system

Abstract

We provide the system. [Solution] A means for storing information related to product attributes in an integrated manner, A language analysis tool that interprets user questions in natural language, A notification mechanism to inform the appropriate department based on the analysis results, A response-forming means that provides the interpreted information to the user in voice or text, A conversion means that enables the output of information using numerical representation software according to the user's request, A means of facilitating communication that dynamically responds to questions from customers in the store and automatically contacts sales staff, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Fragmented management of product information within an organization causes a lot of time and resources to be consumed in inquiry response. Also, due to frequent organizational changes, past product information may not be accurately carried over, leading to further confusion in inquiry response. There is a need to provide a system that can solve such problems and provide information quickly and accurately.

Means for Solving the Problems

[0005] This invention enables rapid access to information by using a database that centrally stores product-related features. Furthermore, it analyzes user inquiries using a natural language processing engine and automatically notifies the appropriate department based on the analysis results, thereby achieving efficient notification to the relevant department. In addition, it provides a user-friendly interface by generating responses in voice or text format and allowing information to be exported to spreadsheet software as needed. This allows for more efficient and optimized handling of inquiries.

[0006] A "database" is a management system that stores information in a unified and systematic manner, enabling rapid searching and retrieval.

[0007] "Natural language processing" is a technology that enables computers to understand, analyze, and process human language.

[0008] "Notification processing" is a function that sends information to relevant departments or personnel based on specific information.

[0009] "Response generation" is the process of creating information appropriate to the inquiry and providing it to the user.

[0010] "Speech recognition" is a technology that analyzes speech data and converts it into text data.

[0011] "Output method" refers to a function that converts and provides data in a format usable by the user, and specifically refers to exporting to spreadsheet software. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system that streamlines the management of product information and the handling of user inquiries within a company. The server has a database that comprehensively stores detailed product information and is designed to be quickly accessible from any department within the organization. This prevents the fragmentation of complex information and enables rapid information retrieval.

[0034] The terminal provides an interface for users to make inquiries. Through this interface, users can input questions into the system via voice or text. When voice input is used, the terminal utilizes the Google® Cloud Speech-to-Text API to convert the voice data into text data.

[0035] The server uses a natural language processing engine to analyze the user's question and extract relevant keywords. This process allows the server to accurately understand the user's intent and retrieve relevant information from the database.

[0036] Furthermore, the server has a function to automatically notify the appropriate department based on the content of the inquiry. The server quickly retrieves product information and defect information related to the extracted keywords and sends alerts to the department according to the analysis results.

[0037] The server then generates a text or voice response for the user based on the analysis results. The response is sent to the user in real time and, if necessary, provided as voice via the Google Cloud Text-to-Speech service.

[0038] Furthermore, users can export the acquired information to a spreadsheet program via their device. This feature allows users to easily record the information they obtain and use it for later analysis and reporting.

[0039] For example, if a user asks, "What is the release date of the new model Y?", the server performs natural language processing, recognizes "model Y" and "release date" as keywords, and retrieves the relevant information from the database. The retrieved information is then provided to the user in either text or audio format. In this way, the present invention achieves appropriate management of product information and highly automated user support.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users enter product inquiries through the interface provided by the device. Inquiries can be made via text or voice.

[0043] Step 2:

[0044] If the user selects voice input, the device receives the voice data and uses the Google Cloud Speech-to-Text API to convert this voice data into text format.

[0045] Step 3:

[0046] The server receives text data sent from the terminal and analyzes the question content using a natural language processing engine. This analysis extracts the user's intent and keywords.

[0047] Step 4:

[0048] The server searches the database based on the extracted keywords, retrieving relevant product information and departmental data. By extracting the relevant information, it prepares accurate answers to user inquiries.

[0049] Step 5:

[0050] The server automatically notifies the appropriate department based on the content of the inquiry. This process is carried out based on the analyzed results and pre-configured rules.

[0051] Step 6:

[0052] The server generates a response to provide the user with information retrieved from the database. The information is sent to the terminal in text format and, if necessary, also provided as audio via Google Cloud Text-to-Speech.

[0053] Step 7:

[0054] Users can view the information provided through their device and, if necessary, choose to export it to a spreadsheet program. This feature allows users to record the information they obtain and use it for further analysis and reporting.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In order to efficiently manage product-related information within companies and respond to user inquiries, there is a need for prompt and accurate information provision. However, currently, information is often fragmented, and notifications of inquiries to the appropriate departments are frequently delayed, reducing the efficiency of information utilization. In addition, although the use of voice input is increasing, the system for accurately converting voice to text information is not yet fully developed. As a result, there is a challenge in providing real-time responses to the diverse inquiries of users.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for a data storage structure for integrally storing product-related information, means for a language analysis mechanism for processing user requests in natural language, and means for a notification mechanism for transmitting information to relevant departments based on the analyzed information. This enables centralized information management and rapid information retrieval, as well as real-time appropriate responses to diverse user inquiries.

[0060] "Product-related information" refers to all data and metadata about a product managed within a company, and includes a wide range of content such as product specifications, release dates, related documents, and customer feedback.

[0061] A "data storage structure" refers to a database or data storage system designed to centrally store information and allow for quick retrieval and searching as needed.

[0062] A "language analysis mechanism" is a processing engine that uses natural language processing technology to analyze the user's linguistic input and automatically extract their intent and important keywords.

[0063] A "notification mechanism" is a communication system that automatically transmits information to relevant departments and personnel based on analysis results, and is a means of promoting appropriate action.

[0064] A "voice conversion function" is a technical mechanism that converts voice input into text information using advanced technology, making it a format that can be recorded and analyzed.

[0065] The "response generation function" is a function that automatically generates answers to the user based on the analyzed information and presents them in text or audio format.

[0066] A "data extraction function" is a function that outputs managed information in a specific format according to the user's request, making it available for use in other software or systems.

[0067] Modes for carrying out the invention

[0068] This invention provides a system for effectively managing product-related information and responding to user inquiries within a company. The server features a data storage structure that integrates and stores product information, enabling centralized information management. This data storage structure is implemented using relational database software. The server also uses a natural language processing engine to analyze user requests and quickly retrieve necessary information based on these analyses. Based on the analysis results, the server includes a notification mechanism for automatically sending notifications to relevant departments.

[0069] The terminal provides a user input interface and accepts inquiries via voice or text. When voice input is received, the terminal uses the Google Cloud Speech-to-Text API as its speech-to-text conversion function to convert the audio into text data. This enables efficient handling of voice data. Furthermore, the server has a response generation function and provides the user with information generated based on the analysis results in either voice or text format. When providing responses via voice, the Google Cloud Text-to-Speech service is used.

[0070] Users can use the provided information to export the data to spreadsheet software for further analysis and reporting. Specifically, when a user enters a question via a terminal, such as "What is the release date of the new model Y?", the server generates a quick response and provides accurate information.

[0071] In this way, the system achieves centralized management of product information and a high degree of automation in user support.

[0072] An example of a prompt message is: "Please describe the process of adding details for the new model Y to the product information system and setting the release date."

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user enters their inquiry via the device. Input can be in voice or text format. In the case of voice input, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text data. This results in output data in text format, which is then used for subsequent processing.

[0076] Step 2:

[0077] The terminal sends the converted text data to the server. The server uses a natural language processing engine to analyze the received text data. This analysis extracts keywords from the text data and processes the data to identify the user's intent. The analysis results output the necessary keywords and intent.

[0078] Step 3:

[0079] The server searches the product information database based on the analysis results. It then performs data search operations using the extracted keywords to retrieve relevant information. The information obtained through the search is output, and the server is ready to respond to the user's inquiry.

[0080] Step 4:

[0081] The server generates a response to provide to the user based on the information obtained from the search. This generation process combines the obtained information to create a response in natural language. The generated response is output as text data.

[0082] Step 5:

[0083] The server sends the generated response to the terminal. The terminal displays the received text data and, if necessary, plays it back as audio using the Google Cloud Text-to-Speech service. At this stage, the user can confirm the system's response and obtain appropriate output for their input.

[0084] Step 6:

[0085] The user uses the terminal to export the output information in spreadsheet software format. Using this function, the user can record and save the acquired data for further analysis and reporting. The exported data is the final output.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] In traditional stores, customers often had to wait for staff assistance to obtain detailed product information, a process that was often time-consuming. Furthermore, there was a lack of systems in place to ensure staff could provide accurate information quickly. This resulted in a decline in the quality of customer service and negatively impacted customer satisfaction. Therefore, there is a need for a system that allows customers to quickly obtain product information and streamlines inquiries to staff.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes an information storage means for integrally storing attributes related to products, a language analysis means for interpreting user questions in natural language, and a communication facilitator means that dynamically responds to user questions in the store and automatically contacts sales staff. This enables customers to obtain product information quickly and accurately in stores, and also allows for more efficient instructions to be given to staff.

[0091] An "information storage system" is a mechanism for centrally collecting and storing attributes related to a product.

[0092] "Language analysis means" refers to technology that interprets customer questions in natural language and analyzes their content.

[0093] A "notification method" is a system for informing relevant departments and personnel based on the analysis results.

[0094] A "response formation means" is a method for providing interpreted information to the customer in the form of audio or text.

[0095] A "conversion method" is a method for outputting information to numerical representation software according to employee instructions.

[0096] A "means of facilitating communication" refers to a system that quickly responds to customer questions within a store and automatically sends a message to the sales staff.

[0097] To realize this system, the server integrates various information processing technologies. First, to manage attribute information about products, the information storage method utilizes a database. This database comprehensively stores a wide range of attribute information about products and enables rapid access to it.

[0098] Users can use devices such as smartphones or smart glasses to ask questions about products in the store. These devices use the Google Cloud Speech-to-Text API to convert voice input into text data. This speech recognition process converts the user's voice data into text and sends it to the server.

[0099] The server analyzes the transmitted text data using a natural language processing engine to understand the intent of the user's question. The latest natural language processing technology is used for language analysis, accurately analyzing the question content in real time. Based on the analysis results, a communication-facilitating mechanism is activated to notify the appropriate department or person in charge within the store. This notification is intended to enable store staff to quickly provide the information the customer is looking for.

[0100] Furthermore, based on the analyzed information, the server uses the Google Cloud Text-to-Speech service to provide customers with a response in either voice or text. This response generation method enhances the convenience of customers receiving information. Responses are provided in real time, and the results are sent to the user's device.

[0101] For example, if a user asks, "Please tell me about the water resistance of the new smartwatch," the system will immediately retrieve relevant information from its database and send a notification to the staff. It will also respond to the customer with the analyzed information via voice.

[0102] An example of a prompt for a generative AI model is: "A user has asked, 'Please tell me about the water resistance of the new smartwatch.' Please generate appropriate information as a voice response." This prompt serves as a guideline for how the system should generate a response and address the customer's question.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] Users ask questions about products using voice input via their smartphone or smart glasses. This voice data becomes the initial input data for the system.

[0106] Step 2:

[0107] The device uses the Google Cloud Speech-to-Text API to convert the received audio data into text data. This speech recognition process converts the audio data into text. The converted text data is then sent to the server.

[0108] Step 3:

[0109] The server analyzes the received text data using a natural language processing engine. This language analysis is performed to understand the user's intent. Keywords are extracted and used as input to identify relevant information from the database based on the question.

