a secure communication protocol (e.g., HTTPS). By ensuring secure communication, the risk of customer information being leaked is reduced.

[0580] 5. Visual representation of suggestions

[0581] The device displays the received suggestions on the user interface, visually presenting information such as "Model A: weight 1.2 kg, battery life 12 hours."

[0582] Specific examples

[0583] For example, if a user inputs "I'm looking for a smartphone that's light and has a long battery life," the system operates as follows:

[0584] The user inputs the consultation content into the input form on the terminal.

[0585] The server tokenizes the input and extracts keywords such as "lightweight" and "good battery life."

[0586] The generative AI refers to a related past database and generates suggestions such as "Model X: weight 1.1 kg, battery life 15 hours" and "Model Y: weight 1.0 kg, battery life 14 hours."

[0587] The generated proposal is transmitted to the terminal using a secure communication protocol.

[0588] The proposed content is displayed on the user interface, and the user selects products based on this.

[0589] Prompt Sentence Examples

[0590] Based on the user's token "lightweight laptop with good battery life," prompt the user with products that meet the following criteria: 1. A lightweight, high-performance model. 2. A long battery life. 3. Multiple options are offered.

[0591] In this way, the technical scope of the invention is specifically indicated, and a system is provided that enables users to receive prompt and accurate product proposals even without specialized knowledge.

[0592] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0593] Step 1:

[0594] The user inputs the desired product specifications into the input form on the device. For example, the user may write "I want a smartphone that is lightweight and has a long battery life." The input data is retrieved by the device's user interface.

[0595] (input)

[0596] Product specifications entered by the user (e.g., "A lightweight smartphone with good battery life")

[0597] (output)

[0598] Text data entered into the terminal

[0599] Step 2:

[0600] The terminal sends the entered text data to the server using a secure communication protocol (e.g., HTTPS), which ensures the confidentiality and integrity of the data.

[0601] (input)

[0602] Text data transmitted using a secure communication protocol

[0603] (output)

[0604] Text data that reaches the server

[0605] Step 3:

[0606] The server preprocesses the received text data, which includes tokenizing and normalizing the input (e.g., removing unnecessary whitespace and grammatical errors) to convert it into a format that is easy for the generative AI model to parse.

[0607] (input)

[0608] Text data that reaches the server

[0609] (output)

[0610] Tokenized and normalized data

[0611] Step 4:

[0612] The server sends the preprocessed data to a generative AI model, which references a database and past cases to extract relevant keywords and features (e.g., "lightweight" and "battery life") and then generates optimal suggestions based on those features.

[0613] (input)

[0614] Tokenized and normalized data

[0615] (output)

[0616] Generated product suggestions (e.g., "Model X: weight 1.1 kg, battery life 15 hours" "Model Y: weight 1.0 kg, battery life 14 hours")

[0617] Step 5:

[0618] The server converts the generated product proposals into an appropriate format (e.g., JSON) and sends them to the terminal, again using a secure communication protocol.

[0619] (input)

[0620] Generated product suggestion data

[0621] (output)

[0622] Proposal data in JSON format

[0623] Step 6:

[0624] The terminal analyzes the received product suggestion data and visually displays it on the user interface, such as product name, features, price, etc.

[0625] (input)

[0626] Proposal data in JSON format

[0627] (output)

[0628] Suggestions displayed in the user interface (e.g., "Model X: weight 1.1 kg, battery life 15 hours" or "Model Y: weight 1.0 kg, battery life 14 hours")

[0629] Step 7:

[0630] The user can then check the proposed content and select products as necessary. This allows the user to select products quickly and accurately, even if they do not have specialized knowledge.

[0631] (input)

[0632] Suggestions displayed in the user interface

[0633] (output)

[0634] User decision-making and product selection

[0635] In this way, a system is realized in which each processing step works together to provide the user with optimal suggestions.

[0636] 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.

[0637] This invention is a system that allows users (store clerks) to input customer inquiries and provides optimal suggestions using artificial intelligence (AI). The system also incorporates an emotion engine that recognizes the user's emotional state and adjusts the suggestions accordingly.

[0638] Program processing flow

[0639] Capturing User Input

[0640] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry. For example, the user might input, "I'm looking for a smartphone with a large battery and a high-performance camera."

[0641] Emotion recognition

[0642] Device: The device uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotional state. For example, the device uses a camera and microphone to determine whether the user is smiling or nervous.

[0643] Sending data

[0644] Terminal: The input data and the emotion information recognized by the emotion engine are sent to the server using a secure protocol (e.g., HTTPS).

[0645] Data Preprocessing

[0646] Server: The server takes the received user data and emotion information and performs preprocessing, which includes text normalization, tokenization, and emotion data formatting.

[0647] Generative AI analysis

[0648] Server: Passes the preprocessed data to the generation AI, which analyzes the input data and emotional information. The generation AI extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state.

[0649] Proposal Generation

[0650] Server: The AI ​​generator uses a database to generate suggestions that match the user's input and emotional state. For example, if the user is nervous, the generated suggestions will include reassuring expressions.

[0651] Submitting proposal data

[0652] Server: Converts the generated proposal into JSON or XML format and sends it to the device using a secure protocol.

[0653] What the user sees

[0654] Terminal: The proposal received from the server is visually displayed on the user interface. Specifically, the proposed product models and their features are displayed in a list or tile format. If the user is nervous, a concise and reassuring explanation is added to the proposal.

[0655] Specific examples

[0656] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life," and the device recognizes through its emotion engine that the user is a little nervous.

[0657] 1. User Input

[0658] Terminal: The user (store clerk) inputs the inquiry content, "a lightweight laptop with good battery life," into the input form on the terminal.

[0659] 2. Emotional Recognition

[0660] Device: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and recognizes whether the user is nervous.

[0661] 3. Data transmission

[0662] Terminal: The content of the consultation and the user's emotional information (state of tension) are sent to the server using a secure protocol.

[0663] 4. Data Preprocessing

[0664] Server: Normalizes the received data and formats the tokenized and sentiment data.

[0665] 5. Generative AI Analysis

[0666] Server: Analyzes data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's state of tension.

[0667] 6. Proposal Generation

[0668] Server: The generative AI searches the database for laptop models that match the keywords and emotional state. The generated suggestions include "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours," as well as reassuring language to ease the user's tension.

[0669] 7. Submitting Proposal Data

[0670] Server: Converts the generated proposal into JSON format and sends it to the device using a secure protocol.

[0671] 8. Display of Suggestions

[0672] Device: The received suggestions are displayed in the user interface. For example, "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[0673] 9. User Actions

[0674] User: Based on the displayed suggestions, the user (store clerk) explains to the customer, "These models are laptops that meet your requirements," and "Even beginners can use them with confidence."

[0675] In this way, by incorporating an emotion engine, it is possible to provide suggestions that are adapted to the user's emotional state, which is expected to lead to smoother communication with customers.

[0676] The processing flow will be explained below.

[0677] Step 1:

[0678] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry. For example, the user might enter "I want a lightweight laptop with good battery life" in the text box and press the send button.

[0679] Step 2:

[0680] Device: The device uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotional state. For example, the device uses a camera and microphone to determine whether the user is tense or relaxed.

[0681] Step 3:

[0682] Terminal: The input consultation content and recognized emotion information are sent to the server using a secure communication protocol (e.g., HTTPS). The data is properly encoded before being sent.

[0683] Step 4:

[0684] Server: The server takes in the received data and emotion information and performs preprocessing, which includes text normalization (removing unnecessary whitespace, standardizing characters, etc.), tokenization (splitting into words), and emotional data formatting.

[0685] Step 5:

[0686] Server: Passes the preprocessed data to the generation AI. The generation AI analyzes the input data using natural language processing (NLP) techniques and extracts keywords and important features. For example, "lightweight" and "long battery life" are extracted as features.

[0687] Step 6:

[0688] Server: The generation AI adjusts the analysis results by taking into account emotional information. For example, if the user is nervous, the generated suggestions will include explanations that will reassure the user.

[0689] Step 7:

[0690] Server: The generative AI searches the database for relevant information based on the extracted keywords and features. For example, it retrieves laptop models that match the criteria of "lightweight" and "long battery life."

[0691] Step 8:

[0692] Server: Based on information retrieved from the database, the generative AI generates optimal suggestions. The suggestions are written in natural language, such as "Model A: weight 1.2 kg, battery life 12 hours" or "Model B: weight 1.1 kg, battery life 10 hours." If the user is nervous, they can add an explanation such as "Even beginners can use it with confidence."

[0693] Step 9:

[0694] Server: Converts the generated proposal into an appropriate format (e.g., JSON, XML) and sends it to the device using a secure communication protocol.

[0695] Step 10:

[0696] Device: The device analyzes the suggestions received from the server and displays them on the user interface. For example, it might display "Model A: Weight 1.2 kg, 12-hour battery life. Easy to use even for beginners," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[0697] Step 11:

[0698] User: Based on the suggestions displayed on the user interface, the user (store clerk) explains to the customer, "These models are laptops that meet your requirements." If the user seems nervous, they can also provide additional information such as, "Even beginners can use it with confidence."

[0699] In this way, a system including an emotion engine can tailor its suggestions to the user's emotional state, providing more personalized information.

[0700] Example 2

[0701] 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."

[0702] Conventional recommendation systems make suggestions based solely on user input data, and therefore are unable to provide optimal suggestions that take into account the user's emotional state. Furthermore, the visual display of suggestions in the user interface is often insufficient, making the content of the suggestions difficult for users to understand. This leads to a poor user experience and makes smooth communication with customers difficult.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0704] In this invention, the server includes a means for taking in and preprocessing data entered by the user and the user's emotional state, a means for transmitting the preprocessed data and emotional information to an artificial intelligence that generates the data, and a means for visually presenting the generated suggestions to the user, thereby enabling optimal suggestions that take the user's emotional state into consideration.

[0705] "Data entered by the user" refers to the details of the consultation or request received by the user (store clerk) from the customer, and is information collected in the form of text input, voice input, or the like.

[0706] "Emotional state" refers to the user's psychological state and emotional movements, and is recognized by analyzing the user's facial expressions and tone of voice.

[0707] "Preprocessing" is the process of analyzing data entered by a user and performing operations such as normalization, tokenization, and formatting of emotion data.

[0708] "Generative AI" is a device that uses machine learning models and algorithms to analyze pre-processed data entered by users and generate optimal suggestions.

[0709] The "visual presentation means" is a method of displaying the generated proposal content on a user interface and providing information in a form that can be easily understood by the user.

[0710] A "secure communication protocol" is a communication protocol used to prevent information tampering or eavesdropping when sending and receiving data, and typical examples include HTTPS and TLS.

[0711] "Past cases" refers to historical data such as consultation details and proposal results previously made by users or customers that are stored in the system.

[0712] A "relevant database" is a collection of information that is referenced to generate recommendations, and is data storage that may include product information, user reviews, technical specifications, etc.

[0713] This invention is a system that allows users (store clerks) to input customer inquiries and provides optimal suggestions using artificial intelligence (AI). This system incorporates an emotion engine that recognizes the user's emotional state and adjusts the suggestions accordingly.

[0714] System configuration

[0715] This system is broadly divided into two components: a terminal and a server. Below, we will explain each component and its role.

[0716] Terminal

[0717] The device provides a user interface (UI) and has a means for users to input customer inquiries. It also uses a camera and microphone to analyze the user's facial expressions and tone of voice, and an emotion engine to recognize the user's emotional state.

[0718] Specifically, the user inputs text into an input form on the device or uses voice input. By inputting a prompt such as "a smartphone with good battery life," the user's needs are specified.

[0719] server

[0720] The server receives data sent from the device and performs preprocessing, which includes text normalization, tokenization, and emotional data formatting.

[0721] The preprocessed data is passed to a generative AI model, which analyzes the input data and emotional information. The generative AI model extracts important keywords and features and generates optimal suggestions based on the user's emotional state. The generated suggestions are also based on a database of past cases and related information.

[0722] The generated proposal is converted back into JSON format and sent to the device using a secure protocol (e.g., HTTPS).

[0723] Specific examples

[0724] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life," and the device recognizes through its emotion engine that the user is a little nervous.

[0725] 1. User Input

[0726] Terminal: The user (store clerk) inputs the inquiry content, "a lightweight laptop with good battery life," into the input form on the terminal.

[0727] 2. Emotional Recognition

[0728] Device: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and recognizes whether the user is nervous.

[0729] 3. Data transmission

[0730] Terminal: The content of the consultation and the user's emotional information (state of tension) are sent to the server using a secure protocol.

[0731] 4. Data Preprocessing

[0732] Server: Normalizes the received data and formats the tokenized and sentiment data.

[0733] 5. Generative AI Analysis

[0734] Server: Analyzes data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's state of tension.

[0735] 6. Proposal Generation

[0736] Server: The generative AI searches the database for laptop models that match the keywords and emotional state. The generated suggestions include "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours," as well as reassuring language to ease the user's tension.

