system

Abstract

We provide the system. [Solution] A voice recognition means that recognizes the user's voice command and converts it into a digital signal, An analysis means that analyzes voice commands using a generative artificial intelligence model and recognizes requests, External information gathering means for obtaining the analyzed request information, A response generation means that generates a response in natural language from the acquired information, A speech synthesis means for outputting the generated response to the user as audio, A design generation method for generating and presenting customized designs using generative artificial intelligence, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional accessory devices lack functions that can be intuitively used in daily life by leveraging user - generated artificial intelligence, so improving user usability is an issue. Also, there is a problem that it is difficult to provide a customization function according to user requirements and apply it to the market as a product.

Means for Solving the Problems

[0005] This invention provides a system that recognizes user voice commands, analyzes them using a generative artificial intelligence model, and acquires external information. Furthermore, it has means to generate the acquired information as a natural language voice response and output it to the user, thereby realizing an intuitive interface. In addition, by generating customized designs using generative artificial intelligence and presenting them to the user, it is possible to provide designs that meet the individual needs of the user.

[0006] "Voice recognition means" refers to a function or device that detects the voice spoken by the user and converts it into a digital signal.

[0007] "Analysis means" refers to a function or device that uses a generative artificial intelligence model to analyze voice commands and understand the user's intent and request content.

[0008] "External information gathering means" refers to a function or device that accesses external resources to obtain necessary data based on the analyzed request information.

[0009] A "response generation means" is a function or device that generates a natural language response to present to the user based on acquired information.

[0010] "Speech synthesis means" refers to a technology or device for providing a user with a generated natural language response as speech.

[0011] "Design generation means" refers to a function or device that uses artificial intelligence to create and present customized designs in response to user requests. [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] 1]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.

Mode 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 according 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 relates to an accessory device that utilizes generative artificial intelligence. This device is designed to allow users to naturally utilize AI technology in everyday situations. The operation of the system will be specifically described below as an embodiment of the invention.

[0034] When a user uses the device, they first initiate interaction with voice. Voice recognition converts the user's voice commands into digital signals on the device. These signals are then transmitted to a server via the network. The server analyzes the voice data using a generative artificial intelligence model and interprets the user's request.

[0035] Based on the analysis results, the server uses external information gathering means to acquire the necessary data. For example, if a user asks for weather information, the server retrieves the latest weather data from an appropriate external API. This information is then processed into natural language using a response generation means and sent back to the terminal.

[0036] The terminal uses speech synthesis technology to output natural language responses received from the server as speech that is easy for the user to understand. This allows the user to intuitively obtain information.

[0037] Furthermore, if a user requests a customized design, the design generation system utilizes artificial intelligence to generate a design tailored to their individual needs and display it on the device. This feature allows users to not only receive information but also enjoy a more personalized experience.

[0038] For example, if a user issues a voice command such as "Tell me today's news," the voice recognition system transmits this information to the server. The server collects the news information and generates a natural language summary. The terminal then outputs this summary as voice and provides it to the user. In addition, the news display layout and headline design can be customized, allowing for presentations tailored to the user's preferences.

[0039] Thus, the present invention provides an environment in which users can smoothly utilize live artificial intelligence through a series of processes from speech recognition to information gathering, response generation, and presentation of customized designs.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user issues voice commands to the accessory device. The user speaks to the device in natural language, specifying the information they want to know or the action they want to perform.

[0043] Step 2:

[0044] The device uses a voice sensor to convert the user's voice into a digital signal. This conversion process is performed in real time.

[0045] Step 3:

[0046] The terminal encodes the digital signal into the appropriate format and sends it to the server over the network. This makes the audio data available to the server in a remote location.

[0047] Step 4:

[0048] The server uses a generated artificial intelligence model to analyze the received voice data. The server understands the user's intent and identifies the specific request.

[0049] Step 5:

[0050] The server collects necessary information based on the analysis results. For example, it may refer to external APIs or databases to retrieve information requested by the user.

[0051] Step 6:

[0052] The server generates natural language responses based on the information it acquires. Generative artificial intelligence is used to format the responses in a way that is easy for the user to understand.

[0053] Step 7:

[0054] The server sends the generated natural language response to the terminal. The response data is encoded in a format suitable for speech output.

[0055] Step 8:

[0056] The terminal receives a response from the server, converts it using speech synthesis technology, and outputs it to the user as audio. This allows the user to hear the information they were looking for.

[0057] Step 9:

[0058] When a user requests a customized design, they input the design details on their device. Based on the user's request, the customized settings are then applied.

[0059] Step 10:

[0060] The server generates a design in response to a customization request and sends it to the device. The generated design is displayed in real time on the device's screen or within the app.

[0061] (Example 1)

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

[0063] The present invention aims to provide a system that allows users to acquire various types of information smoothly and intuitively, and to enable personalized experiences based on user instructions. In particular, there is a need to efficiently acquire information and present designs using voice instructions.

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

[0065] In this invention, the server includes recognition means for interpreting user voice instructions and converting them into information signals, analysis means for analyzing voice instructions and understanding commands using a generative intelligence model, and external information acquisition means for obtaining the analyzed command information. This enables users to efficiently acquire information through voice and to have a personalized design experience.

[0066] "Recognition means" refers to a device or program that has the function of converting voice instructions into digital information signals.

[0067] "Analysis means" refers to a device or program that uses a generative intelligence model to analyze voice commands and understand the user's intended instructions.

[0068] "External information acquisition means" refers to a device or program that acquires relevant information from an external source based on an analyzed command.

[0069] A "response formation means" is a device or program that creates a response in natural language based on acquired information.

[0070] "Speech synthesis means" refers to a device or program for outputting a synthesized natural language response as speech.

[0071] A "design generation means" is a device or program that uses generative intelligence to generate individual designs and present them to the user.

[0072] A "generative intelligence model" is an artificial intelligence model that uses learning algorithms to analyze voice commands and generate designs.

[0073] The system related to this invention analyzes the user's voice commands using a generative AI model and provides information through natural dialogue. Specifically, when a user uses an accessory device, the system initiates interaction through voice commands.

[0074] The terminal uses a voice capture device equipped with recognition capabilities to convert the user's voice into digital information signals. This digital data is then transmitted to a server using network communication technology.

[0075] The server uses analysis tools to analyze the voice data received through a generative AI model. The generative AI model incorporates an algorithm that understands voice instructions and analyzes them as commands. Based on the analysis results, it then uses external information acquisition tools to collect information from appropriate data sources.

[0076] The collected information is converted into a response generated in natural language by a response formation mechanism. The terminal then utilizes a speech synthesis mechanism to vocalize this response and convey it to the user. Through this process, the user can intuitively obtain useful information.

[0077] As a concrete example, when a user asks the device, "Tell me the weather for tomorrow," the device transmits this information to the server. The server retrieves the latest weather data through a weather information API and generates a response such as, "Tomorrow will be sunny, with a high of 22 degrees Celsius." The device then outputs this as audio.

[0078] Furthermore, using design generation tools, individual designs are created in response to user instructions and visually presented on the device. This customized design experience can also offer a variety of content to meet user needs.

[0079] Examples of prompt messages include "Retrieve the latest movie information and inform the user." Thus, this system is designed to provide users with advanced information and a customized experience.

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

[0081] Step 1:

[0082] The user provides voice instructions. The terminal records the user's voice using a voice interface and converts it into a digital signal through recognition means. In this process, the voice data is encoded into a digital format and made into a format that can be transmitted to the server. The input is the user's raw voice, and the output is a digital voice signal.

[0083] Step 2:

[0084] The server receives a digital audio signal transmitted from the terminal. Using an analysis tool, it analyzes the audio data with a generative AI model and interprets the user's intent. Specifically, it converts the audio signal into text and extracts the user's request (e.g., "weather information") from that text. In this step, the input is a digital audio signal, and the output is a textual representation of the audio and the analyzed user intent.

[0085] Step 3:

[0086] Based on the analyzed intent, the server uses external information acquisition methods to obtain the necessary information over the network. For example, if the user is requesting weather information, the server accesses a weather information API to collect the latest weather forecast data. In this step, the input is the user's intent, and the output is the data acquired from an external source.

[0087] Step 4:

[0088] The server generates a natural language response based on the information collected by the response formation mechanism. It utilizes a generative AI model to convert the data into a user-friendly format. For example, it might convert collected weather data into a concise response such as, "Tomorrow will be sunny, and the maximum temperature will be 20 degrees Celsius." In this step, the input is information obtained from an external source, and the output is a natural language response.

[0089] Step 5:

[0090] The server sends the generated natural language response to the terminal. The terminal uses speech synthesis to play the received text as speech. This allows the user to receive information through digital speech. In this step, the input is the natural language response sentence, and the output is the spoken response.

[0091] Step 6:

[0092] Furthermore, when a user makes a specific design request, the design generation system utilizes AI to generate a design tailored to the individual user's needs and displays that design on the device. This process allows users to receive visually customized content. In this step, the input is the user's design request, and the output is the display of the customized design.

[0093] (Application Example 1)

[0094] 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."

[0095] In today's commercial environment, there is a need to provide real-time information to visitors in physical stores and to enable more personalized purchasing support. However, because the optimal use of voice interfaces and AI technology for efficient information provision is not being fully utilized, there is a challenge in that visitors have difficulty obtaining product information and recommendations quickly within stores.

[0096] 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.

[0097] In this invention, the server includes voice recognition means for recognizing voice commands and converting them into digital signals, external information gathering means for acquiring analyzed request information, and guidance display means for providing commercial information to visitors. This enables visitors to instantly obtain information and purchase support within the store simply by speaking.

[0098] "Voice recognition means" refers to a device or software that has the function of converting voice commands uttered by a user into digital signals.

[0099] A "generative artificial intelligence model" is an artificial intelligence algorithm or program used to analyze a user's voice commands and recognize requests.

[0100] "Analysis means" refers to a process or device for analyzing voice commands and recognizing user requests.

[0101] "External information gathering means" refers to devices or means for obtaining necessary information from external sources based on analyzed request information.

[0102] A "response generation means" refers to a function or device for generating a response in natural language based on acquired information.

[0103] "Speech synthesis means" refers to software or devices that output generated natural language responses as speech that is easy for the user to understand.

[0104] A "design generation method" is a means of generating and presenting customized designs based on the individual needs of users, using generative artificial intelligence.

[0105] "Information display means" refers to display devices or functions that provide commercial information to visitors.

[0106] The system of this invention is designed to provide users with product information and recommended outfits in a commercial environment. The core of the system is a speech recognition and generative AI model, which enables seamless interaction with the user. The server first uses speech recognition means to convert voice commands from the user into digital signals. This is done, for example, using speech recognition software. The digital signals are sent to the server and analyzed using a generative artificial intelligence model. During the analysis process, the requested information is decoded, and necessary information is obtained from databases or external APIs, for example, through external information gathering means.

[0107] The server generates natural language responses using response generation means based on the acquired information, and outputs them as user-friendly speech using speech synthesis means. During this process, the generation AI model performs sophisticated natural language processing to produce the optimal answer to the user's request. For example, if a user says, "Tell me your recommended products," the system will respond through speech synthesis with, "Our current recommended product is XX." This system can also use design generation means to present customized designs according to the user's preferences. As a specific example, when suggesting a design for a particular product, it will provide the user with information such as, "This is the suggested design."

[0108] The implementation of such a system will create an environment where visitors can use in-store services more intuitively and efficiently. The natural interface provided by the generative AI model can offer store users a unique and customized experience. An example of a prompt used would be, "Please tell me about the latest recommended products in the store." This prompt serves as the foundation for the AI ​​to accurately understand the user's request and provide appropriate information.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user speaks into the terminal and inputs a voice command. This voice command is captured by the terminal via the microphone. The terminal converts this voice command into a digital signal using voice recognition. The converted digital signal is sent to the server.

[0112] Step 2:

[0113] The server uses a generative AI model to analyze the digital signals received from the speech recognition system. This analysis system understands the request content from the user's voice commands. The understood request is used as input data for the next processing step, in which the digital signals are converted into a request in natural language format.

[0114] Step 3:

[0115] Based on the analysis, the server collects necessary information using external information gathering means. In this case, the server connects to an external API, for example, to obtain information related to the user's request. The collected information is then used as the basis for generating the response. In this step, information corresponding to the request content is collected from databases and external information sources.

[0116] Step 4:

[0117] The server generates a natural language response using response generation tools based on the collected information. The generative AI model structures the data and transforms it into a user-friendly format during this process. The generated response becomes output data for speech synthesis. In this step, the information is converted into human-readable sentences.

[0118] Step 5:

[0119] The server converts the generated natural language response into speech output using speech synthesis. The terminal plays this audio, presenting the information to the user. Through this process, the user can receive the requested information in audio form. In this step, the natural language is converted back into an audio signal and presented to the user through the speaker.

