system

Abstract

We provide the system. [Solution] A detection means for receiving voice input from the user, A conversion means for converting the aforementioned voice input into text data, An interpretation means for analyzing the user's intent from the aforementioned text data, A generation means that generates and presents suggestions to support the user's daily life based on the aforementioned analysis results, A means for providing feedback to the user on the generated proposal via voice or display, 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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, individuals leading busy lives have the problem that it is difficult to make optimal decisions in schedule management and prioritization of daily tasks. Also, due to the lack of personalized support according to the needs of individual users, the improvement of the quality of life is hindered. The purpose of the present invention is to solve the above problems by providing a digital butler-type system that efficiently and effectively supports an individual's daily life.

Means for Solving the Problems

[0005] This invention provides a system that includes a sensor means for receiving voice input from a user, a processing means for converting the voice input into text data, and an analysis means for analyzing the user's intentions from the text data. This system includes a generation means for generating and presenting suggestions to support daily life based on the analysis results. Furthermore, the generation means can suggest clothing for the user based on weather information and the user's past behavior data, and the analysis means can adjust the schedule based on behavior patterns obtained from a database. As a result, users can receive support tailored to their individual needs.

[0006] "Sensor means" refers to a device or function for receiving physical input information from a user, particularly sound.

[0007] "Processing means" refers to a device or function for converting input data into the required format, particularly audio data into text data.

[0008] "Analysis means" refers to a device or function that interprets the user's intent from text data and makes decisions about actions based on that information.

[0009] "Generating means" refers to a device or function that generates and presents specific suggestions to support the user's daily life based on the analysis results.

[0010] "Weather information" refers to data related to the weather, and is one of the factors that influences a user's clothing choices and plans.

[0011] "Behavioral data" refers to information about a user's past behavioral history, and is used to provide suggestions based on individual needs.

[0012] "Schedule adjustment" means optimizing and efficiently managing users' schedules based on data. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled 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.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled 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, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a digital butler-type system that efficiently manages the user's daily life and provides personalized support. This system mainly consists of sensor means, processing means, analysis means, and generation means.

[0035] Specific System Operation

[0036] Voice input and processing

[0037] When a user issues a voice command, the terminal receives it using a sensor. The terminal records this voice data in digital format and converts it into text data using a processing device.

[0038] Analysis of intent

[0039] The server receives the processed text data and interprets the user's intent using parsing tools. For example, if the user says, "Set an alarm for 7 AM tomorrow," the server understands that the user intends to set an alarm for 7 AM the following day.

[0040] Proposal generation

[0041] Based on the analysis results, the server uses various generation methods to create suggestions to support the user's daily life. These suggestions are created by referencing the user's behavioral data and weather information. For example, if rain is expected, the server will notify the user with the message, "Rain is expected tomorrow. Don't forget your umbrella."

[0042] Feedback and Action

[0043] The generated suggestions and actions are communicated to the user via the device. The device can notify the user via voice or screen display, allowing the user to make appropriate preparations and adjustments.

[0044] Specific example

[0045] For example, if a user asks "What's the gym schedule for tomorrow?" to remind themselves of their gym appointment, the server will check the gym's congestion and weather forecast and inform the user, "Tomorrow, 8 to 9 a.m. is the least crowded time. Please prepare accordingly."

[0046] In this way, this system makes users' daily lives more efficient and comfortable by providing specific and helpful suggestions based on their voice input.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The user gives a voice command to the device. The device's sensors then capture the voice and store it as digital data.

[0050] Step 2:

[0051] The terminal converts the audio digital data into text data using processing equipment. Speech recognition technology is used in this conversion process.

[0052] Step 3:

[0053] The terminal sends text data to the server. This transmission is performed via a private and secure protocol over the internet.

[0054] Step 4:

[0055] The server processes the received text data using parsing tools to extract the user's intent. Natural language processing algorithms are used to identify the intent.

[0056] Step 5:

[0057] Based on the analysis results, the server references user behavior data and external information (e.g., weather information) to create optimal suggestions. This process utilizes a generation method.

[0058] Step 6:

[0059] The server sends the generated proposal or action to the terminal. This is also done using a secure communication protocol.

[0060] Step 7:

[0061] The device guides the user through received suggestions and actions via voice generation or display. This provides the user with feedback.

[0062] (Example 1)

[0063] 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."

[0064] Conventional digital assistant systems have faced challenges in accurately interpreting user voice commands and providing specific solutions to support the user's daily life more efficiently and comfortably. Furthermore, they have been unable to dynamically generate suggestions that take into account real-time weather information and past user behavior data, and to accurately notify the user.

[0065] 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.

[0066] In this invention, the server includes a sensing device that acquires voice signals from the user, a conversion device that converts the voice signals into encoded data, and an interpretation device that interprets the user's instructions from the encoded data. This makes it possible to generate and accurately provide advice that is tailored to the user's specific needs.

[0067] "User" refers to an individual or group that uses this system to give voice commands.

[0068] "Audio signal" refers to an input format that acquires the voice emitted by the user as an electrical signal.

[0069] A "sensing device" refers to a mechanical or electronic device used to acquire audio signals.

[0070] "Encoded data" refers to digital data obtained by converting an audio signal into a format that can be processed.

[0071] A "conversion device" refers to a device that has the function of converting audio signals into encoded data.

[0072] An "interpretation device" refers to a device that analyzes and understands the user's intentions and instructions from encoded data.

[0073] A "creation device" refers to a device that generates advice for the user based on the analysis results obtained by an interpretation device.

[0074] An "output device" refers to a device that communicates generated advice to the user visually or audibly.

[0075] "Weather data" refers to data that includes information about weather conditions.

[0076] "Historical data" refers to data about user behavior and past operations recorded over time.

[0077] A "data storage device" refers to a system for storing information related to user trends and behavioral patterns.

[0078] "Planning adjustment" refers to the adjustment work performed to optimize a user's schedule and activities.

[0079] This invention is a digital butler system that efficiently supports the user's daily life. The system consists of a series of devices for collecting voice commands from the user and generating suggestions based on those commands.

[0080] The terminal is equipped with a sensing device that receives the user's voice. This sensing device includes a high-precision microphone that can electrically detect the voice signal. The terminal also has a built-in converter that uses voice recognition software to convert this voice signal into encoded data. Specifically, a general-purpose voice recognition API can be used as the unit that converts voice input into text data.

[0081] Upon receiving encoded data, the server uses an interpreter to interpret the user's intent using a deep learning algorithm. Based on the information obtained from the interpretation, a creation device equipped with a generative AI model creates helpful suggestions for the user. This AI model generates more personalized suggestions by linking with the user's past behavior history and external weather information databases.

[0082] The terminal uses an output device equipped with speech synthesis software and a display to notify the user of the generation suggestions sent from the server. The generated information is then provided to the user visually or audibly.

[0083] For example, if a user says, "Tell me the gym schedule for tomorrow," the device converts this voice into encoded data and sends it to the server. The server checks the gym's congestion level and uses this information to generate the most suitable suggestion using a generative AI model. For example, the server might indicate, "Tomorrow morning from 8 to 9 am is relatively less crowded."

[0084] An example of a prompt would be, "Generate the best suggestion for when the user says, 'Tell me tomorrow's gym schedule.'" By supplying this prompt to the model, it provides specific support tailored to the situation.

[0085] As a result, this system enables users to plan their lives more effectively and flexibly, thereby improving the user experience.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The user issues a voice command. The user communicates their request by speaking to the system. This voice input becomes the initial data. Based on this input, the terminal uses a microphone to acquire a voice signal. The acquired voice signal is processed in digital format and prepared for the next processing step.

[0089] Step 2:

[0090] The terminal converts the audio signal into encoded data. Using speech recognition software, it analyzes the audio signal and converts it into text data. This data processing converts the user's voice commands into text format. The output is text data, which becomes the input to the server.

[0091] Step 3:

[0092] The server receives text data from the terminal. The server uses an interpreter to analyze the text data and interpret the user's intent. This performs specific data calculations, clarifying the actions or suggestions the user desires. The output of this step is the interpreted user intent.

[0093] Step 4:

[0094] Based on the interpreted intent, the server uses a generative AI model to generate specific suggestions. The generative AI model references past user data and weather information obtained from external sources to design the most appropriate suggestions for the user. The output of this step is specific and practical suggestions that should be provided to the user.

[0095] Step 5:

[0096] The terminal receives suggestions from the server and outputs them to the user. The suggestions are communicated to the user via speech synthesis or display. Based on this information, the user can decide on their next course of action. The output of this final step is advice provided to the user, presented visually or audibly.

[0097] (Application Example 1)

[0098] 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."

[0099] In modern society, daily life is increasingly busy, making it difficult for individual users to efficiently manage their schedules and receive lifestyle suggestions. Furthermore, there is a growing need for flexible and personalized information tailored to each user's lifestyle. However, conventional digital assistants generally only provide information and have not yet reached the point of providing effective suggestions in conjunction with the user's behavioral history and real-time information.

[0100] 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.

[0101] In this invention, the server includes detection means for receiving voice input from a user, conversion means for converting the voice input into text data, interpretation means for analyzing the user's intent from the text data, generation means for generating and presenting suggestions to support the user's daily life based on the analysis results, and communication means for providing feedback of the generated suggestions to the user by voice or display. This makes it possible to quickly and effectively provide personalized suggestions tailored to the user's lifestyle and environment.

[0102] "Detection means" refers to a device or mechanism for sensing and recording voice input from the user.

[0103] "Conversion means" refers to processing devices or algorithms that convert audio data into text data.

[0104] "Interpretation tools" refer to devices or functions used to analyze and understand user intent from text data.

[0105] A "generation means" is a device or program that creates specific suggestions to support the user's daily life based on the analyzed intention.

[0106] "Means of communication" refers to devices or functions that convey generated proposals to the user through voice or display.

[0107] "Weather information" refers to data about the environment, such as weather and temperature, and is fundamental information that forms the basis for suggesting lifestyle changes to users.

[0108] "Behavioral history" refers to records and data about a user's past activities and actions.

[0109] "Schedule management" refers to the processes and methods for organizing and optimizing a user's schedule and appointments.

[0110] "Learning results" refer to information obtained as new insights by analyzing behavioral patterns acquired from a database.

[0111] The system that implements this application consists of a server, a terminal, and a user interface. The server utilizes speech recognition software (e.g., Google® Cloud Speech-to-Text) to convert voice input into text data. It also uses natural language processing (NLP) libraries (e.g., NLTK, spaCy) to analyze the user's intent from the text data. Based on this information, the server uses Python as a generation method to analyze the user's behavior history and weather data to generate appropriate suggestions. Furthermore, the generated suggestions are converted into speech using speech synthesis software such as Amazon Polly and transmitted to the user via the terminal. For example, if the user instructs, "Suggest what to wear tomorrow," the server analyzes weather information and the user's past clothing history and notifies them, "It will be cold tomorrow, so please wear a coat."

[0112] Examples of prompt messages are as follows:

[0113] "Suggest the best travel plan for this weekend based on the user's schedule."

[0114] "Generate a message recommending 15 minutes of exercise per day to users who are not getting enough exercise."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user gives voice commands to the device. The input here is the user's spoken voice, which the device's microphone receives. The output is the analog audio signal collected on the device.

