system

Abstract

We provide the system. [Solution] A speech recognition means that acquires voice input from a user, analyzes the voice input, and converts it into text data, An analysis and information acquisition means that uses the text data to identify the user's intent and obtains related information from a database or external information source, A speech synthesis and presentation means that generates a voice response based on acquired information and supplies the voice response to the user, A management system for setting reminders to assist with daily activities in a care support environment, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the elderly, social isolation often progresses along with the decline of physical and cognitive abilities, and they may face various problems in daily life. In conventional support systems, it is difficult to flexibly respond to individual needs, and it is particularly difficult to provide services tailored to the lifestyle of the elderly. Furthermore, the complexity of operation when using technology becomes a burden on users, and it may not be fully utilized effectively. There is a demand for providing an effective support system to solve these problems and improve the quality of life of the elderly.

Means for Solving the Problems

[0005] This invention provides a system that automatically analyzes information based on voice input from a user and provides personalized services. Specifically, it acquires the user's voice input using voice recognition means, identifies the user's intent from the voice using analysis and information acquisition means, and acquires relevant information from a database or external information source. Furthermore, it generates the acquired information as a voice response using voice synthesis and presentation means and presents it to the user. This makes it possible to make suggestions based on the specific lifestyle habits and preferences of elderly people, and solves conventional problems by also providing a function that allows users to easily set reminders and memos by voice.

[0006] "Speech recognition means" refers to a device or software that has the function of analyzing a speech signal input by a user and converting it into corresponding text data.

[0007] "Analysis and information acquisition means" refers to a device or software that has the function of identifying the user's intent based on text data obtained by speech recognition means and acquiring related information from a database or external information source.

[0008] "Speech synthesis and presentation means" refers to a device or software that has the function of generating a speech response based on acquired information and presenting that speech to the user.

[0009] "Profiling tools" refer to devices or software that have the function of managing data necessary to provide personalized suggestions and services by collecting and storing information about users' preferences and lifestyles.

[0010] A "schedule management device" refers to a device or software that has the function of setting reminders and creating notes based on the user's voice input. [Brief explanation of the drawing]

[0011] [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]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention is a system designed to allow elderly people to easily receive support for their daily lives. In this system, the user, terminal, and server communicate with each other to provide the user with the information and support they need in their daily lives.

[0033] To implement this system, the terminal is equipped with a microphone and speech recognition software to receive user voice input. The speech recognition software analyzes the user's voice in real time and converts it into text data. For example, if a user says, "I want to know tomorrow's weather," that voice is instantly converted into text.

[0034] Text data sent from the device is sent to the server. The server analyzes the text data and uses natural language processing (NLP) techniques to understand the user's intent. After the intent is interpreted, the server retrieves the necessary information from the user's personal database or via the internet. For example, it might retrieve the latest weather forecast from a weather information API.

[0035] The information collected by the server is then sent back to the terminal. The terminal uses speech synthesis technology to play the text data as speech and provide the information to the user. If the user asked for weather information, it can say, "Tomorrow will be sunny, and the high temperature will be 25 degrees Celsius."

[0036] The system also features profiling capabilities, providing personalized suggestions based on user preferences and past usage history. For example, it can periodically notify users about local events they have previously shown interest in.

[0037] Furthermore, the scheduling function allows users to set reminders and create notes using voice commands. For example, if a user says, "Set a reminder to take my medicine at 10 AM," the device sends the instruction to the server and sets a reminder alarm at the appropriate time.

[0038] Thus, the system of the present invention can support the user's lifestyle through voice communication and comprehensively provide services ranging from information provision to daily support.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device receives voice input from the user. The user speaks a voice command into the device's microphone.

[0042] Step 2:

[0043] The terminal converts the acquired audio into text data using speech recognition software. The converted text data is then prepared for the next processing step.

[0044] Step 3:

[0045] The device sends the converted text data to the server. The transmitted data serves as the basis for analyzing the user's intent.

[0046] Step 4:

[0047] The server analyzes the received text data. It uses natural language processing (NLP) techniques to identify user requests and questions.

[0048] Step 5:

[0049] The server retrieves information from a database or external API based on the specified request. The information retrieved is related to what the user has instructed.

[0050] Step 6:

[0051] The server generates an audio response to the user based on the acquired information. The response content is created in text format.

[0052] Step 7:

[0053] The server sends the generated voice response as text data to the terminal. The terminal receives this data.

[0054] Step 8:

[0055] The device converts the received voice response text into speech using speech synthesis technology. It then plays the generated speech to the user, providing them with information.

[0056] Step 9:

[0057] Based on the information provided, users can ask additional questions or make new requests. This causes the process to repeat from step 1.

[0058] (Example 1)

[0059] 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."

[0060] A key challenge is ensuring that elderly individuals can effectively access the information and support they need in their daily lives. In particular, there is a need for systems that allow elderly individuals to easily obtain information and improve their quality of life. Such systems require a means for users to obtain necessary information and receive personalized suggestions simply by issuing voice commands.

[0061] 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.

[0062] In this invention, the server includes acoustic recognition means, analysis and information acquisition means, and acoustic synthesis and presentation means. This enables users to efficiently obtain information via acoustic input and receive support based on their individual preferences and habits.

[0063] A "user" is an individual who uses a system to obtain information or receive support.

[0064] "Audio input" refers to audio data generated when a user gives instructions or requests information to the system using their voice.

[0065] "Character data" refers to text-formatted data obtained by analyzing and converting acoustic input using acoustic recognition means.

[0066] "Acoustic recognition means" refers to technologies and devices that receive acoustic input and convert it into text data in real time.

[0067] "Analysis and information acquisition means" refers to a function that analyzes converted character data to identify the user's intent and acquires relevant information from storage or external sources.

[0068] "Sound synthesis and presentation means" refers to technologies and devices for generating and presenting voice responses to a user based on acquired information.

[0069] "Profiling tools" are functions that record a user's past usage history and preferences, and provide personalized information and suggestions based on that information.

[0070] "Time management features" refer to functions that allow users to set schedules and reminders using sound commands, enabling efficient time management.

[0071] This system is designed to enable elderly individuals to easily obtain information and receive personalized support through voice communication. It primarily utilizes a combination of acoustic recognition, natural language processing, and sound synthesis technologies.

[0072] The device is equipped with acoustic recognition software to receive acoustic input from the user. This software can utilize a general-purpose speech recognition engine. When the user speaks a specific command, the device receives the acoustic input and converts it into text data in real time. For example, if the user says, "Tell me the weather tomorrow," this acoustic information is converted into text data.

[0073] Next, the text data sent from the terminal to the server is parsed by the server's natural language processing engine. The server utilizes general-purpose natural language processing techniques to accurately understand the user's intent. This allows, for example, access to a weather information API and retrieve the necessary information.

[0074] The acquired information is returned to the terminal, which uses sound synthesis software to convert the text data back into sound. This sound information is then presented directly to the user. For example, in response to a user's question, it can generate an audio response such as, "Tomorrow will be sunny, and the maximum temperature will be 25 degrees Celsius."

[0075] Furthermore, the system includes profiling capabilities, providing personalized information based on the user's past behavior and preferences. This allows users to manage their schedules and set reminders using voice commands. Specific prompts are expected to include instructions such as, "Set a reminder to take my medicine at 10 AM."