[0120] 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.

[0121] This invention is an accessory device system that combines generative artificial intelligence and emotion recognition with user interaction. Its aim is to provide users with a more personalized experience in their daily lives. The following describes specific embodiments of the system.

[0122] The terminal receives the user's voice command and converts it into a digital signal using voice recognition. Next, an emotion recognition engine analyzes the user's emotions from this voice data and identifies basic emotional states such as joy and anger. This emotion data is sent to the server simultaneously with the analysis of the voice command.

[0123] The server utilizes a generative artificial intelligence model to analyze the transmitted voice commands. Simultaneously, it generates responses appropriate to the user's emotions based on emotion data from an emotion recognition engine. This ensures that responses are not merely informational but also considerate of the user's feelings. For example, if the user is tired, the server can choose a softer tone of voice in its response.

[0124] Based on the acquired data, a natural language response is generated by a response generation system and sent from the server to the terminal. The terminal uses a speech synthesis system to output this response to the user as natural-sounding speech. The user can receive information that is tailored to their own emotions and circumstances.

[0125] Furthermore, if a user requests a customized design, the design generation system utilizes artificial intelligence to create a design that meets the user's requirements. This design is then displayed on the device, with its colors and layout adjusted based on the user's emotions.

[0126] For example, when a user issues the voice command "Tell me the news," if the emotion recognition engine detects that the voice lacks composure, the device will prioritize providing relaxing news topics to reduce stress via the server. In this way, it is possible to provide information services that take the user's state of mind into consideration.

[0127] This invention provides a technology that takes user emotions into consideration to create a more personalized experience and make user-system interaction more natural and comfortable.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user issues voice commands to the accessory device. The user speaks in natural language about the information they want to know or the actions they want to perform.

[0131] Step 2:

[0132] The device uses a voice sensor to convert the user's voice into a digital signal. The voice data is processed within the device.

[0133] Step 3:

[0134] The device uses an emotion recognition engine to analyze the user's emotional state from voice data. For example, it can identify the user's level of tension or relaxation from their voice tone and tempo.

[0135] Step 4:

[0136] The device transmits voice digital signals and emotion data to the server. The transmission takes place in real time over the network.

[0137] Step 5:

[0138] The server uses an artificial intelligence model to analyze voice data and understand the user's intent. Based on the analysis results, it identifies the information and actions the user is seeking.

[0139] Step 6:

[0140] The server considers emotional data from the emotion recognition engine and generates responses with appropriate content and tone. Responses are prepared that are intended to provide feedback tailored to the user's emotions.

[0141] Step 7:

[0142] The server collects necessary information from external sources. For example, it retrieves the latest news and weather forecasts from relevant APIs.

[0143] Step 8:

[0144] The server converts the acquired information into a natural language response and makes adjustments to reflect emotions. The generated response is then sent to the terminal.

[0145] Step 9:

[0146] The terminal receives a response from the server, converts it into speech using speech synthesis technology, and provides it to the user. This allows the user to obtain information through listening.

[0147] Step 10:

[0148] If a user requests a customized design, their device sends the request to the server. The design is then adjusted based on the user's emotional state.

[0149] Step 11:

[0150] The server uses artificial intelligence to generate an emotion-based customized design and sends it to the device. The device then displays the generated design on its screen.

[0151] Through these steps, users can receive information and designs that match their emotions.

[0152] (Example 2)

[0153] 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 will be referred to as the "terminal."

[0154] Conventional information delivery systems often provide information unilaterally without considering the user's emotions, making it difficult to meet individual user needs. Furthermore, there is a need not only for systems that simply recognize voice commands, but also for systems that respond in accordance with the user's emotional state. Additionally, providing designs and content based on user requests in real time presents challenges that were not present in conventional technologies.

[0155] 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.

[0156] In this invention, the server includes a speech recognition means that recognizes the user's voice commands and converts them into numerical signals, a means that analyzes emotions from voice data using emotion recognition technology and extracts emotional information, and an analysis means that analyzes voice commands and recognizes requests using a generative artificial intelligence model. This makes it possible to provide personalized responses and designs based on the user's emotions.

[0157] "Voice commands" are instructions that a user issues to a device via voice, regarding operations or the retrieval of information.

[0158] A "numerical signal" is a signal obtained by converting audio or analog information into a digital format, and is in a format that can be processed by a computer.

[0159] "Voice recognition means" refers to technology that analyzes voice information, converts it into numerical signals, and identifies voice commands.

[0160] "Emotion recognition technology" is a technology that analyzes a user's emotions from their voice, facial expressions, etc., and evaluates specific emotional states.

[0161] "Emotional information" refers to data about the user's emotional state, and is an emotional indicator extracted after analysis.

[0162] A "generative artificial intelligence model" is an algorithm that learns from large amounts of data and generates natural-sounding responses and content that resemble those of a human.

[0163] "Analysis means" refers to technologies that analyze input data and commands to understand their content and recognize requests.

[0164] "Personalized responses" refer to information and feedback provided in a way that is adapted to the user's specific situation and emotions.

[0165] "Design generation methods" refer to technologies that effectively generate visual content and layouts based on user requests and emotions.

[0166] This invention is a system that combines generative artificial intelligence and emotion recognition in user interaction, enabling personalized information provision and design for the user. This system is configured using a terminal and a server.

[0167] The terminal receives voice commands from the user via a microphone and converts this voice into numerical signals using voice recognition technology. This voice recognition can utilize general-purpose voice recognition software or cloud-based voice recognition services. Next, the terminal extracts emotional information from the voice data using emotion recognition technology. Various cloud services can be used for this emotion analysis.

[0168] Voice commands and emotional information sent from the terminal to the server are analyzed on the server by a generative artificial intelligence model (for example, a large-scale natural language processing model). Based on this information, the server can generate an appropriate text response that corresponds to the user's emotions. In this generation process, prompts are used to customize the response according to specific conditions and contexts.

[0169] The generated response is returned from the server to the terminal and output to the user as natural-sounding speech through a speech synthesis system. A common speech synthesis engine can be used for speech synthesis, and the response is delivered to the user in real time.

[0170] Furthermore, if a user makes a design request, the server uses design generation tools and artificial intelligence to generate a personalized design based on the user's emotions and requests. This design is then adjusted in terms of color and layout and displayed on the terminal screen.

[0171] For example, if a user asks "What's the weather like today?", the device will interpret the user's voice as reassuring, and the server will generate a response such as "It's a sunny day today, have a pleasant day." Furthermore, in response to design requests, the system will generate background designs based on the user's needs and emotions, providing a visually pleasing user experience.

[0172] An example of a prompt message is, "Generate a reassuring response based on the user's request."

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The terminal receives voice commands from the user via a microphone. The input is the user's voice information. This voice is converted into a numerical signal using speech recognition means and identified as a voice command. The output is digitized voice command data. Specifically, speech recognition software analyzes the voiceprint and generates text data.

[0176] Step 2:

[0177] The device uses emotion recognition technology to extract user emotion information from received audio data. The input is digitized audio data. Data analysis is performed to identify and output emotions such as joy, anger, and sadness. Specifically, the analysis algorithm analyzes the pitch and tone of the voice and generates an emotional index.

[0178] Step 3:

[0179] The terminal transmits voice command data and emotion information to the server. The input consists of digitized voice commands and extracted emotion information. This is sent to the server as a data packet, which the server receives to prepare for the next processing step. The output is the data packet sent to the server.

[0180] Step 4:

[0181] The server uses a generative AI model to analyze the received voice command data and emotion information, and generates an appropriate response. The input consists of voice command and emotion information data. The generative AI model analyzes the command and generates a response text appropriate to its content and emotion. The output is the response text to the user. Specifically, the AI ​​model uses prompt sentences to construct the optimal response in text format.

[0182] Step 5:

[0183] The server sends the generated response text to the terminal. The input is the generated response text. This data is sent to the terminal and is ready for audio output to the user. The output is the response data sent to the terminal.

[0184] Step 6:

[0185] The terminal converts the received response text into natural-sounding speech using a speech synthesis system and outputs it to the user. The input is the response text sent from the server. It is converted into audio data using a speech synthesis engine and output through the speaker. The output is the audio response that the user can hear. Specifically, the speech synthesis software analyzes the text and generates an audio file.

[0186] Step 7:

[0187] When a user submits a design request, the terminal sends this request to the server. The input is the design request from the user. Using the design generation mechanism, the process prepares to build a design based on the user's feelings and requests. The output is the design request data sent to the server.

[0188] Step 8:

[0189] The server creates personalized designs using a generative AI model based on user requests. The input consists of a design request and sentiment information. The generative AI model analyzes this data and generates a design that meets the request. The output is a design file viewable on the terminal. Specifically, the AI ​​generates graphic data with optimized colors and layout.

[0190] Step 9:

[0191] The terminal displays the design sent from the server on its screen. The input is the design file from the server. By displaying this on the screen, the user can immediately review it. The output is a design display that the user can visually review. Specifically, the display controller renders the design data and displays it on the terminal's screen.

[0192] (Application Example 2)

[0193] 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 device 14 will be referred to as the "terminal."

[0194] Existing systems that unilaterally provide information without considering user emotions make it difficult to provide user-optimized responses and designs, resulting in the standardization of individual user experiences. Furthermore, in security settings, there is a need for more reliable communication that addresses the user's internal psychological state, but there is a lack of technology to achieve this.

[0195] 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.

[0196] In this invention, the server includes speech recognition means, emotion recognition means, and response adjustment means. This makes it possible to provide optimized responses in real time based on the user's emotional state, making the user experience more personal and effective.

[0197] "Voice recognition means" refers to a device or technology that converts a user's voice into a digital signal.

[0198] A "generative artificial intelligence model" is an artificial intelligence algorithm that analyzes a user's voice commands and generates appropriate responses and designs.

[0199] "Analysis means" refers to technology for analyzing a user's voice commands and recognizing their requests.

[0200] "External information gathering means" refers to technologies for obtaining necessary information from external sources based on analyzed request information.

[0201] A "response generation means" is a technology for generating user-facing responses in natural language based on acquired information.

[0202] "Speech synthesis means" refers to a device or technology for outputting a generated natural language response as speech.

[0203] "Design generation means" refers to technology that uses generative artificial intelligence to generate customized designs and present them.

[0204] "Emotion recognition means" refers to technology that analyzes emotions from a user's voice and identifies their emotional state.

[0205] "Response adjustment means" refers to techniques for optimizing responses generated based on recognized emotions.

[0206] The system of this invention operates to convert a user's voice commands into digital signals, analyze the voice data, and generate an appropriate response. The system consists of a voice recognition means, an emotion recognition means, a server incorporating a generative artificial intelligence model, and a terminal for outputting the response.

[0207] When the server receives a voice command from a user, it first converts it into text data using speech recognition. The text data obtained at this stage is then analyzed by emotion recognition to determine the user's emotional state. Based on the results, a generative artificial intelligence model performs further analysis and obtains information corresponding to the user's request. This acquired information is then used by a response generation system to generate a natural language response that takes the user's emotional state into consideration.

[0208] The generated response is output to the user as natural-sounding speech using speech synthesis technology by the terminal. Through interaction with the system, the user can receive information that is appropriate to their emotions and situation. Furthermore, by using design generation technology, customized designs are also provided according to the user's requests.

[0209] For example, if a security guard receives a visitor's "Good morning" greeting through smart glasses and detects tension from the tone, the server can suggest a response such as "Please respond in a relaxed, friendly tone." In this way, interaction that takes into account the user's emotions and situation is realized.

[0210] An example of a prompt is, "The user seems nervous. Please create a greeting in a gentler tone." Based on this prompt, the generative AI model generates a response.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The server receives the user's voice command. This input voice is converted into text data using speech recognition. The voice data is analyzed by a speech recognition algorithm and output as a string.

[0214] Step 2:

[0215] The server processes the obtained text data using emotion recognition means to identify the user's emotional state. In this step, the text data is input into an emotion analysis algorithm, and a determination result indicating an emotional state such as joy, anger, sadness, or pleasure is output.

[0216] Step 3:

[0217] The server uses a generative artificial intelligence model to generate responses based on text data and emotional states. It inputs prompt sentences corresponding to the emotional state into the generative AI, which then outputs appropriate response sentences. These response sentences include information and advice that takes the user's emotions into consideration.

[0218] Step 4:

[0219] The generated text response is transferred to the terminal's speech synthesis system and converted into natural-sounding speech. The terminal then outputs the generated speech data to the user through its speaker, completing the communication. The response is achieved by converting text into speech using a speech synthesis engine and outputting that speech.

[0220] Step 5:

[0221] When a user submits a design request, the server uses a generative artificial intelligence model to generate a customized design and displays it on the terminal. The user's request is received as text input, processed by the design generation algorithm, and the individualized design is output as image data.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] This invention relates to an accessory device that utilizes generative artificial intelligence. This device is designed to allow users to naturally utilize AI technology in everyday situations. The operation of the system will be specifically described below as an embodiment of the invention.