[0118] Step 2:

[0119] The terminal converts the received analog audio signal into digital audio data. This conversion is done using audio signal processing technology. The output is digital audio data.

[0120] Step 3:

[0121] Digital audio data is sent to a server and converted into text data by speech recognition software (e.g., Google Cloud Speech-to-Text). In this step, digital audio data is taken as input, and the converted text data is output using speech recognition technology.

[0122] Step 4:

[0123] The server analyzes the text data and interprets the user's intent. Here, it uses NLP (Neural Language Processing) technology to understand the user's request based on the input text data. This analysis yields the interpreted intent as output.

[0124] Step 5:

[0125] The server generates suggestions tailored to the user's needs based on the analysis results. This process references the user's behavioral history and weather information, and outputs the resulting suggestions.

[0126] Step 6:

[0127] The generated proposals are then converted into speech using speech synthesis software (e.g., Amazon Polly). The input is the generated proposal text, and the output is the synthesized speech data.

[0128] Step 7:

[0129] Finally, the device delivers the voiced suggestion to the user. The synthesized voice is delivered to the user through the speaker. This allows the user to confirm the content of the suggestion.

[0130] 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.

[0131] This invention provides more personalized and appropriate support by combining a voice input system with emotion recognition capabilities to assist users in their daily lives. This system receives voice commands from the user and consists of sensor means, processing means, analysis means, generation means, and an emotion engine.

[0132] Specific System Operation

[0133] Voice input and processing

[0134] When a user gives a voice command to a device, the device's sensor captures this command and saves it as audio data. A processing unit then converts this audio data into text data.

[0135] Recognition of emotions

[0136] This text data is sent from the terminal to the server and analyzed by the emotion engine. The emotion engine extracts the user's emotional characteristics from the audio and text data and determines the user's emotional state. This process is influenced by factors such as the tone and speed of the voice and the frequency of use of specific words.

[0137] Intent analysis and proposal generation

[0138] The server extracts the user's intent through analysis and combines it with their emotional state. Based on this, the generation system creates the most appropriate suggestions for the user. Weather information and past behavioral data are also referenced as needed.

[0139] Personalized feedback

[0140] The server-generated suggestions are adjusted to the user's emotional state and sent to the terminal. The terminal receives this information and guides the user via voice or display. This makes it possible to provide emotionally optimized support.

[0141] Specific example

[0142] For example, if a user speaks to their device in a tired voice saying, "I don't want to go to work," the emotion engine will determine that the user is experiencing negative emotions. The server will take this into account and generate a suggestion such as, "Shall I play some music to help you relax?" and guide the user through the device. This ensures that the user receives support and a supportive environment that takes their emotions into consideration.

[0143] Thus, this system is designed to provide more advanced personalized support that takes user emotions into account.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The user gives voice commands to the device. The device captures the voice using sensory means and stores it as digital audio data.

[0147] Step 2:

[0148] The terminal uses processing means to convert audio data into text data. This conversion utilizes speech recognition technology.

[0149] Step 3:

[0150] The device sends the converted text data to the server, and at the same time, it also sends voice characteristic data for emotion recognition.

[0151] Step 4:

[0152] The server uses an emotion engine to analyze text data and voice characteristic data to evaluate the user's emotional state.

[0153] Step 5:

[0154] The server extracts the user's specific intentions from text data through analysis. Based on these intentions, it generates basic suggestions for life support.

[0155] Step 6:

[0156] The server adjusts the content of the suggestions, taking into account the emotional state, and the generation method determines the suggestions that are appropriate for the user's current emotions.

[0157] Step 7:

[0158] The server sends the adjusted suggestions to the terminal. The terminal then guides the user through the suggestions via voice or display.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] Conventional speech recognition systems can generate text data based on voice input from users and analyze the user's intent from that text data. However, they could not take into account the user's emotional state, resulting in uniform support for users and difficulty in providing flexible responses tailored to the individual needs of each user. This invention aims to solve these problems and provide more personalized and accurate support for daily life by generating suggestions that reflect the user's emotional state.

[0162] 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.

[0163] In this invention, the server includes a device for receiving voice input from a user, a device for converting the voice input into text data, and a device for determining the user's emotional state from the text data and voice characteristic data. This makes it possible to generate personalized suggestions that take the user's emotional state into account.

[0164] A "device that receives voice input from a user" is a device that captures the voice spoken by a user and takes that voice in order to process it.

[0165] A "device that converts voice input to text data" is a device that analyzes acquired voice data and converts it into a text format that can be read by humans.

[0166] A "device for determining a user's emotional state from text data and voice characteristic data" is a device that analyzes characteristics obtained from voice and the content of text to infer and determine a user's emotions.

[0167] A "device that analyzes user intent and generates suggestions considering emotional state" is a device that, based on the user's input and emotional state, presents the most appropriate course of action and support for that user.

[0168] "Environmental information and user's past activity history" refers to information about the user's surroundings, weather conditions, and records of the user's past actions and habits.

[0169] A "device that acquires behavioral history from a storage device and optimizes schedules based on learned information" is a device that acquires data on a user's past actions and activities from a storage device and uses that data to organize the user's schedule and plans more efficiently.

[0170] Modes for carrying out the invention

[0171] This invention is a system for providing personalized assistance based on user voice input. The system aims to generate suggestions tailored to the user's specific needs by combining voice recognition, sentiment recognition, and intent analysis.

[0172] composition

[0173] 1. Voice input device

[0174] The terminal is a device that detects voice input from the user and captures it as digital voice data. This device includes a microphone and a voice sensor.

[0175] 2. Speech recognition device

[0176] The device uses speech recognition software to convert the captured audio data into text data. Commonly used software includes "speech recognition APIs" and "natural language processing engines."

[0177] 3. Emotion discrimination device

[0178] The server analyzes the user's emotional state based on the generated text data and voice characteristics data. In this step, the emotion engine determines the emotional state by considering factors such as voice tone, speed, and word choice.

[0179] 4. Intention Analysis Device

[0180] The server uses natural language processing techniques to analyze the user's intent from text data and understand the user's current situation.

[0181] 5. Proposal generation device

[0182] The server uses a generative AI model to generate personalized suggestions for the user based on their emotional state and analyzed intentions.

[0183] Specific example

[0184] For example, if a user says to the device in the morning, "I don't really feel like it today," the emotion recognition device will determine from the tone of voice and phrasing that the user is feeling unmotivated. The server can then use this result to generate a suggestion to play relaxing music and notify the user via voice.

[0185] Example of a prompt

[0186] The prompt can be input to the generating AI model in the form of, "The user said, 'I don't feel like doing anything today.' Please provide an appropriate suggestion for this situation." Based on this prompt, the model will suggest the most appropriate course of action for the user.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] The terminal receives voice input from the user. The input is raw voice data. The terminal's voice input device captures this voice through the microphone and saves it as digital audio data. The output at this stage is digital audio data.

[0190] Step 2:

[0191] The terminal's speech recognition device receives the audio data obtained in step 1 as input and converts it into text data using speech recognition software. During this process, data processing is performed by the speech signal processing and recognition engine. The output of this step is text data representing the content of the user's speech.

[0192] Step 3:

[0193] The terminal sends the text data and voice characteristic data created in step 2 to the server. The communication is encrypted, ensuring data security. The input consists of text data and voice characteristic data, and the output is the secure delivery of that data to the server.

[0194] Step 4:

[0195] The server analyzes the user's emotional state using an emotion recognition device based on the received text data and voice characteristic data. The input consists of text and voice data, and this analysis utilizes natural language processing and pattern recognition technologies. The output is data representing the determined emotional state.

[0196] Step 5:

[0197] The server uses the emotional state and text data identified in step 4 to analyze the user's purpose and needs using an intent analysis device. The input is the emotional state and text data, and the output is the analyzed user intent. A machine learning algorithm is applied here.

[0198] Step 6:

[0199] The server uses a generative AI model to generate specific suggestions for the user based on the analysis results from step 5. The input is the user's intention and emotional state, and the output is a personalized suggestion. Natural language generation technology is used as the specific operation.

[0200] Step 7:

[0201] The server generates suggestions, which are sent to the terminal and communicated to the user through the terminal's output device. The input is the generated suggestions, and the output is user guidance using voice and display. The terminal provides information to the user using speech synthesis and display technologies.

[0202] (Application Example 2)

[0203] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0204] In modern society, it is crucial to alleviate stress and frustration in users' daily lives and provide more effective and emotionally resonant support. However, conventional support systems do not take into account users' emotional states and are insufficiently personalized in meeting user needs. To address this challenge, there is a need for technology that recognizes emotions from user voice input and provides more appropriate support for daily life.

[0205] 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.

[0206] In this invention, the server includes detection means for receiving voice input from a user, processing means for converting the voice input into text data, and analysis means for analyzing the user's intent and emotional state from the text data. This makes it possible to propose an appropriate solution that takes the user's emotions into consideration.

[0207] "Detection means" refers to a device or method for receiving voice input from a user and sensing its content.

[0208] "Processing means" refers to an apparatus or method that performs the process of converting audio data into text data.

[0209] "Analysis means" refers to a device or method for analyzing and inferring a user's intentions and emotional state from text data.

[0210] "Generating means" refers to a device or method for generating and presenting suggestions to support daily tasks based on the analyzed intentions and emotions of the user.

[0211] "Emotional state" refers to the psychological and emotional state of the user when they perform voice input.

[0212] A "proposal" refers to a solution or option generated based on information obtained through analysis, in order to support the user's daily life.

[0213] To realize this application, the system implements a program that processes voice input based on user interaction and performs emotion recognition. The server uses a speech recognition library to convert the user's voice input into text data. At this stage, APIs such as Google Cloud Speech-to-Text are utilized. The text data is then analyzed for emotional state using the Microsoft® Azure® Text Analytics API to understand the user's intent.

[0214] The server generates personalized suggestions to support the user's daily life based on the acquired information. This suggestion generation takes into account past behavioral data and current environmental information. The content of the suggestions is selected by a Python program, presenting the user with the most suitable solution.

[0215] The device communicates these generated suggestions to the user via voice or display. As an interface in a home robot, the device utilizes a microphone, speaker, and display to support the user in using and implementing the suggestions.

[0216] For example, if a user asks the robot, "What can I do to relax today?", the robot will offer emotionally supportive advice such as, "Perhaps listening to your favorite music or going for a walk would be good."

[0217] An example prompt for the generating AI model is: "Please provide an example of a consumer robot application that analyzes the user's emotions through voice input and supports the user's daily life in accordance with those emotions."

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The user makes questions and requests to the home robot using voice commands. The device detects this voice input through its microphone. The input is real-time voice data, which is then recorded.

[0221] Step 2:

[0222] The device converts detected audio data into text data using a processing tool. The tool used is the Google Cloud Speech-to-Text API. Audio analysis is performed to convert audio data (input) into text data (output).

[0223] Step 3:

[0224] The server analyzes the user's emotional state and intent from text data. It uses the Microsoft Azure Text Analytics API to perform analysis that converts text data (input) into emotional labels and intent information (output). This process considers word tone, speed, and emotional characteristics.

[0225] Step 4:

[0226] The server generates suggestions to support the user's daily life based on the analysis results. Using a Python program, the system creates optimal suggestions (output) based on emotion labels and intent information (input). The suggestions are determined by considering past behavioral data and current environmental information.