[0110] Step 4:

[0111] The server searches the product information database based on keywords obtained through language analysis and retrieves the relevant product information. This database search outputs detailed information related to the user's inquiry.

[0112] Step 5:

[0113] After acquiring the information, the server generates a voice response using the Google Cloud Text-to-Speech service, or prepares the answer as text. At this stage, output data is generated in either voice or text format.

[0114] Step 6:

[0115] The server notifies the appropriate store staff based on the product information the user inquired about. In this notification process, a communication facilitation mechanism is activated based on the analysis results, and the staff are automatically contacted.

[0116] Step 7:

[0117] A voice or text response is sent to the user's device. The user can then listen to the product information or read it on their device screen. At this stage, the user's question is answered.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] This invention is a system aimed at efficiently managing product information and responding quickly and appropriately to user inquiries. This system analyzes user inquiries in natural language and combines this with sentiment analysis to grasp not only the intent of the inquiry but also the user's emotions.

[0120] The server centrally stores all product-related information in a database, and a system is in place to quickly retrieve data when a user makes a request. This makes it easy to obtain complex data such as product specifications and defect information.

[0121] The terminal provides an interface for users to make inquiries and accepts input via voice or text. In the case of voice input, the terminal has the ability to convert speech to text using speech recognition technology.

[0122] The server analyzes user queries using a natural language processing engine and retrieves relevant information from the database. Simultaneously, it uses an emotion engine to assess the user's emotions and understand the emotional state behind the query.

[0123] Based on the acquired emotional information, the server adjusts the tone and content of its response, providing information in a way that is considerate of the user's feelings. For example, if it determines that the user is dissatisfied, it will provide a more helpful and detailed explanation to prevent complaints from arising.

[0124] For example, if a user anxiously asks, "Please tell me about the problems with the new Model Z," the server will use an emotion engine to analyze the user's anxious feelings, retrieve more detailed data about the product's problems than usual, and provide that information through the device along with a reassuring message.

[0125] Furthermore, the server monitors the progress of inquiries and sends reminders to the relevant departments for unresolved issues. This ensures that inquiries are properly managed and supports quick resolution. This system further streamlines internal information management within the company, particularly improving the user experience.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] Users enter product information inquiries through the terminal's interface. Input is accepted via text or voice.

[0129] Step 2:

[0130] For users who have selected voice input, the device uses speech recognition technology to convert the voice data into text data.

[0131] Step 3:

[0132] The server analyzes the text data received from the terminal using a natural language processing engine to extract the intent of the inquiry and related keywords.

[0133] Step 4:

[0134] The server searches the database based on the analyzed keywords and retrieves relevant information. Simultaneously, it uses an emotion engine to analyze the user's inquiry from an emotional perspective.

[0135] Step 5:

[0136] The server uses the results of the emotion engine to adjust the content and tone of the responses to the information it has received. It includes gentle language and reassuring explanations as needed.

[0137] Step 6:

[0138] The server generates an emotionally sensitive response and returns the information to the terminal. This information is provided to the user in text or voice.

[0139] Step 7:

[0140] The server monitors the progress of inquiries and automatically sends reminders to the appropriate department if the inquiry remains unresolved.

[0141] Step 8:

[0142] Users can view the information provided via their terminal and, if necessary, have the option to export it to spreadsheet software. This feature is useful for later data analysis and reporting.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] In recent years, a vast amount of product-related information has been generated, and companies need to manage it efficiently and respond to customers quickly and appropriately. However, traditional systems are insufficient in analyzing inquiries and responding to user emotions, contributing to decreased customer satisfaction and complaints. Furthermore, when information is managed in a distributed manner, access to necessary data is often delayed. A new system is needed to address these issues.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes information storage means for centrally managing product-related information, information processing means for analyzing user inquiries in natural language and identifying their intent, and response generation means for adjusting and generating response content according to the identified emotions. This makes it possible to quickly and accurately understand the user's intent and emotions and automatically take appropriate action.

[0148] "Information storage means" refers to a system or device that centrally manages information related to a product and enables centralized data management.

[0149] An "information processing means" is a system or device that analyzes inquiries received from users and understands their content and intent in natural language.

[0150] "Sentiment analysis means" refers to a system or device for evaluating and identifying the emotions of a user included in an inquiry, and for taking appropriate action accordingly.

[0151] A "response generation means" is a system or device for generating and providing an appropriate response based on analyzed information and the user's emotions.

[0152] "Information provision means" refers to a system or device capable of transmitting analyzed information to users in various formats, such as audio or text.

[0153] A "progress management tool" is a system or device for tracking the progress of an inquiry and automatically notifying the relevant departments.

[0154] "Speech conversion means" refers to a system or device for analyzing speech data and converting it into text information.

[0155] In this invention, the user makes product inquiries using a terminal. The terminal accepts voice input and converts it to text using speech recognition technology. Specifically, it can convert speech to text using a "speech recognition API" or the like. Direct text input is also possible.

[0156] The server analyzes the text sent from the terminal using a natural language processing engine. This engine could include a "natural language processing API." Through this analysis, the server identifies the intent behind the user's inquiry and extracts relevant information.

[0157] Furthermore, the server uses a sentiment analysis engine to evaluate the user's emotional state included in the query. A typical example is a "sentiment analysis API." Based on the results of the sentiment analysis, the response is adjusted.

[0158] Product-related information is centrally managed in a database on a server. This database uses a "database management system" to centrally store product specifications, past defect information, and other data. This enables rapid data retrieval, allowing for the immediate provision of relevant information.

[0159] For example, if a user asks, "I want to know the technical features of the new Model X," the server performs natural language processing to retrieve relevant information. At the same time, it uses a sentiment analysis engine to assess the user's level of interest and provides deeper levels of detail as needed.

[0160] Furthermore, the server manages the progress of inquiries and automatically sends reminders to the relevant departments if an issue remains unresolved. This facilitates quick and appropriate problem resolution.

[0161] Example of a prompt:

[0162] "What natural language processing technologies and sentiment analysis tools should we implement to build a system that efficiently manages product information and can respond quickly to user inquiries?"

[0163] In this way, this system enables efficient and effective responses to user inquiries, thereby improving customer satisfaction.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] Users make product inquiries using devices such as smartphones. Input is provided as either voice or text. When the device receives voice input, it uses a "speech recognition API" to convert the voice into text data, and prepares this converted text data as output.

[0167] Step 2:

[0168] The terminal sends text data to the server. The server uses the received text data as input and performs analysis using a natural language processing engine. Using the "Natural Language Processing API," it determines the intent of the query and extracts relevant keywords. It then generates the analysis results as output.

[0169] Step 3:

[0170] The server uses the "Sentiment Analysis API" to evaluate the user's emotions based on the analysis results. This sentiment analysis applies the query content as input, identifying the user's emotional state (e.g., reassurance, anxiety, interest, etc.). The output provides the emotional state and its intensity.

[0171] Step 4:

[0172] The server accesses a database containing product-related information and searches for relevant information based on the analysis results. It uses a database management system to retrieve necessary product specifications, solutions, and other information. The input is the analysis results, and the output is the retrieved information.

[0173] Step 5:

[0174] The server generates an appropriate response message based on the acquired information and the results of sentiment analysis. Using a "generative AI model," it creates a response with a tone that takes the user's emotions into consideration. Search results and emotional state are used as input, and an adjusted response message is generated as output.

[0175] Step 6:

[0176] The server sends the generated response message to the terminal. The terminal provides the received message to the user in both audio and text formats. Specifically, it displays the text on the terminal screen and reads the message aloud using speech synthesis technology.

[0177] Step 7:

[0178] The server monitors the progress of inquiries and sends reminders to the relevant internal teams if they remain unresolved. This ensures timely responses to inquiries. Progress information is used as input, and reminders are issued as output.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0181] In modern information management systems, providing prompt and emotionally sensitive responses to diverse user inquiries is challenging. Especially when users are experiencing anxiety or dissatisfaction, appropriate responses are crucial, requiring technologies that accurately analyze emotions and provide corresponding information. Furthermore, maintaining rapid inquiry processing while simultaneously improving user satisfaction remains a challenge.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes a storage means for centrally storing product-related features, a means equipped with a natural language processing engine for analyzing user inquiries in natural language, and a processing means for adjusting the content and tone of the response and notifying the user based on the analysis results and the user's emotional state. This makes it possible to understand the emotions the user is feeling and to quickly provide an appropriate and considerate response.

[0184] A "memory device" is a device or technology for managing and centrally storing information, including product-related characteristics.

[0185] A "natural language processing engine" is a computational method that analyzes user inquiries in natural language and extracts relevant information.

[0186] "Emotional state" refers to the process of analyzing and judging the user's psychological and emotional state, as well as the results of that analysis.

[0187] A "notification processing mechanism" is a system for transmitting appropriate information to users based on the analyzed results.

[0188] "Response content and tone" refers to the concept of the specific information provided in a response to a user, as well as the way and attitude with which that information is presented.

[0189] A specific embodiment of this invention is a system that improves user interaction and aims to efficiently manage product-related information. The system mainly consists of a server and terminals.

[0190] The server uses a database management system (e.g., MySQL®) as a storage method to centrally store product-related features. For natural language processing, it uses Python-based libraries such as spaCy or NLTK to analyze user queries and extract relevant information. In addition, to determine emotional states, it utilizes the Hugging Face Transformers library for sentiment analysis. Based on the analysis results and the user's emotional state, the device determines and notifies the user of an appropriate response in terms of content and tone.

[0191] The device uses the Google Cloud Speech-to-Text API as its speech recognition processing method, converting user voice input into text data. This allows users to ask for product information either by voice or text. The information is then provided to the user, adjusted based on the analysis results from the server.

[0192] As a concrete example, if a user contacts the system because they are feeling anxious about a problem with an electronic payment, the server will use a sentiment analysis engine to detect their anxiety and provide reassurance by explaining the necessary security procedures in detail. In response to a prompt such as, "User inquiry: 'My payment problem hasn't been resolved. What should I do?' Sentiment: Anxiety, confusion. Please generate an appropriate response," the system will present the necessary solution in a friendly tone.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The user enters their inquiry into the device via voice or text. For voice input, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text. The entered voice is then sent to the server as text data.

[0196] Step 2:

[0197] The server passes the received text data to a natural language processing engine for analysis. This analysis extracts keywords and context necessary to accurately identify product-related information.

[0198] Step 3:

[0199] The server performs emotion analysis using the Hugging Face Transformers library based on the results of the natural language processing engine. Here, the user's emotional state is classified into categories such as "anxiety," "anger," and "joy," and used as data for adjusting responses.

[0200] Step 4:

[0201] Based on the analyzed information and sentiment analysis results, the server retrieves relevant product information from the database. Using MySQL, product specifications, troubleshooting procedures, or FAQ information are extracted as a result of the query.

[0202] Step 5:

[0203] The server combines acquired information with emotional information to generate a message that responds to the user in an appropriate tone and content. This message is formatted in natural language and adjusted to ensure that the content provided is sensitive to the user's emotions.

[0204] Step 6:

[0205] The terminal displays the response message sent from the server to the user or reads it aloud using speech synthesis technology. The user receives appropriately tailored information and can take specific actions to resolve their inquiry.

[0206] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0207] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0213] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0217] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] This invention is a system that streamlines the management of product information and the handling of user inquiries within a company. The server has a database that comprehensively stores detailed product information and is designed to be quickly accessible from any department within the organization. This prevents the fragmentation of complex information and enables rapid information retrieval.