[0076] This system is designed with the aim of supporting the daily lives of the elderly through voice communication, utilizing generative AI models.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user provides audio input to the device. The user gives a specific voice command, such as "Tell me the weather tomorrow." This audio input is received by the device's microphone and sent to speech recognition software. The speech recognition software analyzes the audio data and converts it into text data. In this case, the input is audio data, and the output is text data.

[0080] Step 2:

[0081] The terminal sends text data to the server. The server receives the text data and processes it using a natural language processing engine to analyze the user's intent. The input is text data, and the output is a data structure that represents the user's intent. The server uses this data structure to access appropriate external information sources (e.g., weather information API) and retrieve the requested information.

[0082] Step 3:

[0083] When the server retrieves information, it organizes it and sends it back to the terminal. The input is external information retrieved based on the user's intent, and the output is information structured in a format that is easy for the user to understand. This structured information is then sent back to the terminal.

[0084] Step 4:

[0085] The terminal passes the received information to the sound synthesis engine, converting the text data into speech data. This speech data is then played back to the user, providing information such as, "Tomorrow will be sunny, with a high of 25 degrees Celsius." The input is structured information, and the output is speech data.

[0086] Step 5:

[0087] User behavior and preferences are recorded on the server and used for profiling. This allows for personalized suggestions during subsequent inquiries. The input here is the user's past usage history, and the output is profile data.

[0088] (Application Example 1)

[0089] 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."

[0090] In situations where elderly people require assistance with daily living, it is necessary to address the challenges of obtaining necessary information without complex operations and maintaining independent living. In particular, there is a need to improve the quality of life by easily obtaining information through voice and setting reminders for daily activities.

[0091] 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.

[0092] In this invention, the server includes a speech recognition means that analyzes voice input and converts it into text data; an analysis and information acquisition means that identifies the user's intent using the text data and acquires relevant information; a speech synthesis and presentation means that generates a voice response based on the acquired information and presents it to the user; and a management means for setting reminders to assist daily activities in a care support environment. This makes it possible for elderly people to acquire necessary information through simple voice operations and to use reminders to maintain their daily routines.

[0093] "Speech recognition means" refers to technology that acquires voice input from a user, analyzes that voice, and converts it into text data.

[0094] "Analysis and information acquisition means" refers to functions that use text data to identify user intent and acquire relevant information from a database or external information source.

[0095] "Speech synthesis and presentation means" refers to a technology for generating a speech response based on acquired information and presenting that speech response to the user.

[0096] "Management means" refers to functions for setting and managing reminders to assist with daily activities in a care support environment.

[0097] The system implementing this invention is designed to allow users to obtain information through voice and to support their daily lives. The system mainly consists of a terminal used by the user and a server that processes the data.

[0098] The device has a built-in microphone for receiving voice input and uses the Google® Speech-to-Text API for speech recognition. This process allows voice input to be converted into text data in real time. The text data is then sent to a server on Amazon Web Services (AWS®) via the internet.

[0099] The server analyzes the received text data using the Google Cloud Natural Language API to identify the user's intent. Based on this intent, it retrieves the necessary information from various data sources on the internet. For example, if it's a weather forecast, it retrieves the latest information from the relevant API.

[0100] The acquired information is synthesized into speech using Amazon Polly and presented to the user as an audio response. The device can play the generated audio and communicate the answer to the user. Furthermore, through the management system, reminders related to daily activities can be set using the user's voice commands. This ensures that elderly individuals can easily receive timely notifications to avoid forgetting medication or important appointments.

[0101] As a concrete example, if an elderly user A says, "Tell me the weather in my area today," the system immediately transcribes it into text, retrieves the weather information from the server, and returns it. The terminal can then respond by voice, "Today's weather is sunny with a high of 20 degrees Celsius."

[0102] Example of a prompt:

[0103] "Develop an application that allows users to easily obtain everyday information using voice. Design a system where voice recognition and information provision work together seamlessly."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The device receives the user's voice input. The microphone captures the user's voice, and this audio data is sent as input to the Google Speech-to-Text API. This API analyzes the audio data, converts it into corresponding text data, and outputs it.

[0107] Step 2:

[0108] The device sends the converted text data to the server. Upon receiving the text data, the server uses the Google Cloud Natural Language API to analyze it and identify the user's intent. The input is text data, and the output is the user's query intent.

[0109] Step 3:

[0110] The server retrieves the necessary information from various data sources and APIs based on the identified user's intent. In this step, requests are sent to databases and external APIs to obtain the required information. The input is the user's intent, and the output is the retrieved information.

[0111] Step 4:

[0112] Based on the information acquired by the server, Amazon Polly is used to generate a voice response. The server takes text-based information as input, converts it into voice data, and outputs it.

[0113] Step 5:

[0114] The device receives the generated audio data and presents it to the user. It plays the audio through the device's speaker, providing information to the user. This allows the user to receive an audio response.

[0115] Step 6:

[0116] Based on the user's voice command, the device uses its management mechanisms to set reminders. The input is the user's voice command, which is analyzed and then the device outputs reminder information set to send notifications at the appropriate time.

[0117] 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.

[0118] This invention is a life support system equipped with a function to recognize the user's emotions in order to enrich the lives of elderly users. This system combines the functions of speech recognition, data analysis, speech synthesis, and emotion recognition.

[0119] The terminal is equipped with a microphone to acquire user voice input and speech recognition software to convert speech into text. When the user inputs a question or command by voice, the terminal converts the voice into text data and sends it to the server.

[0120] The server analyzes voice input by using natural language processing (NLP) techniques to analyze the text and identify the user's intent. Furthermore, the server uses an emotion engine to recognize the user's emotions from the voice. For example, if the user is feeling stressed, that emotion will be recognized.

[0121] Based on identified intentions and emotions, the server retrieves necessary information from databases and external sources. Simultaneously, it considers the perceived emotions and adjusts the tone and content of its responses. For example, a user experiencing stress will receive a calmer, more reassuring response.

[0122] The voice response text prepared by the server is sent to the terminal. The terminal uses speech synthesis technology to convert this text into speech and play it back to the user. At this stage, the tone and speed of the voice are also adjusted according to the emotion.

[0123] For example, if a user voice-inputs "I'm feeling a bit down today," the device converts the voice into text, and the emotion engine recognizes that the user is feeling down. The server then provides encouraging messages or suggests relaxing music tailored to this emotion.

[0124] Furthermore, this system can collect user emotional data using profiling techniques and analyze long-term emotional trends to provide personalized lifestyle suggestions. For example, it can learn what activities or information have improved a user's mood in the past and make similar suggestions again.

[0125] Thus, by incorporating emotion recognition functionality, the present invention realizes richer and more personalized life support.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user issues a voice command to the device. For example, they might say, "I'm feeling a bit down today."

[0129] Step 2:

[0130] The device captures the user's voice and converts it into text data using speech recognition software.

[0131] Step 3:

[0132] The device sends the converted text data to the server. This text data serves as the basis for analyzing the user's intent and emotions.

[0133] Step 4:

[0134] The server analyzes the received text data using natural language processing techniques. This is where the user's requests and questions are identified.

[0135] Step 5:

[0136] The server utilizes an emotion engine to recognize the user's emotions from voice input. In this example, the emotion engine recognizes that the user is "depressed."

[0137] Step 6:

[0138] The server selects appropriate information and actions based on the recognized user's intentions and emotions. For example, it might retrieve information suggesting cheerful music or videos to brighten the user's mood.

[0139] Step 7:

[0140] The server uses the acquired information to generate a voice response to the user. The tone and content of the response are adjusted according to the user's emotions.