[0227] Step 5:

[0228] The terminal presents the generated suggestions to the user. The suggestions (input) are communicated to the user via voice or display (output). The terminal uses its speaker or display to perform specific actions that guide the user through possible actions.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] This invention is a digital butler-type system that efficiently manages the user's daily life and provides personalized support. This system mainly consists of sensor means, processing means, analysis means, and generation means.

[0246] Specific System Operation

[0247] Voice input and processing

[0248] When a user issues a voice command, the terminal receives it using a sensor. The terminal records this voice data in digital format and converts it into text data using a processing device.

[0249] Analysis of intent

[0250] The server receives the processed text data and interprets the user's intent using parsing tools. For example, if the user says, "Set an alarm for 7 AM tomorrow," the server understands that the user intends to set an alarm for 7 AM the following day.

[0251] Proposal generation

[0252] Based on the analysis results, the server uses various generation methods to create suggestions to support the user's daily life. These suggestions are created by referencing the user's behavioral data and weather information. For example, if rain is expected, the server will notify the user with the message, "Rain is expected tomorrow. Don't forget your umbrella."

[0253] Feedback and Action

[0254] The generated suggestions and actions are communicated to the user via the device. The device can notify the user via voice or screen display, allowing the user to make appropriate preparations and adjustments.

[0255] Specific example

[0256] For example, if a user asks "What's the gym schedule for tomorrow?" to remind themselves of their gym appointment, the server will check the gym's congestion and weather forecast and inform the user, "Tomorrow, 8 to 9 a.m. is the least crowded time. Please prepare accordingly."

[0257] In this way, this system makes users' daily lives more efficient and comfortable by providing specific and helpful suggestions based on their voice input.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] The user gives a voice command to the device. The device's sensors then capture the voice and store it as digital data.

[0261] Step 2:

[0262] The terminal converts the audio digital data into text data using processing equipment. Speech recognition technology is used in this conversion process.

[0263] Step 3:

[0264] The terminal sends text data to the server. This transmission is performed via a private and secure protocol over the internet.

[0265] Step 4:

[0266] The server processes the received text data using parsing tools to extract the user's intent. Natural language processing algorithms are used to identify the intent.

[0267] Step 5:

[0268] Based on the analysis results, the server references user behavior data and external information (e.g., weather information) to create optimal suggestions. This process utilizes a generation method.

[0269] Step 6:

[0270] The server sends the generated proposal or action to the terminal. This is also done using a secure communication protocol.

[0271] Step 7:

[0272] The device guides the user through received suggestions and actions via voice generation or display. This provides the user with feedback.

[0273] (Example 1)

[0274] 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."

[0275] Conventional digital assistant systems have faced challenges in accurately interpreting user voice commands and providing specific solutions to support the user's daily life more efficiently and comfortably. Furthermore, they have been unable to dynamically generate suggestions that take into account real-time weather information and past user behavior data, and to accurately notify the user.

[0276] 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.

[0277] In this invention, the server includes a sensing device that acquires voice signals from the user, a conversion device that converts the voice signals into encoded data, and an interpretation device that interprets the user's instructions from the encoded data. This makes it possible to generate and accurately provide advice that is tailored to the user's specific needs.

[0278] "User" refers to an individual or group that uses this system to give voice commands.

[0279] "Audio signal" refers to an input format that acquires the voice emitted by the user as an electrical signal.

[0280] A "sensing device" refers to a mechanical or electronic device used to acquire audio signals.

[0281] "Encoded data" refers to digital data obtained by converting an audio signal into a format that can be processed.

[0282] A "conversion device" refers to a device that has the function of converting audio signals into encoded data.

[0283] The "interpretation device" refers to a device for analyzing and understanding the user's intentions and instructions from the encoded data.

[0284] The "creation device" refers to a device that generates advice for the user based on the analysis results obtained by the interpretation device.

[0285] The "output device" refers to a device for visually or audibly transmitting the generated advice to the user.

[0286] The "weather data" refers to data containing information related to the weather.

[0287] The "history data" refers to data related to the user's actions and past operations recorded over time.

[0288] The "data storage device" refers to a system for storing information related to the user's trends and behavior patterns.

[0289] The "schedule adjustment" refers to an adjustment operation performed to optimize the user's schedule and activity content.

[0290] This invention is a digital butler-type system that efficiently supports the user's daily life. This system is composed of a series of devices that collect voice instructions from the user and generate proposals based on those instructions.

[0291] The terminal is equipped with a sensing device that receives the user's voice. This sensing device includes a highly accurate microphone and can electrically detect voice signals. Then, the terminal incorporates a conversion device that uses voice recognition software to convert this voice signal into encoded data. Specifically, a general voice recognition API can be used as a unit for converting voice input into text data.

[0292] Upon receiving encoded data, the server uses an interpreter to interpret the user's intent using a deep learning algorithm. Based on the information obtained from the interpretation, a creation device equipped with a generative AI model creates helpful suggestions for the user. This AI model generates more personalized suggestions by linking with the user's past behavior history and external weather information databases.

[0293] The terminal uses an output device equipped with speech synthesis software and a display to notify the user of the generation suggestions sent from the server. The generated information is then provided to the user visually or audibly.

[0294] For example, if a user says, "Tell me the gym schedule for tomorrow," the device converts this voice into encoded data and sends it to the server. The server checks the gym's congestion level and uses this information to generate the most suitable suggestion using a generative AI model. For example, the server might indicate, "Tomorrow morning from 8 to 9 am is relatively less crowded."

[0295] An example of a prompt would be, "Generate the best suggestion for when the user says, 'Tell me tomorrow's gym schedule.'" By supplying this prompt to the model, it provides specific support tailored to the situation.

[0296] As a result, this system enables users to plan their lives more effectively and flexibly, thereby improving the user experience.

[0297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0298] Step 1:

[0299] The user issues a voice command. The user communicates their request by speaking to the system. This voice input becomes the initial data. Based on this input, the terminal uses a microphone to acquire a voice signal. The acquired voice signal is processed in digital format and prepared for the next processing step.

[0300] Step 2:

[0301] The terminal converts the audio signal into encoded data. Using speech recognition software, it analyzes the audio signal and converts it into text data. This data processing converts the user's voice commands into text format. The output is text data, which becomes the input to the server.

[0302] Step 3:

[0303] The server receives text data from the terminal. The server uses an interpreter to analyze the text data and interpret the user's intent. This performs specific data calculations, clarifying the actions or suggestions the user desires. The output of this step is the interpreted user intent.

[0304] Step 4:

[0305] Based on the interpreted intent, the server uses a generative AI model to generate specific suggestions. The generative AI model references past user data and weather information obtained from external sources to design the most appropriate suggestions for the user. The output of this step is specific and practical suggestions that should be provided to the user.

[0306] Step 5:

[0307] The terminal receives suggestions from the server and outputs them to the user. The suggestions are communicated to the user via speech synthesis or display. Based on this information, the user can decide on their next course of action. The output of this final step is advice provided to the user, presented visually or audibly.

[0308] (Application Example 1)

[0309] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0310] In modern society, daily life is becoming busier, and it is difficult for individual users to efficiently receive schedule management and life suggestions. Also, the need for flexible and personalized information provision according to the user's lifestyle is increasing. However, conventional digital assistants generally only provide information and have not yet reached the point of making effective suggestions in cooperation with the user's behavior history and real-time information.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0312] In this invention, the server includes a detection means for receiving a voice input from a user, a conversion means for converting the voice input into text data, an interpretation means for analyzing the user's intention from the text data, a generation means for generating and presenting a suggestion for supporting the user's daily life based on the analysis result, and a transmission means for feeding back the generated suggestion to the user by voice or display. Thereby, it becomes possible to quickly and effectively provide personalized suggestions according to the user's lifestyle and environment.

[0313] The "detection means" is a device or mechanism for sensing and recording a voice input from a user.

[0314] The "conversion means" refers to a processing device or algorithm for converting voice data into text data.

[0315] The "interpretation means" is a device or function for analyzing and understanding the user's intention from text data.

[0316] A "generation means" is a device or program that creates specific suggestions to support the user's daily life based on the analyzed intention.

[0317] "Means of communication" refers to devices or functions that convey generated proposals to the user through voice or display.

[0318] "Weather information" refers to data about the environment, such as weather and temperature, and is fundamental information that forms the basis for suggesting lifestyle changes to users.

[0319] "Behavioral history" refers to records and data about a user's past activities and actions.

[0320] "Schedule management" refers to the processes and methods for organizing and optimizing a user's schedule and appointments.

[0321] "Learning results" refer to information obtained as new insights by analyzing behavioral patterns acquired from a database.

[0322] The system that implements this application consists of a server, a terminal, and a user interface. The server utilizes speech recognition software (e.g., Google Cloud Speech-to-Text) to convert voice input into text data. It also uses natural language processing (NLP) libraries (e.g., NLTK, spaCy) to analyze the user's intent from the text data. Based on this information, the server uses Python as a generation method to analyze the user's behavior history and weather data to generate appropriate suggestions. Furthermore, the generated suggestions are converted into speech using speech synthesis software such as Amazon Polly and transmitted to the user via the terminal. For example, if the user instructs, "Suggest what to wear tomorrow," the server analyzes weather information and the user's past clothing history and notifies them, "It will be cold tomorrow, so please wear a coat."

[0323] Examples of prompt messages are as follows:

[0324] "Suggest the best travel plan for this weekend based on the user's schedule."

[0325] "Generate a message recommending 15 minutes of exercise per day to users who are not getting enough exercise."

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] The user gives voice commands to the device. The input here is the user's spoken voice, which the device's microphone receives. The output is the analog audio signal collected on the device.

[0329] Step 2:

[0330] The terminal converts the received analog audio signal into digital audio data. This conversion is done using audio signal processing technology. The output is digital audio data.

[0331] Step 3:

[0332] Digital audio data is sent to a server and converted into text data by speech recognition software (e.g., Google Cloud Speech-to-Text). In this step, digital audio data is taken as input, and the converted text data is output using speech recognition technology.

[0333] Step 4:

[0334] The server analyzes the text data and interprets the user's intent. Here, it uses NLP (Neural Language Processing) technology to understand the user's request based on the input text data. This analysis yields the interpreted intent as output.

[0335] Step 5:

[0336] The server generates suggestions tailored to the user's needs based on the analysis results. This process references the user's behavioral history and weather information, and outputs the resulting suggestions.

[0337] Step 6:

[0338] The generated proposals are then converted into speech using speech synthesis software (e.g., Amazon Polly). The input is the generated proposal text, and the output is the synthesized speech data.

[0339] Step 7:

[0340] Finally, the device delivers the voiced suggestion to the user. The synthesized voice is delivered to the user through the speaker. This allows the user to confirm the content of the suggestion.

[0341] 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.

[0342] This invention provides more personalized and appropriate support by combining a voice input system with emotion recognition capabilities to assist users in their daily lives. This system receives voice commands from the user and consists of sensor means, processing means, analysis means, generation means, and an emotion engine.

[0343] Specific System Operation

[0344] Voice input and processing

[0345] When a user gives a voice command to a device, the device's sensor captures this command and saves it as audio data. A processing unit then converts this audio data into text data.

[0346] Recognition of emotions

[0347] This text data is sent from the terminal to the server and analyzed by the emotion engine. The emotion engine extracts the user's emotional characteristics from the audio and text data and determines the user's emotional state. This process is influenced by factors such as the tone and speed of the voice and the frequency of use of specific words.