[0737] 7. Submitting Proposal Data

[0738] Server: Converts the generated proposal into JSON format and sends it to the device using a secure protocol.

[0739] 8. Display of Suggestions

[0740] Device: The received suggestions are displayed in the user interface. For example, "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[0741] This system is expected to provide suggestions that take into account the user's emotional state, facilitating smoother communication with customers. Furthermore, the combination of a generative AI model and an emotion engine will improve the user experience.

[0742] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0743] Program processing flow

[0744] Step 1:

[0745] The user inputs the consultation content.

[0746] Terminal: Using the text box or voice input function provided by the user interface (UI), the user (store clerk) inputs the customer's inquiry. For example, the user might input, "I'm looking for a smartphone with a large battery and a high-performance camera."

[0747] Input: User's consultation content (text or voice)

[0748] Output: Text data of consultation content

[0749] Step 2:

[0750] Recognize the user's emotional state.

[0751] Device: Uses a camera and microphone to analyze the user's facial expressions and tone of voice, and uses an emotion engine (e.g., emotion recognition software) to identify the user's emotional state. For example, it can determine whether the user is smiling, nervous, surprised, etc.

[0752] Input: User's facial expression data, voice data

[0753] Output: Emotional state (e.g., nervous, relieved, happy)

[0754] Step 3:

[0755] Send the data to the server.

[0756] Terminal: Converts the input consultation data and recognized emotion information into JSON format and sends it to the server using a secure protocol (HTTPS).

[0757] Input: Text data of consultation content, emotional state information

[0758] Output: Confirmation of successful transmission

[0759] Step 4:

[0760] Preprocessing of received data is performed.

[0761] Server: The server takes the data received from the device and performs preprocessing, which includes text normalization (e.g., converting all data to lowercase), tokenization (splitting data into words), and emotional data formatting (e.g., converting data into numerical data).

[0762] Input: Text data of consultation content, emotional state information

[0763] Output: Preprocessed data

[0764] Step 5:

[0765] The transmitted data is analyzed by the generation AI.

[0766] Server: Passes the preprocessed data to the generative AI model, which analyzes the input data and emotional information. The generative AI model extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state. For example, it extracts keywords such as "battery" and "camera" and adds elements that will give the user a sense of security if they are nervous.

[0767] Input: Preprocessed data

[0768] Output: Proposal

[0769] Step 6:

[0770] Retrieve suggestions from the database.

[0771] Server: Based on the keywords and emotion information extracted by the generative AI model, the server references a database to retrieve information on suitable products and services. For example, it searches the database for smartphones with "12-hour battery life" and "high-performance cameras."

[0772] Input: Extracted keywords, emotional information

[0773] Output: Specific product suggestions

[0774] Step 7:

[0775] The generated proposal data is transmitted to the terminal.

[0776] Server: The generated proposal is converted back to JSON format and sent to the device using a secure protocol, using an authentication token to prevent data tampering.

[0777] Input: Proposal content (product information)

[0778] Output: Confirmation of successful transmission

[0779] Step 8:

[0780] The suggestions are displayed in the user interface.

[0781] Device: The received proposal is visually displayed on the user interface. For example, it may be displayed as "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[0782] Input: Received proposal

[0783] Output: Visual suggestions

[0784] In this way, the user's input and emotional information are appropriately utilized at each step to generate and display optimal suggestions, thereby improving the user experience.

[0785] (Application example 2)

[0786] 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."

[0787] Conventional customer service systems were unable to consider the emotional state of individual customers when making proposals based on their wishes and consultation details. This made it difficult to respond to subtle changes in emotions, such as when a customer was nervous or when a particular expression made them feel anxious. As a result, proposals were sometimes not fully accepted by customers. To solve this problem, a system was needed that could recognize the customer's emotional state in real time and make optimal proposals based on that.

[0788] 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.

[0789] In this invention, the server includes means for taking in and preprocessing data and emotional state input by the user, means for transmitting the preprocessed data and emotional information to an artificial intelligence that generates the data, means for presenting the generated suggestions to the user and adjusting them based on the emotional state, and means for transmitting and receiving data using a secure communication protocol, thereby enabling optimal product suggestions that take the user's emotional state into account in real time.

[0790] "Data entered by the user" refers to information that the user provides to the system through the input interface, such as the customer's wishes and consultation details.

[0791] "Emotional state" refers to psychological and emotional information obtained from a user's or customer's facial expressions, tone of voice, etc.

[0792] "Preprocessing" refers to a series of steps that normalize, tokenize, and convert input data and emotional information into a format that is easier to analyze.

[0793] "Generative AI" refers to systems that include algorithms and models for generating optimal suggestions and responses based on input data and emotional information.

[0794] "Secure communications protocol" means a communications protocol (e.g., HTTPS) used to ensure the security of data communications.

[0795] The "means for presenting suggestions to the user and adjusting them based on the user's emotional state" refers to a mechanism for displaying generated suggestions to the user and appropriately adjusting the content and expression of the suggestions, taking into account the user's emotional state.

[0796] "Optimal product proposals" refer to proposals for products or services that best suit the customer's wishes, consultation content, and emotional state.

[0797] The present invention is a system that takes in data and emotional state input by a user and makes optimal product recommendations based on that data. This system operates by having a salesperson input the customer's wishes and inquiries using a device such as a smartphone, tablet, or smart glasses when the customer is searching for a product in a physical store, and then using an emotion engine to recognize the customer's emotional state.

[0798] The system consists of hardware including a smartphone, tablet, and smart glasses (e.g., general-purpose smart glasses), and software including a user interface (UI) library, an emotion recognition engine (e.g., a general-purpose emotion recognition API), a secure communication protocol (HTTPS), and a generative AI model (e.g., a general-purpose generative model).

[0799] Program processing

[0800] User Input and Emotion Recognition

[0801] The terminal first receives the customer's requests and inquiries from the user (store clerk) through an input interface. For example, if a customer says, "I want a large-screen tablet that I can share with my friends," the store clerk enters this information into the terminal.

[0802] The device then uses an emotion engine to recognize the customer's emotional state. For example, it uses a camera and microphone to analyze the customer's facial expressions and tone of voice to determine whether they are feeling a little nervous. This emotional information is then processed along with the input data.

[0803] Data submission and preprocessing

[0804] The device sends the input data and emotion information to the server using a secure communication protocol (HTTPS). The server takes the received data and performs preprocessing such as text normalization, tokenization, and emotion data formatting.

[0805] Proposal generation using generative AI models

[0806] The server passes the preprocessed data to a generative AI model, which analyzes the input data and emotional information. This generative AI model extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state. For example, based on keywords such as "large screen," "shared," and "tablet," it compares them with past cases and database information to make product suggestions. If the customer is nervous, the suggestions will be generated to include expressions that will give them a sense of security.

[0807] Viewing Proposals

[0808] The generated proposal is converted into JSON format or similar and sent to the device again using a secure communication protocol. The device visually displays the received proposal content on the user interface. For example, the proposal content may include a reassuring description such as "Model A: 10 inches, easy to use even for beginners."

[0809] Specific examples

[0810] For example, suppose a customer asks, "I'm looking for a lightweight laptop with good battery life," and the device's emotion engine recognizes that the customer is a little nervous.

[0811] 1. User Input:

[0812] The terminal receives a request from the user (store clerk) via an input interface for a "lightweight laptop with good battery life."

[0813] 2. Emotion recognition:

[0814] The device uses a camera and microphone to analyze the customer's facial expressions and tone of voice, and recognizes if they are a little nervous.

[0815] 3. Data submission and preprocessing:

[0816] The device transmits the consultation content and emotional information (tension state) to the server using a secure protocol, and the server normalizes, tokenizes, and formats the emotional data.

[0817] 4. Generative AI analysis:

[0818] The server analyzes the data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's level of tension.

[0819] 5. Proposal generation:

[0820] The generative AI searches the database to find laptop models that match the keywords and emotional state. For example, generated suggestions might include "Model A: weight 1.2 kg, battery life 12 hours" or "Model B: weight 1.1 kg, battery life 10 hours," and include reassuring language to ease the user's tension.

[0821] 6. Suggestion display:

[0822] The proposal is converted into JSON format and sent to the device using a secure protocol. The device then displays the received proposal in its user interface, for example, "Model A: Weight 1.2 kg, Battery life 12 hours. Safe for beginners to use," or "Model B: Weight 1.1 kg, Battery life 10 hours. Lightweight and ideal for carrying around."

[0823] Prompt Sentence Examples

[0824] The following text could be fed into a generative AI model as a prompt:

[0825] text

[0826] Customer's request: "A large-screen tablet that can be shared with colleagues."

[0827] Customer sentiment: "I'm a little nervous"

[0828] Generate a proposal for a tablet with a large screen that's easy to share, including its features, beginner-friendly features, and a reassuring description.

[0829] As described above, the present invention provides a system that grasps the emotional state of a user and makes appropriate product suggestions in real time, thereby realizing smooth communication with customers.

[0830] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0831] Step 1:

[0832] The terminal accepts user input. The user (store clerk) inputs the customer's wishes or inquiries into the input interface. For example, the user might input, "I'm looking for a lightweight laptop with good battery life." This becomes the input data.

[0833] Step 2:

[0834] The device performs emotion recognition. It uses a camera and microphone to analyze the user's (store clerk's) facial expressions and tone of voice, and uses an emotion engine to identify the customer's emotional state. Emotional information is obtained in this step.

[0835] Step 3:

[0836] The device preprocesses the data, integrating the input data with sentiment information, normalizing and tokenizing the text, and formatting the sentiment data. This preprocessed data is the input for the next step.

[0837] Step 4:

[0838] The device sends the preprocessed data to the server. The preprocessed data is transferred to the server using a secure communication protocol (HTTPS). The server receives this data and begins processing it as new input data.

[0839] Step 5:

[0840] The server analyzes the data using a generative AI model. The server passes the received input data and emotional information to a generative AI model (general-purpose generative model) to extract important keywords and features. For example, keywords such as "lightweight" and "long battery life" are extracted, and analysis is performed taking into account the emotional state.

[0841] Step 6:

[0842] The server generates optimal suggestions. The generative AI model references past cases and databases to generate suggestions that best fit the user's requirements and emotional state. The generated suggestions include specific product information and reassuring descriptions.

[0843] Step 7:

[0844] The server sends the generated proposal to the device. The server converts the generated proposal into JSON format or similar and sends it to the device using a secure communication protocol. The device receives this data.

[0845] Step 8:

[0846] The device presents the suggestions to the user. The received suggestions are visually displayed on the user interface. For example, it may display "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[0847] Step 9:

[0848] The user (store clerk) responds to the customer. Based on the generated proposal, the user (store clerk) provides an appropriate product explanation to the customer. The user explains the proposed product model and its features, and makes a proposal that best suits the customer's needs.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] [Third embodiment]

[0853] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0854] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0855] 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).

[0856] 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.

[0857] 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.

[0858] 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).

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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."

[0865] This invention is a system in which a user (store clerk) inputs the customer's inquiry details and an artificial intelligence (AI) generates and provides optimal suggestions. This system involves taking in user input, sending the data, processing it with the AI, and displaying the suggestions.

[0866] Program processing flow

[0867] Capturing User Input

[0868] Terminal: A user interface (UI) is provided where the user inputs the customer's inquiry. For example, "I'm looking for a smartphone with a large battery and a high-performance camera."

[0869] Sending data

[0870] Terminal: The entered data is sent to the server using a secure communication protocol (e.g., HTTPS).

[0871] NPL pretreatment

[0872] Server: The server performs preprocessing such as tokenization and normalization on the received data, converting it into a form that can be easily analyzed by the generative AI.

[0873] Proposal generation by generative AI

[0874] Server: The generative AI analyzes the preprocessed data and extracts the input keywords and features. For example, "large capacity battery" and "high performance camera" are extracted. The generative AI then searches for products that meet these features based on a database and past cases, and generates optimal suggestions. The generative AI creates suggestions in natural language, including relevant information (e.g., battery capacity and camera performance).

[0875] Sending response data

[0876] Server: Converts the generated proposal into an appropriate format (e.g., JSON or XML) and sends it back to the device using a secure communication protocol.

[0877] What the user sees

[0878] Terminal: The terminal displays the proposal received from the server on the user interface. The user can interact with the customer based on this information and provide professional proposals, such as "Model X: 5000mAh battery, 48MP camera."

[0879] Specific examples

[0880] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life."

[0881] 1. Step 1: User Input

[0882] Terminal: The user (store clerk) inputs the customer's request into the terminal's input form. The request is for a "lightweight laptop with good battery life."

[0883] 2. Step 2: Sending data

[0884] Terminal: The entered data is sent to the server using a secure protocol.

[0885] 3. Step 3: Pretreatment

[0886] Server: Normalizes and tokenizes the received data into a format that is easy to analyze.