[0223] The device provides an interface for users to make inquiries. Through this interface, users can input questions into the system via voice or text. When voice input is used, the device utilizes the Google Cloud Speech-to-Text API to convert the voice data into text data.

[0224] The server uses a natural language processing engine to analyze the user's question and extract relevant keywords. This process allows the server to accurately understand the user's intent and retrieve relevant information from the database.

[0225] Furthermore, the server has a function to automatically notify the appropriate department based on the content of the inquiry. The server quickly retrieves product information and defect information related to the extracted keywords and sends alerts to the department according to the analysis results.

[0226] The server then generates a text or voice response for the user based on the analysis results. The response is sent to the user in real time and, if necessary, provided as voice via the Google Cloud Text-to-Speech service.

[0227] Furthermore, users can export the acquired information to a spreadsheet program via their device. This feature allows users to easily record the information they obtain and use it for later analysis and reporting.

[0228] For example, if a user asks, "What is the release date of the new model Y?", the server performs natural language processing, recognizes "model Y" and "release date" as keywords, and retrieves the relevant information from the database. The retrieved information is then provided to the user in either text or audio format. In this way, the present invention achieves appropriate management of product information and highly automated user support.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] Users enter product inquiries through the interface provided by the device. Inquiries can be made via text or voice.

[0232] Step 2:

[0233] If the user selects voice input, the device receives the voice data and uses the Google Cloud Speech-to-Text API to convert this voice data into text format.

[0234] Step 3:

[0235] The server receives text data sent from the terminal and analyzes the question content using a natural language processing engine. This analysis extracts the user's intent and keywords.

[0236] Step 4:

[0237] The server searches the database based on the extracted keywords, retrieving relevant product information and departmental data. By extracting the relevant information, it prepares accurate information to respond to user inquiries.

[0238] Step 5:

[0239] The server automatically notifies the appropriate department based on the content of the inquiry. This process is carried out based on the analyzed results and pre-configured rules.

[0240] Step 6:

[0241] The server generates a response to provide the user with information retrieved from the database. The information is sent to the terminal in text format and, if necessary, also provided as audio via Google Cloud Text-to-Speech.

[0242] Step 7:

[0243] Users can view the information provided through their device and, if necessary, choose to export it to a spreadsheet program. This feature allows users to record the information they obtain and use it for further analysis and reporting.

[0244] (Example 1)

[0245] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0246] In order to efficiently manage product-related information within companies and respond to user inquiries, there is a need for prompt and accurate information provision. However, currently, information is often fragmented, and notifications of inquiries to the appropriate departments are frequently delayed, reducing the efficiency of information utilization. In addition, although the use of voice input is increasing, the system for accurately converting voice to text information is not yet fully developed. As a result, there is a challenge in providing real-time responses to the diverse inquiries of users.

[0247] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0248] In this invention, the server includes means for a data storage structure for integrally storing product-related information, means for a language analysis mechanism for processing user requests in natural language, and means for a notification mechanism for transmitting information to relevant departments based on the analyzed information. This enables centralized information management and rapid information retrieval, as well as real-time appropriate responses to diverse user inquiries.

[0249] "Product-related information" refers to all data and metadata about a product managed within a company, and includes a wide range of content such as product specifications, release dates, related documents, and customer feedback.

[0250] A "data storage structure" refers to a database or data storage system designed to centrally store information and allow for quick retrieval and searching as needed.

[0251] A "language analysis mechanism" is a processing engine that uses natural language processing technology to analyze the user's linguistic input and automatically extract their intent and important keywords.

[0252] A "notification mechanism" is a communication system that automatically transmits information to relevant departments and personnel based on analysis results, and is a means of promoting appropriate action.

[0253] A "voice conversion function" is a technical mechanism that converts voice input into text information using advanced technology, making it a format that can be recorded and analyzed.

[0254] The "response generation function" is a function that automatically generates answers to the user based on the analyzed information and presents them in text or audio format.

[0255] A "data extraction function" is a function that outputs managed information in a specific format according to the user's request, making it available for use in other software or systems.

[0256] Modes for carrying out the invention

[0257] This invention provides a system for effectively managing product-related information and responding to user inquiries within a company. The server features a data storage structure that integrates and stores product information, enabling centralized information management. This data storage structure is implemented using relational database software. The server also uses a natural language processing engine to analyze user requests and quickly retrieve necessary information based on these analyses. Based on the analysis results, the server includes a notification mechanism for automatically sending notifications to relevant departments.

[0258] The terminal provides a user input interface and accepts inquiries via voice or text. When voice input is received, the terminal uses the Google Cloud Speech-to-Text API as its speech-to-text conversion function to convert the audio into text data. This enables efficient handling of voice data. Furthermore, the server has a response generation function and provides the user with information generated based on the analysis results in either voice or text format. When providing responses via voice, the Google Cloud Text-to-Speech service is used.

[0259] Users can use the provided information to export the data to spreadsheet software for further analysis and reporting. Specifically, when a user enters a question via a terminal, such as "What is the release date of the new model Y?", the server generates a quick response and provides accurate information.

[0260] In this way, the system achieves centralized management of product information and a high degree of automation in user support.

[0261] An example of a prompt message is: "Please describe the process of adding details for the new model Y to the product information system and setting the release date."

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The user enters their inquiry via the device. Input can be in voice or text format. In the case of voice input, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text data. This results in output data in text format, which is then used for subsequent processing.

[0265] Step 2:

[0266] The terminal sends the converted text data to the server. The server uses a natural language processing engine to analyze the received text data. This analysis extracts keywords from the text data and processes the data to identify the user's intent. The analysis results output the necessary keywords and intent.

[0267] Step 3:

[0268] The server searches the product information database based on the analysis results. It then performs data search operations using the extracted keywords to retrieve relevant information. The information obtained through the search is output, and the server is ready to respond to the user's inquiry.

[0269] Step 4:

[0270] The server generates a response to provide to the user based on the information obtained from the search. This generation process combines the obtained information to create a response in natural language. The generated response is output as text data.

[0271] Step 5:

[0272] The server sends the generated response to the terminal. The terminal displays the received text data and, if necessary, plays it back as audio using the Google Cloud Text-to-Speech service. At this stage, the user can confirm the system's response and obtain appropriate output for their input.

[0273] Step 6:

[0274] The user uses the terminal to export the output information in spreadsheet software format. Using this function, the user can record and save the acquired data for further analysis and reporting. The exported data is the final output.

[0275] (Application Example 1)

[0276] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0277] In traditional stores, customers often had to wait for staff assistance to obtain detailed product information, a process that was often time-consuming. Furthermore, there was a lack of systems in place to ensure staff could provide accurate information quickly. This resulted in a decline in the quality of customer service and negatively impacted customer satisfaction. Therefore, there is a need for a system that allows customers to quickly obtain product information and streamlines inquiries to staff.

[0278] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0279] In this invention, the server includes an information storage means for integrally storing attributes related to products, a language analysis means for interpreting user questions in natural language, and a communication facilitator means that dynamically responds to user questions in the store and automatically contacts sales staff. This enables customers to obtain product information quickly and accurately in stores, and also allows for more efficient instructions to be given to staff.

[0280] The "information storage means" is a mechanism for centrally storing and managing attributes related to products.

[0281] The "language analysis means" is a technology for interpreting a customer's question in natural language and analyzing its content.

[0282] The "notification means" is a mechanism for notifying relevant departments and personnel based on the analysis results.

[0283] The "response generation means" is a method for providing the interpreted information to the customer as voice or text.

[0284] The "conversion means" is a method for outputting information to numerical expression software according to an employee's instructions.

[0285] The "communication facilitation means" is a mechanism for promptly responding to a customer's question in the store and automatically sending a notification to the salesperson.

[0286] To implement this system, the server integrates various information processing technologies. First, to manage attribute information related to products, the information storage means utilizes a database. This database centrally stores a wide range of attribute information related to products and enables rapid access.

[0287] When a user wants to ask a question about a product in the store, they can use a terminal such as a smartphone or smart glasses. The terminal uses the Google Cloud Speech-to-Text API to convert voice input into character data. This voice recognition process serves to convert the user's voice data into text and transmit it to the server.

[0288] The server analyzes the transmitted text data using a natural language processing engine to understand the intent of the user's question. The latest natural language processing technology is used for language analysis, accurately analyzing the question content in real time. Based on the analysis results, a communication-facilitating mechanism is activated to notify the appropriate department or person in charge within the store. This notification is intended to enable store staff to quickly provide the information the customer is looking for.

[0289] Furthermore, based on the analyzed information, the server uses the Google Cloud Text-to-Speech service to provide customers with a response in either voice or text. This response generation method enhances the convenience of customers receiving information. Responses are provided in real time, and the results are sent to the user's device.

[0290] For example, if a user asks, "Please tell me about the water resistance of the new smartwatch," the system will immediately retrieve relevant information from its database and send a notification to the staff. It will also respond to the customer with the analyzed information via voice.

[0291] An example of a prompt for a generative AI model is: "A user has asked, 'Please tell me about the water resistance of the new smartwatch.' Please generate appropriate information as a voice response." This prompt serves as a guideline for how the system should generate a response and address the customer's question.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] Users ask questions about products using voice input via their smartphone or smart glasses. This voice data becomes the initial input data for the system.

[0295] Step 2:

[0296] The device uses the Google Cloud Speech-to-Text API to convert the received audio data into text data. This speech recognition process converts the audio data into text. The converted text data is then sent to the server.

[0297] Step 3:

[0298] The server analyzes the received text data using a natural language processing engine. This language analysis is performed to understand the user's intent. Keywords are extracted and used as input to identify relevant information from the database based on the question.

[0299] Step 4:

[0300] The server searches the product information database based on keywords obtained through language analysis and retrieves the relevant product information. This database search outputs detailed information related to the user's inquiry.

[0301] Step 5:

[0302] After acquiring the information, the server generates a voice response using the Google Cloud Text-to-Speech service, or prepares the answer as text. At this stage, output data is generated in either voice or text format.

[0303] Step 6:

[0304] The server notifies the appropriate store staff based on the product information the user inquired about. In this notification process, a communication facilitation mechanism is activated based on the analysis results, and the staff are automatically contacted.

[0305] Step 7:

[0306] A response by voice or text is sent to the user's terminal. The user can actually listen to the answers about product information or read them on the device screen. At this stage, the response to the user's question is completed.

[0307] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0308] The present invention is a system aimed at efficiently managing product information and quickly and appropriately responding to inquiries from users. By analyzing the user's inquiries in natural language and combining sentiment analysis, this system can grasp not only the intention of the inquiry but also the user's emotion.

[0309] The server stores all information related to products in a database in a unified manner, and has a mechanism for quickly searching for data when there is an inquiry from a user. As a result, complex data such as product specifications and defect information can be easily obtained.

[0310] The terminal provides an interface when the user makes an inquiry and accepts input by voice or text. In the case of voice input, the terminal has a function of converting voice into text using voice recognition technology.

[0311] The server analyzes the inquiry received from the user with a natural language processing engine and retrieves relevant information from the database. At the same time, it uses an emotion engine to evaluate the user's emotion and understand the emotional state behind the inquiry.