[0348] Intent analysis and proposal generation

[0349] The server extracts the user's intent through analysis and combines it with their emotional state. Based on this, the generation system creates the most appropriate suggestions for the user. Weather information and past behavioral data are also referenced as needed.

[0350] Personalized feedback

[0351] The server-generated suggestions are adjusted to the user's emotional state and sent to the terminal. The terminal receives this information and guides the user via voice or display. This makes it possible to provide emotionally optimized support.

[0352] Specific example

[0353] For example, if a user speaks to their device in a tired voice saying, "I don't want to go to work," the emotion engine will determine that the user is experiencing negative emotions. The server will take this into account and generate a suggestion such as, "Shall I play some music to help you relax?" and guide the user through the device. This ensures that the user receives support and a supportive environment that takes their emotions into consideration.

[0354] Thus, this system is designed to provide more advanced personalized support that takes user emotions into account.

[0355] The following describes the processing flow.

[0356] Step 1:

[0357] The user gives voice commands to the device. The device captures the voice using sensory means and stores it as digital audio data.

[0358] Step 2:

[0359] The terminal uses processing means to convert audio data into text data. This conversion utilizes speech recognition technology.

[0360] Step 3:

[0361] The device sends the converted text data to the server, and at the same time, it also sends voice characteristic data for emotion recognition.

[0362] Step 4:

[0363] The server uses an emotion engine to analyze text data and voice characteristic data to evaluate the user's emotional state.

[0364] Step 5:

[0365] The server extracts the user's specific intentions from text data through analysis. Based on these intentions, it generates basic suggestions for life support.

[0366] Step 6:

[0367] The server adjusts the content of the suggestions, taking into account the emotional state, and the generation method determines the suggestions that are appropriate for the user's current emotions.

[0368] Step 7:

[0369] The server sends the adjusted suggestions to the terminal. The terminal then guides the user through the suggestions via voice or display.

[0370] (Example 2)

[0371] 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".

[0372] Conventional speech recognition systems can generate text data based on voice input from users and analyze the user's intent from that text data. However, they could not take into account the user's emotional state, resulting in uniform support for users and difficulty in providing flexible responses tailored to the individual needs of each user. This invention aims to solve these problems and provide more personalized and accurate support for daily life by generating suggestions that reflect the user's emotional state.

[0373] 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.

[0374] In this invention, the server includes a device for receiving voice input from a user, a device for converting the voice input into text data, and a device for determining the user's emotional state from the text data and voice characteristic data. This makes it possible to generate personalized suggestions that take the user's emotional state into account.

[0375] A "device that receives voice input from a user" is a device that captures the voice spoken by a user and takes that voice in order to process it.

[0376] A "device that converts voice input to text data" is a device that analyzes acquired voice data and converts it into a text format that can be read by humans.

[0377] A "device for determining a user's emotional state from text data and voice characteristic data" is a device that analyzes characteristics obtained from voice and the content of text to infer and determine a user's emotions.

[0378] A "device that analyzes user intent and generates suggestions considering emotional state" is a device that, based on the user's input and emotional state, presents the most appropriate course of action and support for that user.

[0379] "Environmental information and user's past activity history" refers to information about the user's surroundings, weather conditions, and records of the user's past actions and habits.

[0380] A "device that acquires behavioral history from a storage device and optimizes schedules based on learned information" is a device that acquires data on a user's past actions and activities from a storage device and uses that data to organize the user's schedule and plans more efficiently.

[0381] Modes for carrying out the invention

[0382] This invention is a system for providing personalized assistance based on user voice input. The system aims to generate suggestions tailored to the user's specific needs by combining voice recognition, sentiment recognition, and intent analysis.

[0383] composition

[0384] 1. Voice input device

[0385] The terminal is a device that detects voice input from the user and captures it as digital voice data. This device includes a microphone and a voice sensor.

[0386] 2. Speech recognition device

[0387] The device uses speech recognition software to convert the captured audio data into text data. Commonly used software includes "speech recognition APIs" and "natural language processing engines."

[0388] 3. Emotion discrimination device

[0389] The server analyzes the user's emotional state based on the generated text data and voice characteristics data. In this step, the emotion engine determines the emotional state by considering factors such as voice tone, speed, and word choice.

[0390] 4. Intention Analysis Device

[0391] The server uses natural language processing techniques to analyze the user's intent from text data and understand the user's current situation.

[0392] 5. Proposal generation device

[0393] The server uses a generative AI model to generate personalized suggestions for the user based on their emotional state and analyzed intentions.

[0394] Specific example

[0395] For example, if a user says to the device in the morning, "I don't really feel like it today," the emotion recognition device will determine from the tone of voice and phrasing that the user is feeling unmotivated. The server can then use this result to generate a suggestion to play relaxing music and notify the user via voice.

[0396] Example of a prompt

[0397] The prompt can be input to the generating AI model in the form of, "The user said, 'I don't feel like doing anything today.' Please provide an appropriate suggestion for this situation." Based on this prompt, the model will suggest the most appropriate course of action for the user.

[0398] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0399] Step 1:

[0400] The terminal receives voice input from the user. The input is raw voice data. The terminal's voice input device captures this voice through the microphone and saves it as digital audio data. The output at this stage is digital audio data.

[0401] Step 2:

[0402] The terminal's speech recognition device receives the audio data obtained in step 1 as input and converts it into text data using speech recognition software. During this process, data processing is performed by the speech signal processing and recognition engine. The output of this step is text data representing the content of the user's speech.

[0403] Step 3:

[0404] The terminal sends the text data and voice characteristic data created in step 2 to the server. The communication is encrypted, ensuring data security. The input consists of text data and voice characteristic data, and the output is the secure delivery of that data to the server.

[0405] Step 4:

[0406] The server analyzes the user's emotional state using an emotion recognition device based on the received text data and voice characteristic data. The input consists of text and voice data, and this analysis utilizes natural language processing and pattern recognition technologies. The output is data representing the determined emotional state.

[0407] Step 5:

[0408] The server uses the emotional state and text data identified in step 4 to analyze the user's purpose and needs using an intent analysis device. The input is the emotional state and text data, and the output is the analyzed user intent. A machine learning algorithm is applied here.

[0409] Step 6:

[0410] The server uses a generative AI model to generate specific suggestions for the user based on the analysis results from step 5. The input is the user's intention and emotional state, and the output is a personalized suggestion. Natural language generation technology is used as the specific operation.

[0411] Step 7:

[0412] The server generates suggestions, which are sent to the terminal and communicated to the user through the terminal's output device. The input is the generated suggestions, and the output is user guidance using voice and display. The terminal provides information to the user using speech synthesis and display technologies.

[0413] (Application Example 2)

[0414] 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."

[0415] In modern society, it is crucial to alleviate stress and frustration in users' daily lives and provide more effective and emotionally resonant support. However, conventional support systems do not take into account users' emotional states and are insufficiently personalized in meeting user needs. To address this challenge, there is a need for technology that recognizes emotions from user voice input and provides more appropriate support for daily life.

[0416] 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.

[0417] In this invention, the server includes detection means for receiving voice input from a user, processing means for converting the voice input into text data, and analysis means for analyzing the user's intent and emotional state from the text data. This makes it possible to propose an appropriate solution that takes the user's emotions into consideration.

[0418] "Detection means" refers to a device or method for receiving voice input from a user and sensing its content.

[0419] "Processing means" refers to an apparatus or method that performs the process of converting audio data into text data.

[0420] "Analysis means" refers to a device or method for analyzing and inferring a user's intentions and emotional state from text data.

[0421] "Generating means" refers to a device or method for generating and presenting suggestions to support daily tasks based on the analyzed intentions and emotions of the user.

[0422] "Emotional state" refers to the psychological and emotional state of the user when they perform voice input.

[0423] A "proposal" refers to a solution or option generated based on information obtained through analysis, in order to support the user's daily life.

[0424] To realize this application, the system implements a program that processes voice input based on user interaction and performs emotion recognition. The server uses a speech recognition library to convert the user's voice input into text data. At this stage, APIs such as Google Cloud Speech-to-Text are utilized. The text data is then analyzed for emotional state using the Microsoft Azure Text Analytics API to understand the user's intent.

[0425] The server generates personalized suggestions to support the user's daily life based on the acquired information. This suggestion generation takes into account past behavioral data and current environmental information. The content of the suggestions is selected by a Python program, presenting the user with the most suitable solution.

[0426] The device communicates these generated suggestions to the user via voice or display. As an interface in a home robot, the device utilizes a microphone, speaker, and display to support the user in using and implementing the suggestions.

[0427] For example, if a user asks the robot, "What can I do to relax today?", the robot will offer emotionally supportive advice such as, "Perhaps listening to your favorite music or going for a walk would be good."

[0428] An example prompt for the generating AI model is: "Please provide an example of a consumer robot application that analyzes the user's emotions through voice input and supports the user's daily life in accordance with those emotions."

[0429] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0430] Step 1:

[0431] The user makes questions and requests to the home robot using voice commands. The device detects this voice input through its microphone. The input is real-time voice data, which is then recorded.

[0432] Step 2:

[0433] The device converts detected audio data into text data using a processing tool. The tool used is the Google Cloud Speech-to-Text API. Audio analysis is performed to convert audio data (input) into text data (output).

[0434] Step 3:

[0435] The server analyzes the user's emotional state and intent from text data. It uses the Microsoft Azure Text Analytics API to perform analysis that converts text data (input) into emotional labels and intent information (output). This process considers word tone, speed, and emotional characteristics.

[0436] Step 4:

[0437] The server generates suggestions to support the user's daily life based on the analysis results. Using a Python program, the system creates optimal suggestions (output) based on emotion labels and intent information (input). The suggestions are determined by considering past behavioral data and current environmental information.

[0438] Step 5:

[0439] The terminal presents the generated suggestions to the user. The suggestions (input) are communicated to the user via voice or display (output). The terminal uses its speaker or display to perform specific actions that guide the user through possible actions.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] [Third Embodiment]

[0444] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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".

[0456] This invention is a digital butler-type system that efficiently manages the user's daily life and provides personalized support. This system mainly consists of sensor means, processing means, analysis means, and generation means.

[0457] Specific System Operation

[0458] Voice input and processing

[0459] When a user issues a voice command, the terminal receives it using a sensor. The terminal records this voice data in digital format and converts it into text data using a processing device.

[0460] Analysis of intent

[0461] The server receives the processed text data and interprets the user's intent using parsing tools. For example, if the user says, "Set an alarm for 7 AM tomorrow," the server understands that the user intends to set an alarm for 7 AM the following day.

[0462] Proposal generation

[0463] Based on the analysis results, the server uses various generation methods to create suggestions to support the user's daily life. These suggestions are created by referencing the user's behavioral data and weather information. For example, if rain is expected, the server will notify the user with the message, "Rain is expected tomorrow. Don't forget your umbrella."

[0464] Feedback and Action

[0465] The generated suggestions and actions are communicated to the user via the device. The device can notify the user via voice or screen display, allowing the user to make appropriate preparations and adjustments.

[0466] Specific example

[0467] For example, if a user asks "What's the gym schedule for tomorrow?" to remind themselves of their gym appointment, the server will check the gym's congestion and weather forecast and inform the user, "Tomorrow, 8 to 9 a.m. is the least crowded time. Please prepare accordingly."