[0141] Step 8:

[0142] The server sends the generated voice response to the terminal. The terminal receives this data.

[0143] Step 9:

[0144] The device converts the received voice response text into speech using speech synthesis technology and plays it back to the user. The speech is played back in an emotionally appropriate tone.

[0145] Step 10:

[0146] Based on the information provided, the user can make additional requests or ask new questions. This will cause the process to resume from step 1.

[0147] (Example 2)

[0148] 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".

[0149] The challenges that elderly people face in their daily lives include managing their emotions and physical condition appropriately. However, conventional life support systems have struggled to fully understand users' emotions and intentions and provide appropriate support accordingly. Therefore, there is a need for more personalized and appropriate support systems that meet the individual needs of elderly people.

[0150] 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.

[0151] In this invention, the server includes means for identifying the user's intent from the voice and analyzing the user's emotions from the voice; means for acquiring relevant information based on the identified intent and emotions and adjusting the response; and means for generating and presenting a voice response to the user based on the acquired and adjusted information. This enables advanced support that responds to the user's evolving needs and emotional state.

[0152] A "speech recognition system" is a mechanism that acquires voice input from a user and converts it into text data.

[0153] "Emotion recognition means" refers to technology that analyzes voice data obtained from users and identifies the user's emotional state from that data.

[0154] "Analysis and information acquisition means" refers to a method for clarifying the user's intent from voice input and retrieving relevant information from a database or external source based on the identified intent and emotions.

[0155] "Speech synthesis and presentation means" refers to a process for generating new speech data based on acquired information and adjusted response content, and for presenting it to the user in an easily understandable manner.

[0156] "Profiling methods" are systems for recording and analyzing a user's personal preferences, lifestyle habits, and long-term changes in their emotions.

[0157] The "schedule management function" is a feature that allows users to set reminders and create notes in response to voice commands, thereby managing their daily schedule.

[0158] This invention is a system that supports the lives of elderly users, integrating voice recognition, emotion recognition, and information provision technologies. When a user provides voice input, the terminal uses voice recognition software to convert that input into text data. This process utilizes widely used voice recognition technology. The converted text data is then sent to a server.

[0159] The server analyzes the received text data using natural language processing techniques and identifies the user's intent based on the results. Natural language processing models such as BERT may be used here. Simultaneously, an emotion engine is used to analyze the user's emotional state. In this process, if the user inputs, for example, "I'm feeling a bit down today," the emotion engine recognizes this feeling of depression.

[0160] The server searches for relevant information from databases and external sources based on identified intentions and emotions, and generates an appropriate response. In particular, the tone and content of the response are adjusted to match the user's emotions. For example, if the user is feeling stressed, relaxing music or words of encouragement may be suggested.

[0161] This response is sent as text to the terminal, which then converts it back into speech using speech synthesis technology and outputs it to the user. At this stage, the speed and tone of the speech are adjusted to match the emotion.

[0162] Furthermore, this system incorporates profiling tools to accumulate and analyze users' emotions and reactions over the long term. This enables personalized lifestyle suggestions for each user. Based on past data, for example, activities that have previously improved the user's mood may be suggested again.

[0163] By utilizing generative AI models, the system aims to create rich conversational experiences tailored to individual user needs and emotions. An example of a prompt would be: "Recognize that the elderly user is experiencing stress and generate a response suggesting relaxing music." This system aims to enrich the lives of seniors by combining these technologies.

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

[0165] Step 1:

[0166] The device acquires the user's voice input via the microphone. When the user says, "What's the weather like today?", this voice data is treated as input. The device uses speech recognition software to convert the voice data into text data. This conversion outputs the user's speech as text data. Specifically, this involves operations that perform high-precision text conversion using noise filtering and acoustic modeling techniques.

[0167] Step 2:

[0168] Text data is sent from the terminal to the server. The server uses the received text data as input and interprets the user's intent using natural language processing technology. This process involves data analysis to extract keywords and context from the text and identify the content of the user's question. The output is an interpretation that the user is "seeking weather information." Furthermore, emotion recognition is also employed, analyzing the user's emotions from the tone and content of their voice, and outputting emotion data such as "curious."

[0169] Step 3:

[0170] The server retrieves relevant information from a database or external sources based on the interpretation results and sentiment data. The input consists of data interpreted as the user's intent and the results of the sentiment analysis. At this stage, it accesses a weather information database to obtain real-time weather information based on the user's location and date. The resulting weather data is further refined into a format that matches the user's sentiment.

[0171] Step 4:

[0172] The server then generates a voice response based on the acquired information. The inputs used are interpretation results, sentiment data, and the acquired information. The generated response is produced using a template-based language generation model to create a natural conversation. The output is a voice response text, such as "It's sunny today. It's a good day to go out."

[0173] Step 5:

[0174] The generated voice response text is sent from the server to the terminal. The terminal uses speech synthesis technology to convert the text into speech. This conversion adjusts the tone and pace of the voice to produce an emotionally responsive, easy-to-understand, and friendly voice. The output includes playing the synthesized voice for the user to hear.

[0175] (Application Example 2)

[0176] 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".

[0177] In modern society, there is a need for advanced life support tailored to individual needs so that elderly people can live their daily lives safely and comfortably. However, conventional life support systems have the challenge of not considering the user's emotional state and having difficulty providing individualized responses and suggestions. As a result, there are few systems that can adequately alleviate the stress and anxiety that elderly people experience in their daily lives, leading to a problem where their quality of life cannot be improved.

[0178] 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.

[0179] In this invention, the server includes an analysis and information acquisition means for analyzing voice input to identify intent, an emotion analysis and response adjustment means for analyzing emotional state to adjust response, and a profiling means for storing and analyzing user information. This makes it possible to generate personalized responses according to the user's emotional state and to provide appropriate lifestyle suggestions based on the user's long-term emotional tendencies.

[0180] A "speech recognition means" is a technical device that acquires speech input from a user, analyzes that speech input, and converts it into text data.

[0181] "Analysis and information acquisition means" refers to a technical device for identifying the user's intent using text data and acquiring related information from a storage device or external information source.

[0182] "Speech synthesis and presentation means" refers to a technical device for generating a speech response based on acquired information and presenting that speech response to the user.

[0183] "Emotion analysis and response adjustment means" refers to a technical device for analyzing a user's emotional state and adjusting the response based on those emotions.

[0184] A "profiling tool" is a technological device used to store and analyze information based on a user's individual preferences and lifestyle.

[0185] "Emotional profiling tools" are technological devices used to analyze a user's long-term emotional tendencies.

[0186] "Assistance support devices" are technological devices that provide appropriate information and support according to the user's emotions.

[0187] This invention is a life support system that acquires user voice input, analyzes emotions, and generates personalized responses. This system consists of a terminal and a server, each playing a specific role.

[0188] First, the device acquires voice input from the user. It captures the voice using a microphone and converts it into text data using speech recognition software. This converted text data is then sent to the server.

[0189] The server uses natural language processing (NLP) techniques to identify the user's intent from the received text data. It then runs an emotion analysis engine to analyze the user's emotional state. Based on the analyzed emotional state and user intent, the server retrieves relevant information from a database or external sources. Furthermore, based on the retrieved information, it generates an appropriate voice response, adjusting the content and tone of the response according to the user's emotions.

[0190] The generated voice response is returned to the device. The device uses speech synthesis technology to convert the text response into speech and plays it back to the user. The tone and speed of the voice are also adjusted to take the user's emotional state into consideration.