[0239] When a user uses the device, they first initiate interaction with voice. Voice recognition converts the user's voice commands into digital signals on the device. These signals are then transmitted to a server via the network. The server analyzes the voice data using a generative artificial intelligence model and interprets the user's request.

[0240] Based on the analysis results, the server uses external information gathering means to acquire the necessary data. For example, if a user asks for weather information, the server retrieves the latest weather data from an appropriate external API. This information is then processed into natural language using a response generation means and sent back to the terminal.

[0241] The terminal uses speech synthesis technology to output natural language responses received from the server as speech that is easy for the user to understand. This allows the user to intuitively obtain information.

[0242] Furthermore, if a user requests a customized design, the design generation system utilizes artificial intelligence to generate a design tailored to their individual needs and display it on the device. This feature allows users to not only receive information but also enjoy a more personalized experience.

[0243] For example, if a user issues a voice command such as "Tell me today's news," the voice recognition system transmits this information to the server. The server collects the news information and generates a natural language summary. The terminal then outputs this summary as voice and provides it to the user. In addition, the news display layout and headline design can be customized, allowing for presentations tailored to the user's preferences.

[0244] Thus, the present invention provides an environment in which users can smoothly utilize live artificial intelligence through a series of processes from speech recognition to information gathering, response generation, and presentation of customized designs.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user issues voice commands to the accessory device. The user speaks to the device in natural language, specifying the information they want to know or the action they want to perform.

[0248] Step 2:

[0249] The device uses a voice sensor to convert the user's voice into a digital signal. This conversion process is performed in real time.

[0250] Step 3:

[0251] The terminal encodes the digital signal into the appropriate format and sends it to the server over the network. This makes the audio data available to the server in a remote location.

[0252] Step 4:

[0253] The server uses a generated artificial intelligence model to analyze the received voice data. The server understands the user's intent and identifies the specific request.

[0254] Step 5:

[0255] The server collects necessary information based on the analysis results. For example, it may refer to external APIs or databases to retrieve information requested by the user.

[0256] Step 6:

[0257] The server generates natural language responses based on the information it acquires. Generative artificial intelligence is used to format the responses in a way that is easy for the user to understand.

[0258] Step 7:

[0259] The server sends the generated natural language response to the terminal. The response data is encoded in a format suitable for speech output.

[0260] Step 8:

[0261] The terminal receives a response from the server, converts it using speech synthesis technology, and outputs it to the user as audio. This allows the user to hear the information they were looking for.

[0262] Step 9:

[0263] When a user requests a customized design, they input the design details on their device. Based on the user's request, the customized settings are then applied.

[0264] Step 10:

[0265] The server generates a design in response to a customization request and sends it to the device. The generated design is displayed in real time on the device's screen or within the app.

[0266] (Example 1)

[0267] 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."

[0268] The present invention aims to provide a system that allows users to acquire various types of information smoothly and intuitively, and to enable personalized experiences based on user instructions. In particular, there is a need to efficiently acquire information and present designs using voice instructions.

[0269] 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.

[0270] In this invention, the server includes recognition means for interpreting user voice instructions and converting them into information signals, analysis means for analyzing voice instructions and understanding commands using a generative intelligence model, and external information acquisition means for obtaining the analyzed command information. This enables users to efficiently acquire information through voice and to have a personalized design experience.

[0271] "Recognition means" refers to a device or program that has the function of converting voice instructions into digital information signals.

[0272] "Analysis means" refers to a device or program that uses a generative intelligence model to analyze voice commands and understand the user's intended instructions.

[0273] "External information acquisition means" refers to a device or program that acquires relevant information from an external source based on an analyzed command.

[0274] A "response formation means" is a device or program that creates a response in natural language based on acquired information.

[0275] "Speech synthesis means" refers to a device or program for outputting a synthesized natural language response as speech.

[0276] A "design generation means" is a device or program that uses generative intelligence to generate individual designs and present them to the user.

[0277] A "generative intelligence model" is an artificial intelligence model that uses learning algorithms to analyze voice commands and generate designs.

[0278] The system related to this invention analyzes the user's voice commands using a generative AI model and provides information through natural dialogue. Specifically, when a user uses an accessory device, the system initiates interaction through voice commands.

[0279] The terminal uses a voice capture device equipped with recognition capabilities to convert the user's voice into digital information signals. This digital data is then transmitted to a server using network communication technology.

[0280] The server uses analysis tools to analyze the voice data received through a generative AI model. The generative AI model incorporates an algorithm that understands voice instructions and analyzes them as commands. Based on the analysis results, it then uses external information acquisition tools to collect information from appropriate data sources.

[0281] The collected information is converted into a response generated in natural language by the response generation means. The terminal further utilizes voice synthesis means to vocalize this response and convey it to the user. Through this process, the user can intuitively obtain useful information.

[0282] As a specific example, when the user speaks to the device saying "Tell me the weather for tomorrow", the terminal conveys the content to the server. The server obtains the latest weather data through the weather information API and generates a response such as "Tomorrow will be sunny and the maximum temperature will be 22 degrees". Then the terminal outputs this as voice.

[0283] Furthermore, using the design generation means, an individual design corresponding to the user's instruction is generated and visually presented on the terminal. This customized design experience can also provide various contents according to the user's needs.

[0284] Examples of prompt sentences include "Obtain the latest movie information and notify the user", etc. Thus, this system is designed to provide the user with high-level information provision and a customized experience.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The user gives a voice instruction. The terminal uses the voice interface to record the user's voice and converts it into a digital signal through the recognition means. In this process, the voice data is encoded into a digital format and made into a format that can be transmitted to the server. The input is the user's raw voice, and the output is a digital voice signal.

[0288] Step 2:

[0289] The server receives a digital audio signal transmitted from the terminal. Using an analysis tool, it analyzes the audio data with a generative AI model and interprets the user's intent. Specifically, it converts the audio signal into text and extracts the user's request (e.g., "weather information") from that text. In this step, the input is a digital audio signal, and the output is a textual representation of the audio and the analyzed user intent.

[0290] Step 3:

[0291] Based on the analyzed intent, the server uses external information acquisition methods to obtain the necessary information over the network. For example, if the user is requesting weather information, the server accesses a weather information API to collect the latest weather forecast data. In this step, the input is the user's intent, and the output is the data acquired from an external source.

[0292] Step 4:

[0293] The server generates a natural language response based on the information collected by the response formation mechanism. It utilizes a generative AI model to convert the data into a user-friendly format. For example, it might convert collected weather data into a concise response such as, "Tomorrow will be sunny, and the maximum temperature will be 20 degrees Celsius." In this step, the input is information obtained from an external source, and the output is a natural language response.

[0294] Step 5:

[0295] The server sends the generated natural language response to the terminal. The terminal uses speech synthesis to play the received text as speech. This allows the user to receive information through digital speech. In this step, the input is the natural language response sentence, and the output is the spoken response.

[0296] Step 6:

[0297] Furthermore, when a user makes a specific design request, the design generation system utilizes AI to generate a design tailored to the individual user's needs and displays that design on the device. This process allows users to receive visually customized content. In this step, the input is the user's design request, and the output is the display of the customized design.

[0298] (Application Example 1)

[0299] 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."

[0300] In today's commercial environment, there is a need to provide real-time information to visitors in physical stores and to enable more personalized purchasing support. However, because the optimal use of voice interfaces and AI technology for efficient information provision is not being fully utilized, there is a challenge in that visitors have difficulty obtaining product information and recommendations quickly within stores.

[0301] 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.

[0302] In this invention, the server includes voice recognition means for recognizing voice commands and converting them into digital signals, external information gathering means for acquiring analyzed request information, and guidance display means for providing commercial information to visitors. This enables visitors to instantly obtain information and purchase support within the store simply by speaking.

[0303] "Voice recognition means" refers to a device or software that has the function of converting voice commands uttered by a user into digital signals.

[0304] A "generative artificial intelligence model" is an artificial intelligence algorithm or program used to analyze a user's voice commands and recognize requests.

[0305] The "analysis means" is a process or device for analyzing voice commands and recognizing user requests.

[0306] The "external information collection means" is a device or means for obtaining necessary information from an external information source based on the analyzed request information.

[0307] The "response generation means" is a function or device for generating a response in natural language based on the obtained information.

[0308] The "voice synthesis means" is software or a device for outputting the generated natural language response as voice that is easy for the user to understand.

[0309] The "design generation means" is a means for generating and presenting a customized design based on the individual needs of the user using a generative artificial intelligence.

[0310] The "guidance display means" is a display device or function for providing commercial information to visitors.

[0311] The system of this invention is designed to provide product information and recommended coordination to users in a commercial environment. The core of the system is a voice recognition and generative AI model, through which seamless interaction with the user is realized. First, the server uses voice recognition means to convert a voice command from the user into a digital signal. This is done, for example, using voice recognition software. The digital signal is transmitted to the server and analyzed using a generative artificial intelligence model. In the process of analysis, the requested information is decoded, and necessary information is obtained from a database or an external API, for example, through the external information collection means.

[0312] The server generates natural language responses using response generation means based on the acquired information, and outputs them as user-friendly speech using speech synthesis means. During this process, the generation AI model performs sophisticated natural language processing to produce the optimal answer to the user's request. For example, if a user says, "Tell me your recommended products," the system will respond through speech synthesis with, "Our current recommended product is XX." This system can also use design generation means to present customized designs according to the user's preferences. As a specific example, when suggesting a design for a particular product, it will provide the user with information such as, "This is the suggested design."

[0313] The implementation of such a system will create an environment where visitors can use in-store services more intuitively and efficiently. The natural interface provided by the generative AI model can offer store users a unique and customized experience. An example of a prompt used would be, "Please tell me about the latest recommended products in the store." This prompt serves as the foundation for the AI ​​to accurately understand the user's request and provide appropriate information.

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The user speaks into the terminal and inputs a voice command. This voice command is captured by the terminal via the microphone. The terminal converts this voice command into a digital signal using voice recognition. The converted digital signal is sent to the server.

[0317] Step 2:

[0318] The server uses a generative AI model to analyze the digital signals received from the speech recognition system. This analysis system understands the request content from the user's voice commands. The understood request is used as input data for the next processing step, in which the digital signals are converted into a request in natural language format.

[0319] Step 3:

[0320] Based on the analysis, the server collects necessary information using external information gathering means. In this case, the server connects to an external API, for example, to obtain information related to the user's request. The collected information is then used as the basis for generating the response. In this step, information corresponding to the request content is collected from databases and external information sources.

[0321] Step 4:

[0322] The server generates a natural language response using response generation tools based on the collected information. The generative AI model structures the data and transforms it into a user-friendly format during this process. The generated response becomes output data for speech synthesis. In this step, the information is converted into human-readable sentences.

[0323] Step 5:

[0324] The server converts the generated natural language response into speech output using speech synthesis. The terminal plays this audio, presenting the information to the user. Through this process, the user can receive the requested information in audio form. In this step, the natural language is converted back into an audio signal and presented to the user through the speaker.

[0325] 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.

[0326] This invention is an accessory device system that combines generative artificial intelligence and emotion recognition with user interaction. Its aim is to provide users with a more personalized experience in their daily lives. The following describes specific embodiments of the system.

[0327] The terminal receives the user's voice command and converts it into a digital signal using voice recognition. Next, an emotion recognition engine analyzes the user's emotions from this voice data and identifies basic emotional states such as joy and anger. This emotion data is sent to the server simultaneously with the analysis of the voice command.

[0328] The server utilizes a generative artificial intelligence model to analyze the transmitted voice commands. Simultaneously, it generates responses appropriate to the user's emotions based on emotion data from an emotion recognition engine. This ensures that responses are not merely informational but also considerate of the user's feelings. For example, if the user is tired, the server can choose a softer tone of voice in its response.

[0329] Based on the acquired data, a natural language response is generated by a response generation system and sent from the server to the terminal. The terminal uses a speech synthesis system to output this response to the user as natural-sounding speech. The user can receive information that is tailored to their own emotions and circumstances.

[0330] Furthermore, if a user requests a customized design, the design generation system utilizes artificial intelligence to create a design that meets the user's requirements. This design is then displayed on the device, with its colors and layout adjusted based on the user's emotions.

[0331] For example, when a user issues the voice command "Tell me the news," if the emotion recognition engine detects that the voice lacks composure, the device will prioritize providing relaxing news topics to reduce stress via the server. In this way, it is possible to provide information services that take the user's state of mind into consideration.

[0332] This invention provides a technology that takes user emotions into consideration to create a more personalized experience and make user-system interaction more natural and comfortable.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] The user issues voice commands to the accessory device. The user speaks in natural language about the information they want to know or the actions they want to perform.