[0468] In this way, this system makes users' daily lives more efficient and comfortable by providing specific and helpful suggestions based on their voice input.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The user gives a voice command to the device. The device's sensors then capture the voice and store it as digital data.

[0472] Step 2:

[0473] The terminal converts the audio digital data into text data using processing equipment. Speech recognition technology is used in this conversion process.

[0474] Step 3:

[0475] The terminal sends text data to the server. This transmission is performed via a private and secure protocol over the internet.

[0476] Step 4:

[0477] The server processes the received text data using parsing tools to extract the user's intent. Natural language processing algorithms are used to identify the intent.

[0478] Step 5:

[0479] Based on the analysis results, the server references user behavior data and external information (e.g., weather information) to create optimal suggestions. This process utilizes a generation method.

[0480] Step 6:

[0481] The server sends the generated proposal or action to the terminal. This is also done using a secure communication protocol.

[0482] Step 7:

[0483] The device guides the user through received suggestions and actions via voice generation or display. This provides the user with feedback.

[0484] (Example 1)

[0485] 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."

[0486] Conventional digital assistant systems have faced challenges in accurately interpreting user voice commands and providing specific solutions to support the user's daily life more efficiently and comfortably. Furthermore, they have been unable to dynamically generate suggestions that take into account real-time weather information and past user behavior data, and to accurately notify the user.

[0487] 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.

[0488] In this invention, the server includes a sensing device that acquires voice signals from the user, a conversion device that converts the voice signals into encoded data, and an interpretation device that interprets the user's instructions from the encoded data. This makes it possible to generate and accurately provide advice that is tailored to the user's specific needs.

[0489] "User" refers to an individual or group that uses this system to give voice commands.

[0490] "Audio signal" refers to an input format that acquires the voice emitted by the user as an electrical signal.

[0491] A "sensing device" refers to a mechanical or electronic device used to acquire audio signals.

[0492] "Encoded data" refers to digital data obtained by converting an audio signal into a format that can be processed.

[0493] A "conversion device" refers to a device that has the function of converting audio signals into encoded data.

[0494] An "interpretation device" refers to a device that analyzes and understands the user's intentions and instructions from encoded data.

[0495] A "creation device" refers to a device that generates advice for the user based on the analysis results obtained by an interpretation device.

[0496] An "output device" refers to a device that communicates generated advice to the user visually or audibly.

[0497] "Weather data" refers to data that includes information about weather conditions.

[0498] "Historical data" refers to data about user behavior and past operations recorded over time.

[0499] A "data storage device" refers to a system for storing information related to user trends and behavioral patterns.

[0500] "Planning adjustment" refers to the adjustment work performed to optimize a user's schedule and activities.

[0501] This invention is a digital butler system that efficiently supports the user's daily life. The system consists of a series of devices for collecting voice commands from the user and generating suggestions based on those commands.

[0502] The terminal is equipped with a sensing device that receives the user's voice. This sensing device includes a high-precision microphone that can electrically detect the voice signal. The terminal also has a built-in converter that uses voice recognition software to convert this voice signal into encoded data. Specifically, a general-purpose voice recognition API can be used as the unit that converts voice input into text data.

[0503] Upon receiving encoded data, the server uses an interpreter to interpret the user's intent using a deep learning algorithm. Based on the information obtained from the interpretation, a creation device equipped with a generative AI model creates helpful suggestions for the user. This AI model generates more personalized suggestions by linking with the user's past behavior history and external weather information databases.

[0504] The terminal uses an output device equipped with speech synthesis software and a display to notify the user of the generation suggestions sent from the server. The generated information is then provided to the user visually or audibly.

[0505] For example, if a user says, "Tell me the gym schedule for tomorrow," the device converts this voice into encoded data and sends it to the server. The server checks the gym's congestion level and uses this information to generate the most suitable suggestion using a generative AI model. For example, the server might indicate, "Tomorrow morning from 8 to 9 am is relatively less crowded."

[0506] An example of a prompt would be, "Generate the best suggestion for when the user says, 'Tell me tomorrow's gym schedule.'" By supplying this prompt to the model, it provides specific support tailored to the situation.

[0507] As a result, this system enables users to plan their lives more effectively and flexibly, thereby improving the user experience.

[0508] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0509] Step 1:

[0510] The user issues a voice command. The user communicates their request by speaking to the system. This voice input becomes the initial data. Based on this input, the terminal uses a microphone to acquire a voice signal. The acquired voice signal is processed in digital format and prepared for the next processing step.

[0511] Step 2:

[0512] The terminal converts the audio signal into encoded data. Using speech recognition software, it analyzes the audio signal and converts it into text data. This data processing converts the user's voice commands into text format. The output is text data, which becomes the input to the server.

[0513] Step 3:

[0514] The server receives text data from the terminal. The server uses an interpreter to analyze the text data and interpret the user's intent. This performs specific data calculations, clarifying the actions or suggestions the user desires. The output of this step is the interpreted user intent.

[0515] Step 4:

[0516] Based on the interpreted intent, the server uses a generative AI model to generate specific suggestions. The generative AI model references past user data and weather information obtained from external sources to design the most appropriate suggestions for the user. The output of this step is specific and practical suggestions that should be provided to the user.

[0517] Step 5:

[0518] The terminal receives suggestions from the server and outputs them to the user. The suggestions are communicated to the user via speech synthesis or display. Based on this information, the user can decide on their next course of action. The output of this final step is advice provided to the user, presented visually or audibly.

[0519] (Application Example 1)

[0520] 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."

[0521] In modern society, daily life is increasingly busy, making it difficult for individual users to efficiently manage their schedules and receive lifestyle suggestions. Furthermore, there is a growing need for flexible and personalized information tailored to each user's lifestyle. However, conventional digital assistants generally only provide information and have not yet reached the point of providing effective suggestions in conjunction with the user's behavioral history and real-time information.

[0522] 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.

[0523] In this invention, the server includes detection means for receiving voice input from a user, conversion means for converting the voice input into text data, interpretation means for analyzing the user's intent from the text data, generation means for generating and presenting suggestions to support the user's daily life based on the analysis results, and communication means for providing feedback of the generated suggestions to the user by voice or display. This makes it possible to quickly and effectively provide personalized suggestions tailored to the user's lifestyle and environment.

[0524] "Detection means" refers to a device or mechanism for sensing and recording voice input from the user.

[0525] "Conversion means" refers to processing devices or algorithms that convert audio data into text data.

[0526] "Interpretation tools" refer to devices or functions used to analyze and understand user intent from text data.

[0527] A "generation means" is a device or program that creates specific suggestions to support the user's daily life based on the analyzed intention.

[0528] "Means of communication" refers to devices or functions that convey generated proposals to the user through voice or display.

[0529] "Weather information" refers to data about the environment, such as weather and temperature, and is fundamental information that forms the basis for suggesting lifestyle changes to users.

[0530] "Behavioral history" refers to records and data about a user's past activities and actions.

[0531] "Schedule management" refers to the processes and methods for organizing and optimizing a user's schedule and appointments.

[0532] "Learning results" refer to information obtained as new insights by analyzing behavioral patterns acquired from a database.

[0533] The system that implements this application consists of a server, a terminal, and a user interface. The server utilizes speech recognition software (e.g., Google Cloud Speech-to-Text) to convert voice input into text data. It also uses natural language processing (NLP) libraries (e.g., NLTK, spaCy) to analyze the user's intent from the text data. Based on this information, the server uses Python as a generation method to analyze the user's behavior history and weather data to generate appropriate suggestions. Furthermore, the generated suggestions are converted into speech using speech synthesis software such as Amazon Polly and transmitted to the user via the terminal. For example, if the user instructs, "Suggest what to wear tomorrow," the server analyzes weather information and the user's past clothing history and notifies them, "It will be cold tomorrow, so please wear a coat."

[0534] Examples of prompt messages are as follows:

[0535] "Suggest the best travel plan for this weekend based on the user's schedule."

[0536] "Generate a message recommending 15 minutes of exercise per day to users who are not getting enough exercise."

[0537] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0538] Step 1:

[0539] The user gives voice commands to the device. The input here is the user's spoken voice, which the device's microphone receives. The output is the analog audio signal collected on the device.

[0540] Step 2:

[0541] The terminal converts the received analog audio signal into digital audio data. This conversion is done using audio signal processing technology. The output is digital audio data.

[0542] Step 3:

[0543] Digital audio data is sent to a server and converted into text data by speech recognition software (e.g., Google Cloud Speech-to-Text). In this step, digital audio data is taken as input, and the converted text data is output using speech recognition technology.

[0544] Step 4:

[0545] The server analyzes the text data and interprets the user's intent. Here, it uses NLP (Neural Language Processing) technology to understand the user's request based on the input text data. This analysis yields the interpreted intent as output.

[0546] Step 5:

[0547] The server generates suggestions tailored to the user's needs based on the analysis results. This process references the user's behavioral history and weather information, and outputs the resulting suggestions.

[0548] Step 6:

[0549] The generated proposals are then converted into speech using speech synthesis software (e.g., Amazon Polly). The input is the generated proposal text, and the output is the synthesized speech data.

[0550] Step 7:

[0551] Finally, the device delivers the voiced suggestion to the user. The synthesized voice is delivered to the user through the speaker. This allows the user to confirm the content of the suggestion.

[0552] 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.

[0553] This invention provides more personalized and appropriate support by combining a voice input system with emotion recognition capabilities to assist users in their daily lives. This system receives voice commands from the user and consists of sensor means, processing means, analysis means, generation means, and an emotion engine.

[0554] Specific System Operation

[0555] Voice input and processing

[0556] When a user gives a voice command to a device, the device's sensor captures this command and saves it as audio data. A processing unit then converts this audio data into text data.

[0557] Recognition of emotions

[0558] This text data is sent from the terminal to the server and analyzed by the emotion engine. The emotion engine extracts the user's emotional characteristics from the audio and text data and determines the user's emotional state. This process is influenced by factors such as the tone and speed of the voice and the frequency of use of specific words.

[0559] Intent analysis and proposal generation

[0560] The server extracts the user's intent through analysis and combines it with their emotional state. Based on this, the generation system creates the most appropriate suggestions for the user. Weather information and past behavioral data are also referenced as needed.

[0561] Personalized feedback

[0562] The server-generated suggestions are adjusted to the user's emotional state and sent to the terminal. The terminal receives this information and guides the user via voice or display. This makes it possible to provide emotionally optimized support.

[0563] Specific example

[0564] For example, if a user speaks to their device in a tired voice saying, "I don't want to go to work," the emotion engine will determine that the user is experiencing negative emotions. The server will take this into account and generate a suggestion such as, "Shall I play some music to help you relax?" and guide the user through the device. This ensures that the user receives support and a supportive environment that takes their emotions into consideration.

[0565] Thus, this system is designed to provide more advanced personalized support that takes user emotions into account.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The user gives voice commands to the device. The device captures the voice using sensory means and stores it as digital audio data.

[0569] Step 2:

[0570] The terminal uses processing means to convert audio data into text data. This conversion utilizes speech recognition technology.

[0571] Step 3:

[0572] The device sends the converted text data to the server, and at the same time, it also sends voice characteristic data for emotion recognition.