[0887] 4. Step 4: Proposal Generation

[0888] Server: The generation AI extracts the keywords "lightweight" and "long battery life" and searches the database for laptop models that match these keywords. The generation AI generates the following suggestions: "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours."

[0889] 5. Step 5: Sending response data

[0890] Server: Converts the generated proposal into JSON format and sends it to the device.

[0891] 6. Step 6: Suggestions

[0892] Terminal: Visually displays the received proposal to the user. The user interface displays "Model A: Weight 1.2 kg, Battery life 12 hours" and "Model B: Weight 1.1 kg, Battery life 10 hours."

[0893] 7. Step 7: User Actions

[0894] User: Based on the proposal, the salesperson (user) explains to the customer, "These models are laptops that meet your requirements."

[0895] This allows store clerks to quickly provide highly specialized information to customers without relying on their specialized knowledge or experience, which is expected to improve customer satisfaction and increase sales.

[0896] The processing flow will be explained below.

[0897] Step 1:

[0898] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry details. The user enters the inquiry details in the text box and presses the send button.

[0899] Step 2:

[0900] Terminal: Sends user-entered data to the server using a secure protocol (e.g., HTTPS), properly encoding the data before sending it.

[0901] Step 3:

[0902] Server: The server receives the received data and performs preprocessing, which includes text normalization (removing unnecessary whitespace, standardizing characters, etc.) and tokenization (splitting into words).

[0903] Step 4:

[0904] Server: Passes the preprocessed data to the generative AI, which uses natural language processing (NLP) techniques to analyze the input data and extract keywords and important features.

[0905] Step 5:

[0906] Server: The generation AI searches the database for relevant information based on the extracted keywords and features. For example, it retrieves product information that matches keywords such as "large-capacity battery" and "high-performance camera."

[0907] Step 6:

[0908] Server: Based on the information retrieved from the database, the generative AI generates optimal suggestions. Suggestions are written in natural language and are provided in the form of, for example, "Model X: 5000mAh battery, 48MP camera."

[0909] Step 7:

[0910] Server: Converts the generated proposal into an appropriate format (e.g., JSON, XML) and sends it to the device using a secure protocol.

[0911] Step 8:

[0912] Terminal: The terminal analyzes the proposals received from the server and displays them on the user interface. Specifically, the proposed product models and their features are displayed in a list or tile format.

[0913] Step 9:

[0914] User: Based on the suggestions displayed in the user interface, the user (store clerk) provides expert advice to the customer, for example, explaining that "Model X and Model Y are products that meet your requirements."

[0915] In this way, the entire system operates in cooperation with one another, allowing users to make appropriate proposals to customers without relying on specialized knowledge.

[0916] Example 1

[0917] 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."

[0918] In conventional systems, store staff and users required advanced expertise to properly understand customer inquiries and provide optimal proposals. This resulted in problems such as the quality and consistency of proposals depending on the staff's skills, leading to inconsistencies in customer satisfaction. Furthermore, proposals were generated manually, resulting in a lack of speed. The present invention aims to solve these problems and provide high-quality proposals to customers quickly and consistently.

[0919] 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.

[0920] In this invention, the server includes a means for taking in data entered by a user and performing preprocessing, a means for sending the preprocessed data to a generative AI model and extracting keywords, and a means for searching a related database and generating optimal suggestions using the generative AI model, thereby enabling users to quickly make high-quality suggestions even if they do not have specialized knowledge.

[0921] "Means for capturing and preprocessing data entered by users" refers to a function that collects data such as consultation details entered by users via their terminals and processes them using natural language processing techniques such as tokenization and normalization.

[0922] "Means for sending preprocessed data to a generative AI model and extracting keywords" refers to the process of sending preprocessed data to a generative AI model and extracting important information and keywords from the input data.

[0923] "Means for searching relevant databases and generating optimal suggestions using a generative AI model" refers to a function that searches for similar or related information from a database based on keywords extracted by the generative AI model, and generates optimal suggestions based on that information.

[0924] "Means for transmitting the generated proposal to the user terminal using a secure communication protocol" refers to a process for securely transmitting the generated proposal content to the user terminal using a secure communication protocol (e.g., HTTPS).

[0925] The "means for displaying the generated proposal to the user" is a function for visually displaying the proposal content received on the user terminal so that the user can easily confirm it.

[0926] This invention is a system in which a user (store clerk) inputs the customer's inquiry details and the system generates artificial intelligence (AI) to provide optimal suggestions. This system uses a terminal, a server, a user interface (UI), a natural language processing (NLP) library, and a generative AI model.

[0927] System Overview

[0928] The system includes a means for taking in data entered by a user and performing preprocessing, a means for sending the preprocessed data to a generative AI model and extracting keywords, a means for searching a relevant database and generating optimal suggestions using the generative AI model, a means for sending the generated suggestions to a user terminal using a secure communication protocol, and a means for displaying the generated suggestions to a user.

[0929] Hardware and software used

[0930] Terminal: A device operated by a user (store clerk), including a PC, tablet, smartphone, etc. These terminals have a user interface that allows the user to input data.

[0931] Server: A remote server used for data processing and analysis of AI models. The database stores past cases and related information.

[0932] Natural language processing libraries: Software libraries used to preprocess data, including tokenization and normalization. Examples include NLP libraries in Python.

[0933] Generative AI model: An artificial intelligence model used to generate optimal recommendations based on user input. Examples include generative AI models such as OpenAI's GPT-3.

[0934] Secure communication protocol: A communication protocol used to protect the transmission and reception of data, such as HTTPS.

[0935] Specific examples

[0936] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with a long battery life." In this case, the process will proceed as follows:

[0937] User input: The store clerk enters "a lightweight laptop with good battery life" into the input form on the terminal and clicks the submit button.

[0938] Data transmission: The terminal encodes the input data into JSON format and sends it to the server using a secure communication protocol (HTTPS).

[0939] Preprocessing: The server receives the data and normalizes and tokenizes it using a natural language processing library. It breaks down "lightweight laptop with good battery life" into tokens such as "lightweight", "battery life", "good", and "laptop".

[0940] Proposal generation by generative AI: Preprocessed data is input into the generative AI model, and the keywords "lightweight" and "long battery life" are extracted. Based on this, information on laptops that fit the criteria is searched for in the database, and the generative AI generates the following proposals: "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours."

[0941] Sending response data: The generated proposal is converted into JSON format and sent to the terminal again using a secure communication protocol.

[0942] Proposal display: The device parses the received proposal and visually displays "Model A: weight 1.2 kg, battery 12 hours" and "Model B: weight 1.1 kg, battery 10 hours" on the user interface.

[0943] User action: The salesperson explains the proposal to the customer, saying, "These models of laptops meet your requirements."

[0944] Prompt Sentence Examples

[0945] Input prompt:

[0946] "I'm looking for a lightweight laptop with good battery life."

[0947] Output of the generative AI model:

[0948] "We recommend the following models: Model A (weight 1.2 kg, 12-hour battery life), Model B (weight 1.1 kg, 10-hour battery life)"

[0949] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0950] Step 1: Capturing User Input

[0951] Terminal: Through the user interface (UI), the user (store clerk) inputs the customer's inquiry. At this point, the input is text data, such as "I'm looking for a lightweight laptop with good battery life." The terminal receives this input and prepares for the next processing step.

[0952] Step 2: Sending data

[0953] Terminal: Encodes input data into JSON format and sends it to the server using a secure communication protocol (e.g., HTTPS). This operation involves sending the data via an HTTP POST request. The input is the encoded JSON data, and the output is a status code indicating successful submission to the server.

[0954] Step 3: Preprocessing

[0955] Server: The received data is first normalized to remove unnecessary whitespace and special characters. Next, a natural language processing library (e.g., a Python NLP library) is used to tokenize the data and break it into words and phrases. For example, "lightweight laptop with good battery life" is broken down into "lightweight", "battery life", "good", and "laptop". The input is the received raw data, and the output is the tokenized data.

[0956] Step 4: Keyword extraction and suggestion generation

[0957] Server: A generative AI model is used to extract key keywords from the preprocessed data. After extracting the keywords "lightweight" and "long battery life," information on products related to these keywords is searched for in the database. Based on the search results, the generative AI model generates optimal suggestions in natural language. For example, suggestions such as "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours" are generated. The input is tokenized data, and the output is suggestions in natural language format.

[0958] Step 5: Sending response data

[0959] Server: Converts the generated proposal into JSON format and sends it back to the terminal using a secure communication protocol (HTTPS). The server sets the response header and returns an HTTP 200 status code. The input is the generated proposal, and the output is the encoded JSON data and the sending status code.

[0960] Step 6: Display suggestions

[0961] Terminal: Parses the JSON format proposal received from the server and displays it visually to the user. Specifically, proposals such as "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours" are displayed on the UI. The input is the JSON data received from the server, and the output is the visual information displayed on the user interface.

[0962] Step 7: User Actions

[0963] User: The salesperson (user) explains the proposal to the customer based on the content of the proposal. They introduce the laptop models that meet the customer's requirements and support the customer in making a selection. The input is the proposal information displayed on the terminal, and the output is the dialogue with the customer and the implementation of the proposal.

[0964] This allows users to quickly make high-quality proposals even without specialized knowledge.

[0965] (Application example 1)

[0966] 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."

[0967] Conventional systems have had difficulty quickly and accurately proposing products that meet the user's desired conditions. In particular, proposing products that meet multiple conditions requires the user to have specialized knowledge, which can be time-consuming. Furthermore, from a security standpoint, there is a risk of incomplete data transmission and customer information leaks. A system that solves these problems is needed.

[0968] 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.

[0969] In this invention, the server includes a means for receiving and preprocessing data entered by the user, a means for transmitting the preprocessed data to the AI ​​that generates the data, and a means for visually displaying the generated suggestions on a user interface. This allows the user to receive product suggestions quickly and accurately without having specialized knowledge, and furthermore, a secure communication protocol prevents the leakage of customer information.

[0970] A "user interface" is something that provides a visual or operational means for a user to interact with a system.

[0971] "Preprocessing" is the initial data processing that converts the data entered by the user into a form that is easy to analyze.

[0972] "Generative AI" is a system that uses specific algorithms to analyze input data and generate optimal suggestions.

[0973] A "secure communication protocol" is a communication method that ensures confidentiality, integrity, authenticity, etc. of information when sending and receiving data.

[0974] "Visually displaying" refers to a process of displaying the generated information on the screen in a format that is easy for the user to understand.

[0975] This invention is a system that integrates a user interface, data preprocessing, generative AI, and a secure communication protocol. This system uses AI to generate optimal suggestions based on data entered by the user and displays them to the user.

[0976] System Configuration

[0977] Hardware

[0978] Terminal: A device such as a smartphone or tablet on which a user interface is provided.

[0979] Server: The data center or cloud server where the generative AI runs.

[0980] software

[0981] Requests library: A library for sending HTTP requests.

[0982] Flask: A web framework used on the server side.

[0983] Generative AI: An artificial intelligence model that generates optimal suggestions based on input data.

[0984] Secure communication protocol: A protocol that ensures confidentiality, integrity, and authenticity of information when transmitting and receiving data (e.g., HTTPS).

[0985] Data processing flow

[0986] 1. User Interface

[0987] The user inputs the desired product specifications into the input form on the device. For example, the user may write in their inquiry about a "lightweight smartphone with good battery life."

[0988] 2. Data Preprocessing

[0989] The server tokenizes and normalizes the received user input, converting it into a format that is easy for the generative AI to analyze.

[0990] 3. Generation AI

[0991] Generative AI extracts relevant keywords and features from pre-processed data, and generates optimal product suggestions by referencing past cases and databases. The suggestions are expressed in natural language.

[0992] 4. Secure Data Communication

[0993] The proposed data is then sent back to the terminal using a secure communication protocol (e.g., HTTPS). By ensuring secure communication, the risk of customer information being leaked is reduced.

[0994] 5. Visual representation of suggestions

[0995] The device displays the received suggestions on the user interface, visually presenting information such as "Model A: weight 1.2 kg, battery life 12 hours."

[0996] Specific examples

[0997] For example, if a user inputs "I'm looking for a smartphone that's light and has a long battery life," the system operates as follows:

[0998] The user inputs the consultation content into the input form on the terminal.

[0999] The server tokenizes the input and extracts keywords such as "lightweight" and "good battery life."

[1000] The generative AI refers to a related past database and generates suggestions such as "Model X: weight 1.1 kg, battery life 15 hours" and "Model Y: weight 1.0 kg, battery life 14 hours."

[1001] The generated proposal is transmitted to the terminal using a secure communication protocol.

[1002] The proposed content is displayed on the user interface, and the user selects products based on this.

[1003] Prompt Sentence Examples

[1004] Based on the user's token "lightweight laptop with good battery life," prompt the user with products that meet the following criteria: 1. A lightweight, high-performance model. 2. A long battery life. 3. Multiple options are offered.