[0336] Step 2:

[0337] The device uses a voice sensor to convert the user's voice into a digital signal. The voice data is processed within the device.

[0338] Step 3:

[0339] The device uses an emotion recognition engine to analyze the user's emotional state from voice data. For example, it can identify the user's level of tension or relaxation from their voice tone and tempo.

[0340] Step 4:

[0341] The device transmits voice digital signals and emotion data to the server. The transmission takes place in real time over the network.

[0342] Step 5:

[0343] The server uses an artificial intelligence model to analyze voice data and understand the user's intent. Based on the analysis results, it identifies the information and actions the user is seeking.

[0344] Step 6:

[0345] The server considers emotional data from the emotion recognition engine and generates responses with appropriate content and tone. Responses are prepared that are intended to provide feedback tailored to the user's emotions.

[0346] Step 7:

[0347] The server collects necessary information from external sources. For example, it retrieves the latest news and weather forecasts from relevant APIs.

[0348] Step 8:

[0349] The server converts the acquired information into a natural language response and makes adjustments to reflect emotions. The generated response is then sent to the terminal.

[0350] Step 9:

[0351] The terminal receives a response from the server, converts it into speech using speech synthesis technology, and provides it to the user. This allows the user to obtain information through listening.

[0352] Step 10:

[0353] If a user requests a customized design, their device sends the request to the server. The design is then adjusted based on the user's emotional state.

[0354] Step 11:

[0355] The server uses artificial intelligence to generate an emotion-based customized design and sends it to the device. The device then displays the generated design on its screen.

[0356] Through these steps, users can receive information and designs that match their emotions.

[0357] (Example 2)

[0358] 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".

[0359] Conventional information delivery systems often provide information unilaterally without considering the user's emotions, making it difficult to meet individual user needs. Furthermore, there is a need not only for systems that simply recognize voice commands, but also for systems that respond in accordance with the user's emotional state. Additionally, providing designs and content based on user requests in real time presents challenges that were not present in conventional technologies.

[0360] 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.

[0361] In this invention, the server includes a speech recognition means that recognizes the user's voice commands and converts them into numerical signals, a means that analyzes emotions from voice data using emotion recognition technology and extracts emotional information, and an analysis means that analyzes voice commands and recognizes requests using a generative artificial intelligence model. This makes it possible to provide personalized responses and designs based on the user's emotions.

[0362] "Voice commands" are instructions that a user issues to a device via voice, regarding operations or the retrieval of information.

[0363] A "numerical signal" is a signal obtained by converting audio or analog information into a digital format, and is in a format that can be processed by a computer.

[0364] "Voice recognition means" refers to technology that analyzes voice information, converts it into numerical signals, and identifies voice commands.

[0365] "Emotion recognition technology" is a technology that analyzes a user's emotions from their voice, facial expressions, etc., and evaluates specific emotional states.

[0366] "Emotional information" refers to data about the user's emotional state, and is an emotional indicator extracted after analysis.

[0367] A "generative artificial intelligence model" is an algorithm that learns from large amounts of data and generates natural-sounding responses and content that resemble those of a human.

[0368] "Analysis means" refers to technologies that analyze input data and commands to understand their content and recognize requests.

[0369] "Personalized responses" refer to information and feedback provided in a way that is adapted to the user's specific situation and emotions.

[0370] "Design generation methods" refer to technologies that effectively generate visual content and layouts based on user requests and emotions.

[0371] This invention is a system that combines generative artificial intelligence and emotion recognition in user interaction, enabling personalized information provision and design for the user. This system is configured using a terminal and a server.

[0372] The terminal receives voice commands from the user via a microphone and converts this voice into numerical signals using voice recognition technology. This voice recognition can utilize general-purpose voice recognition software or cloud-based voice recognition services. Next, the terminal extracts emotional information from the voice data using emotion recognition technology. Various cloud services can be used for this emotion analysis.

[0373] Voice commands and emotional information sent from the terminal to the server are analyzed on the server by a generative artificial intelligence model (for example, a large-scale natural language processing model). Based on this information, the server can generate an appropriate text response that corresponds to the user's emotions. In this generation process, prompts are used to customize the response according to specific conditions and contexts.

[0374] The generated response is returned from the server to the terminal and output to the user as natural-sounding speech through a speech synthesis system. A common speech synthesis engine can be used for speech synthesis, and the response is delivered to the user in real time.

[0375] Furthermore, if a user makes a design request, the server uses design generation tools and artificial intelligence to generate a personalized design based on the user's emotions and requests. This design is then adjusted in terms of color and layout and displayed on the terminal screen.

[0376] For example, if a user asks "What's the weather like today?", the device will interpret the user's voice as reassuring, and the server will generate a response such as "It's a sunny day today, have a pleasant day." Furthermore, in response to design requests, the system will generate background designs based on the user's needs and emotions, providing a visually pleasing user experience.

[0377] An example of a prompt message is, "Generate a reassuring response based on the user's request."

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] The terminal receives voice commands from the user via a microphone. The input is the user's voice information. This voice is converted into a numerical signal using speech recognition means and identified as a voice command. The output is digitized voice command data. Specifically, speech recognition software analyzes the voiceprint and generates text data.

[0381] Step 2:

[0382] The device uses emotion recognition technology to extract user emotion information from received audio data. The input is digitized audio data. Data analysis is performed to identify and output emotions such as joy, anger, and sadness. Specifically, the analysis algorithm analyzes the pitch and tone of the voice and generates an emotional index.

[0383] Step 3:

[0384] The terminal transmits voice command data and emotion information to the server. The input consists of digitized voice commands and extracted emotion information. This is sent to the server as a data packet, which the server receives to prepare for the next processing step. The output is the data packet sent to the server.

[0385] Step 4:

[0386] The server uses a generative AI model to analyze the received voice command data and emotion information, and generates an appropriate response. The input consists of voice command and emotion information data. The generative AI model analyzes the command and generates a response text appropriate to its content and emotion. The output is the response text to the user. Specifically, the AI ​​model uses prompt sentences to construct the optimal response in text format.

[0387] Step 5:

[0388] The server sends the generated response text to the terminal. The input is the generated response text. This data is sent to the terminal and is ready for audio output to the user. The output is the response data sent to the terminal.

[0389] Step 6:

[0390] The terminal converts the received response text into natural-sounding speech using a speech synthesis system and outputs it to the user. The input is the response text sent from the server. It is converted into audio data using a speech synthesis engine and output through the speaker. The output is the audio response that the user can hear. Specifically, the speech synthesis software analyzes the text and generates an audio file.

[0391] Step 7:

[0392] When a user submits a design request, the terminal sends this request to the server. The input is the design request from the user. Using the design generation mechanism, the process prepares to build a design based on the user's feelings and requests. The output is the design request data sent to the server.

[0393] Step 8:

[0394] The server creates personalized designs using a generative AI model based on user requests. The input consists of a design request and sentiment information. The generative AI model analyzes this data and generates a design that meets the request. The output is a design file viewable on the terminal. Specifically, the AI ​​generates graphic data with optimized colors and layout.

[0395] Step 9:

[0396] The terminal displays the design sent from the server on its screen. The input is the design file from the server. By displaying this on the screen, the user can immediately review it. The output is a design display that the user can visually review. Specifically, the display controller renders the design data and displays it on the terminal's screen.

[0397] (Application Example 2)

[0398] 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."

[0399] Existing systems that unilaterally provide information without considering user emotions make it difficult to provide user-optimized responses and designs, resulting in the standardization of individual user experiences. Furthermore, in security settings, there is a need for more reliable communication that addresses the user's internal psychological state, but there is a lack of technology to achieve this.

[0400] 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.

[0401] In this invention, the server includes speech recognition means, emotion recognition means, and response adjustment means. This makes it possible to provide optimized responses in real time based on the user's emotional state, making the user experience more personal and effective.

[0402] "Voice recognition means" refers to a device or technology that converts a user's voice into a digital signal.

[0403] A "generative artificial intelligence model" is an artificial intelligence algorithm that analyzes a user's voice commands and generates appropriate responses and designs.

[0404] "Analysis means" refers to technology for analyzing a user's voice commands and recognizing their requests.

[0405] "External information gathering means" refers to technologies for obtaining necessary information from external sources based on analyzed request information.

[0406] A "response generation means" is a technology for generating user-facing responses in natural language based on acquired information.

[0407] "Speech synthesis means" refers to a device or technology for outputting a generated natural language response as speech.

[0408] "Design generation means" refers to technology that uses generative artificial intelligence to generate customized designs and present them.

[0409] "Emotion recognition means" refers to technology that analyzes emotions from a user's voice and identifies their emotional state.

[0410] "Response adjustment means" refers to techniques for optimizing responses generated based on recognized emotions.

[0411] The system of this invention operates to convert a user's voice commands into digital signals, analyze the voice data, and generate an appropriate response. The system consists of a voice recognition means, an emotion recognition means, a server incorporating a generative artificial intelligence model, and a terminal for outputting the response.

[0412] When the server receives a voice command from a user, it first converts it into text data using speech recognition. The text data obtained at this stage is then analyzed by emotion recognition to determine the user's emotional state. Based on the results, a generative artificial intelligence model performs further analysis and obtains information corresponding to the user's request. This acquired information is then used by a response generation system to generate a natural language response that takes the user's emotional state into consideration.

[0413] The generated response is output to the user as natural-sounding speech using speech synthesis technology by the terminal. Through interaction with the system, the user can receive information that is appropriate to their emotions and situation. Furthermore, by using design generation technology, customized designs are also provided according to the user's requests.

[0414] For example, if a security guard receives a visitor's "Good morning" greeting through smart glasses and detects tension from the tone, the server can suggest a response such as "Please respond in a relaxed, friendly tone." In this way, interaction that takes into account the user's emotions and situation is realized.

[0415] An example of a prompt is, "The user seems nervous. Please create a greeting in a gentler tone." Based on this prompt, the generative AI model generates a response.

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The server receives the user's voice command. This input voice is converted into text data using speech recognition. The voice data is analyzed by a speech recognition algorithm and output as a string.

[0419] Step 2:

[0420] The server processes the obtained text data using emotion recognition means to identify the user's emotional state. In this step, the text data is input into an emotion analysis algorithm, and a determination result indicating an emotional state such as joy, anger, sadness, or pleasure is output.

[0421] Step 3:

[0422] The server uses a generative artificial intelligence model to generate responses based on text data and emotional states. It inputs prompt sentences corresponding to the emotional state into the generative AI, which then outputs appropriate response sentences. These response sentences include information and advice that takes the user's emotions into consideration.

[0423] Step 4:

[0424] The generated text response is transferred to the terminal's speech synthesis system and converted into natural-sounding speech. The terminal then outputs the generated speech data to the user through its speaker, completing the communication. The response is achieved by converting text into speech using a speech synthesis engine and outputting that speech.

[0425] Step 5:

[0426] When a user submits a design request, the server uses a generative artificial intelligence model to generate a customized design and displays it on the terminal. The user's request is received as text input, processed by the design generation algorithm, and the individualized design is output as image data.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] [Third Embodiment]

[0431] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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".

[0443] This invention relates to an accessory device that utilizes generative artificial intelligence. This device is designed to allow users to naturally utilize AI technology in everyday situations. The operation of the system will be specifically described below as an embodiment of the invention.

[0444] When a user uses the device, they first initiate interaction with voice. Voice recognition converts the user's voice commands into digital signals on the device. These signals are then transmitted to a server via the network. The server analyzes the voice data using a generative artificial intelligence model and interprets the user's request.

[0445] Based on the analysis results, the server uses external information gathering means to acquire the necessary data. For example, if a user asks for weather information, the server acquires the latest weather data from an appropriate external API. This information is then processed into natural language using a response generation means and sent back to the terminal.

[0446] The terminal uses speech synthesis technology to output natural language responses received from the server as speech that is easy for the user to understand. This allows the user to intuitively obtain information.

[0447] Furthermore, if a user requests a customized design, the design generation system utilizes artificial intelligence to generate a design tailored to their individual needs and display it on the device. This feature allows users to not only receive information but also enjoy a more personalized experience.

[0448] For example, if a user issues a voice command such as "Tell me today's news," the voice recognition system transmits this information to the server. The server collects the news information and generates a natural language summary. The terminal then outputs this summary as voice and provides it to the user. In addition, the news display layout and headline design can be customized, allowing for presentations tailored to the user's preferences.

[0449] Thus, the present invention provides an environment in which users can smoothly utilize live artificial intelligence through a series of processes from speech recognition to information gathering, response generation, and presentation of customized designs.

[0450] The following describes the processing flow.

[0451] Step 1:

[0452] The user issues voice commands to the accessory device. The user speaks to the device in natural language, specifying the information they want to know or the action they want to perform.

[0453] Step 2:

[0454] The device uses a voice sensor to convert the user's voice into a digital signal. This conversion process is performed in real time.