[0573] Step 4:

[0574] The server uses an emotion engine to analyze text data and voice characteristic data to evaluate the user's emotional state.

[0575] Step 5:

[0576] The server extracts the user's specific intentions from text data through analysis. Based on these intentions, it generates basic suggestions for life support.

[0577] Step 6:

[0578] The server adjusts the content of the suggestions, taking into account the emotional state, and the generation method determines the suggestions that are appropriate for the user's current emotions.

[0579] Step 7:

[0580] The server sends the adjusted suggestions to the terminal. The terminal then guides the user through the suggestions via voice or display.

[0581] (Example 2)

[0582] 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."

[0583] Conventional speech recognition systems can generate text data based on voice input from users and analyze the user's intent from that text data. However, they could not take into account the user's emotional state, resulting in uniform support for users and difficulty in providing flexible responses tailored to the individual needs of each user. This invention aims to solve these problems and provide more personalized and accurate support for daily life by generating suggestions that reflect the user's emotional state.

[0584] 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.

[0585] In this invention, the server includes a device for receiving voice input from a user, a device for converting the voice input into text data, and a device for determining the user's emotional state from the text data and voice characteristic data. This makes it possible to generate personalized suggestions that take the user's emotional state into account.

[0586] A "device that receives voice input from a user" is a device that captures the voice spoken by a user and takes that voice in order to process it.

[0587] A "device that converts voice input to text data" is a device that analyzes acquired voice data and converts it into a text format that can be read by humans.

[0588] A "device for determining a user's emotional state from text data and voice characteristic data" is a device that analyzes characteristics obtained from voice and the content of text to infer and determine a user's emotions.

[0589] A "device that analyzes user intent and generates suggestions considering emotional state" is a device that, based on the user's input and emotional state, presents the most appropriate course of action and support for that user.

[0590] "Environmental information and user's past activity history" refers to information about the user's surroundings, weather conditions, and records of the user's past actions and habits.

[0591] A "device that acquires behavioral history from a storage device and optimizes schedules based on learned information" is a device that acquires data on a user's past actions and activities from a storage device and uses that data to organize the user's schedule and plans more efficiently.

[0592] Modes for carrying out the invention

[0593] This invention is a system for providing personalized assistance based on user voice input. The system aims to generate suggestions tailored to the user's specific needs by combining voice recognition, sentiment recognition, and intent analysis.

[0594] composition

[0595] 1. Voice input device

[0596] The terminal is a device that detects voice input from the user and captures it as digital voice data. This device includes a microphone and a voice sensor.

[0597] 2. Speech recognition device

[0598] The device uses speech recognition software to convert the captured audio data into text data. Commonly used software includes "speech recognition APIs" and "natural language processing engines."

[0599] 3. Emotion discrimination device

[0600] The server analyzes the user's emotional state based on the generated text data and voice characteristics data. In this step, the emotion engine determines the emotional state by considering factors such as voice tone, speed, and word choice.

[0601] 4. Intention Analysis Device

[0602] The server uses natural language processing techniques to analyze the user's intent from text data and understand the user's current situation.

[0603] 5. Proposal generation device

[0604] The server uses a generative AI model to generate personalized suggestions for the user based on their emotional state and analyzed intentions.

[0605] Specific example

[0606] For example, if a user says to the device in the morning, "I don't really feel like it today," the emotion recognition device will determine from the tone of voice and phrasing that the user is feeling unmotivated. The server can then use this result to generate a suggestion to play relaxing music and notify the user via voice.

[0607] Example of a prompt

[0608] The prompt can be input to the generating AI model in the form of, "The user said, 'I don't feel like doing anything today.' Please provide an appropriate suggestion for this situation." Based on this prompt, the model will suggest the most appropriate course of action for the user.

[0609] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0610] Step 1:

[0611] The terminal receives voice input from the user. The input is raw voice data. The terminal's voice input device captures this voice through the microphone and saves it as digital audio data. The output at this stage is digital audio data.

[0612] Step 2:

[0613] The terminal's speech recognition device receives the audio data obtained in step 1 as input and converts it into text data using speech recognition software. During this process, data processing is performed by the speech signal processing and recognition engine. The output of this step is text data representing the content of the user's speech.

[0614] Step 3:

[0615] The terminal sends the text data and voice characteristic data created in step 2 to the server. The communication is encrypted, ensuring data security. The input consists of text data and voice characteristic data, and the output is the secure delivery of that data to the server.

[0616] Step 4:

[0617] The server analyzes the user's emotional state using an emotion recognition device based on the received text data and voice characteristic data. The input consists of text and voice data, and this analysis utilizes natural language processing and pattern recognition technologies. The output is data representing the determined emotional state.

[0618] Step 5:

[0619] The server uses the emotional state and text data identified in step 4 to analyze the user's purpose and needs using an intent analysis device. The input is the emotional state and text data, and the output is the analyzed user intent. A machine learning algorithm is applied here.

[0620] Step 6:

[0621] The server uses a generative AI model to generate specific suggestions for the user based on the analysis results from step 5. The input is the user's intention and emotional state, and the output is a personalized suggestion. Natural language generation technology is used as the specific operation.

[0622] Step 7:

[0623] The server generates suggestions, which are sent to the terminal and communicated to the user through the terminal's output device. The input is the generated suggestions, and the output is user guidance using voice and display. The terminal provides information to the user using speech synthesis and display technologies.

[0624] (Application Example 2)

[0625] 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."

[0626] In modern society, it is crucial to alleviate stress and frustration in users' daily lives and provide more effective and emotionally resonant support. However, conventional support systems do not take into account users' emotional states and are insufficiently personalized in meeting user needs. To address this challenge, there is a need for technology that recognizes emotions from user voice input and provides more appropriate support for daily life.

[0627] 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.

[0628] In this invention, the server includes detection means for receiving voice input from a user, processing means for converting the voice input into text data, and analysis means for analyzing the user's intent and emotional state from the text data. This makes it possible to propose an appropriate solution that takes the user's emotions into consideration.

[0629] "Detection means" refers to a device or method for receiving voice input from a user and sensing its content.

[0630] "Processing means" refers to an apparatus or method that performs the process of converting audio data into text data.

[0631] "Analysis means" refers to a device or method for analyzing and inferring a user's intentions and emotional state from text data.

[0632] "Generating means" refers to a device or method for generating and presenting suggestions to support daily tasks based on the analyzed intentions and emotions of the user.

[0633] "Emotional state" refers to the psychological and emotional state of the user when they perform voice input.

[0634] A "proposal" refers to a solution or option generated based on information obtained through analysis, in order to support the user's daily life.

[0635] To realize this application, the system implements a program that processes voice input based on user interaction and performs emotion recognition. The server uses a speech recognition library to convert the user's voice input into text data. At this stage, APIs such as Google Cloud Speech-to-Text are utilized. The text data is then analyzed for emotional state using the Microsoft Azure Text Analytics API to understand the user's intent.

[0636] The server generates personalized suggestions to support the user's daily life based on the acquired information. This suggestion generation takes into account past behavioral data and current environmental information. The content of the suggestions is selected by a Python program, presenting the user with the most suitable solution.

[0637] The device communicates these generated suggestions to the user via voice or display. As an interface in a home robot, the device utilizes a microphone, speaker, and display to support the user in using and implementing the suggestions.

[0638] For example, if a user asks the robot, "What can I do to relax today?", the robot will offer emotionally supportive advice such as, "Perhaps listening to your favorite music or going for a walk would be good."

[0639] An example prompt for the generating AI model is: "Please provide an example of a consumer robot application that analyzes the user's emotions through voice input and supports the user's daily life in accordance with those emotions."

[0640] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0641] Step 1:

[0642] The user makes questions and requests to the home robot using voice commands. The device detects this voice input through its microphone. The input is real-time voice data, which is then recorded.

[0643] Step 2:

[0644] The device converts detected audio data into text data using a processing tool. The tool used is the Google Cloud Speech-to-Text API. Audio analysis is performed to convert audio data (input) into text data (output).

[0645] Step 3:

[0646] The server analyzes the user's emotional state and intent from text data. It uses the Microsoft Azure Text Analytics API to perform analysis that converts text data (input) into emotional labels and intent information (output). This process considers word tone, speed, and emotional characteristics.

[0647] Step 4:

[0648] The server generates suggestions to support the user's daily life based on the analysis results. Using a Python program, the system creates optimal suggestions (output) based on emotion labels and intent information (input). The suggestions are determined by considering past behavioral data and current environmental information.

[0649] Step 5:

[0650] The terminal presents the generated suggestions to the user. The suggestions (input) are communicated to the user via voice or display (output). The terminal uses its speaker or display to perform specific actions that guide the user through possible actions.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] [Fourth Embodiment]

[0655] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0656] 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.

[0657] 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).

[0658] 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.

[0659] 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.

[0660] 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).

[0661] 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.

[0662] 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.

[0663] 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.

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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".

[0668] This invention is a digital butler-type system that efficiently manages the user's daily life and provides personalized support. This system mainly consists of sensor means, processing means, analysis means, and generation means.

[0669] Specific System Operation

[0670] Voice input and processing

[0671] When a user issues a voice command, the terminal receives it using a sensor. The terminal records this voice data in digital format and converts it into text data using a processing device.

[0672] Analysis of intent

[0673] The server receives the processed text data and interprets the user's intent using parsing tools. For example, if the user says, "Set an alarm for 7 AM tomorrow," the server understands that the user intends to set an alarm for 7 AM the following day.

[0674] Proposal generation

[0675] Based on the analysis results, the server uses various generation methods to create suggestions to support the user's daily life. These suggestions are created by referencing the user's behavioral data and weather information. For example, if rain is expected, the server will notify the user with the message, "Rain is expected tomorrow. Don't forget your umbrella."

[0676] Feedback and Action

[0677] The generated suggestions and actions are communicated to the user via the device. The device can notify the user via voice or screen display, allowing the user to make appropriate preparations and adjustments.

[0678] Specific example

[0679] For example, if a user asks "What's the gym schedule for tomorrow?" to remind themselves of their gym appointment, the server will check the gym's congestion and weather forecast and inform the user, "Tomorrow, 8 to 9 a.m. is the least crowded time. Please prepare accordingly."

[0680] In this way, this system makes users' daily lives more efficient and comfortable by providing specific and helpful suggestions based on their voice input.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] The user gives a voice command to the device. The device's sensors then capture the voice and store it as digital data.

[0684] Step 2:

[0685] The terminal converts the audio digital data into text data using processing equipment. Speech recognition technology is used in this conversion process.

[0686] Step 3:

[0687] The terminal sends text data to the server. This transmission is performed via a private and secure protocol over the internet.

[0688] Step 4:

[0689] The server processes the received text data using parsing tools to extract the user's intent. Natural language processing algorithms are used to identify the intent.

[0690] Step 5:

[0691] Based on the analysis results, the server references user behavior data and external information (e.g., weather information) to create optimal suggestions. This process utilizes a generation method.

[0692] Step 6:

[0693] The server sends the generated proposal or action to the terminal. This is also done using a secure communication protocol.

[0694] Step 7:

[0695] The device guides the user through received suggestions and actions via voice generation or display. This provides the user with feedback.

[0696] (Example 1)

[0697] 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".

[0698] Conventional digital assistant systems have faced challenges in accurately interpreting user voice commands and providing specific solutions to support the user's daily life more efficiently and comfortably. Furthermore, they have been unable to dynamically generate suggestions that take into account real-time weather information and past user behavior data, and to accurately notify the user.