[0312] Based on the obtained emotion information, the server adjusts the tone and content of the response and provides information in a form that takes into account the user's emotion. For example, if it is determined that the user is dissatisfied, a more friendly and detailed explanation is given to prevent claims in advance.

[0313] For example, if a user anxiously asks, "Please tell me about the problems with the new Model Z," the server will use an emotion engine to analyze the user's anxious feelings, retrieve more detailed data about the product's problems than usual, and provide that information through the device along with a reassuring message.

[0314] Furthermore, the server monitors the progress of inquiries and sends reminders to the relevant departments for unresolved issues. This ensures that inquiries are properly managed and supports quick resolution. This system further streamlines internal information management within the company, particularly improving the user experience.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] Users enter product information inquiries through the terminal's interface. Input is accepted via text or voice.

[0318] Step 2:

[0319] For users who have selected voice input, the device uses speech recognition technology to convert the voice data into text data.

[0320] Step 3:

[0321] The server analyzes the text data received from the terminal using a natural language processing engine to extract the intent of the inquiry and related keywords.

[0322] Step 4:

[0323] The server searches the database based on the analyzed keywords and retrieves relevant information. Simultaneously, it uses an emotion engine to analyze the user's inquiry from an emotional perspective.

[0324] Step 5:

[0325] The server uses the results of the emotion engine to adjust the content and tone of the responses to the information it has received. It includes gentle language and reassuring explanations as needed.

[0326] Step 6:

[0327] The server generates an emotionally sensitive response and returns the information to the terminal. This information is provided to the user in text or voice.

[0328] Step 7:

[0329] The server monitors the progress of inquiries and automatically sends reminders to the appropriate department if the inquiry remains unresolved.

[0330] Step 8:

[0331] Users can view the information provided via their terminal and, if necessary, have the option to export it to spreadsheet software. This feature is useful for later data analysis and reporting.

[0332] (Example 2)

[0333] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0334] In recent years, a vast amount of product-related information has been generated, and companies need to manage it efficiently and respond to customers quickly and appropriately. However, traditional systems are insufficient in analyzing inquiries and responding to user emotions, contributing to decreased customer satisfaction and complaints. Furthermore, when information is managed in a distributed manner, access to necessary data is often delayed. A new system is needed to address these issues.

[0335] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0336] In this invention, the server includes information storage means for centrally managing product-related information, information processing means for analyzing user inquiries in natural language and identifying their intent, and response generation means for adjusting and generating response content according to the identified emotions. This makes it possible to quickly and accurately understand the user's intent and emotions and automatically take appropriate action.

[0337] "Information storage means" refers to a system or device that centrally manages information related to a product and enables centralized data management.

[0338] An "information processing means" is a system or device that analyzes inquiries received from users and understands their content and intent in natural language.

[0339] "Sentiment analysis means" refers to a system or device for evaluating and identifying the emotions of a user included in an inquiry, and for taking appropriate action accordingly.

[0340] A "response generation means" is a system or device for generating and providing an appropriate response based on analyzed information and the user's emotions.

[0341] "Information provision means" refers to a system or device capable of transmitting analyzed information to users in various formats, such as audio or text.

[0342] A "progress management tool" is a system or device for tracking the progress of an inquiry and automatically notifying the relevant departments.

[0343] "Speech conversion means" refers to a system or device for analyzing speech data and converting it into text information.

[0344] In this invention, the user makes product inquiries using a terminal. The terminal accepts voice input and converts it to text using speech recognition technology. Specifically, it can convert speech to text using a "speech recognition API" or the like. Direct text input is also possible.

[0345] The server analyzes the text sent from the terminal using a natural language processing engine. This engine could include a "natural language processing API." Through this analysis, the server identifies the intent behind the user's inquiry and extracts relevant information.

[0346] Furthermore, the server uses a sentiment analysis engine to evaluate the user's emotional state included in the query. A typical example is a "sentiment analysis API." Based on the results of the sentiment analysis, the response is adjusted.

[0347] Product-related information is centrally managed in a database on a server. This database uses a "database management system" to centrally store product specifications, past defect information, and other data. This enables rapid data retrieval, allowing for the immediate provision of relevant information.

[0348] For example, if a user asks, "I want to know the technical features of the new Model X," the server performs natural language processing to retrieve relevant information. At the same time, it uses a sentiment analysis engine to assess the user's level of interest and provides deeper levels of detail as needed.

[0349] Furthermore, the server manages the progress of inquiries and automatically sends reminders to the relevant departments if an issue remains unresolved. This facilitates quick and appropriate problem resolution.

[0350] Example of a prompt:

[0351] "What natural language processing technologies and sentiment analysis tools should we implement to build a system that efficiently manages product information and can respond quickly to user inquiries?"

[0352] In this way, this system enables efficient and effective responses to user inquiries, thereby improving customer satisfaction.

[0353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0354] Step 1:

[0355] Users make product inquiries using devices such as smartphones. Input is provided as either voice or text. When the device receives voice input, it uses a "speech recognition API" to convert the voice into text data, and prepares this converted text data as output.

[0356] Step 2:

[0357] The terminal sends text data to the server. The server uses the received text data as input and performs analysis using a natural language processing engine. Using the "Natural Language Processing API," it determines the intent of the query and extracts relevant keywords. It then generates the analysis results as output.

[0358] Step 3:

[0359] The server uses the "Sentiment Analysis API" to evaluate the user's emotions based on the analysis results. This sentiment analysis applies the query content as input, identifying the user's emotional state (e.g., reassurance, anxiety, interest, etc.). The output provides the emotional state and its intensity.

[0360] Step 4:

[0361] The server accesses a database containing product-related information and searches for relevant information based on the analysis results. It uses a database management system to retrieve necessary product specifications, solutions, and other information. The input is the analysis results, and the output is the retrieved information.

[0362] Step 5:

[0363] The server generates an appropriate response message based on the acquired information and the results of sentiment analysis. Using a "generative AI model," it creates a response with a tone that takes the user's emotions into consideration. Search results and emotional state are used as input, and an adjusted response message is generated as output.

[0364] Step 6:

[0365] The server sends the generated response message to the terminal. The terminal provides the received message to the user in both audio and text formats. Specifically, it displays the text on the terminal screen and reads the message aloud using speech synthesis technology.

[0366] Step 7:

[0367] The server monitors the progress of inquiries and sends reminders to the relevant internal teams if they remain unresolved. This ensures timely responses to inquiries. Progress information is used as input, and reminders are issued as output.

[0368] (Application Example 2)

[0369] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0370] In modern information management systems, providing prompt and emotionally sensitive responses to diverse user inquiries is challenging. Especially when users are experiencing anxiety or dissatisfaction, appropriate responses are crucial, requiring technologies that accurately analyze emotions and provide corresponding information. Furthermore, maintaining rapid inquiry processing while simultaneously improving user satisfaction remains a challenge.

[0371] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0372] In this invention, the server includes a storage means for centrally storing product-related features, a means equipped with a natural language processing engine for analyzing user inquiries in natural language, and a processing means for adjusting the content and tone of the response and notifying the user based on the analysis results and the user's emotional state. This makes it possible to understand the emotions the user is feeling and to quickly provide an appropriate and considerate response.

[0373] A "memory device" is a device or technology for managing and centrally storing information, including product-related characteristics.

[0374] A "natural language processing engine" is a computational method that analyzes user inquiries in natural language and extracts relevant information.

[0375] "Emotional state" refers to the process of analyzing and judging the user's psychological and emotional state, as well as the results of that analysis.

[0376] A "notification processing mechanism" is a system for transmitting appropriate information to users based on the analyzed results.

[0377] "Response content and tone" refers to the concept of the specific information provided in a response to a user, as well as the way and attitude with which that information is presented.

[0378] A specific embodiment of this invention is a system that improves user interaction and aims to efficiently manage product-related information. The system mainly consists of a server and terminals.

[0379] The server uses a database management system (e.g., MySQL) as a storage method to centrally store product-related features. For natural language processing, it uses Python-based libraries such as spaCy or NLTK to analyze user queries and extract relevant information. In addition, to determine emotional states, it utilizes the Hugging Face Transformers library for sentiment analysis. Based on the analysis results and the user's emotional state, this device determines and notifies the user of an appropriate response in terms of content and tone.

[0380] The device uses the Google Cloud Speech-to-Text API as its speech recognition processing method, converting user voice input into text data. This allows users to ask for product information either by voice or text. The information is then provided to the user, adjusted based on the analysis results from the server.

[0381] As a concrete example, if a user contacts the system because they are feeling anxious about a problem with an electronic payment, the server will use a sentiment analysis engine to detect their anxiety and provide reassurance by explaining the necessary security procedures in detail. In response to a prompt such as, "User inquiry: 'My payment problem hasn't been resolved. What should I do?' Sentiment: Anxiety, confusion. Please generate an appropriate response," the system will present the necessary solution in a friendly tone.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The user enters their inquiry into the device via voice or text. For voice input, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text. The entered voice is then sent to the server as text data.

[0385] Step 2:

[0386] The server passes the received text data to a natural language processing engine for analysis. This analysis extracts keywords and context necessary to accurately identify product-related information.

[0387] Step 3:

[0388] The server performs emotion analysis using the Hugging Face Transformers library based on the results of the natural language processing engine. Here, the user's emotional state is classified into categories such as "anxiety," "anger," and "joy," and used as data for adjusting responses.

[0389] Step 4:

[0390] Based on the analyzed information and sentiment analysis results, the server retrieves relevant product information from the database. Using MySQL, product specifications, troubleshooting procedures, or FAQ information are extracted as a result of the query.

[0391] Step 5:

[0392] The server combines acquired information with emotional information to generate a message that responds to the user in an appropriate tone and content. This message is formatted in natural language and adjusted to ensure that the content provided is sensitive to the user's emotions.

[0393] Step 6:

[0394] The terminal displays the response message sent from the server to the user or reads it aloud using speech synthesis technology. The user receives appropriately tailored information and can take specific actions to resolve their inquiry.

[0395] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0396] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0397] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0401] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0402] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0403] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0404] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0405] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0406] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0407] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0408] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0409] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0410] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0411] This invention is a system that streamlines the management of product information and the handling of user inquiries within a company. The server has a database that comprehensively stores detailed product information and is designed to be quickly accessible from any department within the organization. This prevents the fragmentation of complex information and enables rapid information retrieval.

[0412] The device provides an interface for users to make inquiries. Through this interface, users can input questions into the system via voice or text. When voice input is used, the device utilizes the Google Cloud Speech-to-Text API to convert the voice data into text data.

[0413] The server uses a natural language processing engine to analyze the user's question and extract relevant keywords. This process allows the server to accurately understand the user's intent and retrieve relevant information from the database.

[0414] Furthermore, the server has a function to automatically notify the appropriate department based on the content of the inquiry. The server quickly retrieves product information and defect information related to the extracted keywords and sends alerts to the department according to the analysis results.

[0415] The server then generates a text or voice response for the user based on the analysis results. The response is sent to the user in real time and, if necessary, provided as voice via the Google Cloud Text-to-Speech service.

[0416] Furthermore, users can export the acquired information to a spreadsheet program via their device. This feature allows users to easily record the information they obtain and use it for later analysis and reporting.

[0417] For example, if a user asks, "What is the release date of the new model Y?", the server performs natural language processing, recognizes "model Y" and "release date" as keywords, and retrieves the relevant information from the database. The retrieved information is then provided to the user in either text or audio format. In this way, the present invention achieves appropriate management of product information and highly automated user support.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] Users enter product inquiries through the interface provided by the device. Inquiries can be made via text or voice.