[0455] Step 3:

[0456] The terminal encodes the digital signal into the appropriate format and sends it to the server over the network. This makes the audio data available to the server in a remote location.

[0457] Step 4:

[0458] The server uses a generated artificial intelligence model to analyze the received voice data. The server understands the user's intent and identifies the specific request.

[0459] Step 5:

[0460] The server collects necessary information based on the analysis results. For example, it may refer to external APIs or databases to retrieve information requested by the user.

[0461] Step 6:

[0462] The server generates natural language responses based on the information it acquires. Generative artificial intelligence is used to format the responses in a way that is easy for the user to understand.

[0463] Step 7:

[0464] The server sends the generated natural language response to the terminal. The response data is encoded in a format suitable for speech output.

[0465] Step 8:

[0466] The terminal receives a response from the server, converts it using speech synthesis technology, and outputs it to the user as audio. This allows the user to hear the information they were looking for.

[0467] Step 9:

[0468] When a user requests a customized design, they input the design details on their device. Based on the user's request, the customized settings are applied.

[0469] Step 10:

[0470] The server generates a design in response to a customization request and sends it to the device. The generated design is displayed in real time on the device's screen or within the app.

[0471] (Example 1)

[0472] 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."

[0473] The present invention aims to provide a system that allows users to acquire various types of information smoothly and intuitively, and to enable personalized experiences based on user instructions. In particular, there is a need to efficiently acquire information and present designs using voice instructions.

[0474] 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.

[0475] In this invention, the server includes recognition means for interpreting user voice instructions and converting them into information signals, analysis means for analyzing voice instructions and understanding commands using a generative intelligence model, and external information acquisition means for obtaining the analyzed command information. This enables users to efficiently acquire information through voice and to have a personalized design experience.

[0476] "Recognition means" refers to a device or program that has the function of converting voice instructions into digital information signals.

[0477] "Analysis means" refers to a device or program that uses a generative intelligence model to analyze voice commands and understand the user's intended instructions.

[0478] "External information acquisition means" refers to a device or program that acquires relevant information from an external source based on an analyzed command.

[0479] A "response formation means" is a device or program that creates a response in natural language based on acquired information.

[0480] "Speech synthesis means" refers to a device or program for outputting a synthesized natural language response as speech.

[0481] A "design generation means" is a device or program that uses generative intelligence to generate individual designs and present them to the user.

[0482] A "generative intelligence model" is an artificial intelligence model that uses learning algorithms to analyze voice commands and generate designs.

[0483] The system related to this invention analyzes the user's voice commands using a generative AI model and provides information through natural dialogue. Specifically, when a user uses an accessory device, the system initiates interaction through voice commands.

[0484] The terminal uses a voice capture device equipped with recognition capabilities to convert the user's voice into digital information signals. This digital data is then transmitted to a server using network communication technology.

[0485] The server uses analysis tools to analyze the voice data received through a generative AI model. The generative AI model incorporates an algorithm that understands voice instructions and analyzes them as commands. Based on the analysis results, it then uses external information acquisition tools to collect information from appropriate data sources.

[0486] The collected information is converted into a response generated in natural language by a response formation mechanism. The terminal then utilizes a speech synthesis mechanism to vocalize this response and convey it to the user. Through this process, the user can intuitively obtain useful information.

[0487] As a concrete example, when a user asks the device, "Tell me the weather for tomorrow," the device transmits this information to the server. The server retrieves the latest weather data through a weather information API and generates a response such as, "Tomorrow will be sunny, with a high of 22 degrees Celsius." The device then outputs this as audio.

[0488] Furthermore, using design generation tools, individual designs are created in response to user instructions and visually presented on the device. This customized design experience can also offer a variety of content to meet user needs.

[0489] Examples of prompt messages include "Retrieve the latest movie information and inform the user." Thus, this system is designed to provide users with advanced information and a customized experience.

[0490] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0491] Step 1:

[0492] The user provides voice instructions. The terminal records the user's voice using a voice interface and converts it into a digital signal through recognition means. In this process, the voice data is encoded into a digital format and made into a format that can be transmitted to the server. The input is the user's raw voice, and the output is a digital voice signal.

[0493] Step 2:

[0494] The server receives a digital audio signal transmitted from the terminal. Using an analysis tool, it analyzes the audio data with a generative AI model and interprets the user's intent. Specifically, it converts the audio signal into text and extracts the user's request (e.g., "weather information") from that text. In this step, the input is a digital audio signal, and the output is a textual representation of the audio and the analyzed user intent.

[0495] Step 3:

[0496] Based on the analyzed intent, the server uses external information acquisition methods to obtain the necessary information over the network. For example, if the user is requesting weather information, the server accesses a weather information API to collect the latest weather forecast data. In this step, the input is the user's intent, and the output is the data acquired from an external source.

[0497] Step 4:

[0498] The server generates a natural language response based on the information collected by the response formation mechanism. It utilizes a generative AI model to convert the data into a user-friendly format. For example, it might convert collected weather data into a concise response such as, "Tomorrow will be sunny, and the maximum temperature will be 20 degrees Celsius." In this step, the input is information obtained from an external source, and the output is a natural language response.

[0499] Step 5:

[0500] The server sends the generated natural language response to the terminal. The terminal uses speech synthesis to play the received text as speech. This allows the user to receive information through digital speech. In this step, the input is the natural language response sentence, and the output is the spoken response.

[0501] Step 6:

[0502] Furthermore, when a user makes a specific design request, the design generation system utilizes AI to generate a design tailored to the individual user's needs and displays that design on the device. This process allows users to receive visually customized content. In this step, the input is the user's design request, and the output is the display of the customized design.

[0503] (Application Example 1)

[0504] 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."

[0505] In today's commercial environment, there is a need to provide real-time information to visitors in physical stores and to enable more personalized purchasing support. However, because the optimal use of voice interfaces and AI technology for efficient information provision is not being fully utilized, there is a challenge in that visitors have difficulty obtaining product information and recommendations quickly within stores.

[0506] 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.

[0507] In this invention, the server includes voice recognition means for recognizing voice commands and converting them into digital signals, external information gathering means for acquiring analyzed request information, and guidance display means for providing commercial information to visitors. This enables visitors to instantly obtain information and purchase support within the store simply by speaking.

[0508] "Voice recognition means" refers to a device or software that has the function of converting voice commands uttered by a user into digital signals.

[0509] A "generative artificial intelligence model" is an artificial intelligence algorithm or program used to analyze a user's voice commands and recognize requests.

[0510] "Analysis means" refers to a process or device for analyzing voice commands and recognizing user requests.

[0511] "External information gathering means" refers to devices or means for obtaining necessary information from external sources based on analyzed request information.

[0512] A "response generation means" refers to a function or device for generating a response in natural language based on acquired information.

[0513] "Speech synthesis means" refers to software or devices that output generated natural language responses as speech that is easy for the user to understand.

[0514] A "design generation method" is a means of generating and presenting customized designs based on the individual needs of users, using generative artificial intelligence.

[0515] "Information display means" refers to display devices or functions that provide commercial information to visitors.

[0516] The system of this invention is designed to provide users with product information and recommended outfits in a commercial environment. The core of the system is a speech recognition and generative AI model, which enables seamless interaction with the user. The server first uses speech recognition means to convert voice commands from the user into digital signals. This is done, for example, using speech recognition software. The digital signals are sent to the server and analyzed using a generative artificial intelligence model. During the analysis process, the requested information is decoded, and necessary information is obtained from databases or external APIs, for example, through external information gathering means.

[0517] The server generates natural language responses using response generation means based on the acquired information, and outputs them as user-friendly speech using speech synthesis means. During this process, the generation AI model performs sophisticated natural language processing to produce the optimal answer to the user's request. For example, if a user says, "Tell me your recommended products," the system will respond through speech synthesis with, "Our current recommended product is XX." This system can also use design generation means to present customized designs according to the user's preferences. As a specific example, when suggesting a design for a particular product, it will provide the user with information such as, "This is the suggested design."

[0518] The implementation of such a system will create an environment where visitors can use in-store services more intuitively and efficiently. The natural interface provided by the generative AI model can offer store users a unique and customized experience. An example of a prompt used would be, "Please tell me about the latest recommended products in the store." This prompt serves as the foundation for the AI ​​to accurately understand the user's request and provide appropriate information.

[0519] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0520] Step 1:

[0521] The user speaks into the terminal and inputs a voice command. This voice command is captured by the terminal via the microphone. The terminal converts this voice command into a digital signal using voice recognition. The converted digital signal is sent to the server.

[0522] Step 2:

[0523] The server uses a generative AI model to analyze the digital signals received from the speech recognition system. This analysis system understands the request content from the user's voice commands. The understood request is used as input data for the next processing step, in which the digital signals are converted into a request in natural language format.

[0524] Step 3:

[0525] Based on the analysis, the server collects necessary information using external information gathering means. In this case, the server connects to an external API, for example, to obtain information related to the user's request. The collected information is then used as the basis for generating the response. In this step, information corresponding to the request content is collected from databases and external information sources.

[0526] Step 4:

[0527] The server generates a natural language response using response generation tools based on the collected information. The generative AI model structures the data and transforms it into a user-friendly format during this process. The generated response becomes output data for speech synthesis. In this step, the information is converted into human-readable sentences.

[0528] Step 5:

[0529] The server converts the generated natural language response into speech output using speech synthesis. The terminal plays this audio, presenting the information to the user. Through this process, the user can receive the requested information in audio form. In this step, the natural language is converted back into an audio signal and presented to the user through the speaker.

[0530] 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.

[0531] This invention is an accessory device system that combines generative artificial intelligence and emotion recognition with user interaction. Its aim is to provide users with a more personalized experience in their daily lives. The following describes specific embodiments of the system.

[0532] The terminal receives the user's voice command and converts it into a digital signal using voice recognition. Next, an emotion recognition engine analyzes the user's emotions from this voice data and identifies basic emotional states such as joy and anger. This emotion data is sent to the server simultaneously with the analysis of the voice command.

[0533] The server utilizes a generative artificial intelligence model to analyze the transmitted voice commands. Simultaneously, it generates responses appropriate to the user's emotions based on emotion data from an emotion recognition engine. This ensures that responses are not merely informational but also considerate of the user's feelings. For example, if the user is tired, the server can choose a softer tone of voice in its response.

[0534] Based on the acquired data, a natural language response is generated by a response generation system and sent from the server to the terminal. The terminal uses a speech synthesis system to output this response to the user as natural-sounding speech. The user can receive information that is tailored to their own emotions and circumstances.

[0535] Furthermore, if a user requests a customized design, the design generation system utilizes artificial intelligence to create a design that meets the user's requirements. This design is then displayed on the device, with its colors and layout adjusted based on the user's emotions.

[0536] For example, when a user issues the voice command "Tell me the news," if the emotion recognition engine detects that the voice lacks composure, the device will prioritize providing relaxing news topics to reduce stress via the server. In this way, it is possible to provide information services that take the user's state of mind into consideration.

[0537] This invention provides a technology that takes user emotions into consideration to create a more personalized experience and make user-system interaction more natural and comfortable.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] The user issues voice commands to the accessory device. The user speaks in natural language about the information they want to know or the actions they want to perform.

[0541] Step 2:

[0542] The device uses a voice sensor to convert the user's voice into a digital signal. The voice data is processed within the device.

[0543] Step 3:

[0544] The device uses an emotion recognition engine to analyze the user's emotional state from voice data. For example, it can identify the user's level of tension or relaxation from their voice tone and tempo.

[0545] Step 4:

[0546] The device transmits voice digital signals and emotion data to the server. The transmission takes place in real time over the network.

[0547] Step 5:

[0548] The server uses an artificial intelligence model to analyze voice data and understand the user's intent. Based on the analysis results, it identifies the information and actions the user is seeking.

[0549] Step 6:

[0550] The server considers emotional data from the emotion recognition engine and generates responses with appropriate content and tone. Responses are prepared that are intended to provide feedback tailored to the user's emotions.

[0551] Step 7:

[0552] The server collects necessary information from external sources. For example, it retrieves the latest news and weather forecasts from relevant APIs.

[0553] Step 8:

[0554] The server converts the acquired information into a natural language response and makes adjustments to reflect emotions. The generated response is then sent to the terminal.

[0555] Step 9:

[0556] The terminal receives a response from the server, converts it into speech using speech synthesis technology, and provides it to the user. This allows the user to obtain information through listening.

[0557] Step 10:

[0558] If a user requests a customized design, their device sends the request to the server. The design is then adjusted based on the user's emotional state.

[0559] Step 11:

[0560] The server uses artificial intelligence to generate an emotion-based customized design and sends it to the device. The device then displays the generated design on its screen.

[0561] Through these steps, users can receive information and designs that match their emotions.

[0562] (Example 2)

[0563] 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."