[0699] 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.

[0700] In this invention, the server includes a sensing device that acquires voice signals from the user, a conversion device that converts the voice signals into encoded data, and an interpretation device that interprets the user's instructions from the encoded data. This makes it possible to generate and accurately provide advice that is tailored to the user's specific needs.

[0701] "User" refers to an individual or group that uses this system to give voice commands.

[0702] "Audio signal" refers to an input format that acquires the voice emitted by the user as an electrical signal.

[0703] A "sensing device" refers to a mechanical or electronic device used to acquire audio signals.

[0704] "Encoded data" refers to digital data obtained by converting an audio signal into a format that can be processed.

[0705] A "conversion device" refers to a device that has the function of converting audio signals into encoded data.

[0706] An "interpretation device" refers to a device that analyzes and understands the user's intentions and instructions from encoded data.

[0707] A "creation device" refers to a device that generates advice for the user based on the analysis results obtained by an interpretation device.

[0708] An "output device" refers to a device that communicates generated advice to the user visually or audibly.

[0709] "Weather data" refers to data that includes information about weather conditions.

[0710] "Historical data" refers to data about user behavior and past operations recorded over time.

[0711] A "data storage device" refers to a system for storing information related to user trends and behavioral patterns.

[0712] "Planning adjustment" refers to the adjustment work performed to optimize a user's schedule and activities.

[0713] This invention is a digital butler system that efficiently supports the user's daily life. The system consists of a series of devices for collecting voice commands from the user and generating suggestions based on those commands.

[0714] The terminal is equipped with a sensing device that receives the user's voice. This sensing device includes a high-precision microphone that can electrically detect the voice signal. The terminal also has a built-in converter that uses voice recognition software to convert this voice signal into encoded data. Specifically, a general-purpose voice recognition API can be used as the unit that converts voice input into text data.

[0715] Upon receiving encoded data, the server uses an interpreter to interpret the user's intent using a deep learning algorithm. Based on the information obtained from the interpretation, a creation device equipped with a generative AI model creates helpful suggestions for the user. This AI model generates more personalized suggestions by linking with the user's past behavior history and external weather information databases.

[0716] The terminal uses an output device equipped with speech synthesis software and a display to notify the user of the generation suggestions sent from the server. The generated information is then provided to the user visually or audibly.

[0717] For example, if a user says, "Tell me the gym schedule for tomorrow," the device converts this voice into encoded data and sends it to the server. The server checks the gym's congestion level and uses this information to generate the most suitable suggestion using a generative AI model. For example, the server might indicate, "Tomorrow morning from 8 to 9 am is relatively less crowded."

[0718] An example of a prompt would be, "Generate the best suggestion for when the user says, 'Tell me tomorrow's gym schedule.'" By supplying this prompt to the model, it provides specific support tailored to the situation.

[0719] As a result, this system enables users to plan their lives more effectively and flexibly, thereby improving the user experience.

[0720] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0721] Step 1:

[0722] The user issues a voice command. The user communicates their request by speaking to the system. This voice input becomes the initial data. Based on this input, the terminal uses a microphone to acquire a voice signal. The acquired voice signal is processed in digital format and prepared for the next processing step.

[0723] Step 2:

[0724] The terminal converts the audio signal into encoded data. Using speech recognition software, it analyzes the audio signal and converts it into text data. This data processing converts the user's voice commands into text format. The output is text data, which becomes the input to the server.

[0725] Step 3:

[0726] The server receives text data from the terminal. The server uses an interpreter to analyze the text data and interpret the user's intent. This performs specific data calculations, clarifying the actions or suggestions the user desires. The output of this step is the interpreted user intent.

[0727] Step 4:

[0728] Based on the interpreted intent, the server uses a generative AI model to generate specific suggestions. The generative AI model references past user data and weather information obtained from external sources to design the most appropriate suggestions for the user. The output of this step is specific and practical suggestions that should be provided to the user.

[0729] Step 5:

[0730] The terminal receives suggestions from the server and outputs them to the user. The suggestions are communicated to the user via speech synthesis or display. Based on this information, the user can decide on their next course of action. The output of this final step is advice provided to the user, presented visually or audibly.

[0731] (Application Example 1)

[0732] 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".

[0733] In modern society, daily life is increasingly busy, making it difficult for individual users to efficiently manage their schedules and receive lifestyle suggestions. Furthermore, there is a growing need for flexible and personalized information tailored to each user's lifestyle. However, conventional digital assistants generally only provide information and have not yet reached the point of providing effective suggestions in conjunction with the user's behavioral history and real-time information.

[0734] 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.

[0735] In this invention, the server includes detection means for receiving voice input from a user, conversion means for converting the voice input into text data, interpretation means for analyzing the user's intent from the text data, generation means for generating and presenting suggestions to support the user's daily life based on the analysis results, and communication means for providing feedback of the generated suggestions to the user by voice or display. This makes it possible to quickly and effectively provide personalized suggestions tailored to the user's lifestyle and environment.

[0736] "Detection means" refers to a device or mechanism for sensing and recording voice input from the user.

[0737] "Conversion means" refers to processing devices or algorithms that convert audio data into text data.

[0738] "Interpretation tools" refer to devices or functions used to analyze and understand user intent from text data.

[0739] A "generation means" is a device or program that creates specific suggestions to support the user's daily life based on the analyzed intention.

[0740] "Means of communication" refers to devices or functions that convey generated proposals to the user through voice or display.

[0741] "Weather information" refers to data about the environment, such as weather and temperature, and is fundamental information that forms the basis for suggesting lifestyle changes to users.

[0742] "Behavioral history" refers to records and data about a user's past activities and actions.

[0743] "Schedule management" refers to the processes and methods for organizing and optimizing a user's schedule and appointments.

[0744] "Learning results" refer to information obtained as new insights by analyzing behavioral patterns acquired from a database.

[0745] The system that implements this application consists of a server, a terminal, and a user interface. The server utilizes speech recognition software (e.g., Google Cloud Speech-to-Text) to convert voice input into text data. It also uses natural language processing (NLP) libraries (e.g., NLTK, spaCy) to analyze the user's intent from the text data. Based on this information, the server uses Python as a generation method to analyze the user's behavior history and weather data to generate appropriate suggestions. Furthermore, the generated suggestions are converted into speech using speech synthesis software such as Amazon Polly and transmitted to the user via the terminal. For example, if the user instructs, "Suggest what to wear tomorrow," the server analyzes weather information and the user's past clothing history and notifies them, "It will be cold tomorrow, so please wear a coat."

[0746] Examples of prompt messages are as follows:

[0747] "Suggest the best travel plan for this weekend based on the user's schedule."

[0748] "Generate a message recommending 15 minutes of exercise per day to users who are not getting enough exercise."

[0749] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0750] Step 1:

[0751] The user gives voice commands to the device. The input here is the user's spoken voice, which the device's microphone receives. The output is the analog audio signal collected on the device.

[0752] Step 2:

[0753] The terminal converts the received analog audio signal into digital audio data. This conversion is done using audio signal processing technology. The output is digital audio data.

[0754] Step 3:

[0755] Digital audio data is sent to a server and converted into text data by speech recognition software (e.g., Google Cloud Speech-to-Text). In this step, digital audio data is taken as input, and the converted text data is output using speech recognition technology.

[0756] Step 4:

[0757] The server analyzes the text data and interprets the user's intent. Here, it uses NLP (Neural Language Processing) technology to understand the user's request based on the input text data. This analysis yields the interpreted intent as output.

[0758] Step 5:

[0759] The server generates suggestions tailored to the user's needs based on the analysis results. This process references the user's behavioral history and weather information, and outputs the resulting suggestions.

[0760] Step 6:

[0761] The generated proposals are then converted into speech using speech synthesis software (e.g., Amazon Polly). The input is the generated proposal text, and the output is the synthesized speech data.

[0762] Step 7:

[0763] Finally, the device delivers the voiced suggestion to the user. The synthesized voice is delivered to the user through the speaker. This allows the user to confirm the content of the suggestion.

[0764] 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.

[0765] This invention provides more personalized and appropriate support by combining a voice input system with emotion recognition capabilities to assist users in their daily lives. This system receives voice commands from the user and consists of sensor means, processing means, analysis means, generation means, and an emotion engine.

[0766] Specific System Operation

[0767] Voice input and processing

[0768] When a user gives a voice command to a device, the device's sensor captures this command and saves it as audio data. A processing unit then converts this audio data into text data.

[0769] Recognition of emotions

[0770] This text data is sent from the terminal to the server and analyzed by the emotion engine. The emotion engine extracts the user's emotional characteristics from the audio and text data and determines the user's emotional state. This process is influenced by factors such as the tone and speed of the voice and the frequency of use of specific words.

[0771] Intent analysis and proposal generation

[0772] The server extracts the user's intent through analysis and combines it with their emotional state. Based on this, the generation system creates the most appropriate suggestions for the user. Weather information and past behavioral data are also referenced as needed.

[0773] Personalized feedback

[0774] The server-generated suggestions are adjusted to the user's emotional state and sent to the terminal. The terminal receives this information and guides the user via voice or display. This makes it possible to provide emotionally optimized support.

[0775] Specific example

[0776] For example, if a user speaks to their device in a tired voice saying, "I don't want to go to work," the emotion engine will determine that the user is experiencing negative emotions. The server will take this into account and generate a suggestion such as, "Shall I play some music to help you relax?" and guide the user through the device. This ensures that the user receives support and a supportive environment that takes their emotions into consideration.

[0777] Thus, this system is designed to provide more advanced personalized support that takes user emotions into account.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] The user gives voice commands to the device. The device captures the voice using sensory means and stores it as digital audio data.

[0781] Step 2:

[0782] The terminal uses processing means to convert audio data into text data. This conversion utilizes speech recognition technology.

[0783] Step 3:

[0784] The device sends the converted text data to the server, and at the same time, it also sends voice characteristic data for emotion recognition.

[0785] Step 4:

[0786] The server uses an emotion engine to analyze text data and voice characteristic data to evaluate the user's emotional state.

[0787] Step 5:

[0788] The server extracts the user's specific intentions from text data through analysis. Based on these intentions, it generates basic suggestions for life support.

[0789] Step 6:

[0790] The server adjusts the content of the suggestions, taking into account the emotional state, and the generation method determines the suggestions that are appropriate for the user's current emotions.

[0791] Step 7:

[0792] The server sends the adjusted suggestions to the terminal. The terminal then guides the user through the suggestions via voice or display.

[0793] (Example 2)

[0794] 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".

[0795] Conventional speech recognition systems can generate text data based on voice input from users and analyze the user's intent from that text data. However, they could not take into account the user's emotional state, resulting in uniform support for users and difficulty in providing flexible responses tailored to the individual needs of each user. This invention aims to solve these problems and provide more personalized and accurate support for daily life by generating suggestions that reflect the user's emotional state.

[0796] 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.

[0797] In this invention, the server includes a device for receiving voice input from a user, a device for converting the voice input into text data, and a device for determining the user's emotional state from the text data and voice characteristic data. This makes it possible to generate personalized suggestions that take the user's emotional state into account.

[0798] A "device that receives voice input from a user" is a device that captures the voice spoken by a user and takes that voice in order to process it.