[0421] Step 2:

[0422] If the user selects voice input, the device receives the voice data and uses the Google Cloud Speech-to-Text API to convert this voice data into text format.

[0423] Step 3:

[0424] The server receives text data sent from the terminal and analyzes the question content using a natural language processing engine. This analysis extracts the user's intent and keywords.

[0425] Step 4:

[0426] The server searches the database based on the extracted keywords, retrieving relevant product information and departmental data. By extracting the relevant information, it prepares accurate answers to user inquiries.

[0427] Step 5:

[0428] The server automatically notifies the appropriate department based on the content of the inquiry. This process is carried out based on the analyzed results and pre-configured rules.

[0429] Step 6:

[0430] The server generates a response to provide the user with information retrieved from the database. The information is sent to the terminal in text format and, if necessary, also provided as audio via Google Cloud Text-to-Speech.

[0431] Step 7:

[0432] Users can view the information provided through their device and, if necessary, choose to export it to a spreadsheet program. This feature allows users to record the information they obtain and use it for further analysis and reporting.

[0433] (Example 1)

[0434] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0435] In order to efficiently manage product-related information within companies and respond to user inquiries, there is a need for prompt and accurate information provision. However, currently, information is often fragmented, and notifications of inquiries to the appropriate departments are frequently delayed, reducing the efficiency of information utilization. In addition, although the use of voice input is increasing, the system for accurately converting voice to text information is not yet fully developed. As a result, there is a challenge in providing real-time responses to the diverse inquiries of users.

[0436] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0437] In this invention, the server includes means for a data storage structure for integrally storing product-related information, means for a language analysis mechanism for processing user requests in natural language, and means for a notification mechanism for transmitting information to relevant departments based on the analyzed information. This enables centralized information management and rapid information retrieval, as well as real-time appropriate responses to diverse user inquiries.

[0438] "Product-related information" refers to all data and metadata about a product managed within a company, and includes a wide range of content such as product specifications, release dates, related documents, and customer feedback.

[0439] A "data storage structure" refers to a database or data storage system designed to centrally store information and allow for quick retrieval and searching as needed.

[0440] A "language analysis mechanism" is a processing engine that uses natural language processing technology to analyze the user's linguistic input and automatically extract their intent and important keywords.

[0441] A "notification mechanism" is a communication system that automatically transmits information to relevant departments and personnel based on analysis results, and is a means of promoting appropriate action.

[0442] A "voice conversion function" is a technical mechanism that converts voice input into text information using advanced technology, making it a format that can be recorded and analyzed.

[0443] The "response generation function" is a function that automatically generates answers to the user based on the analyzed information and presents them in text or audio format.

[0444] A "data extraction function" is a function that outputs managed information in a specific format according to the user's request, making it available for use in other software or systems.

[0445] Modes for carrying out the invention

[0446] This invention provides a system for effectively managing product-related information and responding to user inquiries within a company. The server features a data storage structure that integrates and stores product information, enabling centralized information management. This data storage structure is implemented using relational database software. The server also uses a natural language processing engine to analyze user requests and quickly retrieve necessary information based on these analyses. Based on the analysis results, the server includes a notification mechanism for automatically sending notifications to relevant departments.

[0447] The terminal provides a user input interface and accepts inquiries via voice or text. When voice input is received, the terminal uses the Google Cloud Speech-to-Text API as its speech-to-text conversion function to convert the audio into text data. This enables efficient handling of voice data. Furthermore, the server has a response generation function and provides the user with information generated based on the analysis results in either voice or text format. When providing responses via voice, the Google Cloud Text-to-Speech service is used.

[0448] Users can use the provided information to export the data to spreadsheet software for further analysis and reporting. Specifically, when a user enters a question via a terminal, such as "What is the release date of the new model Y?", the server generates a quick response and provides accurate information.

[0449] In this way, the system achieves centralized management of product information and a high degree of automation in user support.

[0450] An example of a prompt message is: "Please describe the process of adding details for the new model Y to the product information system and setting the release date."

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] The user enters their inquiry via the device. Input can be in voice or text format. In the case of voice input, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text data. This results in output data in text format, which is then used for subsequent processing.

[0454] Step 2:

[0455] The terminal sends the converted text data to the server. The server uses a natural language processing engine to analyze the received text data. This analysis extracts keywords from the text data and processes the data to identify the user's intent. The analysis results output the necessary keywords and intent.

[0456] Step 3:

[0457] The server searches the product information database based on the analysis results. It then performs data search operations using the extracted keywords to retrieve relevant information. The information obtained through the search is output, and the server is ready to respond to the user's inquiry.

[0458] Step 4:

[0459] The server generates a response to provide to the user based on the information obtained from the search. This generation process combines the obtained information to create a response in natural language. The generated response is output as text data.

[0460] Step 5:

[0461] The server sends the generated response to the terminal. The terminal displays the received text data and, if necessary, plays it back as audio using the Google Cloud Text-to-Speech service. At this stage, the user can confirm the system's response and obtain appropriate output for their input.

[0462] Step 6:

[0463] The user uses the terminal to export the output information in spreadsheet software format. Using this function, the user can record and save the acquired data for further analysis and reporting. The exported data is the final output.

[0464] (Application Example 1)

[0465] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0466] In traditional stores, customers often had to wait for staff assistance to obtain detailed product information, a process that was often time-consuming. Furthermore, there was a lack of systems in place to ensure staff could provide accurate information quickly. This resulted in a decline in the quality of customer service and negatively impacted customer satisfaction. Therefore, there is a need for a system that allows customers to quickly obtain product information and streamlines inquiries to staff.

[0467] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0468] In this invention, the server includes an information storage means for integrally storing attributes related to products, a language analysis means for interpreting user questions in natural language, and a communication facilitator means that dynamically responds to user questions in the store and automatically contacts sales staff. This enables customers to obtain product information quickly and accurately in stores, and also allows for more efficient instructions to be given to staff.

[0469] An "information storage system" is a mechanism for centrally collecting and storing attributes related to a product.

[0470] "Language analysis means" refers to technology that interprets customer questions in natural language and analyzes their content.

[0471] A "notification method" is a system for informing relevant departments and personnel based on the analysis results.

[0472] A "response formation means" is a method for providing interpreted information to the customer in the form of audio or text.

[0473] A "conversion method" is a method for outputting information to numerical representation software according to employee instructions.

[0474] A "means of facilitating communication" refers to a system that quickly responds to customer questions within a store and automatically sends a message to the sales staff.

[0475] To realize this system, the server integrates various information processing technologies. First, to manage attribute information about products, the information storage method utilizes a database. This database comprehensively stores a wide range of attribute information about products and enables rapid access to it.

[0476] Users can use devices such as smartphones or smart glasses to ask questions about products in the store. These devices use the Google Cloud Speech-to-Text API to convert voice input into text data. This speech recognition process converts the user's voice data into text and sends it to the server.

[0477] The server analyzes the transmitted text data using a natural language processing engine to understand the intent of the user's question. The latest natural language processing technology is used for language analysis, accurately analyzing the question content in real time. Based on the analysis results, a communication-facilitating mechanism is activated to notify the appropriate department or person in charge within the store. This notification is intended to enable store staff to quickly provide the information the customer is looking for.

[0478] Furthermore, based on the analyzed information, the server uses the Google Cloud Text-to-Speech service to provide customers with a response in either voice or text. This response generation method enhances the convenience of customers receiving information. Responses are provided in real time, and the results are sent to the user's device.

[0479] For example, if a user asks, "Please tell me about the water resistance of the new smartwatch," the system will immediately retrieve relevant information from its database and send a notification to the staff. It will also respond to the customer with the analyzed information via voice.

[0480] An example of a prompt for a generative AI model is: "A user has asked, 'Please tell me about the water resistance of the new smartwatch.' Please generate appropriate information as a voice response." This prompt serves as a guideline for how the system should generate a response and address the customer's question.

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] Users ask questions about products using voice input via their smartphone or smart glasses. This voice data becomes the initial input data for the system.

[0484] Step 2:

[0485] The device uses the Google Cloud Speech-to-Text API to convert the received audio data into text data. This speech recognition process converts the audio data into text. The converted text data is then sent to the server.

[0486] Step 3:

[0487] The server analyzes the received text data using a natural language processing engine. This language analysis is performed to understand the user's intent. Keywords are extracted and used as input to identify relevant information from the database based on the question.

[0488] Step 4:

[0489] The server searches the product information database based on keywords obtained through language analysis and retrieves the relevant product information. This database search outputs detailed information related to the user's inquiry.

[0490] Step 5:

[0491] After acquiring the information, the server generates a voice response using the Google Cloud Text-to-Speech service, or prepares the answer as text. At this stage, output data is generated in either voice or text format.

[0492] Step 6:

[0493] The server notifies the appropriate store staff based on the product information the user inquired about. In this notification process, a communication facilitation mechanism is activated based on the analysis results, and the staff are automatically contacted.

[0494] Step 7:

[0495] A voice or text response is sent to the user's device. The user can then listen to the product information or read it on their device screen. At this stage, the user's question is answered.

[0496] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0497] This invention is a system aimed at efficiently managing product information and responding quickly and appropriately to user inquiries. This system analyzes user inquiries in natural language and combines this with sentiment analysis to grasp not only the intent of the inquiry but also the user's emotions.

[0498] The server centrally stores all product-related information in a database, and a system is in place to quickly retrieve data when a user makes a request. This makes it easy to obtain complex data such as product specifications and defect information.

[0499] The terminal provides an interface for users to make inquiries and accepts input via voice or text. In the case of voice input, the terminal has the ability to convert speech to text using speech recognition technology.

[0500] The server analyzes user queries using a natural language processing engine and retrieves relevant information from the database. Simultaneously, it uses an emotion engine to assess the user's emotions and understand the emotional state behind the query.

[0501] Based on the acquired emotional information, the server adjusts the tone and content of its response, providing information in a way that is considerate of the user's feelings. For example, if it determines that the user is dissatisfied, it will provide a more helpful and detailed explanation to prevent complaints from arising.

[0502] For example, if a user anxiously asks, "Please tell me about the problems with the new Model Z," the server will use an emotion engine to analyze the user's anxious feelings, retrieve more detailed data about the product's problems than usual, and provide that information through the device along with a reassuring message.

[0503] Furthermore, the server monitors the progress of inquiries and sends reminders to the relevant departments for unresolved issues. This ensures that inquiries are properly managed and supports quick resolution. This system further streamlines internal information management within the company, particularly improving the user experience.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] Users enter product information inquiries through the terminal's interface. Input is accepted via text or voice.

[0507] Step 2:

[0508] For users who have selected voice input, the device uses speech recognition technology to convert the voice data into text data.

[0509] Step 3:

[0510] The server analyzes the text data received from the terminal using a natural language processing engine to extract the intent of the inquiry and related keywords.

[0511] Step 4:

[0512] The server searches the database based on the analyzed keywords and retrieves relevant information. Simultaneously, it uses an emotion engine to analyze the user's inquiry from an emotional perspective.

[0513] Step 5:

[0514] The server uses the results of the emotion engine to adjust the content and tone of the responses to the information it has received. It includes gentle language and reassuring explanations as needed.