[0564] Conventional information delivery systems often provide information unilaterally without considering the user's emotions, making it difficult to meet individual user needs. Furthermore, there is a need not only for systems that simply recognize voice commands, but also for systems that respond in accordance with the user's emotional state. Additionally, providing designs and content based on user requests in real time presents challenges that were not present in conventional technologies.

[0565] 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.

[0566] In this invention, the server includes a speech recognition means that recognizes the user's voice commands and converts them into numerical signals, a means that analyzes emotions from voice data using emotion recognition technology and extracts emotional information, and an analysis means that analyzes voice commands and recognizes requests using a generative artificial intelligence model. This makes it possible to provide personalized responses and designs based on the user's emotions.

[0567] "Voice commands" are instructions that a user issues to a device via voice, regarding operations or the retrieval of information.

[0568] A "numerical signal" is a signal obtained by converting audio or analog information into a digital format, and is in a format that can be processed by a computer.

[0569] "Voice recognition means" refers to technology that analyzes voice information, converts it into numerical signals, and identifies voice commands.

[0570] "Emotion recognition technology" is a technology that analyzes a user's emotions from their voice, facial expressions, etc., and evaluates specific emotional states.

[0571] "Emotional information" refers to data about the user's emotional state, and is an emotional indicator extracted after analysis.

[0572] A "generative artificial intelligence model" is an algorithm that learns from large amounts of data and generates natural-sounding responses and content that resemble those of a human.

[0573] "Analysis means" refers to technologies that analyze input data and commands to understand their content and recognize requests.

[0574] "Personalized responses" refer to information and feedback provided in a way that is adapted to the user's specific situation and emotions.

[0575] "Design generation methods" refer to technologies that effectively generate visual content and layouts based on user requests and emotions.

[0576] This invention is a system that combines generative artificial intelligence and emotion recognition in user interaction, enabling personalized information provision and design for the user. This system is configured using a terminal and a server.

[0577] The terminal receives voice commands from the user via a microphone and converts this voice into numerical signals using voice recognition technology. This voice recognition can utilize general-purpose voice recognition software or cloud-based voice recognition services. Next, the terminal extracts emotional information from the voice data using emotion recognition technology. Various cloud services can be used for this emotion analysis.

[0578] Voice commands and emotional information sent from the terminal to the server are analyzed on the server by a generative artificial intelligence model (for example, a large-scale natural language processing model). Based on this information, the server can generate an appropriate text response that corresponds to the user's emotions. In this generation process, prompts are used to customize the response according to specific conditions and contexts.

[0579] The generated response is returned from the server to the terminal and output to the user as natural-sounding speech through a speech synthesis system. A common speech synthesis engine can be used for speech synthesis, and the response is delivered to the user in real time.

[0580] Furthermore, if a user makes a design request, the server uses design generation tools and artificial intelligence to generate a personalized design based on the user's emotions and requests. This design is then adjusted in terms of color and layout and displayed on the terminal screen.

[0581] For example, if a user asks "What's the weather like today?", the device will interpret the user's voice as reassuring, and the server will generate a response such as "It's a sunny day today, have a pleasant day." Furthermore, in response to design requests, the system will generate background designs based on the user's needs and emotions, providing a visually pleasing user experience.

[0582] An example of a prompt message is, "Generate a reassuring response based on the user's request."

[0583] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0584] Step 1:

[0585] The terminal receives voice commands from the user via a microphone. The input is the user's voice information. This voice is converted into a numerical signal using speech recognition means and identified as a voice command. The output is digitized voice command data. Specifically, speech recognition software analyzes the voiceprint and generates text data.

[0586] Step 2:

[0587] The device uses emotion recognition technology to extract user emotion information from received audio data. The input is digitized audio data. Data analysis is performed to identify and output emotions such as joy, anger, and sadness. Specifically, the analysis algorithm analyzes the pitch and tone of the voice and generates an emotional index.

[0588] Step 3:

[0589] The terminal transmits voice command data and emotion information to the server. The input consists of digitized voice commands and extracted emotion information. This is sent to the server as a data packet, which the server receives to prepare for the next processing step. The output is the data packet sent to the server.

[0590] Step 4:

[0591] The server uses a generative AI model to analyze the received voice command data and emotion information, and generates an appropriate response. The input consists of voice command and emotion information data. The generative AI model analyzes the command and generates a response text appropriate to its content and emotion. The output is the response text to the user. Specifically, the AI ​​model uses prompt sentences to construct the optimal response in text format.

[0592] Step 5:

[0593] The server sends the generated response text to the terminal. The input is the generated response text. This data is sent to the terminal and is ready for audio output to the user. The output is the response data sent to the terminal.

[0594] Step 6:

[0595] The terminal converts the received response text into natural-sounding speech using a speech synthesis system and outputs it to the user. The input is the response text sent from the server. It is converted into audio data using a speech synthesis engine and output through the speaker. The output is the audio response that the user can hear. Specifically, the speech synthesis software analyzes the text and generates an audio file.

[0596] Step 7:

[0597] When a user submits a design request, the terminal sends this request to the server. The input is the design request from the user. Using the design generation mechanism, the process prepares to build a design based on the user's feelings and requests. The output is the design request data sent to the server.

[0598] Step 8:

[0599] The server creates personalized designs using a generative AI model based on user requests. The input consists of a design request and sentiment information. The generative AI model analyzes this data and generates a design that meets the request. The output is a design file viewable on the terminal. Specifically, the AI ​​generates graphic data with optimized colors and layout.

[0600] Step 9:

[0601] The terminal displays the design sent from the server on its screen. The input is the design file from the server. By displaying this on the screen, the user can immediately review it. The output is a design display that the user can visually review. Specifically, the display controller renders the design data and displays it on the terminal's screen.

[0602] (Application Example 2)

[0603] 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."

[0604] Existing systems that unilaterally provide information without considering user emotions make it difficult to provide user-optimized responses and designs, resulting in the standardization of individual user experiences. Furthermore, in security settings, there is a need for more reliable communication that addresses the user's internal psychological state, but there is a lack of technology to achieve this.

[0605] 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.

[0606] In this invention, the server includes speech recognition means, emotion recognition means, and response adjustment means. This makes it possible to provide optimized responses in real time based on the user's emotional state, making the user experience more personal and effective.

[0607] "Voice recognition means" refers to a device or technology that converts a user's voice into a digital signal.

[0608] A "generative artificial intelligence model" is an artificial intelligence algorithm that analyzes a user's voice commands and generates appropriate responses and designs.

[0609] "Analysis means" refers to technology for analyzing a user's voice commands and recognizing their requests.

[0610] "External information gathering means" refers to technologies for obtaining necessary information from external sources based on analyzed request information.

[0611] A "response generation means" is a technology for generating user-facing responses in natural language based on acquired information.

[0612] "Speech synthesis means" refers to a device or technology for outputting a generated natural language response as speech.

[0613] "Design generation means" refers to technology that uses generative artificial intelligence to generate customized designs and present them.

[0614] "Emotion recognition means" refers to technology that analyzes emotions from a user's voice and identifies their emotional state.

[0615] "Response adjustment means" refers to techniques for optimizing responses generated based on recognized emotions.

[0616] The system of this invention operates to convert a user's voice commands into digital signals, analyze the voice data, and generate an appropriate response. The system consists of a voice recognition means, an emotion recognition means, a server incorporating a generative artificial intelligence model, and a terminal for outputting the response.

[0617] When the server receives a voice command from a user, it first converts it into text data using speech recognition. The text data obtained at this stage is then analyzed by emotion recognition to determine the user's emotional state. Based on the results, a generative artificial intelligence model performs further analysis and obtains information corresponding to the user's request. This acquired information is then used by a response generation system to generate a natural language response that takes the user's emotional state into consideration.

[0618] The generated response is output to the user as natural-sounding speech using speech synthesis technology by the terminal. Through interaction with the system, the user can receive information that is appropriate to their emotions and situation. Furthermore, by using design generation technology, customized designs are also provided according to the user's requests.

[0619] For example, if a security guard receives a visitor's "Good morning" greeting through smart glasses and detects tension from the tone, the server can suggest a response such as "Please respond in a relaxed, friendly tone." In this way, interaction that takes into account the user's emotions and situation is realized.

[0620] An example of a prompt is, "The user seems nervous. Please create a greeting in a gentler tone." Based on this prompt, the generative AI model generates a response.

[0621] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0622] Step 1:

[0623] The server receives the user's voice command. This input voice is converted into text data using speech recognition. The voice data is analyzed by a speech recognition algorithm and output as a string.

[0624] Step 2:

[0625] The server processes the obtained text data using emotion recognition means to identify the user's emotional state. In this step, the text data is input into an emotion analysis algorithm, and a determination result indicating an emotional state such as joy, anger, sadness, or pleasure is output.

[0626] Step 3:

[0627] The server uses a generative artificial intelligence model to generate responses based on text data and emotional states. It inputs prompt sentences corresponding to the emotional state into the generative AI, which then outputs appropriate response sentences. These response sentences include information and advice that takes the user's emotions into consideration.

[0628] Step 4:

[0629] The generated text response is transferred to the terminal's speech synthesis system and converted into natural-sounding speech. The terminal then outputs the generated speech data to the user through its speaker, completing the communication. The response is achieved by converting text into speech using a speech synthesis engine and outputting that speech.

[0630] Step 5:

[0631] When a user submits a design request, the server uses a generative artificial intelligence model to generate a customized design and displays it on the terminal. The user's request is received as text input, processed by the design generation algorithm, and the individualized design is output as image data.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] [Fourth Embodiment]

[0636] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0637] 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.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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".

[0649] This invention relates to an accessory device that utilizes generative artificial intelligence. This device is designed to allow users to naturally utilize AI technology in everyday situations. The operation of the system will be specifically described below as an embodiment of the invention.

[0650] When a user uses the device, they first initiate interaction with voice. Voice recognition converts the user's voice commands into digital signals on the device. These signals are then transmitted to a server via the network. The server analyzes the voice data using a generative artificial intelligence model and interprets the user's request.

[0651] Based on the analysis results, the server uses external information gathering means to acquire the necessary data. For example, if a user asks for weather information, the server acquires the latest weather data from an appropriate external API. This information is then processed into natural language using a response generation means and sent back to the terminal.

[0652] The terminal uses speech synthesis technology to output natural language responses received from the server as speech that is easy for the user to understand. This allows the user to intuitively obtain information.

[0653] Furthermore, if a user requests a customized design, the design generation system utilizes artificial intelligence to generate a design tailored to their individual needs and display it on the device. This feature allows users to not only receive information but also enjoy a more personalized experience.

[0654] For example, if a user issues a voice command such as "Tell me today's news," the voice recognition system transmits this information to the server. The server collects the news information and generates a natural language summary. The terminal then outputs this summary as voice and provides it to the user. In addition, the news display layout and headline design can be customized, allowing for presentations tailored to the user's preferences.

[0655] Thus, the present invention provides an environment in which users can smoothly utilize live artificial intelligence through a series of processes from speech recognition to information gathering, response generation, and presentation of customized designs.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] The user issues voice commands to the accessory device. The user speaks to the device in natural language, specifying the information they want to know or the action they want to perform.

[0659] Step 2:

[0660] The device uses a voice sensor to convert the user's voice into a digital signal. This conversion process is performed in real time.

[0661] Step 3:

[0662] The terminal encodes the digital signal into the appropriate format and sends it to the server over the network. This makes the audio data available to the server in a remote location.

[0663] Step 4:

[0664] The server uses a generated artificial intelligence model to analyze the received voice data. The server understands the user's intent and identifies the specific request.

[0665] Step 5:

[0666] The server collects necessary information based on the analysis results. For example, it may refer to external APIs or databases to retrieve information requested by the user.

[0667] Step 6:

[0668] The server generates natural language responses based on the information it acquires. Generative artificial intelligence is used to format the responses in a way that is easy for the user to understand.

[0669] Step 7:

[0670] The server sends the generated natural language response to the terminal. The response data is encoded in a format suitable for speech output.

[0671] Step 8:

[0672] The terminal receives a response from the server, converts it using speech synthesis technology, and outputs it to the user as audio. This allows the user to hear the information they were looking for.

[0673] Step 9:

[0674] When a user requests a customized design, they input the design details on their device. Based on the user's request, the customized settings are applied.

[0675] Step 10:

[0676] The server generates a design in response to a customization request and sends it to the device. The generated design is displayed in real time on the device's screen or within the app.

[0677] (Example 1)

[0678] 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".

[0679] The present invention aims to provide a system that allows users to acquire various types of information smoothly and intuitively, and to enable personalized experiences based on user instructions. In particular, there is a need to efficiently acquire information and present designs using voice instructions.

[0680] 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.