[1005] In this way, the technical scope of the invention is specifically indicated, and a system is provided that enables users to receive prompt and accurate product proposals even without specialized knowledge.

[1006] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1007] Step 1:

[1008] The user inputs the desired product specifications into the input form on the device. For example, the user may write "I want a smartphone that is lightweight and has a long battery life." The input data is retrieved by the device's user interface.

[1009] (input)

[1010] Product specifications entered by the user (e.g., "A lightweight smartphone with good battery life")

[1011] (output)

[1012] Text data entered into the terminal

[1013] Step 2:

[1014] The terminal sends the entered text data to the server using a secure communication protocol (e.g., HTTPS), which ensures the confidentiality and integrity of the data.

[1015] (input)

[1016] Text data transmitted using a secure communication protocol

[1017] (output)

[1018] Text data that reaches the server

[1019] Step 3:

[1020] The server preprocesses the received text data, which includes tokenizing and normalizing the input (e.g., removing unnecessary whitespace and grammatical errors) to convert it into a format that is easy for the generative AI model to parse.

[1021] (input)

[1022] Text data that reaches the server

[1023] (output)

[1024] Tokenized and normalized data

[1025] Step 4:

[1026] The server sends the preprocessed data to a generative AI model, which references a database and past cases to extract relevant keywords and features (e.g., "lightweight" and "battery life") and then generates optimal suggestions based on those features.

[1027] (input)

[1028] Tokenized and normalized data

[1029] (output)

[1030] Generated product suggestions (e.g., "Model X: weight 1.1 kg, battery life 15 hours" "Model Y: weight 1.0 kg, battery life 14 hours")

[1031] Step 5:

[1032] The server converts the generated product proposals into an appropriate format (e.g., JSON) and sends them to the terminal, again using a secure communication protocol.

[1033] (input)

[1034] Generated product suggestion data

[1035] (output)

[1036] Proposal data in JSON format

[1037] Step 6:

[1038] The terminal analyzes the received product suggestion data and visually displays it on the user interface, such as product name, features, price, etc.

[1039] (input)

[1040] Proposal data in JSON format

[1041] (output)

[1042] Suggestions displayed in the user interface (e.g., "Model X: weight 1.1 kg, battery life 15 hours" or "Model Y: weight 1.0 kg, battery life 14 hours")

[1043] Step 7:

[1044] The user can then check the proposed content and select products as necessary. This allows the user to select products quickly and accurately, even if they do not have specialized knowledge.

[1045] (input)

[1046] Suggestions displayed in the user interface

[1047] (output)

[1048] User decision-making and product selection

[1049] In this way, a system is realized in which each processing step works together to provide the user with optimal suggestions.

[1050] 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.

[1051] This invention is a system that allows users (store clerks) to input customer inquiries and provides optimal suggestions using artificial intelligence (AI). The system also incorporates an emotion engine that recognizes the user's emotional state and adjusts the suggestions accordingly.

[1052] Program processing flow

[1053] Capturing User Input

[1054] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry. For example, the user might input, "I'm looking for a smartphone with a large battery and a high-performance camera."

[1055] Emotion recognition

[1056] Device: The device uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotional state. For example, the device uses a camera and microphone to determine whether the user is smiling or nervous.

[1057] Sending data

[1058] Terminal: The input data and the emotion information recognized by the emotion engine are sent to the server using a secure protocol (e.g., HTTPS).

[1059] Data Preprocessing

[1060] Server: The server takes the received user data and emotion information and performs preprocessing, which includes text normalization, tokenization, and emotion data formatting.

[1061] Generative AI analysis

[1062] Server: Passes the preprocessed data to the generation AI, which analyzes the input data and emotional information. The generation AI extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state.

[1063] Proposal Generation

[1064] Server: The AI ​​generator uses a database to generate suggestions that match the user's input and emotional state. For example, if the user is nervous, the generated suggestions will include reassuring expressions.

[1065] Submitting proposal data

[1066] Server: Converts the generated proposal into JSON or XML format and sends it to the device using a secure protocol.

[1067] What the user sees

[1068] Terminal: The proposal received from the server is visually displayed on the user interface. Specifically, the proposed product models and their features are displayed in a list or tile format. If the user is nervous, a concise and reassuring explanation is added to the proposal.

[1069] Specific examples

[1070] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life," and the device recognizes through its emotion engine that the user is a little nervous.

[1071] 1. User Input

[1072] Terminal: The user (store clerk) inputs the inquiry content, "a lightweight laptop with good battery life," into the input form on the terminal.

[1073] 2. Emotional Recognition

[1074] Device: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and recognizes whether the user is nervous.

[1075] 3. Data transmission

[1076] Terminal: The content of the consultation and the user's emotional information (state of tension) are sent to the server using a secure protocol.

[1077] 4. Data Preprocessing

[1078] Server: Normalizes the received data and formats the tokenized and sentiment data.

[1079] 5. Generative AI Analysis

[1080] Server: Analyzes data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's state of tension.

[1081] 6. Proposal Generation

[1082] Server: The generative AI searches the database for laptop models that match the keywords and emotional state. The generated suggestions include "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours," as well as reassuring language to ease the user's tension.

[1083] 7. Submitting Proposal Data

[1084] Server: Converts the generated proposal into JSON format and sends it to the device using a secure protocol.

[1085] 8. Display of Suggestions

[1086] Device: The received suggestions are displayed in the user interface. For example, "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[1087] 9. User Actions

[1088] User: Based on the displayed suggestions, the user (store clerk) explains to the customer, "These models are laptops that meet your requirements," and "Even beginners can use them with confidence."

[1089] In this way, by incorporating an emotion engine, it is possible to provide suggestions that are adapted to the user's emotional state, which is expected to lead to smoother communication with customers.

[1090] The processing flow will be explained below.

[1091] Step 1:

[1092] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry. For example, the user might enter "I want a lightweight laptop with good battery life" in the text box and press the send button.

[1093] Step 2:

[1094] Device: The device uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotional state. For example, the device uses a camera and microphone to determine whether the user is tense or relaxed.

[1095] Step 3:

[1096] Terminal: The input consultation content and recognized emotion information are sent to the server using a secure communication protocol (e.g., HTTPS). The data is properly encoded before being sent.

[1097] Step 4:

[1098] Server: The server takes in the received data and emotion information and performs preprocessing, which includes text normalization (removing unnecessary whitespace, standardizing characters, etc.), tokenization (splitting into words), and emotional data formatting.

[1099] Step 5:

[1100] Server: Passes the preprocessed data to the generation AI. The generation AI analyzes the input data using natural language processing (NLP) techniques and extracts keywords and important features. For example, "lightweight" and "long battery life" are extracted as features.

[1101] Step 6:

[1102] Server: The generation AI adjusts the analysis results by taking into account emotional information. For example, if the user is nervous, the generated suggestions will include explanations that will reassure the user.

[1103] Step 7:

[1104] Server: The generative AI searches the database for relevant information based on the extracted keywords and features. For example, it retrieves laptop models that match the criteria of "lightweight" and "long battery life."

[1105] Step 8:

[1106] Server: Based on information retrieved from the database, the generative AI generates optimal suggestions. The suggestions are written in natural language, such as "Model A: weight 1.2 kg, battery life 12 hours" or "Model B: weight 1.1 kg, battery life 10 hours." If the user is nervous, they can add an explanation such as "Even beginners can use it with confidence."

[1107] Step 9:

[1108] Server: Converts the generated proposal into an appropriate format (e.g., JSON, XML) and sends it to the device using a secure communication protocol.

[1109] Step 10:

[1110] Device: The device analyzes the suggestions received from the server and displays them on the user interface. For example, it might display "Model A: Weight 1.2 kg, 12-hour battery life. Easy to use even for beginners," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[1111] Step 11:

[1112] User: Based on the suggestions displayed on the user interface, the user (store clerk) explains to the customer, "These models are laptops that meet your requirements." If the user seems nervous, they can also provide additional information such as, "Even beginners can use it with confidence."

[1113] In this way, a system including an emotion engine can tailor its suggestions to the user's emotional state, providing more personalized information.

[1114] Example 2

[1115] 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."

[1116] Conventional recommendation systems make suggestions based solely on user input data, and therefore are unable to provide optimal suggestions that take into account the user's emotional state. Furthermore, the visual display of suggestions in the user interface is often insufficient, making the content of the suggestions difficult for users to understand. This leads to a poor user experience and makes smooth communication with customers difficult.

[1117] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1118] In this invention, the server includes a means for taking in and preprocessing data entered by the user and the user's emotional state, a means for transmitting the preprocessed data and emotional information to an artificial intelligence that generates the data, and a means for visually presenting the generated suggestions to the user, thereby enabling optimal suggestions that take the user's emotional state into consideration.

[1119] "Data entered by the user" refers to the details of the consultation or request received by the user (store clerk) from the customer, and is information collected in the form of text input, voice input, or the like.

[1120] "Emotional state" refers to the user's psychological state and emotional movements, and is recognized by analyzing the user's facial expressions and tone of voice.

[1121] "Preprocessing" is the process of analyzing data entered by a user and performing operations such as normalization, tokenization, and formatting of emotion data.

[1122] "Generative AI" is a device that uses machine learning models and algorithms to analyze pre-processed data entered by users and generate optimal suggestions.

[1123] The "visual presentation means" is a method of displaying the generated proposal content on a user interface and providing information in a form that can be easily understood by the user.

[1124] A "secure communication protocol" is a communication protocol used to prevent information tampering or eavesdropping when sending and receiving data, and typical examples include HTTPS and TLS.

[1125] "Past cases" refers to historical data such as consultation details and proposal results previously made by users or customers that are stored in the system.

[1126] A "relevant database" is a collection of information that is referenced to generate recommendations, and is data storage that may include product information, user reviews, technical specifications, etc.

[1127] This invention is a system that allows users (store clerks) to input customer inquiries and provides optimal suggestions using artificial intelligence (AI). This system incorporates an emotion engine that recognizes the user's emotional state and adjusts the suggestions accordingly.

[1128] System configuration

[1129] This system is broadly divided into two components: a terminal and a server. Below, we will explain each component and its role.

[1130] Terminal

[1131] The device provides a user interface (UI) and has a means for users to input customer inquiries. It also uses a camera and microphone to analyze the user's facial expressions and tone of voice, and an emotion engine to recognize the user's emotional state.

[1132] Specifically, the user inputs text into an input form on the device or uses voice input. By inputting a prompt such as "a smartphone with good battery life," the user's needs are specified.

[1133] server

[1134] The server receives data sent from the device and performs preprocessing, which includes text normalization, tokenization, and emotional data formatting.

[1135] The preprocessed data is passed to a generative AI model, which analyzes the input data and emotional information. The generative AI model extracts important keywords and features and generates optimal suggestions based on the user's emotional state. The generated suggestions are also based on a database of past cases and related information.

[1136] The generated proposal is converted back into JSON format and sent to the device using a secure protocol (e.g., HTTPS).

[1137] Specific examples

[1138] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life," and the device recognizes through its emotion engine that the user is a little nervous.

[1139] 1. User Input

[1140] Terminal: The user (store clerk) inputs the inquiry content, "a lightweight laptop with good battery life," into the input form on the terminal.

[1141] 2. Emotional Recognition

[1142] Device: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and recognizes whether the user is nervous.

[1143] 3. Data transmission

[1144] Terminal: The content of the consultation and the user's emotional information (state of tension) are sent to the server using a secure protocol.

[1145] 4. Data Preprocessing

[1146] Server: Normalizes the received data and formats the tokenized and sentiment data.

[1147] 5. Generative AI Analysis

[1148] Server: Analyzes data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's state of tension.

[1149] 6. Proposal Generation

[1150] Server: The generative AI searches the database for laptop models that match the keywords and emotional state. The generated suggestions include "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours," as well as reassuring language to ease the user's tension.

[1151] 7. Submitting Proposal Data

[1152] Server: Converts the generated proposal into JSON format and sends it to the device using a secure protocol.

[1153] 8. Display of Suggestions

[1154] Device: The received suggestions are displayed in the user interface. For example, "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[1155] This system is expected to provide suggestions that take into account the user's emotional state, facilitating smoother communication with customers. Furthermore, the combination of a generative AI model and an emotion engine will improve the user experience.

[1156] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1157] Program processing flow

[1158] Step 1:

[1159] The user inputs the consultation content.

[1160] Terminal: Using the text box or voice input function provided by the user interface (UI), the user (store clerk) inputs the customer's inquiry. For example, the user might input, "I'm looking for a smartphone with a large battery and a high-performance camera."

[1161] Input: User's consultation content (text or voice)

[1162] Output: Text data of consultation content

[1163] Step 2:

[1164] Recognize the user's emotional state.

[1165] Device: Uses a camera and microphone to analyze the user's facial expressions and tone of voice, and uses an emotion engine (e.g., emotion recognition software) to identify the user's emotional state. For example, it can determine whether the user is smiling, nervous, surprised, etc.