[0515] Step 6:

[0516] The server generates an emotionally sensitive response and returns the information to the terminal. This information is provided to the user in text or voice.

[0517] Step 7:

[0518] The server monitors the progress of inquiries and automatically sends reminders to the appropriate department if the inquiry remains unresolved.

[0519] Step 8:

[0520] Users can view the information provided via their terminal and, if necessary, have the option to export it to spreadsheet software. This feature is useful for later data analysis and reporting.

[0521] (Example 2)

[0522] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] In recent years, a vast amount of product-related information has been generated, and companies need to manage it efficiently and respond to customers quickly and appropriately. However, traditional systems are insufficient in analyzing inquiries and responding to user emotions, contributing to decreased customer satisfaction and complaints. Furthermore, when information is managed in a distributed manner, access to necessary data is often delayed. A new system is needed to address these issues.

[0524] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0525] In this invention, the server includes information storage means for centrally managing product-related information, information processing means for analyzing user inquiries in natural language and identifying their intent, and response generation means for adjusting and generating response content according to the identified emotions. This makes it possible to quickly and accurately understand the user's intent and emotions and automatically take appropriate action.

[0526] "Information storage means" refers to a system or device that centrally manages information related to a product and enables centralized data management.

[0527] An "information processing means" is a system or device that analyzes inquiries received from users and understands their content and intent in natural language.

[0528] "Sentiment analysis means" refers to a system or device for evaluating and identifying the emotions of a user included in an inquiry, and for taking appropriate action accordingly.

[0529] A "response generation means" is a system or device for generating and providing an appropriate response based on analyzed information and the user's emotions.

[0530] "Information provision means" refers to a system or device capable of transmitting analyzed information to users in various formats, such as audio or text.

[0531] A "progress management tool" is a system or device for tracking the progress of an inquiry and automatically notifying the relevant departments.

[0532] "Speech conversion means" refers to a system or device for analyzing speech data and converting it into text information.

[0533] In this invention, the user makes product inquiries using a terminal. The terminal accepts voice input and converts it to text using speech recognition technology. Specifically, it can convert speech to text using a "speech recognition API" or the like. Direct text input is also possible.

[0534] The server analyzes the text sent from the terminal using a natural language processing engine. This engine could include a "natural language processing API." Through this analysis, the server identifies the intent behind the user's inquiry and extracts relevant information.

[0535] Furthermore, the server uses a sentiment analysis engine to evaluate the user's emotional state included in the query. A typical example is a "sentiment analysis API." Based on the results of the sentiment analysis, the response is adjusted.

[0536] Product-related information is centrally managed in a database on a server. This database uses a "database management system" to centrally store product specifications, past defect information, and other data. This enables rapid data retrieval, allowing for the immediate provision of relevant information.

[0537] For example, if a user asks, "I want to know the technical features of the new Model X," the server performs natural language processing to retrieve relevant information. At the same time, it uses a sentiment analysis engine to assess the user's level of interest and provides deeper levels of detail as needed.

[0538] Furthermore, the server manages the progress of inquiries and automatically sends reminders to the relevant departments if an issue remains unresolved. This facilitates quick and appropriate problem resolution.

[0539] Example of a prompt:

[0540] "What natural language processing technologies and sentiment analysis tools should we implement to build a system that efficiently manages product information and can respond quickly to user inquiries?"

[0541] In this way, this system enables efficient and effective responses to user inquiries, thereby improving customer satisfaction.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] Users make product inquiries using devices such as smartphones. Input is provided as either voice or text. When the device receives voice input, it uses a "speech recognition API" to convert the voice into text data, and prepares this converted text data as output.

[0545] Step 2:

[0546] The terminal sends text data to the server. The server uses the received text data as input and performs analysis using a natural language processing engine. Using the "Natural Language Processing API," it determines the intent of the query and extracts relevant keywords. It then generates the analysis results as output.

[0547] Step 3:

[0548] The server uses the "Sentiment Analysis API" to evaluate the user's emotions based on the analysis results. This sentiment analysis applies the query content as input, identifying the user's emotional state (e.g., reassurance, anxiety, interest, etc.). The output provides the emotional state and its intensity.

[0549] Step 4:

[0550] The server accesses a database containing product-related information and searches for relevant information based on the analysis results. It uses a database management system to retrieve necessary product specifications, solutions, and other information. The input is the analysis results, and the output is the retrieved information.

[0551] Step 5:

[0552] The server generates an appropriate response message based on the acquired information and the results of sentiment analysis. Using a "generative AI model," it creates a response with a tone that takes the user's emotions into consideration. Search results and emotional state are used as input, and an adjusted response message is generated as output.

[0553] Step 6:

[0554] The server sends the generated response message to the terminal. The terminal provides the received message to the user in both audio and text formats. Specifically, it displays the text on the terminal screen and reads the message aloud using speech synthesis technology.

[0555] Step 7:

[0556] The server monitors the progress of inquiries and sends reminders to the relevant internal teams if they remain unresolved. This ensures timely responses to inquiries. Progress information is used as input, and reminders are issued as output.

[0557] (Application Example 2)

[0558] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0559] In modern information management systems, providing prompt and emotionally sensitive responses to diverse user inquiries is challenging. Especially when users are experiencing anxiety or dissatisfaction, appropriate responses are crucial, requiring technologies that accurately analyze emotions and provide corresponding information. Furthermore, maintaining rapid inquiry processing while simultaneously improving user satisfaction remains a challenge.

[0560] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0561] In this invention, the server includes a storage means for centrally storing product-related features, a means equipped with a natural language processing engine for analyzing user inquiries in natural language, and a processing means for adjusting the content and tone of the response and notifying the user based on the analysis results and the user's emotional state. This makes it possible to understand the emotions the user is feeling and to quickly provide an appropriate and considerate response.

[0562] A "memory device" is a device or technology for managing and centrally storing information, including product-related characteristics.

[0563] A "natural language processing engine" is a computational method that analyzes user inquiries in natural language and extracts relevant information.

[0564] "Emotional state" refers to the process of analyzing and judging the user's psychological and emotional state, as well as the results of that analysis.

[0565] A "notification processing mechanism" is a system for transmitting appropriate information to users based on the analyzed results.

[0566] "Response content and tone" refers to the concept of the specific information provided in a response to a user, as well as the way and attitude with which that information is presented.

[0567] A specific embodiment of this invention is a system that improves user interaction and aims to efficiently manage product-related information. The system mainly consists of a server and terminals.

[0568] The server uses a database management system (e.g., MySQL) as a storage method to centrally store product-related features. For natural language processing, it uses Python-based libraries such as spaCy or NLTK to analyze user queries and extract relevant information. In addition, to determine emotional states, it utilizes the Hugging Face Transformers library for sentiment analysis. Based on the analysis results and the user's emotional state, this device determines and notifies the user of an appropriate response in terms of content and tone.

[0569] The device uses the Google Cloud Speech-to-Text API as its speech recognition processing method, converting user voice input into text data. This allows users to ask for product information either by voice or text. The information is then provided to the user, adjusted based on the analysis results from the server.

[0570] As a concrete example, if a user contacts the system because they are feeling anxious about a problem with an electronic payment, the server will use a sentiment analysis engine to detect their anxiety and provide reassurance by explaining the necessary security procedures in detail. In response to a prompt such as, "User inquiry: 'My payment problem hasn't been resolved. What should I do?' Sentiment: Anxiety, confusion. Please generate an appropriate response," the system will present the necessary solution in a friendly tone.

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The user enters their inquiry into the device via voice or text. For voice input, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text. The entered voice is then sent to the server as text data.

[0574] Step 2:

[0575] The server passes the received text data to a natural language processing engine for analysis. This analysis extracts keywords and context necessary to accurately identify product-related information.

[0576] Step 3:

[0577] The server performs emotion analysis using the Hugging Face Transformers library based on the results of the natural language processing engine. Here, the user's emotional state is classified into categories such as "anxiety," "anger," and "joy," and used as data for adjusting responses.

[0578] Step 4:

[0579] Based on the analyzed information and sentiment analysis results, the server retrieves relevant product information from the database. Using MySQL, product specifications, troubleshooting procedures, or FAQ information are extracted as a result of the query.

[0580] Step 5:

[0581] The server combines acquired information with emotional information to generate a message that responds to the user in an appropriate tone and content. This message is formatted in natural language and adjusted to ensure that the content provided is sensitive to the user's emotions.

[0582] Step 6:

[0583] The terminal displays the response message sent from the server to the user or reads it aloud using speech synthesis technology. The user receives appropriately tailored information and can take specific actions to resolve their inquiry.

[0584] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0585] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0586] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0590] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0591] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0592] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0593] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0594] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0595] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0596] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0597] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0598] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0599] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0600] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0601] This invention is a system that streamlines the management of product information and the handling of user inquiries within a company. The server has a database that comprehensively stores detailed product information and is designed to be quickly accessible from any department within the organization. This prevents the fragmentation of complex information and enables rapid information retrieval.

[0602] The device provides an interface for users to make inquiries. Through this interface, users can input questions into the system via voice or text. When voice input is used, the device utilizes the Google Cloud Speech-to-Text API to convert the voice data into text data.

[0603] The server uses a natural language processing engine to analyze the user's question and extract relevant keywords. This process allows the server to accurately understand the user's intent and retrieve relevant information from the database.

[0604] Furthermore, the server has a function to automatically notify the appropriate department based on the content of the inquiry. The server quickly retrieves product information and defect information related to the extracted keywords and sends alerts to the department according to the analysis results.

[0605] The server then generates a text or voice response for the user based on the analysis results. The response is sent to the user in real time and, if necessary, provided as voice via the Google Cloud Text-to-Speech service.

[0606] Furthermore, users can export the acquired information to a spreadsheet program via their device. This feature allows users to easily record the information they obtain and use it for later analysis and reporting.

[0607] For example, if a user asks, "What is the release date of the new model Y?", the server performs natural language processing, recognizes "model Y" and "release date" as keywords, and retrieves the relevant information from the database. The retrieved information is then provided to the user in either text or audio format. In this way, the present invention achieves appropriate management of product information and highly automated user support.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] Users enter product inquiries through the interface provided by the device. Inquiries can be made via text or voice.

[0611] Step 2:

[0612] If the user selects voice input, the device receives the voice data and uses the Google Cloud Speech-to-Text API to convert this voice data into text format.

[0613] Step 3:

[0614] The server receives text data sent from the terminal and analyzes the question content using a natural language processing engine. This analysis extracts the user's intent and keywords.

[0615] Step 4:

[0616] The server searches the database based on the extracted keywords, retrieving relevant product information and departmental data. By extracting the relevant information, it prepares accurate answers to user inquiries.

[0617] Step 5:

[0618] The server automatically notifies the appropriate department based on the content of the inquiry. This process is carried out based on the analyzed results and pre-configured rules.

[0619] Step 6:

[0620] The server generates a response to provide the user with information retrieved from the database. The information is sent to the terminal in text format and, if necessary, also provided as audio via Google Cloud Text-to-Speech.

[0621] Step 7:

[0622] Users can view the information provided through their device and, if necessary, choose to export it to a spreadsheet program. This feature allows users to record the information they obtain and use it for further analysis and reporting.