[0191] For example, if a user voice-inputs "I'm feeling a little down today," that input is converted to text, and the emotion engine recognizes the emotion of "feeling down." The server then generates encouraging messages that resonate with the user's feelings, as well as suggestions for relaxing music. This allows the user to receive care tailored to their needs.

[0192] An example of a prompt message is, "How have you been feeling lately? Please tell me in voice." This allows the invention to more accurately understand the user's condition and provide appropriate support.

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

[0194] Step 1:

[0195] The user speaks into the device's microphone. The device uses speech recognition software to convert this speech into text data.

[0196] The input is the user's voice, and the output is converted text data. In this step, the voice data is digitized and phoneme analysis is performed.

[0197] Step 2:

[0198] The terminal sends the converted text data to the server. The server then uses natural language processing techniques to analyze the text data and identify the user's intent.

[0199] The input is text data, and the output is an analysis result that includes the user's intent. This step involves contextual analysis and keyword extraction.

[0200] Step 3:

[0201] The server uses an emotion analysis engine to determine the user's emotional state from text data.

[0202] The input is text data, and the output is an identified emotional state. This step involves detecting elements that indicate emotion (e.g., interjections, adjectives).

[0203] Step 4:

[0204] Based on the analyzed intent and emotional state, the server retrieves relevant information from storage or external sources.

[0205] The input is the user's intent and emotional state, and the output is the relevant information retrieved. This step involves database queries and API calls.

[0206] Step 5:

[0207] Based on the acquired information, the server generates a voice response using natural language generation technology, adjusting the content and tone according to the emotional state.

[0208] The input is the acquired information, and the output is the text of the generated voice response. In this step, the response is structured and refined.

[0209] Step 6:

[0210] The terminal receives the text of the voice response sent from the server, converts it into speech using speech synthesis technology, and plays it back to the user.

[0211] The input is the text of the voice response, and the output is the audio presented to the user. In this step, synthesized speech is generated and output.

[0212] 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.

[0213] Data generation model 58 is a type of 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention is a system designed to allow elderly people to easily receive support for their daily lives. In this system, the user, terminal, and server communicate with each other to provide the user with the information and support they need in their daily lives.

[0229] To implement this system, the terminal is equipped with a microphone and speech recognition software to receive user voice input. The speech recognition software analyzes the user's voice in real time and converts it into text data. For example, if a user says, "I want to know tomorrow's weather," that voice is instantly converted into text.

[0230] Text data sent from the device is sent to the server. The server analyzes the text data and uses natural language processing (NLP) techniques to understand the user's intent. After the intent is interpreted, the server retrieves the necessary information from the user's personal database or via the internet. For example, it might retrieve the latest weather forecast from a weather information API.

[0231] The information collected by the server is then sent back to the terminal. The terminal uses speech synthesis technology to play the text data as speech and provide the information to the user. If the user asked for weather information, it can say, "Tomorrow will be sunny, and the high temperature will be 25 degrees Celsius."

[0232] The system also features profiling capabilities, providing personalized suggestions based on user preferences and past usage history. For example, it can periodically notify users about local events they have previously shown interest in.

[0233] Furthermore, the scheduling function allows users to set reminders and create notes using voice commands. For example, if a user says, "Set a reminder to take my medicine at 10 AM," the device sends the instruction to the server and sets a reminder alarm at the appropriate time.

[0234] Thus, the system of the present invention can support the user's lifestyle through voice communication and comprehensively provide services ranging from information provision to daily support.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The device receives voice input from the user. The user speaks a voice command into the device's microphone.

[0238] Step 2:

[0239] The terminal converts the acquired audio into text data using speech recognition software. The converted text data is then prepared for the next processing step.

[0240] Step 3:

[0241] The device sends the converted text data to the server. The transmitted data serves as the basis for analyzing the user's intent.

[0242] Step 4:

[0243] The server analyzes the received text data. It uses natural language processing (NLP) techniques to identify user requests and questions.

[0244] Step 5:

[0245] The server retrieves information from a database or external API based on the specified request. The information retrieved is related to what the user has instructed.

[0246] Step 6:

[0247] The server generates an audio response to the user based on the acquired information. The response content is created in text format.

[0248] Step 7:

[0249] The server sends the generated voice response as text data to the terminal. The terminal receives this data.

[0250] Step 8:

[0251] The device converts the received voice response text into speech using speech synthesis technology. It then plays the generated speech to the user, providing them with information.

[0252] Step 9:

[0253] Based on the information provided, users can ask additional questions or make new requests. This causes the process to repeat from step 1.

[0254] (Example 1)

[0255] 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."

[0256] A key challenge is ensuring that elderly individuals can effectively access the information and support they need in their daily lives. In particular, there is a need for systems that allow elderly individuals to easily obtain information and improve their quality of life. Such systems require a means for users to obtain necessary information and receive personalized suggestions simply by issuing voice commands.

[0257] 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.

[0258] In this invention, the server includes acoustic recognition means, analysis and information acquisition means, and acoustic synthesis and presentation means. This enables users to efficiently obtain information via acoustic input and receive support based on their individual preferences and habits.

[0259] A "user" is an individual who uses a system to obtain information or receive support.

[0260] "Audio input" refers to audio data generated when a user gives instructions or requests information to the system using their voice.

[0261] "Character data" refers to text-formatted data obtained by analyzing and converting acoustic input using acoustic recognition means.

[0262] "Acoustic recognition means" refers to technologies and devices that receive acoustic input and convert it into text data in real time.

[0263] "Analysis and information acquisition means" refers to a function that analyzes converted character data to identify the user's intent and acquires relevant information from storage or external sources.

[0264] "Sound synthesis and presentation means" refers to technologies and devices for generating and presenting voice responses to a user based on acquired information.

[0265] "Profiling tools" are functions that record a user's past usage history and preferences, and provide personalized information and suggestions based on that information.

[0266] "Time management features" refer to functions that allow users to set schedules and reminders using sound commands, enabling efficient time management.

[0267] This system is designed to enable elderly individuals to easily obtain information and receive personalized support through voice communication. It primarily utilizes a combination of acoustic recognition, natural language processing, and sound synthesis technologies.

[0268] The device is equipped with acoustic recognition software to receive acoustic input from the user. This software can utilize a general-purpose speech recognition engine. When the user speaks a specific command, the device receives the acoustic input and converts it into text data in real time. For example, if the user says, "Tell me the weather tomorrow," this acoustic information is converted into text data.

[0269] Next, the text data sent from the terminal to the server is parsed by the server's natural language processing engine. The server utilizes general-purpose natural language processing techniques to accurately understand the user's intent. This allows, for example, access to a weather information API and retrieve the necessary information.

[0270] The acquired information is returned to the terminal, which uses sound synthesis software to convert the text data back into sound. This sound information is then presented directly to the user. For example, in response to a user's question, it can generate an audio response such as, "Tomorrow will be sunny, and the maximum temperature will be 25 degrees Celsius."

[0271] Furthermore, the system includes profiling capabilities, providing personalized information based on the user's past behavior and preferences. This allows users to manage their schedules and set reminders using voice commands. Specific prompts are expected to include instructions such as, "Set a reminder to take my medicine at 10 AM."

[0272] This system is designed with the aim of supporting the daily lives of the elderly through voice communication, utilizing generative AI models.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The user provides audio input to the device. The user gives a specific voice command, such as "Tell me the weather tomorrow." This audio input is received by the device's microphone and sent to speech recognition software. The speech recognition software analyzes the audio data and converts it into text data. In this case, the input is audio data, and the output is text data.