[1166] Input: User's facial expression data, voice data

[1167] Output: Emotional state (e.g., nervous, relieved, happy)

[1168] Step 3:

[1169] Send the data to the server.

[1170] Terminal: Converts the input consultation data and recognized emotion information into JSON format and sends it to the server using a secure protocol (HTTPS).

[1171] Input: Text data of consultation content, emotional state information

[1172] Output: Confirmation of successful transmission

[1173] Step 4:

[1174] Preprocessing of received data is performed.

[1175] Server: The server takes the data received from the device and performs preprocessing, which includes text normalization (e.g., converting all data to lowercase), tokenization (splitting data into words), and emotional data formatting (e.g., converting data into numerical data).

[1176] Input: Text data of consultation content, emotional state information

[1177] Output: Preprocessed data

[1178] Step 5:

[1179] The transmitted data is analyzed by the generation AI.

[1180] Server: Passes the preprocessed data to the generative AI model, which analyzes the input data and emotional information. The generative AI model extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state. For example, it extracts keywords such as "battery" and "camera" and adds elements that will give the user a sense of security if they are nervous.

[1181] Input: Preprocessed data

[1182] Output: Proposal

[1183] Step 6:

[1184] Retrieve suggestions from the database.

[1185] Server: Based on the keywords and emotion information extracted by the generative AI model, the server references a database to retrieve information on suitable products and services. For example, it searches the database for smartphones with "12-hour battery life" and "high-performance cameras."

[1186] Input: Extracted keywords, emotional information

[1187] Output: Specific product suggestions

[1188] Step 7:

[1189] The generated proposal data is transmitted to the terminal.

[1190] Server: The generated proposal is converted back to JSON format and sent to the device using a secure protocol, using an authentication token to prevent data tampering.

[1191] Input: Proposal content (product information)

[1192] Output: Confirmation of successful transmission

[1193] Step 8:

[1194] The suggestions are displayed in the user interface.

[1195] Device: The received proposal is visually displayed on the user interface. For example, it may be displayed as "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[1196] Input: Received proposal

[1197] Output: Visual suggestions

[1198] In this way, the user's input and emotional information are appropriately utilized at each step to generate and display optimal suggestions, thereby improving the user experience.

[1199] (Application example 2)

[1200] 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."

[1201] Conventional customer service systems were unable to consider the emotional state of individual customers when making proposals based on their wishes and consultation details. This made it difficult to respond to subtle changes in emotions, such as when a customer was nervous or when a particular expression made them feel anxious. As a result, proposals were sometimes not fully accepted by customers. To solve this problem, a system was needed that could recognize the customer's emotional state in real time and make optimal proposals based on that.

[1202] 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.

[1203] In this invention, the server includes means for taking in and preprocessing data and emotional state input by the user, means for transmitting the preprocessed data and emotional information to an artificial intelligence that generates the data, means for presenting the generated suggestions to the user and adjusting them based on the emotional state, and means for transmitting and receiving data using a secure communication protocol, thereby enabling optimal product suggestions that take the user's emotional state into account in real time.

[1204] "Data entered by the user" refers to information that the user provides to the system through the input interface, such as the customer's wishes and consultation details.

[1205] "Emotional state" refers to psychological and emotional information obtained from a user's or customer's facial expressions, tone of voice, etc.

[1206] "Preprocessing" refers to a series of steps that normalize, tokenize, and convert input data and emotional information into a format that is easier to analyze.

[1207] "Generative AI" refers to systems that include algorithms and models for generating optimal suggestions and responses based on input data and emotional information.

[1208] "Secure communications protocol" means a communications protocol (e.g., HTTPS) used to ensure the security of data communications.

[1209] The "means for presenting suggestions to the user and adjusting them based on the user's emotional state" refers to a mechanism for displaying generated suggestions to the user and appropriately adjusting the content and expression of the suggestions, taking into account the user's emotional state.

[1210] "Optimal product proposals" refer to proposals for products or services that best suit the customer's wishes, consultation content, and emotional state.

[1211] The present invention is a system that takes in data and emotional state input by a user and makes optimal product recommendations based on that data. This system operates by having a salesperson input the customer's wishes and inquiries using a device such as a smartphone, tablet, or smart glasses when the customer is searching for a product in a physical store, and then using an emotion engine to recognize the customer's emotional state.

[1212] The system consists of hardware including a smartphone, tablet, and smart glasses (e.g., general-purpose smart glasses), and software including a user interface (UI) library, an emotion recognition engine (e.g., a general-purpose emotion recognition API), a secure communication protocol (HTTPS), and a generative AI model (e.g., a general-purpose generative model).

[1213] Program processing

[1214] User Input and Emotion Recognition

[1215] The terminal first receives the customer's requests and inquiries from the user (store clerk) through an input interface. For example, if a customer says, "I want a large-screen tablet that I can share with my friends," the store clerk enters this information into the terminal.

[1216] The device then uses an emotion engine to recognize the customer's emotional state. For example, it uses a camera and microphone to analyze the customer's facial expressions and tone of voice to determine whether they are feeling a little nervous. This emotional information is then processed along with the input data.

[1217] Data submission and preprocessing

[1218] The device sends the input data and emotion information to the server using a secure communication protocol (HTTPS). The server takes the received data and performs preprocessing such as text normalization, tokenization, and emotion data formatting.

[1219] Proposal generation using generative AI models

[1220] The server passes the preprocessed data to a generative AI model, which analyzes the input data and emotional information. This generative AI model extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state. For example, based on keywords such as "large screen," "shared," and "tablet," it compares them with past cases and database information to make product suggestions. If the customer is nervous, the suggestions will be generated to include expressions that will give them a sense of security.

[1221] Viewing Proposals

[1222] The generated proposal is converted into JSON format or similar and sent to the device again using a secure communication protocol. The device visually displays the received proposal content on the user interface. For example, the proposal content may include a reassuring description such as "Model A: 10 inches, easy to use even for beginners."

[1223] Specific examples

[1224] For example, suppose a customer asks, "I'm looking for a lightweight laptop with good battery life," and the device's emotion engine recognizes that the customer is a little nervous.

[1225] 1. User Input:

[1226] The terminal receives a request from the user (store clerk) via an input interface for a "lightweight laptop with good battery life."

[1227] 2. Emotion recognition:

[1228] The device uses a camera and microphone to analyze the customer's facial expressions and tone of voice, and recognizes if they are a little nervous.

[1229] 3. Data submission and preprocessing:

[1230] The device transmits the consultation content and emotional information (tension state) to the server using a secure protocol, and the server normalizes, tokenizes, and formats the emotional data.

[1231] 4. Generative AI analysis:

[1232] The server analyzes the data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's level of tension.

[1233] 5. Proposal generation:

[1234] The generative AI searches the database to find laptop models that match the keywords and emotional state. For example, generated suggestions might include "Model A: weight 1.2 kg, battery life 12 hours" or "Model B: weight 1.1 kg, battery life 10 hours," and include reassuring language to ease the user's tension.

[1235] 6. Suggestion display:

[1236] The proposal is converted into JSON format and sent to the device using a secure protocol. The device then displays the received proposal in its user interface, for example, "Model A: Weight 1.2 kg, Battery life 12 hours. Safe for beginners to use," or "Model B: Weight 1.1 kg, Battery life 10 hours. Lightweight and ideal for carrying around."

[1237] Prompt Sentence Examples

[1238] The following text could be fed into a generative AI model as a prompt:

[1239] text

[1240] Customer's request: "A large-screen tablet that can be shared with colleagues."

[1241] Customer sentiment: "I'm a little nervous"

[1242] Generate a proposal for a tablet with a large screen that's easy to share, including its features, beginner-friendly features, and a reassuring description.

[1243] As described above, the present invention provides a system that grasps the emotional state of a user and makes appropriate product suggestions in real time, thereby realizing smooth communication with customers.

[1244] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1245] Step 1:

[1246] The terminal accepts user input. The user (store clerk) inputs the customer's wishes or inquiries into the input interface. For example, the user might input, "I'm looking for a lightweight laptop with good battery life." This becomes the input data.

[1247] Step 2:

[1248] The device performs emotion recognition. It uses a camera and microphone to analyze the user's (store clerk's) facial expressions and tone of voice, and uses an emotion engine to identify the customer's emotional state. Emotional information is obtained in this step.

[1249] Step 3:

[1250] The device preprocesses the data, integrating the input data with sentiment information, normalizing and tokenizing the text, and formatting the sentiment data. This preprocessed data is the input for the next step.

[1251] Step 4:

[1252] The device sends the preprocessed data to the server. The preprocessed data is transferred to the server using a secure communication protocol (HTTPS). The server receives this data and begins processing it as new input data.

[1253] Step 5:

[1254] The server analyzes the data using a generative AI model. The server passes the received input data and emotional information to a generative AI model (general-purpose generative model) to extract important keywords and features. For example, keywords such as "lightweight" and "long battery life" are extracted, and analysis is performed taking into account the emotional state.

[1255] Step 6:

[1256] The server generates optimal suggestions. The generative AI model references past cases and databases to generate suggestions that best fit the user's requirements and emotional state. The generated suggestions include specific product information and reassuring descriptions.

[1257] Step 7:

[1258] The server sends the generated proposal to the device. The server converts the generated proposal into JSON format or similar and sends it to the device using a secure communication protocol. The device receives this data.

[1259] Step 8:

[1260] The device presents the suggestions to the user. The received suggestions are visually displayed on the user interface. For example, it may display "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[1261] Step 9:

[1262] The user (store clerk) responds to the customer. Based on the generated proposal, the user (store clerk) provides an appropriate product explanation to the customer. The user explains the proposed product model and its features, and makes a proposal that best suits the customer's needs.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] [Fourth embodiment]

[1267] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1268] 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.

[1269] 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).

[1270] 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.

[1271] 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.

[1272] 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).

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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.

[1279] 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."

[1280] This invention is a system in which a user (store clerk) inputs the customer's inquiry details and an artificial intelligence (AI) generates and provides optimal suggestions. This system involves taking in user input, sending the data, processing it with the AI, and displaying the suggestions.

[1281] Program processing flow

[1282] Capturing User Input

[1283] Terminal: A user interface (UI) is provided where the user inputs the customer's inquiry. For example, "I'm looking for a smartphone with a large battery and a high-performance camera."

[1284] Sending data

[1285] Terminal: The entered data is sent to the server using a secure communication protocol (e.g., HTTPS).

[1286] NPL pretreatment

[1287] Server: The server performs preprocessing such as tokenization and normalization on the received data, converting it into a form that can be easily analyzed by the generative AI.

[1288] Proposal generation by generative AI

[1289] Server: The generative AI analyzes the preprocessed data and extracts the input keywords and features. For example, "large capacity battery" and "high performance camera" are extracted. The generative AI then searches for products that meet these features based on a database and past cases, and generates optimal suggestions. The generative AI creates suggestions in natural language, including relevant information (e.g., battery capacity and camera performance).

[1290] Sending response data

[1291] Server: Converts the generated proposal into an appropriate format (e.g., JSON or XML) and sends it back to the device using a secure communication protocol.

[1292] What the user sees

[1293] Terminal: The terminal displays the proposal received from the server on the user interface. The user can interact with the customer based on this information and provide professional proposals, such as "Model X: 5000mAh battery, 48MP camera."

[1294] Specific examples

[1295] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life."

[1296] 1. Step 1: User Input

[1297] Terminal: The user (store clerk) inputs the customer's request into the terminal's input form. The request is for a "lightweight laptop with good battery life."

[1298] 2. Step 2: Sending data

[1299] Terminal: The entered data is sent to the server using a secure protocol.

[1300] 3. Step 3: Pretreatment

[1301] Server: Normalizes and tokenizes the received data into a format that is easy to analyze.

[1302] 4. Step 4: Proposal Generation

[1303] Server: The generation AI extracts the keywords "lightweight" and "long battery life" and searches the database for laptop models that match these keywords. The generation AI generates the following suggestions: "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours."

[1304] 5. Step 5: Sending response data

[1305] Server: Converts the generated proposal into JSON format and sends it to the device.

[1306] 6. Step 6: Suggestions

[1307] Terminal: Visually displays the received proposal to the user. The user interface displays "Model A: Weight 1.2 kg, Battery life 12 hours" and "Model B: Weight 1.1 kg, Battery life 10 hours."

[1308] 7. Step 7: User Actions

[1309] User: Based on the proposal, the salesperson (user) explains to the customer, "These models are laptops that meet your requirements."

[1310] This allows store clerks to quickly provide highly specialized information to customers without relying on their specialized knowledge or experience, which is expected to improve customer satisfaction and increase sales.

[1311] The processing flow will be explained below.

[1312] Step 1:

[1313] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry details. The user enters the inquiry details in the text box and presses the send button.

[1314] Step 2:

[1315] Terminal: Sends user-entered data to the server using a secure protocol (e.g., HTTPS), properly encoding the data before sending it.