[0799] A "device that converts voice input to text data" is a device that analyzes acquired voice data and converts it into a text format that can be read by humans.

[0800] A "device for determining a user's emotional state from text data and voice characteristic data" is a device that analyzes characteristics obtained from voice and the content of text to infer and determine a user's emotions.

[0801] A "device that analyzes user intent and generates suggestions considering emotional state" is a device that, based on the user's input and emotional state, presents the most appropriate course of action and support for that user.

[0802] "Environmental information and user's past activity history" refers to information about the user's surroundings, weather conditions, and records of the user's past actions and habits.

[0803] A "device that acquires behavioral history from a storage device and optimizes schedules based on learned information" is a device that acquires data on a user's past actions and activities from a storage device and uses that data to organize the user's schedule and plans more efficiently.

[0804] Modes for carrying out the invention

[0805] This invention is a system for providing personalized assistance based on user voice input. The system aims to generate suggestions tailored to the user's specific needs by combining voice recognition, sentiment recognition, and intent analysis.

[0806] composition

[0807] 1. Voice input device

[0808] The terminal is a device that detects voice input from the user and captures it as digital voice data. This device includes a microphone and a voice sensor.

[0809] 2. Speech recognition device

[0810] The device uses speech recognition software to convert the captured audio data into text data. Commonly used software includes "speech recognition APIs" and "natural language processing engines."

[0811] 3. Emotion discrimination device

[0812] The server analyzes the user's emotional state based on the generated text data and voice characteristics data. In this step, the emotion engine determines the emotional state by considering factors such as voice tone, speed, and word choice.

[0813] 4. Intention Analysis Device

[0814] The server uses natural language processing techniques to analyze the user's intent from text data and understand the user's current situation.

[0815] 5. Proposal generation device

[0816] The server uses a generative AI model to generate personalized suggestions for the user based on their emotional state and analyzed intentions.

[0817] Specific example

[0818] For example, if a user says to the device in the morning, "I don't really feel like it today," the emotion recognition device will determine from the tone of voice and phrasing that the user is feeling unmotivated. The server can then use this result to generate a suggestion to play relaxing music and notify the user via voice.

[0819] Example of a prompt

[0820] The prompt can be input to the generating AI model in the form of, "The user said, 'I don't feel like doing anything today.' Please provide an appropriate suggestion for this situation." Based on this prompt, the model will suggest the most appropriate course of action for the user.

[0821] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0822] Step 1:

[0823] The terminal receives voice input from the user. The input is raw voice data. The terminal's voice input device captures this voice through the microphone and saves it as digital audio data. The output at this stage is digital audio data.

[0824] Step 2:

[0825] The terminal's speech recognition device receives the audio data obtained in step 1 as input and converts it into text data using speech recognition software. During this process, data processing is performed by the speech signal processing and recognition engine. The output of this step is text data representing the content of the user's speech.

[0826] Step 3:

[0827] The terminal sends the text data and voice characteristic data created in step 2 to the server. The communication is encrypted, ensuring data security. The input consists of text data and voice characteristic data, and the output is the secure delivery of that data to the server.

[0828] Step 4:

[0829] The server analyzes the user's emotional state using an emotion recognition device based on the received text data and voice characteristic data. The input consists of text and voice data, and this analysis utilizes natural language processing and pattern recognition technologies. The output is data representing the determined emotional state.

[0830] Step 5:

[0831] The server uses the emotional state and text data identified in step 4 to analyze the user's purpose and needs using an intent analysis device. The input is the emotional state and text data, and the output is the analyzed user intent. A machine learning algorithm is applied here.

[0832] Step 6:

[0833] The server uses a generative AI model to generate specific suggestions for the user based on the analysis results from step 5. The input is the user's intention and emotional state, and the output is a personalized suggestion. Natural language generation technology is used as the specific operation.

[0834] Step 7:

[0835] The server generates suggestions, which are sent to the terminal and communicated to the user through the terminal's output device. The input is the generated suggestions, and the output is user guidance using voice and display. The terminal provides information to the user using speech synthesis and display technologies.

[0836] (Application Example 2)

[0837] 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".

[0838] In modern society, it is crucial to alleviate stress and frustration in users' daily lives and provide more effective and emotionally resonant support. However, conventional support systems do not take into account users' emotional states and are insufficiently personalized in meeting user needs. To address this challenge, there is a need for technology that recognizes emotions from user voice input and provides more appropriate support for daily life.

[0839] 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.

[0840] In this invention, the server includes detection means for receiving voice input from a user, processing means for converting the voice input into text data, and analysis means for analyzing the user's intent and emotional state from the text data. This makes it possible to propose an appropriate solution that takes the user's emotions into consideration.

[0841] "Detection means" refers to a device or method for receiving voice input from a user and sensing its content.

[0842] "Processing means" refers to an apparatus or method that performs the process of converting audio data into text data.

[0843] "Analysis means" refers to a device or method for analyzing and inferring a user's intentions and emotional state from text data.

[0844] "Generating means" refers to a device or method for generating and presenting suggestions to support daily tasks based on the analyzed intentions and emotions of the user.

[0845] "Emotional state" refers to the psychological and emotional state of the user when they perform voice input.

[0846] A "proposal" refers to a solution or option generated based on information obtained through analysis, in order to support the user's daily life.

[0847] To realize this application, the system implements a program that processes voice input based on user interaction and performs emotion recognition. The server uses a speech recognition library to convert the user's voice input into text data. At this stage, APIs such as Google Cloud Speech-to-Text are utilized. The text data is then analyzed for emotional state using the Microsoft Azure Text Analytics API to understand the user's intent.

[0848] The server generates personalized suggestions to support the user's daily life based on the acquired information. This suggestion generation takes into account past behavioral data and current environmental information. The content of the suggestions is selected by a Python program, presenting the user with the most suitable solution.

[0849] The device communicates these generated suggestions to the user via voice or display. As an interface in a home robot, the device utilizes a microphone, speaker, and display to support the user in using and implementing the suggestions.

[0850] For example, if a user asks the robot, "What can I do to relax today?", the robot will offer emotionally supportive advice such as, "Perhaps listening to your favorite music or going for a walk would be good."

[0851] An example prompt for the generating AI model is: "Please provide an example of a consumer robot application that analyzes the user's emotions through voice input and supports the user's daily life in accordance with those emotions."

[0852] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0853] Step 1:

[0854] The user makes questions and requests to the home robot using voice commands. The device detects this voice input through its microphone. The input is real-time voice data, which is then recorded.

[0855] Step 2:

[0856] The device converts detected audio data into text data using a processing tool. The tool used is the Google Cloud Speech-to-Text API. Audio analysis is performed to convert audio data (input) into text data (output).

[0857] Step 3:

[0858] The server analyzes the user's emotional state and intent from text data. It uses the Microsoft Azure Text Analytics API to perform analysis that converts text data (input) into emotional labels and intent information (output). This process considers word tone, speed, and emotional characteristics.

[0859] Step 4:

[0860] The server generates suggestions to support the user's daily life based on the analysis results. Using a Python program, the system creates optimal suggestions (output) based on emotion labels and intent information (input). The suggestions are determined by considering past behavioral data and current environmental information.

[0861] Step 5:

[0862] The terminal presents the generated suggestions to the user. The suggestions (input) are communicated to the user via voice or display (output). The terminal uses its speaker or display to perform specific actions that guide the user through possible actions.

[0863] 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.

[0864] 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.

[0865] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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."

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] The following is further disclosed regarding the embodiments described above.

[0885] (Claim 1)

[0886] A sensor means for receiving voice input from the user,

[0887] Processing means for converting the aforementioned voice input into text data,

[0888] An analysis means for analyzing the user's intent from the aforementioned text data,

[0889] A generation means that generates and presents suggestions to support the user's daily life based on the aforementioned analysis results,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, wherein the generation means provides clothing suggestions to the user based on weather information and the user's past behavioral data.

[0893] (Claim 3)

[0894] The system according to claim 1, wherein the analysis means obtains user behavior patterns from a database and adjusts the schedule based on the learned results.

[0895] "Example 1"

[0896] (Claim 1)

[0897] A sensing device that acquires audio signals from the user,

[0898] A conversion device that converts the aforementioned audio signal into encoded data,

[0899] An interpreting device that interprets user instructions from the encoded data,

[0900] Based on the aforementioned interpretation results, a creation device is provided to adjust the user's schedule and create advice.

[0901] An output device that transmits and visually or audibly communicates the generated advice,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The creation device optimizes the user's plan based on weather data and the user's historical data, according to claim 1.

[0905] (Claim 3)

[0906] The system according to claim 1, wherein the interpretation device extracts user behavior patterns from the data storage device and performs plan adjustments based on the learned information.

[0907] "Application Example 1"

[0908] (Claim 1)

[0909] A detection means for receiving voice input from the user,

[0910] A conversion means for converting the aforementioned voice input into text data,

[0911] An interpretation means for analyzing the user's intent from the aforementioned text data,

[0912] A generation means that generates and presents suggestions to support the user's daily life based on the aforementioned analysis results,

[0913] A means for providing feedback to the user on the generated proposal via voice or display,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, wherein the generation means makes suggestions related to lifestyle habits based on weather information and the user's past behavioral history.

[0917] (Claim 3)

[0918] The system according to claim 1, wherein the interpretation means obtains user behavior patterns from a database and manages schedules based on the learned results.

[0919] "Example 2 of combining an emotion engine"

[0920] (Claim 1)

[0921] A device that receives voice input from the user,

[0922] A device that converts the aforementioned voice input into text data,

[0923] A device for determining the user's emotional state from the aforementioned text data and voice characteristic data,

[0924] A device that analyzes the user's intent from the aforementioned text data and generates suggestions to support the user's daily life, taking into account their emotional state.

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, wherein the generating device makes activity suggestions to the user based on environmental information and the user's past activity history.

[0928] (Claim 3)

[0929] The analysis device acquires the user's behavior history from a storage device and optimizes the schedule based on the learned information, as described in claim 1.

[0930] "Application example 2 when combining with an emotional engine"

[0931] (Claim 1)

[0932] A detection means for receiving voice input from the user,

[0933] Processing means for converting the aforementioned voice input into text data,

[0934] An analysis means for analyzing the user's intent and emotional state from the aforementioned text data,

[0935] A generation means that generates and presents suggestions to support daily tasks based on the user's emotions,

[0936] A system that includes this.

[0937] (Claim 2)

[0938] The system according to claim 1, wherein the generation means provides activity suggestions tailored to emotions based on environmental information and the user's historical behavioral data.

[0939] (Claim 3)

[0940] The system according to claim 1, wherein the analysis means acquires user behavioral tendencies from a data collection device and adjusts the activity plan based on the learned results. [Explanation of symbols]

[0941] 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 detection means for receiving voice input from the user, A conversion means for converting the aforementioned voice input into text data, An interpretation means for analyzing the user's intent from the aforementioned text data, A generation means that generates and presents suggestions to support the user's daily life based on the aforementioned analysis results, A means for providing feedback to the user on the generated proposal via voice or display, A system that includes this.

2. The system according to claim 1, wherein the generation means makes suggestions related to lifestyle habits based on weather information and the user's past behavioral history.

3. The system according to claim 1, wherein the interpretation means obtains user behavior patterns from a database and manages schedules based on the learned results.

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