[0276] Step 2:

[0277] The terminal sends text data to the server. The server receives the text data and processes it using a natural language processing engine to analyze the user's intent. The input is text data, and the output is a data structure that represents the user's intent. The server uses this data structure to access appropriate external information sources (e.g., weather information API) and retrieve the requested information.

[0278] Step 3:

[0279] When the server acquires information, it organizes the information and returns it to the terminal. The input is external information acquired based on the user's intention, and the output is information structured in a format that is easy for the user to understand. This structured information is transmitted to the terminal again.

[0280] Step 4:

[0281] The terminal passes the received information to the speech synthesis engine and converts the character data into voice data. This voice data is played back to the user, for example, guiding the user with "Tomorrow will be sunny and the maximum temperature will be 25 degrees." The input is structured information, and the output is voice data.

[0282] Step 5:

[0283] The user's actions and preferences are recorded by the server and used for profiling. This enables personalized proposals during subsequent inquiries. The input here is the user's past usage history, and the output is profile data.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] In a scenario where an elderly person needs life support, it is necessary to obtain the necessary information without complicated operations and solve the problems in maintaining an independent life. In particular, it is required to improve the quality of life by easily obtaining information through voice or setting reminders for daily activities.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0288] In this invention, the server includes a speech recognition means that analyzes voice input and converts it into text data; an analysis and information acquisition means that identifies the user's intent using the text data and acquires relevant information; a speech synthesis and presentation means that generates a voice response based on the acquired information and presents it to the user; and a management means for setting reminders to assist daily activities in a care support environment. This makes it possible for elderly people to acquire necessary information through simple voice operations and to use reminders to maintain their daily routines.

[0289] "Speech recognition means" refers to technology that acquires voice input from a user, analyzes that voice, and converts it into text data.

[0290] "Analysis and information acquisition means" refers to functions that use text data to identify user intent and acquire relevant information from a database or external information source.

[0291] "Speech synthesis and presentation means" refers to a technology for generating a speech response based on acquired information and presenting that speech response to the user.

[0292] "Management means" refers to functions for setting and managing reminders to assist with daily activities in a care support environment.

[0293] The system implementing this invention is designed to allow users to obtain information through voice and to support their daily lives. The system mainly consists of a terminal used by the user and a server that processes the data.

[0294] The device has a built-in microphone for receiving voice input and uses the Google Speech-to-Text API for speech recognition. This process allows for real-time conversion of voice input into text data. The text data is then sent to a server on Amazon Web Services (AWS) via an internet connection.

[0295] The server analyzes the received text data using the Google Cloud Natural Language API to identify the user's intent. Based on this intent, it retrieves the necessary information from various data sources on the internet. For example, if it's a weather forecast, it retrieves the latest information from the relevant API.

[0296] The acquired information is synthesized into speech using Amazon Polly and presented to the user as an audio response. The device can play the generated audio and communicate the answer to the user. Furthermore, through the management system, reminders related to daily activities can be set using the user's voice commands. This ensures that elderly individuals can easily receive timely notifications to avoid forgetting medication or important appointments.

[0297] As a concrete example, if an elderly user A says, "Tell me the weather in my area today," the system immediately transcribes it into text, retrieves the weather information from the server, and returns it. The terminal can then respond by voice, "Today's weather is sunny with a high of 20 degrees Celsius."

[0298] Example of a prompt:

[0299] "Develop an application that allows users to easily obtain everyday information using voice. Design a system where voice recognition and information provision work together seamlessly."

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The device receives the user's voice input. The microphone captures the user's voice, and this audio data is sent as input to the Google Speech-to-Text API. This API analyzes the audio data, converts it into corresponding text data, and outputs it.

[0303] Step 2:

[0304] The terminal sends the converted text data to the server. When the server receives the text data, it analyzes the data using the Google Cloud Natural Language API to identify the user's intention. The input is the text data, and the output is the user's query intention.

[0305] Step 3:

[0306] Based on the identified user intention, the server retrieves the necessary information from various data sources and APIs. In this step, requests are sent to the database and external APIs to obtain the required information. The input is the user's intention, and the output is the retrieved information.

[0307] Step 4:

[0308] Based on the information obtained by the server, Amazon Polly is used to generate an audio response. The server takes the information in text format as input and converts it into audio data for output.

[0309] Step 5:

[0310] The terminal receives the generated audio data and presents it to the user. The audio is played through the terminal's speaker to provide information to the user. This allows the user to obtain an audio response.

[0311] Step 6:

[0312] Based on the user's voice command, the terminal sets a reminder using the management means. The input is the user's voice command, and the output is reminder information that is set by analyzing the voice to perform a notification at an appropriate time.

[0313] 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.

[0314] This invention is a life support system equipped with a function to recognize the user's emotions in order to enrich the lives of elderly users. This system combines the functions of speech recognition, data analysis, speech synthesis, and emotion recognition.

[0315] The terminal is equipped with a microphone to acquire user voice input and speech recognition software to convert speech into text. When the user inputs a question or command by voice, the terminal converts the voice into text data and sends it to the server.

[0316] The server analyzes voice input by using natural language processing (NLP) techniques to analyze the text and identify the user's intent. Furthermore, the server uses an emotion engine to recognize the user's emotions from the voice. For example, if the user is feeling stressed, that emotion will be recognized.

[0317] Based on identified intentions and emotions, the server retrieves necessary information from databases and external sources. Simultaneously, it considers the perceived emotions and adjusts the tone and content of its responses. For example, a user experiencing stress will receive a calmer, more reassuring response.

[0318] The voice response text prepared by the server is sent to the terminal. The terminal uses speech synthesis technology to convert this text into speech and play it back to the user. At this stage, the tone and speed of the voice are also adjusted according to the emotion.

[0319] For example, if a user voice-inputs "I'm feeling a bit down today," the device converts the voice into text, and the emotion engine recognizes that the user is feeling down. The server then provides encouraging messages or suggests relaxing music tailored to this emotion.

[0320] Furthermore, this system can collect user emotional data using profiling techniques and analyze long-term emotional trends to provide personalized lifestyle suggestions. For example, it can learn what activities or information have improved a user's mood in the past and make similar suggestions again.

[0321] Thus, by incorporating emotion recognition functionality, the present invention realizes richer and more personalized life support.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The user issues a voice command to the device. For example, they might say, "I'm feeling a bit down today."

[0325] Step 2:

[0326] The device captures the user's voice and converts it into text data using speech recognition software.

[0327] Step 3:

[0328] The device sends the converted text data to the server. This text data serves as the basis for analyzing the user's intent and emotions.

[0329] Step 4:

[0330] The server analyzes the received text data using natural language processing techniques. This is where the user's requests and questions are identified.

[0331] Step 5:

[0332] The server utilizes an emotion engine to recognize the user's emotions from voice input. In this example, the emotion engine recognizes that the user is "depressed."

[0333] Step 6:

[0334] The server selects appropriate information and actions based on the recognized user's intentions and emotions. For example, it might retrieve information suggesting cheerful music or videos to brighten the user's mood.

[0335] Step 7:

[0336] The server uses the acquired information to generate a voice response to the user. The tone and content of the response are adjusted according to the user's emotions.

[0337] Step 8:

[0338] The server sends the generated voice response to the terminal. The terminal receives this data.

[0339] Step 9:

[0340] The device converts the received voice response text into speech using speech synthesis technology and plays it back to the user. The speech is played back in an emotionally appropriate tone.