[1316] Step 3:

[1317] Server: The server receives the received data and performs preprocessing, which includes text normalization (removing unnecessary whitespace, standardizing characters, etc.) and tokenization (splitting into words).

[1318] Step 4:

[1319] Server: Passes the preprocessed data to the generative AI, which uses natural language processing (NLP) techniques to analyze the input data and extract keywords and important features.

[1320] Step 5:

[1321] Server: The generation AI searches the database for relevant information based on the extracted keywords and features. For example, it retrieves product information that matches keywords such as "large-capacity battery" and "high-performance camera."

[1322] Step 6:

[1323] Server: Based on the information retrieved from the database, the generative AI generates optimal suggestions. Suggestions are written in natural language and are provided in the form of, for example, "Model X: 5000mAh battery, 48MP camera."

[1324] Step 7:

[1325] Server: Converts the generated proposal into an appropriate format (e.g., JSON, XML) and sends it to the device using a secure protocol.

[1326] Step 8:

[1327] Terminal: The terminal analyzes the proposals received from the server and displays them on the user interface. Specifically, the proposed product models and their features are displayed in a list or tile format.

[1328] Step 9:

[1329] User: Based on the suggestions displayed in the user interface, the user (store clerk) provides expert advice to the customer, for example, explaining that "Model X and Model Y are products that meet your requirements."

[1330] In this way, the entire system operates in cooperation with one another, allowing users to make appropriate proposals to customers without relying on specialized knowledge.

[1331] Example 1

[1332] 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."

[1333] In conventional systems, store staff and users required advanced expertise to properly understand customer inquiries and provide optimal proposals. This resulted in problems such as the quality and consistency of proposals depending on the staff's skills, leading to inconsistencies in customer satisfaction. Furthermore, proposals were generated manually, resulting in a lack of speed. The present invention aims to solve these problems and provide high-quality proposals to customers quickly and consistently.

[1334] 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.

[1335] In this invention, the server includes a means for taking in data entered by a user and performing preprocessing, a means for sending the preprocessed data to a generative AI model and extracting keywords, and a means for searching a related database and generating optimal suggestions using the generative AI model, thereby enabling users to quickly make high-quality suggestions even if they do not have specialized knowledge.

[1336] "Means for capturing and preprocessing data entered by users" refers to a function that collects data such as consultation details entered by users via their terminals and processes them using natural language processing techniques such as tokenization and normalization.

[1337] "Means for sending preprocessed data to a generative AI model and extracting keywords" refers to the process of sending preprocessed data to a generative AI model and extracting important information and keywords from the input data.

[1338] "Means for searching relevant databases and generating optimal suggestions using a generative AI model" refers to a function that searches for similar or related information from a database based on keywords extracted by the generative AI model, and generates optimal suggestions based on that information.

[1339] "Means for transmitting the generated proposal to the user terminal using a secure communication protocol" refers to a process for securely transmitting the generated proposal content to the user terminal using a secure communication protocol (e.g., HTTPS).

[1340] The "means for displaying the generated proposal to the user" is a function for visually displaying the proposal content received on the user terminal so that the user can easily confirm it.

[1341] This invention is a system in which a user (store clerk) inputs the customer's inquiry details and the system generates artificial intelligence (AI) to provide optimal suggestions. This system uses a terminal, a server, a user interface (UI), a natural language processing (NLP) library, and a generative AI model.

[1342] System Overview

[1343] The system includes a means for taking in data entered by a user and performing preprocessing, a means for sending the preprocessed data to a generative AI model and extracting keywords, a means for searching a relevant database and generating optimal suggestions using the generative AI model, a means for sending the generated suggestions to a user terminal using a secure communication protocol, and a means for displaying the generated suggestions to a user.

[1344] Hardware and software used

[1345] Terminal: A device operated by a user (store clerk), including a PC, tablet, smartphone, etc. These terminals have a user interface that allows the user to input data.

[1346] Server: A remote server used for data processing and analysis of AI models. The database stores past cases and related information.

[1347] Natural language processing libraries: Software libraries used to preprocess data, including tokenization and normalization. Examples include NLP libraries in Python.

[1348] Generative AI model: An artificial intelligence model used to generate optimal recommendations based on user input. Examples include generative AI models such as OpenAI's GPT-3.

[1349] Secure communication protocol: A communication protocol used to protect the transmission and reception of data, such as HTTPS.

[1350] Specific examples

[1351] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with a long battery life." In this case, the process will proceed as follows:

[1352] User input: The store clerk enters "a lightweight laptop with good battery life" into the input form on the terminal and clicks the submit button.

[1353] Data transmission: The terminal encodes the input data into JSON format and sends it to the server using a secure communication protocol (HTTPS).

[1354] Preprocessing: The server receives the data and normalizes and tokenizes it using a natural language processing library. It breaks down "lightweight laptop with good battery life" into tokens such as "lightweight", "battery life", "good", and "laptop".

[1355] Proposal generation by generative AI: Preprocessed data is input into the generative AI model, and the keywords "lightweight" and "long battery life" are extracted. Based on this, information on laptops that fit the criteria is searched for in the database, and the generative AI generates the following proposals: "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours."

[1356] Sending response data: The generated proposal is converted into JSON format and sent to the terminal again using a secure communication protocol.

[1357] Proposal display: The device parses the received proposal and visually displays "Model A: weight 1.2 kg, battery 12 hours" and "Model B: weight 1.1 kg, battery 10 hours" on the user interface.

[1358] User action: The salesperson explains the proposal to the customer, saying, "These models of laptops meet your requirements."

[1359] Prompt Sentence Examples

[1360] Input prompt:

[1361] "I'm looking for a lightweight laptop with good battery life."

[1362] Output of the generative AI model:

[1363] "We recommend the following models: Model A (weight 1.2 kg, 12-hour battery life), Model B (weight 1.1 kg, 10-hour battery life)"

[1364] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1365] Step 1: Capturing User Input

[1366] Terminal: Through the user interface (UI), the user (store clerk) inputs the customer's inquiry. At this point, the input is text data, such as "I'm looking for a lightweight laptop with good battery life." The terminal receives this input and prepares for the next processing step.

[1367] Step 2: Sending data

[1368] Terminal: Encodes input data into JSON format and sends it to the server using a secure communication protocol (e.g., HTTPS). This operation involves sending the data via an HTTP POST request. The input is the encoded JSON data, and the output is a status code indicating successful submission to the server.

[1369] Step 3: Preprocessing

[1370] Server: The received data is first normalized to remove unnecessary whitespace and special characters. Next, a natural language processing library (e.g., a Python NLP library) is used to tokenize the data and break it into words and phrases. For example, "lightweight laptop with good battery life" is broken down into "lightweight", "battery life", "good", and "laptop". The input is the received raw data, and the output is the tokenized data.

[1371] Step 4: Keyword extraction and suggestion generation

[1372] Server: A generative AI model is used to extract key keywords from the preprocessed data. After extracting the keywords "lightweight" and "long battery life," information on products related to these keywords is searched for in the database. Based on the search results, the generative AI model generates optimal suggestions in natural language. For example, suggestions such as "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours" are generated. The input is tokenized data, and the output is suggestions in natural language format.

[1373] Step 5: Sending response data

[1374] Server: Converts the generated proposal into JSON format and sends it back to the terminal using a secure communication protocol (HTTPS). The server sets the response header and returns an HTTP 200 status code. The input is the generated proposal, and the output is the encoded JSON data and the sending status code.

[1375] Step 6: Display suggestions

[1376] Terminal: Parses the JSON format proposal received from the server and displays it visually to the user. Specifically, proposals such as "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours" are displayed on the UI. The input is the JSON data received from the server, and the output is the visual information displayed on the user interface.

[1377] Step 7: User Actions

[1378] User: The salesperson (user) explains the proposal to the customer based on the content of the proposal. They introduce the laptop models that meet the customer's requirements and support the customer in making a selection. The input is the proposal information displayed on the terminal, and the output is the dialogue with the customer and the implementation of the proposal.

[1379] This allows users to quickly make high-quality proposals even without specialized knowledge.

[1380] (Application example 1)

[1381] 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."

[1382] Conventional systems have had difficulty quickly and accurately proposing products that meet the user's desired conditions. In particular, proposing products that meet multiple conditions requires the user to have specialized knowledge, which can be time-consuming. Furthermore, from a security standpoint, there is a risk of incomplete data transmission and customer information leaks. A system that solves these problems is needed.

[1383] 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.

[1384] In this invention, the server includes a means for receiving and preprocessing data entered by the user, a means for transmitting the preprocessed data to the AI ​​that generates the data, and a means for visually displaying the generated suggestions on a user interface. This allows the user to receive product suggestions quickly and accurately without having specialized knowledge, and furthermore, a secure communication protocol prevents the leakage of customer information.

[1385] A "user interface" is something that provides a visual or operational means for a user to interact with a system.

[1386] "Preprocessing" is the initial data processing that converts the data entered by the user into a form that is easy to analyze.

[1387] "Generative AI" is a system that uses specific algorithms to analyze input data and generate optimal suggestions.

[1388] A "secure communication protocol" is a communication method that ensures confidentiality, integrity, authenticity, etc. of information when sending and receiving data.

[1389] "Visually displaying" refers to a process of displaying the generated information on the screen in a format that is easy for the user to understand.

[1390] This invention is a system that integrates a user interface, data preprocessing, generative AI, and a secure communication protocol. This system uses AI to generate optimal suggestions based on data entered by the user and displays them to the user.

[1391] System Configuration

[1392] Hardware

[1393] Terminal: A device such as a smartphone or tablet on which a user interface is provided.

[1394] Server: The data center or cloud server where the generative AI runs.

[1395] software

[1396] Requests library: A library for sending HTTP requests.

[1397] Flask: A web framework used on the server side.

[1398] Generative AI: An artificial intelligence model that generates optimal suggestions based on input data.

[1399] Secure communication protocol: A protocol that ensures confidentiality, integrity, and authenticity of information when transmitting and receiving data (e.g., HTTPS).

[1400] Data processing flow

[1401] 1. User Interface

[1402] The user inputs the desired product specifications into the input form on the device. For example, the user may write in their inquiry about a "lightweight smartphone with good battery life."

[1403] 2. Data Preprocessing

[1404] The server tokenizes and normalizes the received user input, converting it into a format that is easy for the generative AI to analyze.

[1405] 3. Generation AI

[1406] Generative AI extracts relevant keywords and features from pre-processed data, and generates optimal product suggestions by referencing past cases and databases. The suggestions are expressed in natural language.

[1407] 4. Secure Data Communication

[1408] The proposed data is then sent back to the terminal using a secure communication protocol (e.g., HTTPS). By ensuring secure communication, the risk of customer information being leaked is reduced.

[1409] 5. Visual representation of suggestions

[1410] The device displays the received suggestions on the user interface, visually presenting information such as "Model A: weight 1.2 kg, battery life 12 hours."

[1411] Specific examples

[1412] For example, if a user inputs "I'm looking for a smartphone that's light and has a long battery life," the system operates as follows:

[1413] The user inputs the consultation content into the input form on the terminal.

[1414] The server tokenizes the input and extracts keywords such as "lightweight" and "good battery life."

[1415] The generative AI refers to a related past database and generates suggestions such as "Model X: weight 1.1 kg, battery life 15 hours" and "Model Y: weight 1.0 kg, battery life 14 hours."

[1416] The generated proposal is transmitted to the terminal using a secure communication protocol.

[1417] The proposed content is displayed on the user interface, and the user selects products based on this.

[1418] Prompt Sentence Examples

[1419] Based on the user's token "lightweight laptop with good battery life," prompt the user with products that meet the following criteria: 1. A lightweight, high-performance model. 2. A long battery life. 3. Multiple options are offered.

[1420] In this way, the technical scope of the invention is specifically indicated, and a system is provided that enables users to receive prompt and accurate product proposals even without specialized knowledge.

[1421] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1422] Step 1:

[1423] The user inputs the desired product specifications into the input form on the device. For example, the user may write "I want a smartphone that is lightweight and has a long battery life." The input data is retrieved by the device's user interface.

[1424] (input)

[1425] Product specifications entered by the user (e.g., "A lightweight smartphone with good battery life")

[1426] (output)

[1427] Text data entered into the terminal

[1428] Step 2:

[1429] The terminal sends the entered text data to the server using a secure communication protocol (e.g., HTTPS), which ensures the confidentiality and integrity of the data.

[1430] (input)

[1431] Text data transmitted using a secure communication protocol

[1432] (output)

[1433] Text data that reaches the server

[1434] Step 3:

[1435] The server preprocesses the received text data, which includes tokenizing and normalizing the input (e.g., removing unnecessary whitespace and grammatical errors) to convert it into a format that is easy for the generative AI model to parse.

[1436] (input)

[1437] Text data that reaches the server

[1438] (output)

[1439] Tokenized and normalized data

[1440] Step 4:

[1441] The server sends the preprocessed data to a generative AI model, which references a database and past cases to extract relevant keywords and features (e.g., "lightweight" and "battery life") and then generates optimal suggestions based on those features.