[0623] (Example 1)

[0624] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] In order to efficiently manage product-related information within companies and respond to user inquiries, there is a need for prompt and accurate information provision. However, currently, information is often fragmented, and notifications of inquiries to the appropriate departments are frequently delayed, reducing the efficiency of information utilization. In addition, although the use of voice input is increasing, the system for accurately converting voice to text information is not yet fully developed. As a result, there is a challenge in providing real-time responses to the diverse inquiries of users.

[0626] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0627] In this invention, the server includes means for a data storage structure for integrally storing product-related information, means for a language analysis mechanism for processing user requests in natural language, and means for a notification mechanism for transmitting information to relevant departments based on the analyzed information. This enables centralized information management and rapid information retrieval, as well as real-time appropriate responses to diverse user inquiries.

[0628] "Product-related information" refers to all data and metadata about a product managed within a company, and includes a wide range of content such as product specifications, release dates, related documents, and customer feedback.

[0629] A "data storage structure" refers to a database or data storage system designed to centrally store information and allow for quick retrieval and searching as needed.

[0630] A "language analysis mechanism" is a processing engine that uses natural language processing technology to analyze the user's linguistic input and automatically extract their intent and important keywords.

[0631] A "notification mechanism" is a communication system that automatically transmits information to relevant departments and personnel based on analysis results, and is a means of promoting appropriate action.

[0632] A "voice conversion function" is a technical mechanism that converts voice input into text information using advanced technology, making it a format that can be recorded and analyzed.

[0633] The "response generation function" is a function that automatically generates answers to the user based on the analyzed information and presents them in text or audio format.

[0634] A "data extraction function" is a function that outputs managed information in a specific format according to the user's request, making it available for use in other software or systems.

[0635] Modes for carrying out the invention

[0636] This invention provides a system for effectively managing product-related information and responding to user inquiries within a company. The server features a data storage structure that integrates and stores product information, enabling centralized information management. This data storage structure is implemented using relational database software. The server also uses a natural language processing engine to analyze user requests and quickly retrieve necessary information based on these analyses. Based on the analysis results, the server includes a notification mechanism for automatically sending notifications to relevant departments.

[0637] The terminal provides a user input interface and accepts inquiries via voice or text. When voice input is received, the terminal uses the Google Cloud Speech-to-Text API as its speech-to-text conversion function to convert the audio into text data. This enables efficient handling of voice data. Furthermore, the server has a response generation function and provides the user with information generated based on the analysis results in either voice or text format. When providing responses via voice, the Google Cloud Text-to-Speech service is used.

[0638] Users can use the provided information to export the data to spreadsheet software for further analysis and reporting. Specifically, when a user enters a question via a terminal, such as "What is the release date of the new model Y?", the server generates a quick response and provides accurate information.

[0639] In this way, the system achieves centralized management of product information and a high degree of automation in user support.

[0640] An example of a prompt message is: "Please describe the process of adding details for the new model Y to the product information system and setting the release date."

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] The user enters their inquiry via the device. Input can be in voice or text format. In the case of voice input, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text data. This results in output data in text format, which is then used for subsequent processing.

[0644] Step 2:

[0645] The terminal sends the converted text data to the server. The server uses a natural language processing engine to analyze the received text data. This analysis extracts keywords from the text data and processes the data to identify the user's intent. The analysis results output the necessary keywords and intent.

[0646] Step 3:

[0647] The server searches the product information database based on the analysis results. It then performs data search operations using the extracted keywords to retrieve relevant information. The information obtained through the search is output, and the server is ready to respond to the user's inquiry.

[0648] Step 4:

[0649] The server generates a response to provide to the user based on the information obtained from the search. This generation process combines the obtained information to create a response in natural language. The generated response is output as text data.

[0650] Step 5:

[0651] The server sends the generated response to the terminal. The terminal displays the received text data and, if necessary, plays it back as audio using the Google Cloud Text-to-Speech service. At this stage, the user can confirm the system's response and obtain appropriate output for their input.

[0652] Step 6:

[0653] The user uses the terminal to export the output information in spreadsheet software format. Using this function, the user can record and save the acquired data for further analysis and reporting. The exported data is the final output.

[0654] (Application Example 1)

[0655] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] In traditional stores, customers often had to wait for staff assistance to obtain detailed product information, a process that was often time-consuming. Furthermore, there was a lack of systems in place to ensure staff could provide accurate information quickly. This resulted in a decline in the quality of customer service and negatively impacted customer satisfaction. Therefore, there is a need for a system that allows customers to quickly obtain product information and streamlines inquiries to staff.

[0657] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0658] In this invention, the server includes an information storage means for integrally storing attributes related to products, a language analysis means for interpreting user questions in natural language, and a communication facilitator means that dynamically responds to user questions in the store and automatically contacts sales staff. This enables customers to obtain product information quickly and accurately in stores, and also allows for more efficient instructions to be given to staff.

[0659] An "information storage system" is a mechanism for centrally collecting and storing attributes related to a product.

[0660] "Language analysis means" refers to technology that interprets customer questions in natural language and analyzes their content.

[0661] A "notification method" is a system for informing relevant departments and personnel based on the analysis results.

[0662] A "response formation means" is a method for providing interpreted information to the customer in the form of audio or text.

[0663] A "conversion method" is a method for outputting information to numerical representation software according to employee instructions.

[0664] A "means of facilitating communication" refers to a system that quickly responds to customer questions within a store and automatically sends a message to the sales staff.

[0665] To realize this system, the server integrates various information processing technologies. First, to manage attribute information about products, the information storage method utilizes a database. This database comprehensively stores a wide range of attribute information about products and enables rapid access to it.

[0666] Users can use devices such as smartphones or smart glasses to ask questions about products in the store. These devices use the Google Cloud Speech-to-Text API to convert voice input into text data. This speech recognition process converts the user's voice data into text and sends it to the server.

[0667] The server analyzes the transmitted text data using a natural language processing engine to understand the intent of the user's question. The latest natural language processing technology is used for language analysis, accurately analyzing the question content in real time. Based on the analysis results, a communication-facilitating mechanism is activated to notify the appropriate department or person in charge within the store. This notification is intended to enable store staff to quickly provide the information the customer is looking for.

[0668] Furthermore, based on the analyzed information, the server uses the Google Cloud Text-to-Speech service to provide customers with a response in either voice or text. This response generation method enhances the convenience of customers receiving information. Responses are provided in real time, and the results are sent to the user's device.

[0669] For example, if a user asks, "Please tell me about the water resistance of the new smartwatch," the system will immediately retrieve relevant information from its database and send a notification to the staff. It will also respond to the customer with the analyzed information via voice.

[0670] An example of a prompt for a generative AI model is: "A user has asked, 'Please tell me about the water resistance of the new smartwatch.' Please generate appropriate information as a voice response." This prompt serves as a guideline for how the system should generate a response and address the customer's question.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] Users ask questions about products using voice input via their smartphone or smart glasses. This voice data becomes the initial input data for the system.

[0674] Step 2:

[0675] The device uses the Google Cloud Speech-to-Text API to convert the received audio data into text data. This speech recognition process converts the audio data into text. The converted text data is then sent to the server.

[0676] Step 3:

[0677] The server analyzes the received text data using a natural language processing engine. This language analysis is performed to understand the user's intent. Keywords are extracted and used as input to identify relevant information from the database based on the question.

[0678] Step 4:

[0679] The server searches the product information database based on keywords obtained through language analysis and retrieves the relevant product information. This database search outputs detailed information related to the user's inquiry.

[0680] Step 5:

[0681] After acquiring the information, the server generates a voice response using the Google Cloud Text-to-Speech service, or prepares the answer as text. At this stage, output data is generated in either voice or text format.

[0682] Step 6:

[0683] The server notifies the appropriate store staff based on the product information the user inquired about. In this notification process, a communication facilitation mechanism is activated based on the analysis results, and the staff are automatically contacted.

[0684] Step 7:

[0685] A voice or text response is sent to the user's device. The user can then listen to the product information or read it on their device screen. At this stage, the user's question is answered.

[0686] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0687] This invention is a system aimed at efficiently managing product information and responding quickly and appropriately to user inquiries. This system analyzes user inquiries in natural language and combines this with sentiment analysis to grasp not only the intent of the inquiry but also the user's emotions.

[0688] The server centrally stores all product-related information in a database, and a system is in place to quickly retrieve data when a user makes a request. This makes it easy to obtain complex data such as product specifications and defect information.

[0689] The terminal provides an interface for users to make inquiries and accepts input via voice or text. In the case of voice input, the terminal has the ability to convert speech to text using speech recognition technology.

[0690] The server analyzes user queries using a natural language processing engine and retrieves relevant information from the database. Simultaneously, it uses an emotion engine to assess the user's emotions and understand the emotional state behind the query.

[0691] Based on the acquired emotional information, the server adjusts the tone and content of its response, providing information in a way that is considerate of the user's feelings. For example, if it determines that the user is dissatisfied, it will provide a more helpful and detailed explanation to prevent complaints from arising.

[0692] For example, if a user anxiously asks, "Please tell me about the problems with the new Model Z," the server will use an emotion engine to analyze the user's anxious feelings, retrieve more detailed data about the product's problems than usual, and provide that information through the device along with a reassuring message.

[0693] Furthermore, the server monitors the progress of inquiries and sends reminders to the relevant departments for unresolved issues. This ensures that inquiries are properly managed and supports quick resolution. This system further streamlines internal information management within the company, particularly improving the user experience.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] Users enter product information inquiries through the terminal's interface. Input is accepted via text or voice.

[0697] Step 2:

[0698] For users who have selected voice input, the device uses speech recognition technology to convert the voice data into text data.

[0699] Step 3:

[0700] The server analyzes the text data received from the terminal using a natural language processing engine to extract the intent of the inquiry and related keywords.

[0701] Step 4:

[0702] The server searches the database based on the analyzed keywords and retrieves relevant information. Simultaneously, it uses an emotion engine to analyze the user's inquiry from an emotional perspective.

[0703] Step 5:

[0704] The server uses the results of the emotion engine to adjust the content and tone of the responses to the information it has received. It includes gentle language and reassuring explanations as needed.

[0705] Step 6:

[0706] The server generates an emotionally sensitive response and returns the information to the terminal. This information is provided to the user in text or voice.

[0707] Step 7:

[0708] The server monitors the progress of inquiries and automatically sends reminders to the appropriate department if the inquiry remains unresolved.

[0709] Step 8:

[0710] Users can view the information provided via their terminal and, if necessary, have the option to export it to spreadsheet software. This feature is useful for later data analysis and reporting.

[0711] (Example 2)

[0712] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0713] In recent years, a vast amount of product-related information has been generated, and companies need to manage it efficiently and respond to customers quickly and appropriately. However, traditional systems are insufficient in analyzing inquiries and responding to user emotions, contributing to decreased customer satisfaction and complaints. Furthermore, when information is managed in a distributed manner, access to necessary data is often delayed. A new system is needed to address these issues.

[0714] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0715] In this invention, the server includes information storage means for centrally managing product-related information, information processing means for analyzing user inquiries in natural language and identifying their intent, and response generation means for adjusting and generating response content according to the identified emotions. This makes it possible to quickly and accurately understand the user's intent and emotions and automatically take appropriate action.

[0716] "Information storage means" refers to a system or device that centrally manages information related to a product and enables centralized data management.

[0717] An "information processing means" is a system or device that analyzes inquiries received from users and understands their content and intent in natural language.