[0341] Step 10:

[0342] Based on the information provided, the user can make additional requests or ask new questions. This will cause the process to resume from step 1.

[0343] (Example 2)

[0344] 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".

[0345] The challenges that elderly people face in their daily lives include managing their emotions and physical condition appropriately. However, conventional life support systems have struggled to fully understand users' emotions and intentions and provide appropriate support accordingly. Therefore, there is a need for more personalized and appropriate support systems that meet the individual needs of elderly people.

[0346] 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.

[0347] In this invention, the server includes means for identifying the user's intent from the voice and analyzing the user's emotions from the voice; means for acquiring relevant information based on the identified intent and emotions and adjusting the response; and means for generating and presenting a voice response to the user based on the acquired and adjusted information. This enables advanced support that responds to the user's evolving needs and emotional state.

[0348] A "speech recognition system" is a mechanism that acquires voice input from a user and converts it into text data.

[0349] "Emotion recognition means" refers to technology that analyzes voice data obtained from users and identifies the user's emotional state from that data.

[0350] "Analysis and information acquisition means" refers to a method for clarifying the user's intent from voice input and retrieving relevant information from a database or external source based on the identified intent and emotions.

[0351] "Speech synthesis and presentation means" refers to a process for generating new speech data based on acquired information and adjusted response content, and for presenting it to the user in an easily understandable manner.

[0352] "Profiling methods" are systems for recording and analyzing a user's personal preferences, lifestyle habits, and long-term changes in their emotions.

[0353] The "schedule management function" is a feature that allows users to set reminders and create notes in response to voice commands, thereby managing their daily schedule.

[0354] This invention is a system that supports the lives of elderly users, integrating voice recognition, emotion recognition, and information provision technologies. When a user provides voice input, the terminal uses voice recognition software to convert that input into text data. This process utilizes widely used voice recognition technology. The converted text data is then sent to a server.

[0355] The server analyzes the received text data using natural language processing techniques and identifies the user's intent based on the results. Natural language processing models such as BERT may be used here. Simultaneously, an emotion engine is used to analyze the user's emotional state. In this process, if the user inputs, for example, "I'm feeling a bit down today," the emotion engine recognizes this feeling of depression.

[0356] The server searches for relevant information from databases and external sources based on identified intentions and emotions, and generates an appropriate response. In particular, the tone and content of the response are adjusted to match the user's emotions. For example, if the user is feeling stressed, relaxing music or words of encouragement may be suggested.

[0357] This response is sent as text to the terminal, which then converts it back into speech using speech synthesis technology and outputs it to the user. At this stage, the speed and tone of the speech are adjusted to match the emotion.

[0358] Furthermore, this system incorporates profiling tools to accumulate and analyze users' emotions and reactions over the long term. This enables personalized lifestyle suggestions for each user. Based on past data, for example, activities that have previously improved the user's mood may be suggested again.

[0359] By utilizing generative AI models, the system aims to create rich conversational experiences tailored to individual user needs and emotions. An example of a prompt would be: "Recognize that the elderly user is experiencing stress and generate a response suggesting relaxing music." This system aims to enrich the lives of seniors by combining these technologies.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The device acquires the user's voice input via the microphone. When the user says, "What's the weather like today?", this voice data is treated as input. The device uses speech recognition software to convert the voice data into text data. This conversion outputs the user's speech as text data. Specifically, this involves operations that perform high-precision text conversion using noise filtering and acoustic modeling techniques.

[0363] Step 2:

[0364] Text data is sent from the terminal to the server. The server uses the received text data as input and interprets the user's intent using natural language processing technology. This process involves data analysis to extract keywords and context from the text and identify the content of the user's question. The output is an interpretation that the user is "seeking weather information." Furthermore, emotion recognition is also employed, analyzing the user's emotions from the tone and content of their voice, and outputting emotion data such as "curious."

[0365] Step 3:

[0366] The server retrieves relevant information from a database or external sources based on the interpretation results and sentiment data. The input consists of data interpreted as the user's intent and the results of the sentiment analysis. At this stage, it accesses a weather information database to obtain real-time weather information based on the user's location and date. The resulting weather data is further refined into a format that matches the user's sentiment.

[0367] Step 4:

[0368] The server then generates a voice response based on the acquired information. The inputs used are interpretation results, sentiment data, and the acquired information. The generated response is produced using a template-based language generation model to create a natural conversation. The output is a voice response text, such as "It's sunny today. It's a good day to go out."

[0369] Step 5:

[0370] The generated voice response text is sent from the server to the terminal. The terminal uses speech synthesis technology to convert the text into speech. This conversion adjusts the tone and pace of the voice to produce an emotionally responsive, easy-to-understand, and friendly voice. The output includes playing the synthesized voice for the user to hear.

[0371] (Application Example 2)

[0372] 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."

[0373] In modern society, there is a need for advanced life support tailored to individual needs so that elderly people can live their daily lives safely and comfortably. However, conventional life support systems have the challenge of not considering the user's emotional state and having difficulty providing individualized responses and suggestions. As a result, there are few systems that can adequately alleviate the stress and anxiety that elderly people experience in their daily lives, leading to a problem where their quality of life cannot be improved.

[0374] 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.

[0375] In this invention, the server includes an analysis and information acquisition means for analyzing voice input to identify intent, an emotion analysis and response adjustment means for analyzing emotional state to adjust response, and a profiling means for storing and analyzing user information. This makes it possible to generate personalized responses according to the user's emotional state and to provide appropriate lifestyle suggestions based on the user's long-term emotional tendencies.

[0376] A "speech recognition means" is a technical device that acquires speech input from a user, analyzes that speech input, and converts it into text data.

[0377] "Analysis and information acquisition means" refers to a technical device for identifying the user's intent using text data and acquiring related information from a storage device or external information source.

[0378] "Speech synthesis and presentation means" refers to a technical device for generating a speech response based on acquired information and presenting that speech response to the user.

[0379] "Emotion analysis and response adjustment means" refers to a technical device for analyzing a user's emotional state and adjusting the response based on those emotions.

[0380] A "profiling tool" is a technological device used to store and analyze information based on a user's individual preferences and lifestyle.

[0381] "Emotional profiling tools" are technological devices used to analyze a user's long-term emotional tendencies.

[0382] "Assistance support devices" are technological devices that provide appropriate information and support according to the user's emotions.

[0383] This invention is a life support system that acquires user voice input, analyzes emotions, and generates personalized responses. This system consists of a terminal and a server, each playing a specific role.

[0384] First, the device acquires voice input from the user. It captures the voice using a microphone and converts it into text data using speech recognition software. This converted text data is then sent to the server.

[0385] The server uses natural language processing (NLP) techniques to identify the user's intent from the received text data. It then runs an emotion analysis engine to analyze the user's emotional state. Based on the analyzed emotional state and user intent, the server retrieves relevant information from a database or external sources. Furthermore, based on the retrieved information, it generates an appropriate voice response, adjusting the content and tone of the response according to the user's emotions.

[0386] The generated voice response is returned to the device. The device uses speech synthesis technology to convert the text response into speech and plays it back to the user. The tone and speed of the voice are also adjusted to take the user's emotional state into consideration.

[0387] For example, if a user voice-inputs "I'm feeling a little down today," that input is converted to text, and the emotion engine recognizes the emotion of "feeling down." The server then generates encouraging messages that resonate with the user's feelings, as well as suggestions for relaxing music. This allows the user to receive care tailored to their needs.