[1442] (input)

[1443] Tokenized and normalized data

[1444] (output)

[1445] Generated product suggestions (e.g., "Model X: weight 1.1 kg, battery life 15 hours" "Model Y: weight 1.0 kg, battery life 14 hours")

[1446] Step 5:

[1447] The server converts the generated product proposals into an appropriate format (e.g., JSON) and sends them to the terminal, again using a secure communication protocol.

[1448] (input)

[1449] Generated product suggestion data

[1450] (output)

[1451] Proposal data in JSON format

[1452] Step 6:

[1453] The terminal analyzes the received product suggestion data and visually displays it on the user interface, such as product name, features, price, etc.

[1454] (input)

[1455] Proposal data in JSON format

[1456] (output)

[1457] Suggestions displayed in the user interface (e.g., "Model X: weight 1.1 kg, battery life 15 hours" or "Model Y: weight 1.0 kg, battery life 14 hours")

[1458] Step 7:

[1459] The user can then check the proposed content and select products as necessary. This allows the user to select products quickly and accurately, even if they do not have specialized knowledge.

[1460] (input)

[1461] Suggestions displayed in the user interface

[1462] (output)

[1463] User decision-making and product selection

[1464] In this way, a system is realized in which each processing step works together to provide the user with optimal suggestions.

[1465] 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.

[1466] This invention is a system that allows users (store clerks) to input customer inquiries and provides optimal suggestions using artificial intelligence (AI). The system also incorporates an emotion engine that recognizes the user's emotional state and adjusts the suggestions accordingly.

[1467] Program processing flow

[1468] Capturing User Input

[1469] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry. For example, the user might input, "I'm looking for a smartphone with a large battery and a high-performance camera."

[1470] Emotion recognition

[1471] Device: The device uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotional state. For example, the device uses a camera and microphone to determine whether the user is smiling or nervous.

[1472] Sending data

[1473] Terminal: The input data and the emotion information recognized by the emotion engine are sent to the server using a secure protocol (e.g., HTTPS).

[1474] Data Preprocessing

[1475] Server: The server takes the received user data and emotion information and performs preprocessing, which includes text normalization, tokenization, and emotion data formatting.

[1476] Generative AI analysis

[1477] Server: Passes the preprocessed data to the generation AI, which analyzes the input data and emotional information. The generation AI extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state.

[1478] Proposal Generation

[1479] Server: The AI ​​generator uses a database to generate suggestions that match the user's input and emotional state. For example, if the user is nervous, the generated suggestions will include reassuring expressions.

[1480] Submitting proposal data

[1481] Server: Converts the generated proposal into JSON or XML format and sends it to the device using a secure protocol.

[1482] What the user sees

[1483] Terminal: The proposal received from the server is visually displayed on the user interface. Specifically, the proposed product models and their features are displayed in a list or tile format. If the user is nervous, a concise and reassuring explanation is added to the proposal.

[1484] Specific examples

[1485] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life," and the device recognizes through its emotion engine that the user is a little nervous.

[1486] 1. User Input

[1487] Terminal: The user (store clerk) inputs the inquiry content, "a lightweight laptop with good battery life," into the input form on the terminal.

[1488] 2. Emotional Recognition

[1489] Device: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and recognizes whether the user is nervous.

[1490] 3. Data transmission

[1491] Terminal: The content of the consultation and the user's emotional information (state of tension) are sent to the server using a secure protocol.

[1492] 4. Data Preprocessing

[1493] Server: Normalizes the received data and formats the tokenized and sentiment data.

[1494] 5. Generative AI Analysis

[1495] Server: Analyzes data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's state of tension.

[1496] 6. Proposal Generation

[1497] Server: The generative AI searches the database for laptop models that match the keywords and emotional state. The generated suggestions include "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours," as well as reassuring language to ease the user's tension.

[1498] 7. Submitting Proposal Data

[1499] Server: Converts the generated proposal into JSON format and sends it to the device using a secure protocol.

[1500] 8. Display of Suggestions

[1501] Device: The received suggestions are displayed in the user interface. For example, "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[1502] 9. User Actions

[1503] User: Based on the displayed suggestions, the user (store clerk) explains to the customer, "These models are laptops that meet your requirements," and "Even beginners can use them with confidence."

[1504] In this way, by incorporating an emotion engine, it is possible to provide suggestions that are adapted to the user's emotional state, which is expected to lead to smoother communication with customers.

[1505] The processing flow will be explained below.

[1506] Step 1:

[1507] Terminal: The terminal provides a user interface (UI) and allows the user to input the customer's inquiry. For example, the user might enter "I want a lightweight laptop with good battery life" in the text box and press the send button.

[1508] Step 2:

[1509] Device: The device uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotional state. For example, the device uses a camera and microphone to determine whether the user is tense or relaxed.

[1510] Step 3:

[1511] Terminal: The input consultation content and recognized emotion information are sent to the server using a secure communication protocol (e.g., HTTPS). The data is properly encoded before being sent.

[1512] Step 4:

[1513] Server: The server takes in the received data and emotion information and performs preprocessing, which includes text normalization (removing unnecessary whitespace, standardizing characters, etc.), tokenization (splitting into words), and emotional data formatting.

[1514] Step 5:

[1515] Server: Passes the preprocessed data to the generation AI. The generation AI analyzes the input data using natural language processing (NLP) techniques and extracts keywords and important features. For example, "lightweight" and "long battery life" are extracted as features.

[1516] Step 6:

[1517] Server: The generation AI adjusts the analysis results by taking into account emotional information. For example, if the user is nervous, the generated suggestions will include explanations that will reassure the user.

[1518] Step 7:

[1519] Server: The generative AI searches the database for relevant information based on the extracted keywords and features. For example, it retrieves laptop models that match the criteria of "lightweight" and "long battery life."

[1520] Step 8:

[1521] Server: Based on information retrieved from the database, the generative AI generates optimal suggestions. The suggestions are written in natural language, such as "Model A: weight 1.2 kg, battery life 12 hours" or "Model B: weight 1.1 kg, battery life 10 hours." If the user is nervous, they can add an explanation such as "Even beginners can use it with confidence."

[1522] Step 9:

[1523] Server: Converts the generated proposal into an appropriate format (e.g., JSON, XML) and sends it to the device using a secure communication protocol.

[1524] Step 10:

[1525] Device: The device analyzes the suggestions received from the server and displays them on the user interface. For example, it might display "Model A: Weight 1.2 kg, 12-hour battery life. Easy to use even for beginners," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[1526] Step 11:

[1527] User: Based on the suggestions displayed on the user interface, the user (store clerk) explains to the customer, "These models are laptops that meet your requirements." If the user seems nervous, they can also provide additional information such as, "Even beginners can use it with confidence."

[1528] In this way, a system including an emotion engine can tailor its suggestions to the user's emotional state, providing more personalized information.

[1529] Example 2

[1530] 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."

[1531] Conventional recommendation systems make suggestions based solely on user input data, and therefore are unable to provide optimal suggestions that take into account the user's emotional state. Furthermore, the visual display of suggestions in the user interface is often insufficient, making the content of the suggestions difficult for users to understand. This leads to a poor user experience and makes smooth communication with customers difficult.

[1532] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1533] In this invention, the server includes a means for taking in and preprocessing data entered by the user and the user's emotional state, a means for transmitting the preprocessed data and emotional information to an artificial intelligence that generates the data, and a means for visually presenting the generated suggestions to the user, thereby enabling optimal suggestions that take the user's emotional state into consideration.

[1534] "Data entered by the user" refers to the details of the consultation or request received by the user (store clerk) from the customer, and is information collected in the form of text input, voice input, or the like.

[1535] "Emotional state" refers to the user's psychological state and emotional movements, and is recognized by analyzing the user's facial expressions and tone of voice.

[1536] "Preprocessing" is the process of analyzing data entered by a user and performing operations such as normalization, tokenization, and formatting of emotion data.

[1537] "Generative AI" is a device that uses machine learning models and algorithms to analyze pre-processed data entered by users and generate optimal suggestions.

[1538] The "visual presentation means" is a method of displaying the generated proposal content on a user interface and providing information in a form that can be easily understood by the user.

[1539] A "secure communication protocol" is a communication protocol used to prevent information tampering or eavesdropping when sending and receiving data, and typical examples include HTTPS and TLS.

[1540] "Past cases" refers to historical data such as consultation details and proposal results previously made by users or customers that are stored in the system.

[1541] A "relevant database" is a collection of information that is referenced to generate recommendations, and is data storage that may include product information, user reviews, technical specifications, etc.

[1542] This invention is a system that allows users (store clerks) to input customer inquiries and provides optimal suggestions using artificial intelligence (AI). This system incorporates an emotion engine that recognizes the user's emotional state and adjusts the suggestions accordingly.

[1543] System configuration

[1544] This system is broadly divided into two components: a terminal and a server. Below, we will explain each component and its role.

[1545] Terminal

[1546] The device provides a user interface (UI) and has a means for users to input customer inquiries. It also uses a camera and microphone to analyze the user's facial expressions and tone of voice, and an emotion engine to recognize the user's emotional state.

[1547] Specifically, the user inputs text into an input form on the device or uses voice input. By inputting a prompt such as "a smartphone with good battery life," the user's needs are specified.

[1548] server

[1549] The server receives data sent from the device and performs preprocessing, which includes text normalization, tokenization, and emotional data formatting.

[1550] The preprocessed data is passed to a generative AI model, which analyzes the input data and emotional information. The generative AI model extracts important keywords and features and generates optimal suggestions based on the user's emotional state. The generated suggestions are also based on a database of past cases and related information.

[1551] The generated proposal is converted back into JSON format and sent to the device using a secure protocol (e.g., HTTPS).

[1552] Specific examples

[1553] For example, consider the case where a store clerk inputs, "I'm looking for a lightweight laptop with good battery life," and the device recognizes through its emotion engine that the user is a little nervous.

[1554] 1. User Input

[1555] Terminal: The user (store clerk) inputs the inquiry content, "a lightweight laptop with good battery life," into the input form on the terminal.

[1556] 2. Emotional Recognition

[1557] Device: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and recognizes whether the user is nervous.

[1558] 3. Data transmission

[1559] Terminal: The content of the consultation and the user's emotional information (state of tension) are sent to the server using a secure protocol.

[1560] 4. Data Preprocessing

[1561] Server: Normalizes the received data and formats the tokenized and sentiment data.

[1562] 5. Generative AI Analysis

[1563] Server: Analyzes data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's state of tension.

[1564] 6. Proposal Generation

[1565] Server: The generative AI searches the database for laptop models that match the keywords and emotional state. The generated suggestions include "Model A: weight 1.2 kg, battery life 12 hours" and "Model B: weight 1.1 kg, battery life 10 hours," as well as reassuring language to ease the user's tension.

[1566] 7. Submitting Proposal Data

[1567] Server: Converts the generated proposal into JSON format and sends it to the device using a secure protocol.

[1568] 8. Display of Suggestions

[1569] Device: The received suggestions are displayed in the user interface. For example, "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[1570] This system is expected to provide suggestions that take into account the user's emotional state, facilitating smoother communication with customers. Furthermore, the combination of a generative AI model and an emotion engine will improve the user experience.

[1571] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1572] Program processing flow

[1573] Step 1:

[1574] The user inputs the consultation content.

[1575] Terminal: Using the text box or voice input function provided by the user interface (UI), the user (store clerk) inputs the customer's inquiry. For example, the user might input, "I'm looking for a smartphone with a large battery and a high-performance camera."

[1576] Input: User's consultation content (text or voice)

[1577] Output: Text data of consultation content

[1578] Step 2:

[1579] Recognize the user's emotional state.

[1580] Device: Uses a camera and microphone to analyze the user's facial expressions and tone of voice, and uses an emotion engine (e.g., emotion recognition software) to identify the user's emotional state. For example, it can determine whether the user is smiling, nervous, surprised, etc.

[1581] Input: User's facial expression data, voice data

[1582] Output: Emotional state (e.g., nervous, relieved, happy)

[1583] Step 3:

[1584] Send the data to the server.

[1585] Terminal: Converts the input consultation data and recognized emotion information into JSON format and sends it to the server using a secure protocol (HTTPS).

[1586] Input: Text data of consultation content, emotional state information

[1587] Output: Confirmation of successful transmission

[1588] Step 4:

[1589] Preprocessing of received data is performed.

[1590] Server: The server takes the data received from the device and performs preprocessing, which includes text normalization (e.g., converting all data to lowercase), tokenization (splitting data into words), and emotional data formatting (e.g., converting data into numerical data).

[1591] Input: Text data of consultation content, emotional state information

[1592] Output: Preprocessed data

[1593] Step 5:

[1594] The transmitted data is analyzed by the generation AI.