[0681] In this invention, the server includes recognition means for interpreting user voice instructions and converting them into information signals, analysis means for analyzing voice instructions and understanding commands using a generative intelligence model, and external information acquisition means for obtaining the analyzed command information. This enables users to efficiently acquire information through voice and to have a personalized design experience.

[0682] "Recognition means" refers to a device or program that has the function of converting voice instructions into digital information signals.

[0683] "Analysis means" refers to a device or program that uses a generative intelligence model to analyze voice commands and understand the user's intended instructions.

[0684] "External information acquisition means" refers to a device or program that acquires relevant information from an external source based on an analyzed command.

[0685] A "response formation means" is a device or program that creates a response in natural language based on acquired information.

[0686] "Speech synthesis means" refers to a device or program for outputting a synthesized natural language response as speech.

[0687] A "design generation means" is a device or program that uses generative intelligence to generate individual designs and present them to the user.

[0688] A "generative intelligence model" is an artificial intelligence model that uses learning algorithms to analyze voice commands and generate designs.

[0689] The system related to the present invention analyzes the user's voice commands using a generative AI model and provides information through natural dialogue. Specifically, when a user uses an accessory device, the interaction is initiated through voice commands.

[0690] The terminal uses a voice capture device equipped with recognition capabilities to convert the user's voice into digital information signals. This digital data is then transmitted to a server using network communication technology.

[0691] The server uses analysis tools to analyze the voice data received through a generative AI model. The generative AI model incorporates an algorithm that understands voice instructions and analyzes them as commands. Based on the analysis results, it then uses external information acquisition tools to collect information from appropriate data sources.

[0692] The collected information is converted into a response generated in natural language by a response formation mechanism. The terminal then utilizes a speech synthesis mechanism to vocalize this response and convey it to the user. Through this process, the user can intuitively obtain useful information.

[0693] As a concrete example, when a user asks the device, "Tell me the weather for tomorrow," the device transmits this information to the server. The server retrieves the latest weather data through a weather information API and generates a response such as, "Tomorrow will be sunny, with a high of 22 degrees Celsius." The device then outputs this as audio.

[0694] Furthermore, using design generation tools, individual designs are created in response to user instructions and visually presented on the device. This customized design experience can also offer a variety of content to meet user needs.

[0695] Examples of prompt messages include "Retrieve the latest movie information and inform the user." Thus, this system is designed to provide users with advanced information and a customized experience.

[0696] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0697] Step 1:

[0698] The user provides voice instructions. The terminal records the user's voice using a voice interface and converts it into a digital signal through recognition means. In this process, the voice data is encoded into a digital format and made into a format that can be transmitted to the server. The input is the user's raw voice, and the output is a digital voice signal.

[0699] Step 2:

[0700] The server receives a digital audio signal transmitted from the terminal. Using an analysis tool, it analyzes the audio data with a generative AI model and interprets the user's intent. Specifically, it converts the audio signal into text and extracts the user's request (e.g., "weather information") from that text. In this step, the input is a digital audio signal, and the output is a textual representation of the audio and the analyzed user intent.

[0701] Step 3:

[0702] Based on the analyzed intent, the server uses external information acquisition methods to obtain the necessary information over the network. For example, if the user is requesting weather information, the server accesses a weather information API to collect the latest weather forecast data. In this step, the input is the user's intent, and the output is the data acquired from an external source.

[0703] Step 4:

[0704] The server generates a natural language response based on the information collected by the response formation mechanism. It utilizes a generative AI model to convert the data into a user-friendly format. For example, it might convert collected weather data into a concise response such as, "Tomorrow will be sunny, and the maximum temperature will be 20 degrees Celsius." In this step, the input is information obtained from an external source, and the output is a natural language response.

[0705] Step 5:

[0706] The server sends the generated natural language response to the terminal. The terminal uses speech synthesis to play the received text as speech. This allows the user to receive information through digital speech. In this step, the input is the natural language response sentence, and the output is the spoken response.

[0707] Step 6:

[0708] Furthermore, when a user makes a specific design request, the design generation system utilizes AI to generate a design tailored to the individual user's needs and displays that design on the device. This process allows users to receive visually customized content. In this step, the input is the user's design request, and the output is the display of the customized design.

[0709] (Application Example 1)

[0710] 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".

[0711] In today's commercial environment, there is a need to provide real-time information to visitors in physical stores and to enable more personalized purchasing support. However, because the optimal use of voice interfaces and AI technology for efficient information provision is not being fully utilized, there is a challenge in that visitors have difficulty obtaining product information and recommendations quickly within stores.

[0712] 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.

[0713] In this invention, the server includes voice recognition means for recognizing voice commands and converting them into digital signals, external information gathering means for acquiring analyzed request information, and guidance display means for providing commercial information to visitors. This enables visitors to instantly obtain information and purchase support within the store simply by speaking.

[0714] "Voice recognition means" refers to a device or software that has the function of converting voice commands uttered by a user into digital signals.

[0715] A "generative artificial intelligence model" is an artificial intelligence algorithm or program used to analyze a user's voice commands and recognize requests.

[0716] "Analysis means" refers to a process or device for analyzing voice commands and recognizing user requests.

[0717] "External information gathering means" refers to devices or means for obtaining necessary information from external sources based on analyzed request information.

[0718] A "response generation means" refers to a function or device for generating a response in natural language based on acquired information.

[0719] "Speech synthesis means" refers to software or devices that output generated natural language responses as speech that is easy for the user to understand.

[0720] A "design generation method" is a means of generating and presenting customized designs based on the individual needs of users, using generative artificial intelligence.

[0721] "Information display means" refers to display devices or functions that provide commercial information to visitors.

[0722] The system of this invention is designed to provide users with product information and recommended outfits in a commercial environment. The core of the system is a speech recognition and generative AI model, which enables seamless interaction with the user. The server first uses speech recognition means to convert voice commands from the user into digital signals. This is done, for example, using speech recognition software. The digital signals are sent to the server and analyzed using a generative artificial intelligence model. During the analysis process, the requested information is decoded, and necessary information is obtained from databases or external APIs, for example, through external information gathering means.

[0723] The server generates natural language responses using response generation means based on the acquired information, and outputs them as user-friendly speech using speech synthesis means. During this process, the generation AI model performs sophisticated natural language processing to produce the optimal answer to the user's request. For example, if a user says, "Tell me your recommended products," the system will respond through speech synthesis with, "Our current recommended product is XX." This system can also use design generation means to present customized designs according to the user's preferences. As a specific example, when suggesting a design for a particular product, it will provide the user with information such as, "This is the suggested design."

[0724] The implementation of such a system will create an environment where visitors can use in-store services more intuitively and efficiently. The natural interface provided by the generative AI model can offer store users a unique and customized experience. An example of a prompt used would be, "Please tell me about the latest recommended products in the store." This prompt serves as the foundation for the AI ​​to accurately understand the user's request and provide appropriate information.

[0725] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0726] Step 1:

[0727] The user speaks into the terminal and inputs a voice command. This voice command is captured by the terminal via the microphone. The terminal converts this voice command into a digital signal using voice recognition. The converted digital signal is sent to the server.

[0728] Step 2:

[0729] The server uses a generative AI model to analyze the digital signals received from the speech recognition system. This analysis system understands the request content from the user's voice commands. The understood request is used as input data for the next processing step, in which the digital signals are converted into a request in natural language format.

[0730] Step 3:

[0731] Based on the analysis, the server collects necessary information using external information gathering means. In this case, the server connects to an external API, for example, to obtain information related to the user's request. The collected information is then used as the basis for generating the response. In this step, information corresponding to the request content is collected from databases and external information sources.

[0732] Step 4:

[0733] The server generates a natural language response using response generation tools based on the collected information. The generative AI model structures the data and transforms it into a user-friendly format during this process. The generated response becomes output data for speech synthesis. In this step, the information is converted into human-readable sentences.

[0734] Step 5:

[0735] The server converts the generated natural language response into speech output using speech synthesis. The terminal plays this audio, presenting the information to the user. Through this process, the user can receive the requested information in audio form. In this step, the natural language is converted back into an audio signal and presented to the user through the speaker.

[0736] 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.

[0737] This invention is an accessory device system that combines generative artificial intelligence and emotion recognition with user interaction. Its aim is to provide users with a more personalized experience in their daily lives. The following describes specific embodiments of the system.

[0738] The terminal receives the user's voice command and converts it into a digital signal using voice recognition. Next, an emotion recognition engine analyzes the user's emotions from this voice data and identifies basic emotional states such as joy and anger. This emotion data is sent to the server simultaneously with the analysis of the voice command.

[0739] The server utilizes a generative artificial intelligence model to analyze the transmitted voice commands. Simultaneously, it generates responses appropriate to the user's emotions based on emotion data from an emotion recognition engine. This ensures that responses are not merely informational but also considerate of the user's feelings. For example, if the user is tired, the server can choose a softer tone of voice in its response.

[0740] Based on the acquired data, a natural language response is generated by a response generation system and sent from the server to the terminal. The terminal uses a speech synthesis system to output this response to the user as natural-sounding speech. The user can receive information that is tailored to their own emotions and circumstances.

[0741] Furthermore, if a user requests a customized design, the design generation system utilizes artificial intelligence to create a design that meets the user's requirements. This design is then displayed on the device, with its colors and layout adjusted based on the user's emotions.

[0742] For example, when a user issues the voice command "Tell me the news," if the emotion recognition engine detects that the voice lacks composure, the device will prioritize providing relaxing news topics to reduce stress via the server. In this way, it is possible to provide information services that take the user's state of mind into consideration.

[0743] This invention provides a technology that takes user emotions into consideration to create a more personalized experience and make user-system interaction more natural and comfortable.

[0744] The following describes the processing flow.

[0745] Step 1:

[0746] The user issues voice commands to the accessory device. The user speaks in natural language about the information they want to know or the actions they want to perform.

[0747] Step 2:

[0748] The device uses a voice sensor to convert the user's voice into a digital signal. The voice data is processed within the device.

[0749] Step 3:

[0750] The device uses an emotion recognition engine to analyze the user's emotional state from voice data. For example, it can identify the user's level of tension or relaxation from their voice tone and tempo.

[0751] Step 4:

[0752] The device transmits voice digital signals and emotional data to the server. The transmission takes place in real time over the network.

[0753] Step 5:

[0754] The server uses an artificial intelligence model to analyze voice data and understand the user's intent. Based on the analysis results, it identifies the information and actions the user is seeking.

[0755] Step 6:

[0756] The server considers emotional data from the emotion recognition engine and generates responses with appropriate content and tone. Responses are prepared that are intended to provide feedback tailored to the user's emotions.

[0757] Step 7:

[0758] The server collects necessary information from external sources. For example, it retrieves the latest news and weather forecasts from relevant APIs.

[0759] Step 8:

[0760] The server converts the acquired information into a natural language response and makes adjustments to reflect emotions. The generated response is then sent to the terminal.

[0761] Step 9:

[0762] The terminal receives a response from the server, converts it into speech using speech synthesis technology, and provides it to the user. This allows the user to obtain information through listening.

[0763] Step 10:

[0764] If a user requests a customized design, their device sends the request to the server. The design is then adjusted based on the user's emotional state.

[0765] Step 11:

[0766] The server uses artificial intelligence to generate an emotion-based customized design and sends it to the device. The device then displays the generated design on its screen.

[0767] Through these steps, users can receive information and designs that match their emotions.

[0768] (Example 2)

[0769] 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".

[0770] Conventional information delivery systems often provide information unilaterally without considering the user's emotions, making it difficult to meet individual user needs. Furthermore, there is a need not only for systems that simply recognize voice commands, but also for systems that respond in accordance with the user's emotional state. Additionally, providing designs and content based on user requests in real time presents challenges that were not present in conventional technologies.

[0771] 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.

[0772] In this invention, the server includes a speech recognition means that recognizes the user's voice commands and converts them into numerical signals, a means that analyzes emotions from voice data using emotion recognition technology and extracts emotional information, and an analysis means that analyzes voice commands and recognizes requests using a generative artificial intelligence model. This makes it possible to provide personalized responses and designs based on the user's emotions.

[0773] "Voice commands" are instructions that a user issues to a device via voice, regarding operations or the retrieval of information.

[0774] A "numerical signal" is a signal obtained by converting audio or analog information into a digital format, and is in a format that can be processed by a computer.

[0775] "Voice recognition means" refers to technology that analyzes voice information, converts it into numerical signals, and identifies voice commands.

[0776] "Emotion recognition technology" is a technology that analyzes a user's emotions from their voice, facial expressions, etc., and evaluates specific emotional states.

[0777] "Emotional information" refers to data about the user's emotional state, and is an emotional indicator extracted after analysis.

[0778] A "generative artificial intelligence model" is an algorithm that learns from large amounts of data and generates natural-sounding responses and content that resemble those of a human.

[0779] "Analysis means" refers to technologies that analyze input data and commands to understand their content and recognize requests.