[0388] An example of a prompt message is, "How have you been feeling lately? Please tell me in voice." This allows the invention to more accurately understand the user's condition and provide appropriate support.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The user speaks into the device's microphone. The device uses speech recognition software to convert this speech into text data.

[0392] The input is the user's voice, and the output is converted text data. In this step, the voice data is digitized and phoneme analysis is performed.

[0393] Step 2:

[0394] The terminal sends the converted text data to the server. The server then uses natural language processing techniques to analyze the text data and identify the user's intent.

[0395] The input is text data, and the output is an analysis result that includes the user's intent. This step involves contextual analysis and keyword extraction.

[0396] Step 3:

[0397] The server uses an emotion analysis engine to determine the user's emotional state from text data.

[0398] The input is text data, and the output is an identified emotional state. This step involves detecting elements that indicate emotion (e.g., interjections, adjectives).

[0399] Step 4:

[0400] Based on the analyzed intent and emotional state, the server retrieves relevant information from storage or external sources.

[0401] The input is the user's intent and emotional state, and the output is the relevant information retrieved. This step involves database queries and API calls.

[0402] Step 5:

[0403] Based on the acquired information, the server generates a voice response using natural language generation technology, adjusting the content and tone according to the emotional state.

[0404] The input is the acquired information, and the output is the text of the generated voice response. In this step, the response is structured and refined.

[0405] Step 6:

[0406] The terminal receives the text of the voice response sent from the server, converts it into speech using speech synthesis technology, and plays it back to the user.

[0407] The input is the text of the voice response, and the output is the audio presented to the user. In this step, synthesized speech is generated and output.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention is a system designed to allow elderly people to easily receive support for their daily lives. In this system, the user, terminal, and server communicate with each other to provide the user with the information and support they need in their daily lives.

[0425] To implement this system, the terminal is equipped with a microphone and speech recognition software to receive user voice input. The speech recognition software analyzes the user's voice in real time and converts it into text data. For example, if a user says, "I want to know tomorrow's weather," that voice is instantly converted into text.

[0426] Text data sent from the device is sent to the server. The server analyzes the text data and uses natural language processing (NLP) techniques to understand the user's intent. After the intent is interpreted, the server retrieves the necessary information from the user's personal database or via the internet. For example, it might retrieve the latest weather forecast from a weather information API.

[0427] The information collected by the server is then sent back to the terminal. The terminal uses speech synthesis technology to play the text data as speech and provide the information to the user. If the user asked for weather information, it can say, "Tomorrow will be sunny, and the high temperature will be 25 degrees Celsius."

[0428] The system also features profiling capabilities, providing personalized suggestions based on user preferences and past usage history. For example, it can periodically notify users about local events they have previously shown interest in.

[0429] Furthermore, the scheduling function allows users to set reminders and create notes using voice commands. For example, if a user says, "Set a reminder to take my medicine at 10 AM," the device sends the instruction to the server and sets a reminder alarm at the appropriate time.

[0430] Thus, the system of the present invention can support the user's lifestyle through voice communication and comprehensively provide services ranging from information provision to daily support.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The device receives voice input from the user. The user speaks a voice command into the device's microphone.

[0434] Step 2:

[0435] The terminal converts the acquired audio into text data using speech recognition software. The converted text data is then prepared for the next processing step.

[0436] Step 3:

[0437] The device sends the converted text data to the server. The transmitted data serves as the basis for analyzing the user's intent.

[0438] Step 4:

[0439] The server analyzes the received text data. It uses natural language processing (NLP) techniques to identify user requests and questions.

[0440] Step 5:

[0441] The server retrieves information from a database or external API based on the specified request. The information retrieved is related to what the user has instructed.

[0442] Step 6:

[0443] The server generates an audio response to the user based on the acquired information. The response content is created in text format.

[0444] Step 7:

[0445] The server sends the generated voice response as text data to the terminal. The terminal receives this data.

[0446] Step 8:

[0447] The device converts the received voice response text into speech using speech synthesis technology. It then plays the generated speech to the user, providing them with information.

[0448] Step 9:

[0449] Based on the information provided, users can ask additional questions or make new requests. This causes the process to repeat from step 1.

[0450] (Example 1)

[0451] 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."

[0452] A key challenge is ensuring that elderly individuals can effectively access the information and support they need in their daily lives. In particular, there is a need for systems that allow elderly individuals to easily obtain information and improve their quality of life. Such systems require a means for users to obtain necessary information and receive personalized suggestions simply by issuing voice commands.

[0453] 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.

[0454] In this invention, the server includes acoustic recognition means, analysis and information acquisition means, and acoustic synthesis and presentation means. This enables users to efficiently obtain information via acoustic input and receive support based on their individual preferences and habits.

[0455] A "user" is an individual who uses a system to obtain information or receive support.

[0456] "Audio input" refers to audio data generated when a user gives instructions or requests information to the system using their voice.

[0457] "Character data" refers to text-formatted data obtained by analyzing and converting acoustic input using acoustic recognition means.

[0458] "Acoustic recognition means" refers to technologies and devices that receive acoustic input and convert it into text data in real time.

[0459] "Analysis and information acquisition means" refers to a function that analyzes converted character data to identify the user's intent and acquires relevant information from storage or external sources.

[0460] "Sound synthesis and presentation means" refers to technologies and devices for generating and presenting voice responses to a user based on acquired information.

[0461] "Profiling tools" are functions that record a user's past usage history and preferences, and provide personalized information and suggestions based on that information.

[0462] "Time management features" refer to functions that allow users to set schedules and reminders using sound commands, enabling efficient time management.

[0463] This system is designed to enable elderly individuals to easily obtain information and receive personalized support through voice communication. It primarily utilizes a combination of acoustic recognition, natural language processing, and sound synthesis technologies.

[0464] The device is equipped with acoustic recognition software to receive acoustic input from the user. This software can utilize a general-purpose speech recognition engine. When the user speaks a specific command, the device receives the acoustic input and converts it into text data in real time. For example, if the user says, "Tell me the weather tomorrow," this acoustic information is converted into text data.

[0465] Next, the text data sent from the terminal to the server is parsed by the server's natural language processing engine. The server utilizes general-purpose natural language processing techniques to accurately understand the user's intent. This allows, for example, access to a weather information API and retrieve the necessary information.

[0466] The acquired information is returned to the terminal, which uses sound synthesis software to convert the text data back into sound. This sound information is then presented directly to the user. For example, in response to a user's question, it can generate an audio response such as, "Tomorrow will be sunny, and the maximum temperature will be 25 degrees Celsius."

[0467] Furthermore, the system includes profiling capabilities, providing personalized information based on the user's past behavior and preferences. This allows users to manage their schedules and set reminders using voice commands. Specific prompts are expected to include instructions such as, "Set a reminder to take my medicine at 10 AM."

[0468] This system is designed with the aim of supporting the daily lives of the elderly through voice communication, utilizing generative AI models.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The user provides audio input to the device. The user gives a specific voice command, such as "Tell me the weather tomorrow." This audio input is received by the device's microphone and sent to speech recognition software. The speech recognition software analyzes the audio data and converts it into text data. In this case, the input is audio data, and the output is text data.

[0472] Step 2:

[0473] The terminal sends text data to the server. The server receives the text data and processes it using a natural language processing engine to analyze the user's intent. The input is text data, and the output is a data structure that represents the user's intent. The server uses this data structure to access appropriate external information sources (e.g., weather information API) and retrieve the requested information.