[0718] "Sentiment analysis means" refers to a system or device for evaluating and identifying the emotions of a user included in an inquiry, and for taking appropriate action accordingly.

[0719] A "response generation means" is a system or device for generating and providing an appropriate response based on analyzed information and the user's emotions.

[0720] "Information provision means" refers to a system or device capable of transmitting analyzed information to users in various formats, such as audio or text.

[0721] A "progress management tool" is a system or device for tracking the progress of an inquiry and automatically notifying the relevant departments.

[0722] "Speech conversion means" refers to a system or device for analyzing speech data and converting it into text information.

[0723] In this invention, the user makes product inquiries using a terminal. The terminal accepts voice input and converts it to text using speech recognition technology. Specifically, it can convert speech to text using a "speech recognition API" or the like. Direct text input is also possible.

[0724] The server analyzes the text sent from the terminal using a natural language processing engine. This engine could include a "natural language processing API." Through this analysis, the server identifies the intent behind the user's inquiry and extracts relevant information.

[0725] Furthermore, the server uses a sentiment analysis engine to evaluate the user's emotional state included in the query. A typical example is a "sentiment analysis API." Based on the results of the sentiment analysis, the response is adjusted.

[0726] Product-related information is centrally managed in a database on a server. This database uses a "database management system" to centrally store product specifications, past defect information, and other data. This enables rapid data retrieval, allowing for the immediate provision of relevant information.

[0727] For example, if a user asks, "I want to know the technical features of the new Model X," the server performs natural language processing to retrieve relevant information. At the same time, it uses a sentiment analysis engine to assess the user's level of interest and provides deeper levels of detail as needed.

[0728] Furthermore, the server manages the progress of inquiries and automatically sends reminders to the relevant departments if an issue remains unresolved. This facilitates quick and appropriate problem resolution.

[0729] Example of a prompt:

[0730] "What natural language processing technologies and sentiment analysis tools should we implement to build a system that efficiently manages product information and can respond quickly to user inquiries?"

[0731] In this way, this system enables efficient and effective responses to user inquiries, thereby improving customer satisfaction.

[0732] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0733] Step 1:

[0734] Users make product inquiries using devices such as smartphones. Input is provided as either voice or text. When the device receives voice input, it uses a "speech recognition API" to convert the voice into text data, and prepares this converted text data as output.

[0735] Step 2:

[0736] The terminal sends text data to the server. The server uses the received text data as input and performs analysis using a natural language processing engine. Using the "Natural Language Processing API," it determines the intent of the query and extracts relevant keywords. It then generates the analysis results as output.

[0737] Step 3:

[0738] The server uses the "Sentiment Analysis API" to evaluate the user's emotions based on the analysis results. This sentiment analysis applies the query content as input, identifying the user's emotional state (e.g., reassurance, anxiety, interest, etc.). The output provides the emotional state and its intensity.

[0739] Step 4:

[0740] The server accesses a database containing product-related information and searches for relevant information based on the analysis results. It uses a database management system to retrieve necessary product specifications, solutions, and other information. The input is the analysis results, and the output is the retrieved information.

[0741] Step 5:

[0742] The server generates an appropriate response message based on the acquired information and the results of sentiment analysis. Using a "generative AI model," it creates a response with a tone that takes the user's emotions into consideration. Search results and emotional state are used as input, and an adjusted response message is generated as output.

[0743] Step 6:

[0744] The server sends the generated response message to the terminal. The terminal provides the received message to the user in both audio and text formats. Specifically, it displays the text on the terminal screen and reads the message aloud using speech synthesis technology.

[0745] Step 7:

[0746] The server monitors the progress of inquiries and sends reminders to the relevant internal teams if they remain unresolved. This ensures timely responses to inquiries. Progress information is used as input, and reminders are issued as output.

[0747] (Application Example 2)

[0748] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0749] In modern information management systems, providing prompt and emotionally sensitive responses to diverse user inquiries is challenging. Especially when users are experiencing anxiety or dissatisfaction, appropriate responses are crucial, requiring technologies that accurately analyze emotions and provide corresponding information. Furthermore, maintaining rapid inquiry processing while simultaneously improving user satisfaction remains a challenge.

[0750] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0751] In this invention, the server includes a storage means for centrally storing product-related features, a means equipped with a natural language processing engine for analyzing user inquiries in natural language, and a processing means for adjusting the content and tone of the response and notifying the user based on the analysis results and the user's emotional state. This makes it possible to understand the emotions the user is feeling and to quickly provide an appropriate and considerate response.

[0752] A "memory device" is a device or technology for managing and centrally storing information, including product-related characteristics.

[0753] A "natural language processing engine" is a computational method that analyzes user inquiries in natural language and extracts relevant information.

[0754] "Emotional state" refers to the process of analyzing and judging the user's psychological and emotional state, as well as the results of that analysis.

[0755] A "notification processing mechanism" is a system for transmitting appropriate information to users based on the analyzed results.

[0756] "Response content and tone" refers to the concept of the specific information provided in a response to a user, as well as the way and attitude with which that information is presented.

[0757] A specific embodiment of this invention is a system that improves user interaction and aims to efficiently manage product-related information. The system mainly consists of a server and terminals.

[0758] The server uses a database management system (e.g., MySQL) as a storage method to centrally store product-related features. For natural language processing, it uses Python-based libraries such as spaCy or NLTK to analyze user queries and extract relevant information. In addition, to determine emotional states, it utilizes the Hugging Face Transformers library for sentiment analysis. Based on the analysis results and the user's emotional state, this device determines and notifies the user of an appropriate response in terms of content and tone.

[0759] The device uses the Google Cloud Speech-to-Text API as its speech recognition processing method, converting user voice input into text data. This allows users to ask for product information either by voice or text. The information is then provided to the user, adjusted based on the analysis results from the server.

[0760] As a concrete example, if a user contacts the system because they are feeling anxious about a problem with an electronic payment, the server will use a sentiment analysis engine to detect their anxiety and provide reassurance by explaining the necessary security procedures in detail. In response to a prompt such as, "User inquiry: 'My payment problem hasn't been resolved. What should I do?' Sentiment: Anxiety, confusion. Please generate an appropriate response," the system will present the necessary solution in a friendly tone.

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The user enters their inquiry into the device via voice or text. For voice input, the device uses the Google Cloud Speech-to-Text API to convert the voice data into text. The entered voice is then sent to the server as text data.

[0764] Step 2:

[0765] The server passes the received text data to a natural language processing engine for analysis. This analysis extracts keywords and context necessary to accurately identify product-related information.

[0766] Step 3:

[0767] The server performs emotion analysis using the Hugging Face Transformers library based on the results of the natural language processing engine. Here, the user's emotional state is classified into categories such as "anxiety," "anger," and "joy," and used as data for adjusting responses.

[0768] Step 4:

[0769] Based on the analyzed information and sentiment analysis results, the server retrieves relevant product information from the database. Using MySQL, product specifications, troubleshooting procedures, or FAQ information are extracted as a result of the query.

[0770] Step 5:

[0771] The server combines acquired information with emotional information to generate a message that responds to the user in an appropriate tone and content. This message is formatted in natural language and adjusted to ensure that the content provided is sensitive to the user's emotions.

[0772] Step 6:

[0773] The terminal displays the response message sent from the server to the user or reads it aloud using speech synthesis technology. The user receives appropriately tailored information and can take specific actions to resolve their inquiry.

[0774] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0775] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0776] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0777] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0778] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0779] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0780] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0781] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0782] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0783] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0784] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0785] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0786] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0787] 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.

[0788] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0789] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0790] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0791] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0792] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0793] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0794] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] A means of providing a database for centrally storing data including product-related characteristics,

[0798] A means equipped with a natural language processing engine that analyzes user inquiries in natural language,

[0799] A notification processing means that notifies the appropriate department based on the analysis results,

[0800] A response generation means that provides the analyzed information to the user in voice or text,

[0801] An output method that allows information to be exported to a spreadsheet program according to the user's request,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, comprising progress management means for monitoring the progress of an inquiry and automatically sending reminders.

[0805] (Claim 3)

[0806] The system according to claim 1, comprising speech recognition processing means for converting speech input into text data.

[0807] "Example 1"

[0808] (Claim 1)

[0809] A means for providing a data storage structure for integrally storing product-related information,

[0810] A means equipped with a language analysis mechanism for processing user requests in natural language,

[0811] A means comprising a notification mechanism that transmits information to relevant departments based on the analyzed information,

[0812] A means equipped with a response generation function that provides the user with the analysis results as audio or text information,

[0813] A means equipped with a data extraction function that allows information to be output in a computational processing software format according to the user's wishes,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, comprising a progress management mechanism that tracks the progress of inquiries and automatically sends reminders.

[0817] (Claim 3)

[0818] The system according to claim 1, comprising a voice conversion function that converts voice input into text information.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] A means for storing information related to product attributes in an integrated manner,

[0822] A language analysis tool that interprets user questions in natural language,

[0823] A notification mechanism to inform the appropriate department based on the analysis results,

[0824] A response-forming means that provides the interpreted information to the user in voice or text,

[0825] A conversion means that enables the output of information using numerical representation software according to the user's request,

[0826] A means of facilitating communication that dynamically responds to questions from customers in the store and automatically contacts sales staff,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, comprising progress management means for monitoring the progress of an inquiry and automatically sending communications.

[0830] (Claim 3)

[0831] The system according to claim 1, comprising speech recognition means for converting speech input into symbolic data.

[0832] "Example 2 of combining an emotion engine"

[0833] (Claim 1)

[0834] Information storage means for centrally managing product-related information,

[0835] Information processing means for analyzing user inquiries in natural language and identifying their intent,

[0836] A means of sentiment analysis for evaluating and determining the user's emotions based on the analysis results,

[0837] A response generation means that adjusts the content of the response according to the identified emotion and generates an appropriate response,

[0838] Information provision means that provide users with analyzed information in various communication formats,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, comprising progress management means for tracking the progress of an inquiry and automatically providing notifications.

[0842] (Claim 3)

[0843] The system according to claim 1, comprising a speech conversion means for converting speech data into text information.

[0844] "Application example 2 when combining with an emotional engine"

[0845] (Claim 1)

[0846] A storage means for centrally storing information including product-related features,

[0847] A means equipped with a natural language processing engine that analyzes user inquiries in natural language,

[0848] A processing means that adjusts the content and tone of the response and notifies the user based on the analysis results and the user's emotional state,

[0849] A response generation means that provides the analyzed information to the user in voice or text,

[0850] An output method that allows information to be exported to a spreadsheet program according to the user's request,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, comprising progress management means for monitoring the progress of an inquiry and automatically sending reminders.

[0854] (Claim 3)

[0855] The system according to claim 1, comprising speech recognition processing means for converting input information into character data. [Explanation of Symbols]

[0856] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for storing information related to product attributes in an integrated manner, A language analysis tool that interprets user questions in natural language, A notification mechanism to inform the appropriate department based on the analysis results, A response-forming means that provides the interpreted information to the user in voice or text, A conversion means that enables the output of information using numerical representation software according to the user's request, A means of facilitating communication that dynamically responds to questions from customers in the store and automatically contacts sales staff, A system that includes this.

2. The system according to claim 1, comprising progress management means for monitoring the progress of an inquiry and automatically sending communications.

3. The system according to claim 1, comprising speech recognition means for converting speech input into symbolic data.

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