[0780] "Personalized responses" refer to information and feedback provided in a way that is adapted to the user's specific situation and emotions.

[0781] "Design generation methods" refer to technologies that effectively generate visual content and layouts based on user requests and emotions.

[0782] This invention is a system that combines generative artificial intelligence and emotion recognition in user interaction, enabling personalized information provision and design for the user. This system is configured using a terminal and a server.

[0783] The terminal receives voice commands from the user via a microphone and converts this voice into numerical signals using voice recognition technology. This voice recognition can utilize general-purpose voice recognition software or cloud-based voice recognition services. Next, the terminal extracts emotional information from the voice data using emotion recognition technology. Various cloud services can be used for this emotion analysis.

[0784] Voice commands and emotional information sent from the terminal to the server are analyzed on the server by a generative artificial intelligence model (for example, a large-scale natural language processing model). Based on this information, the server can generate an appropriate text response that corresponds to the user's emotions. In this generation process, prompts are used to customize the response according to specific conditions and contexts.

[0785] The generated response is returned from the server to the terminal and output to the user as natural-sounding speech through a speech synthesis system. A common speech synthesis engine can be used for speech synthesis, and the response is delivered to the user in real time.

[0786] Furthermore, if a user makes a design request, the server uses design generation tools and artificial intelligence to generate a personalized design based on the user's emotions and requests. This design is then adjusted in terms of color and layout and displayed on the terminal screen.

[0787] For example, if a user asks "What's the weather like today?", the device will interpret the user's voice as reassuring, and the server will generate a response such as "It's a sunny day today, have a pleasant day." Furthermore, in response to design requests, the system will generate background designs based on the user's needs and emotions, providing a visually pleasing user experience.

[0788] An example of a prompt message is, "Generate a reassuring response based on the user's request."

[0789] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0790] Step 1:

[0791] The terminal receives voice commands from the user via a microphone. The input is the user's voice information. This voice is converted into a numerical signal using speech recognition means and identified as a voice command. The output is digitized voice command data. Specifically, speech recognition software analyzes the voiceprint and generates text data.

[0792] Step 2:

[0793] The device uses emotion recognition technology to extract user emotion information from received audio data. The input is digitized audio data. Data analysis is performed to identify and output emotions such as joy, anger, and sadness. Specifically, the analysis algorithm analyzes the pitch and tone of the voice and generates an emotional index.

[0794] Step 3:

[0795] The terminal transmits voice command data and emotional information to the server. The input consists of digitized voice commands and extracted emotional information. This is sent to the server as a data packet, which the server receives to prepare for the next processing step. The output is the data packet sent to the server.

[0796] Step 4:

[0797] The server uses a generative AI model to analyze the received voice command data and emotion information, and generates an appropriate response. The input consists of voice command and emotion information data. The generative AI model analyzes the command and generates a response text appropriate to its content and emotion. The output is the response text to the user. Specifically, the AI ​​model uses prompt sentences to construct the optimal response in text format.

[0798] Step 5:

[0799] The server sends the generated response text to the terminal. The input is the generated response text. This data is sent to the terminal and is ready for audio output to the user. The output is the response data sent to the terminal.

[0800] Step 6:

[0801] The terminal converts the received response text into natural-sounding speech using a speech synthesis system and outputs it to the user. The input is the response text sent from the server. It is converted into audio data using a speech synthesis engine and output through the speaker. The output is the audio response that the user can hear. Specifically, the speech synthesis software analyzes the text and generates an audio file.

[0802] Step 7:

[0803] When a user submits a design request, the terminal sends this request to the server. The input is the design request from the user. Using the design generation mechanism, the process prepares to build a design based on the user's feelings and requests. The output is the design request data sent to the server.

[0804] Step 8:

[0805] The server creates personalized designs using a generative AI model based on user requests. The input consists of a design request and sentiment information. The generative AI model analyzes this data and generates a design that meets the request. The output is a design file viewable on the terminal. Specifically, the AI ​​generates graphic data with optimized colors and layout.

[0806] Step 9:

[0807] The terminal displays the design sent from the server on its screen. The input is the design file from the server. By displaying this on the screen, the user can immediately review it. The output is a design display that the user can visually review. Specifically, the display controller renders the design data and displays it on the terminal's screen.

[0808] (Application Example 2)

[0809] 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".

[0810] Existing systems that unilaterally provide information without considering user emotions make it difficult to provide user-optimized responses and designs, resulting in the standardization of individual user experiences. Furthermore, in security settings, there is a need for more reliable communication that addresses the user's internal psychological state, but there is a lack of technology to achieve this.

[0811] 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.

[0812] In this invention, the server includes speech recognition means, emotion recognition means, and response adjustment means. This makes it possible to provide optimized responses in real time based on the user's emotional state, making the user experience more personal and effective.

[0813] "Voice recognition means" refers to a device or technology that converts a user's voice into a digital signal.

[0814] A "generative artificial intelligence model" is an artificial intelligence algorithm that analyzes a user's voice commands and generates appropriate responses and designs.

[0815] "Analysis means" refers to technology for analyzing a user's voice commands and recognizing their requests.

[0816] "External information gathering means" refers to technologies for obtaining necessary information from external sources based on analyzed request information.

[0817] A "response generation means" is a technology for generating user-facing responses in natural language based on acquired information.

[0818] "Speech synthesis means" refers to a device or technology for outputting a generated natural language response as speech.

[0819] "Design generation means" refers to technology that uses generative artificial intelligence to generate customized designs and present them.

[0820] "Emotion recognition means" refers to technology that analyzes emotions from a user's voice and identifies their emotional state.

[0821] "Response adjustment means" refers to techniques for optimizing responses generated based on recognized emotions.

[0822] The system of this invention operates to convert a user's voice commands into digital signals, analyze the voice data, and generate an appropriate response. The system consists of a voice recognition means, an emotion recognition means, a server incorporating a generative artificial intelligence model, and a terminal for outputting the response.

[0823] When the server receives a voice command from a user, it first converts it into text data using speech recognition. The text data obtained at this stage is then analyzed by emotion recognition to determine the user's emotional state. Based on the results, a generative artificial intelligence model performs further analysis and obtains information corresponding to the user's request. This acquired information is then used by a response generation system to generate a natural language response that takes the user's emotional state into consideration.

[0824] The generated response is output to the user as natural-sounding speech using speech synthesis technology by the terminal. Through interaction with the system, the user can receive information that is appropriate to their emotions and situation. Furthermore, by using design generation technology, customized designs are also provided according to the user's requests.

[0825] For example, if a security guard receives a visitor's "Good morning" greeting through smart glasses and detects tension from the tone, the server can suggest a response such as "Please respond in a relaxed, friendly tone." In this way, interaction that takes into account the user's emotions and situation is realized.

[0826] An example of a prompt is, "The user seems nervous. Please create a greeting in a gentler tone." Based on this prompt, the generative AI model generates a response.

[0827] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0828] Step 1:

[0829] The server receives the user's voice command. This input voice is converted into text data using speech recognition. The voice data is analyzed by a speech recognition algorithm and output as a string.

[0830] Step 2:

[0831] The server processes the obtained text data using emotion recognition means to identify the user's emotional state. In this step, the text data is input into an emotion analysis algorithm, and a determination result indicating an emotional state such as joy, anger, sadness, or pleasure is output.

[0832] Step 3:

[0833] The server uses a generative artificial intelligence model to generate responses based on text data and emotional states. It inputs prompt sentences corresponding to the emotional state into the generative AI, which then outputs appropriate response sentences. These response sentences include information and advice that takes the user's emotions into consideration.

[0834] Step 4:

[0835] The generated text response is transferred to the terminal's speech synthesis system and converted into natural-sounding speech. The terminal then outputs the generated speech data to the user through its speaker, completing the communication. The response is achieved by converting text into speech using a speech synthesis engine and outputting that speech.

[0836] Step 5:

[0837] When a user submits a design request, the server uses a generative artificial intelligence model to generate a customized design and displays it on the terminal. The user's request is received as text input, processed by the design generation algorithm, and the individualized design is output as image data.

[0838] 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.

[0839] 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.

[0840] 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 robot 414.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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."

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0859] The following is further disclosed regarding the embodiments described above.

[0860] (Claim 1)

[0861] A voice recognition means that recognizes the user's voice command and converts it into a digital signal,

[0862] An analysis means that analyzes voice commands using a generative artificial intelligence model and recognizes requests,

[0863] External information gathering means for obtaining the analyzed request information,

[0864] A response generation means that generates a response in natural language from the acquired information,

[0865] A speech synthesis means for outputting the generated response to the user as audio,

[0866] A design generation method for generating and presenting customized designs using generative artificial intelligence,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, further comprising a transmission means for transmitting a design request from a user to an artificial intelligence that generates designs.

[0870] (Claim 3)

[0871] The system according to claim 1, further comprising a data collection means for collecting user interaction data to improve the generated artificial intelligence model.

[0872] "Example 1"

[0873] (Claim 1)

[0874] A recognition means that interprets the user's voice instructions and converts them into information signals,

[0875] An analytical means that uses a generative intelligence model to analyze voice instructions and understand commands,

[0876] External information acquisition means for obtaining the analyzed command information,

[0877] A response formation means that forms the acquired information as a response in natural language,

[0878] A speech synthesis means for outputting the formed response to the user as audio,

[0879] A design generation means for generating and presenting individual designs using generative intelligence,

[0880] Through a series of processes from speech recognition to response generation and design presentation, it provides a means for users to intuitively obtain information and enjoy a customized experience.

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, further comprising communication means for transmitting design instructions from a user to a generating intelligence.

[0884] (Claim 3)

[0885] The system according to claim 1, further comprising data collection means for collecting user interaction data to improve a generative intelligence model.

[0886] "Application Example 1"

[0887] (Claim 1)

[0888] A voice recognition means that recognizes the user's voice command and converts it into a digital signal,

[0889] An analysis means that analyzes voice commands using a generative artificial intelligence model and recognizes requests,

[0890] External information gathering means for obtaining the analyzed request information,

[0891] A response generation means that generates a response in natural language from the acquired information,

[0892] A speech synthesis means for outputting the generated response to the user as audio,

[0893] A design generation method for generating and presenting customized designs using generative artificial intelligence,

[0894] A means of providing commercial information to visitors,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, further comprising a transmission means for transmitting a design request from a user to an artificial intelligence that generates designs.

[0898] (Claim 3)

[0899] The system according to claim 1, further comprising a data collection means for collecting user interaction data and improving the generated artificial intelligence model.

[0900] "Example 2 of combining an emotion engine"

[0901] (Claim 1)

[0902] A voice recognition means that recognizes the user's voice command and converts it into a numerical signal,

[0903] A means of analyzing emotions from voice data using emotion recognition technology and extracting emotional information,

[0904] An analysis means that analyzes voice commands and recognizes requests using a generative artificial intelligence model,

[0905] A means for generating a response based on analyzed request information and emotional information,

[0906] A speech synthesis means for outputting the generated response to the user as audio,

[0907] A design generation method for generating and presenting personalized designs using generative artificial intelligence,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, further comprising emotion data analysis means for enhancing the process of generating responses that align with the user's emotions.

[0911] (Claim 3)

[0912] The system according to claim 1, further comprising data collection means for collecting user response data and optimizing the generated artificial intelligence model.

[0913] "Application example 2 when combining with an emotional engine"

[0914] (Claim 1)

[0915] A voice recognition means that recognizes the user's voice command and converts it into a digital signal,

[0916] An analysis means that analyzes voice commands using a generative artificial intelligence model and recognizes requests,

[0917] External information gathering means for obtaining the analyzed request information,

[0918] A response generation means that generates a response in natural language from the acquired information,

[0919] A speech synthesis means for outputting the generated response to the user as audio,

[0920] A design generation method for generating and presenting customized designs using generative artificial intelligence,

[0921] An emotion recognition method that analyzes emotions from the user's voice,

[0922] A response adjustment means that adjusts the response based on recognized emotions,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, further comprising a transmission means for transmitting a design request from a user to an artificial intelligence that generates designs.

[0926] (Claim 3)

[0927] The system according to claim 1, further comprising a data collection means for collecting user interaction data to improve the generated artificial intelligence model. [Explanation of Symbols]

[0928] 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 voice recognition means that recognizes the user's voice command and converts it into a digital signal, An analysis means that analyzes voice commands using a generative artificial intelligence model and recognizes requests, External information gathering means for obtaining the analyzed request information, A response generation means that generates a response in natural language from the acquired information, A speech synthesis means for outputting the generated response to the user as audio, A design generation method for generating and presenting customized designs using generative artificial intelligence, A system that includes this.

2. The system according to claim 1, further comprising a transmission means for transmitting a design request from a user to an artificial intelligence that generates designs.

3. The system according to claim 1, further comprising a data collection means for collecting user interaction data and improving the generated artificial intelligence model.

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