[1595] Server: Passes the preprocessed data to the generative AI model, which analyzes the input data and emotional information. The generative AI model extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state. For example, it extracts keywords such as "battery" and "camera" and adds elements that will give the user a sense of security if they are nervous.

[1596] Input: Preprocessed data

[1597] Output: Proposal

[1598] Step 6:

[1599] Retrieve suggestions from the database.

[1600] Server: Based on the keywords and emotion information extracted by the generative AI model, the server references a database to retrieve information on suitable products and services. For example, it searches the database for smartphones with "12-hour battery life" and "high-performance cameras."

[1601] Input: Extracted keywords, emotional information

[1602] Output: Specific product suggestions

[1603] Step 7:

[1604] The generated proposal data is transmitted to the terminal.

[1605] Server: The generated proposal is converted back to JSON format and sent to the device using a secure protocol, using an authentication token to prevent data tampering.

[1606] Input: Proposal content (product information)

[1607] Output: Confirmation of successful transmission

[1608] Step 8:

[1609] The suggestions are displayed in the user interface.

[1610] Device: The received proposal is visually displayed on the user interface. For example, it may be displayed as "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[1611] Input: Received proposal

[1612] Output: Visual suggestions

[1613] In this way, the user's input and emotional information are appropriately utilized at each step to generate and display optimal suggestions, thereby improving the user experience.

[1614] (Application example 2)

[1615] 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."

[1616] Conventional customer service systems were unable to consider the emotional state of individual customers when making proposals based on their wishes and consultation details. This made it difficult to respond to subtle changes in emotions, such as when a customer was nervous or when a particular expression made them feel anxious. As a result, proposals were sometimes not fully accepted by customers. To solve this problem, a system was needed that could recognize the customer's emotional state in real time and make optimal proposals based on that.

[1617] 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.

[1618] In this invention, the server includes means for taking in and preprocessing data and emotional state input by the user, means for transmitting the preprocessed data and emotional information to an artificial intelligence that generates the data, means for presenting the generated suggestions to the user and adjusting them based on the emotional state, and means for transmitting and receiving data using a secure communication protocol, thereby enabling optimal product suggestions that take the user's emotional state into account in real time.

[1619] "Data entered by the user" refers to information that the user provides to the system through the input interface, such as the customer's wishes and consultation details.

[1620] "Emotional state" refers to psychological and emotional information obtained from a user's or customer's facial expressions, tone of voice, etc.

[1621] "Preprocessing" refers to a series of steps that normalize, tokenize, and convert input data and emotional information into a format that is easier to analyze.

[1622] "Generative AI" refers to systems that include algorithms and models for generating optimal suggestions and responses based on input data and emotional information.

[1623] "Secure communications protocol" means a communications protocol (e.g., HTTPS) used to ensure the security of data communications.

[1624] The "means for presenting suggestions to the user and adjusting them based on the user's emotional state" refers to a mechanism for displaying generated suggestions to the user and appropriately adjusting the content and expression of the suggestions, taking into account the user's emotional state.

[1625] "Optimal product proposals" refer to proposals for products or services that best suit the customer's wishes, consultation content, and emotional state.

[1626] The present invention is a system that takes in data and emotional state input by a user and makes optimal product recommendations based on that data. This system operates by having a salesperson input the customer's wishes and inquiries using a device such as a smartphone, tablet, or smart glasses when the customer is searching for a product in a physical store, and then using an emotion engine to recognize the customer's emotional state.

[1627] The system consists of hardware including a smartphone, tablet, and smart glasses (e.g., general-purpose smart glasses), and software including a user interface (UI) library, an emotion recognition engine (e.g., a general-purpose emotion recognition API), a secure communication protocol (HTTPS), and a generative AI model (e.g., a general-purpose generative model).

[1628] Program processing

[1629] User Input and Emotion Recognition

[1630] The terminal first receives the customer's requests and inquiries from the user (store clerk) through an input interface. For example, if a customer says, "I want a large-screen tablet that I can share with my friends," the store clerk enters this information into the terminal.

[1631] The device then uses an emotion engine to recognize the customer's emotional state. For example, it uses a camera and microphone to analyze the customer's facial expressions and tone of voice to determine whether they are feeling a little nervous. This emotional information is then processed along with the input data.

[1632] Data submission and preprocessing

[1633] The device sends the input data and emotion information to the server using a secure communication protocol (HTTPS). The server takes the received data and performs preprocessing such as text normalization, tokenization, and emotion data formatting.

[1634] Proposal generation using generative AI models

[1635] The server passes the preprocessed data to a generative AI model, which analyzes the input data and emotional information. This generative AI model extracts important keywords and features and generates optimal suggestions taking into account the user's emotional state. For example, based on keywords such as "large screen," "shared," and "tablet," it compares them with past cases and database information to make product suggestions. If the customer is nervous, the suggestions will be generated to include expressions that will give them a sense of security.

[1636] Viewing Proposals

[1637] The generated proposal is converted into JSON format or similar and sent to the device again using a secure communication protocol. The device visually displays the received proposal content on the user interface. For example, the proposal content may include a reassuring description such as "Model A: 10 inches, easy to use even for beginners."

[1638] Specific examples

[1639] For example, suppose a customer asks, "I'm looking for a lightweight laptop with good battery life," and the device's emotion engine recognizes that the customer is a little nervous.

[1640] 1. User Input:

[1641] The terminal receives a request from the user (store clerk) via an input interface for a "lightweight laptop with good battery life."

[1642] 2. Emotion recognition:

[1643] The device uses a camera and microphone to analyze the customer's facial expressions and tone of voice, and recognizes if they are a little nervous.

[1644] 3. Data submission and preprocessing:

[1645] The device transmits the consultation content and emotional information (tension state) to the server using a secure protocol, and the server normalizes, tokenizes, and formats the emotional data.

[1646] 4. Generative AI analysis:

[1647] The server analyzes the data and emotional information, extracts keywords such as "lightweight" and "battery life," and takes into account the user's level of tension.

[1648] 5. Proposal generation:

[1649] The generative AI searches the database to find laptop models that match the keywords and emotional state. For example, generated suggestions might include "Model A: weight 1.2 kg, battery life 12 hours" or "Model B: weight 1.1 kg, battery life 10 hours," and include reassuring language to ease the user's tension.

[1650] 6. Suggestion display:

[1651] The proposal is converted into JSON format and sent to the device using a secure protocol. The device then displays the received proposal in its user interface, for example, "Model A: Weight 1.2 kg, Battery life 12 hours. Safe for beginners to use," or "Model B: Weight 1.1 kg, Battery life 10 hours. Lightweight and ideal for carrying around."

[1652] Prompt Sentence Examples

[1653] The following text could be fed into a generative AI model as a prompt:

[1654] text

[1655] Customer's request: "A large-screen tablet that can be shared with colleagues."

[1656] Customer sentiment: "I'm a little nervous"

[1657] Generate a proposal for a tablet with a large screen that's easy to share, including its features, beginner-friendly features, and a reassuring description.

[1658] As described above, the present invention provides a system that grasps the emotional state of a user and makes appropriate product suggestions in real time, thereby realizing smooth communication with customers.

[1659] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1660] Step 1:

[1661] The terminal accepts user input. The user (store clerk) inputs the customer's wishes or inquiries into the input interface. For example, the user might input, "I'm looking for a lightweight laptop with good battery life." This becomes the input data.

[1662] Step 2:

[1663] The device performs emotion recognition. It uses a camera and microphone to analyze the user's (store clerk's) facial expressions and tone of voice, and uses an emotion engine to identify the customer's emotional state. Emotional information is obtained in this step.

[1664] Step 3:

[1665] The device preprocesses the data, integrating the input data with sentiment information, normalizing and tokenizing the text, and formatting the sentiment data. This preprocessed data is the input for the next step.

[1666] Step 4:

[1667] The device sends the preprocessed data to the server. The preprocessed data is transferred to the server using a secure communication protocol (HTTPS). The server receives this data and begins processing it as new input data.

[1668] Step 5:

[1669] The server analyzes the data using a generative AI model. The server passes the received input data and emotional information to a generative AI model (general-purpose generative model) to extract important keywords and features. For example, keywords such as "lightweight" and "long battery life" are extracted, and analysis is performed taking into account the emotional state.

[1670] Step 6:

[1671] The server generates optimal suggestions. The generative AI model references past cases and databases to generate suggestions that best fit the user's requirements and emotional state. The generated suggestions include specific product information and reassuring descriptions.

[1672] Step 7:

[1673] The server sends the generated proposal to the device. The server converts the generated proposal into JSON format or similar and sends it to the device using a secure communication protocol. The device receives this data.

[1674] Step 8:

[1675] The device presents the suggestions to the user. The received suggestions are visually displayed on the user interface. For example, it may display "Model A: Weight 1.2 kg, 12-hour battery life. Safe for beginners to use," or "Model B: Weight 1.1 kg, 10-hour battery life. Lightweight and ideal for carrying around."

[1676] Step 9:

[1677] The user (store clerk) responds to the customer. Based on the generated proposal, the user (store clerk) provides an appropriate product explanation to the customer. The user explains the proposed product model and its features, and makes a proposal that best suits the customer's needs.

[1678] 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.

[1679] 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.

[1680] 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 robot 414.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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).

[1685] 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.

[1686] 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."

[1687] 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.

[1688] 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).

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] 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.

[1698] 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.

[1699] The following is further disclosed regarding the above embodiment.

[1700] (Claim 1)

[1701] means for capturing and pre-processing user-entered data;

[1702] means for transmitting the pre-processed data to an artificial intelligence generating device;

[1703] means for presenting the generated suggestions to a user;

[1704] A system including:

[1705] (Claim 2)

[1706] 10. The system of claim 1, wherein the generated suggestions are generated based on at least past cases and a related database.

[1707] (Claim 3)

[1708] 10. The system of claim 1, wherein data is sent and received using a secure communication protocol.

[1709] (Claim 4)

[1710] 10. The system of claim 1, further comprising means for visually displaying the suggestions through a user interface to allow the user to easily view the suggestions.

[1711] "Example 1"

[1712] (Claim 1)

[1713] means for capturing and pre-processing user-entered data;

[1714] A means for sending the preprocessed data to a generative AI model to extract keywords;

[1715] A means of searching through relevant databases and generating optimal suggestions using a generative AI model;

[1716] means for transmitting the generated proposal to a user terminal using a secure communication protocol;

[1717] means for displaying the generated suggestions to the user;

[1718] A system including:

[1719] (Claim 2)

[1720] 10. The system of claim 1, wherein the generated suggestions are generated based on at least past cases and a related database.

[1721] (Claim 3)

[1722] 10. The system of claim 1, wherein data is sent and received using a secure communication protocol.

[1723] "Application Example 1"

[1724] (Claim 1)

[1725] means for capturing and pre-processing user-entered data;

[1726] means for transmitting the pre-processed data to an artificial intelligence generating device;

[1727] means for visually displaying the generated suggestions in a user interface;

[1728] A system including:

[1729] (Claim 2)

[1730] 10. The system of claim 1, wherein the generated suggestions are generated based on at least past cases and relevant information sources.

[1731] (Claim 3)

[1732] 10. The system of claim 1, wherein data is sent and received using a secure communication protocol.

[1733] "Example 2: Combining Emotion Engines"

[1734] (Claim 1)

[1735] means for capturing and pre-processing user-input data and the user's emotional state;

[1736] means for transmitting the pre-processed data and emotion information to a generating artificial intelligence;

[1737] means for visually presenting the generated suggestions to a user;

[1738] A system including:

[1739] (Claim 2)

[1740] 10. The system of claim 1, wherein the generated suggestions are generated based on at least past cases and a related database.

[1741] (Claim 3)

[1742] 10. The system of claim 1, wherein data is sent and received using a secure communication protocol.

[1743] "Application example 2 when combining emotion engines"

[1744] (Claim 1)

[1745] means for capturing and pre-processing user-input data and emotional states;

[1746] means for transmitting the pre-processed data and emotion information to a generating artificial intelligence;

[1747] means for presenting the generated suggestions to the user and adjusting them based on the user's emotional state;

[1748] means for transmitting and receiving data using a secure communications protocol;

[1749] A system including:

[1750] (Claim 2)

[1751] 10. The system of claim 1, wherein the generated suggestions are generated based on at least past cases and a related database.

[1752] (Claim 3)

[1753] The system of claim 1, further comprising an artificial intelligence model that generates optimal recommendations based on input data and sentiment information. [Explanation of symbols]

[1754] 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. means for capturing and pre-processing user-entered data; means for transmitting the pre-processed data to an artificial intelligence generating device; means for presenting the generated suggestions to a user; A system including:

2. 10. The system of claim 1, wherein the generated suggestions are generated based on at least past cases and a related database.

3. 10. The system of claim 1, wherein data is sent and received using a secure communication protocol.

4. 10. The system of claim 1, further comprising means for visually displaying the suggestions through a user interface to allow the user to easily view the suggestions.

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