[0474] Step 3:

[0475] When the server retrieves information, it organizes it and sends it back to the terminal. The input is external information retrieved based on the user's intent, and the output is information structured in a format that is easy for the user to understand. This structured information is then sent back to the terminal.

[0476] Step 4:

[0477] The terminal passes the received information to the sound synthesis engine, converting the text data into speech data. This speech data is then played back to the user, providing information such as, "Tomorrow will be sunny, with a high of 25 degrees Celsius." The input is structured information, and the output is speech data.

[0478] Step 5:

[0479] User behavior and preferences are recorded on the server and used for profiling. This allows for personalized suggestions during subsequent inquiries. The input here is the user's past usage history, and the output is profile data.

[0480] (Application Example 1)

[0481] 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."

[0482] In situations where elderly people require assistance with daily living, it is necessary to address the challenges of obtaining necessary information without complex operations and maintaining independent living. In particular, there is a need to improve the quality of life by easily obtaining information through voice and setting reminders for daily activities.

[0483] 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.

[0484] In this invention, the server includes a speech recognition means that analyzes voice input and converts it into text data; an analysis and information acquisition means that identifies the user's intent using the text data and acquires relevant information; a speech synthesis and presentation means that generates a voice response based on the acquired information and presents it to the user; and a management means for setting reminders to assist daily activities in a care support environment. This makes it possible for elderly people to acquire necessary information through simple voice operations and to use reminders to maintain their daily routines.

[0485] "Speech recognition means" refers to technology that acquires voice input from a user, analyzes that voice, and converts it into text data.

[0486] "Analysis and information acquisition means" refers to functions that use text data to identify user intent and acquire relevant information from a database or external information source.

[0487] "Speech synthesis and presentation means" refers to a technology for generating a speech response based on acquired information and presenting that speech response to the user.

[0488] "Management means" refers to functions for setting and managing reminders to assist with daily activities in a care support environment.

[0489] The system implementing this invention is designed to allow users to obtain information through voice and to support their daily lives. The system mainly consists of a terminal used by the user and a server that processes the data.

[0490] The device has a built-in microphone for receiving voice input and uses the Google Speech-to-Text API for speech recognition. This process allows for real-time conversion of voice input into text data. The text data is then sent to a server on Amazon Web Services (AWS) via an internet connection.

[0491] The server analyzes the received text data using the Google Cloud Natural Language API to identify the user's intent. Based on this intent, it retrieves the necessary information from various data sources on the internet. For example, if it's a weather forecast, it retrieves the latest information from the relevant API.

[0492] The acquired information is synthesized into speech using Amazon Polly and presented to the user as an audio response. The device can play the generated audio and communicate the answer to the user. Furthermore, through the management system, reminders related to daily activities can be set using the user's voice commands. This ensures that elderly individuals can easily receive timely notifications to avoid forgetting medication or important appointments.

[0493] As a concrete example, if an elderly user A says, "Tell me the weather in my area today," the system immediately transcribes it into text, retrieves the weather information from the server, and returns it. The terminal can then respond by voice, "Today's weather is sunny with a high of 20 degrees Celsius."

[0494] Example of a prompt:

[0495] "Develop an application that allows users to easily obtain everyday information using voice. Design a system where voice recognition and information provision work together seamlessly."

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The device receives the user's voice input. The microphone captures the user's voice, and this audio data is sent as input to the Google Speech-to-Text API. This API analyzes the audio data, converts it into corresponding text data, and outputs it.

[0499] Step 2:

[0500] The device sends the converted text data to the server. Upon receiving the text data, the server uses the Google Cloud Natural Language API to analyze it and identify the user's intent. The input is text data, and the output is the user's query intent.

[0501] Step 3:

[0502] The server retrieves the necessary information from various data sources and APIs based on the identified user's intent. In this step, requests are sent to databases and external APIs to obtain the required information. The input is the user's intent, and the output is the retrieved information.

[0503] Step 4:

[0504] Based on the information acquired by the server, Amazon Polly is used to generate a voice response. The server takes text-based information as input, converts it into voice data, and outputs it.

[0505] Step 5:

[0506] The device receives the generated audio data and presents it to the user. It plays the audio through the device's speaker, providing information to the user. This allows the user to receive an audio response.

[0507] Step 6:

[0508] Based on the user's voice command, the device uses its management mechanisms to set reminders. The input is the user's voice command, which is analyzed and then the device outputs reminder information set to send notifications at the appropriate time.

[0509] 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.

[0510] This invention is a life support system equipped with a function to recognize the user's emotions in order to enrich the lives of elderly users. This system combines the functions of speech recognition, data analysis, speech synthesis, and emotion recognition.

[0511] The terminal is equipped with a microphone to acquire user voice input and speech recognition software to convert speech into text. When the user inputs a question or command by voice, the terminal converts the voice into text data and sends it to the server.

[0512] The server analyzes voice input by using natural language processing (NLP) techniques to analyze the text and identify the user's intent. Furthermore, the server uses an emotion engine to recognize the user's emotions from the voice. For example, if the user is feeling stressed, that emotion will be recognized.

[0513] Based on identified intentions and emotions, the server retrieves necessary information from databases and external sources. Simultaneously, it considers the perceived emotions and adjusts the tone and content of its responses. For example, a user experiencing stress will receive a calmer, more reassuring response.

[0514] The voice response text prepared by the server is sent to the terminal. The terminal uses speech synthesis technology to convert this text into speech and play it back to the user. At this stage, the tone and speed of the voice are also adjusted according to the emotion.

[0515] For example, if a user voice-inputs "I'm feeling a bit down today," the device converts the voice into text, and the emotion engine recognizes that the user is feeling down. The server then provides encouraging messages or suggests relaxing music tailored to this emotion.

[0516] Furthermore, this system can collect user emotional data using profiling techniques and analyze long-term emotional trends to provide personalized lifestyle suggestions. For example, it can learn what activities or information have improved a user's mood in the past and make similar suggestions again.

[0517] Thus, by incorporating emotion recognition functionality, the present invention realizes richer and more personalized life support.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The user issues a voice command to the device. For example, they might say, "I'm feeling a bit down today."

[0521] Step 2:

[0522] The device captures the user's voice and converts it into text data using speech recognition software.

[0523] Step 3:

[0524] The device sends the converted text data to the server. This text data serves as the basis for analyzing the user's intent and emotions.

[0525] Step 4:

[0526] The server analyzes the received text data using natural language processing techniques. This is where the user's requests and questions are identified.

[0527] Step 5:

[0528] The server utilizes an emotion engine to recognize the user's emotions from voice input. In this example, the emotion engine recognizes that the user is "depressed."

[0529] Step 6:

[0530] The server selects appropriate information and actions based on the recognized user's intentions and emotions. For example, it might retrieve information suggesting cheerful music or videos to brighten the user's mood.

[0531] Step 7:

[0532] The server uses the acquired information to generate a voice response to the user. The tone and content of the response are adjusted according to the user's emotions.

[0533] Step 8:

[0534] The server sends the generated voice response to the terminal. The terminal receives this data.

[0535] Step 9:

[0536] The device converts the received voice response text into speech using speech synthesis technology and plays it back to the user. The speech is played back in an emotionally appropriate tone.

[0537] Step 10:

[0538] Based on the information provided, the user can make additional requests or ask new questions. This will cause the process to resume from step 1.

[0539] (Example 2)

[0540] 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."

[0541] The challenges that elderly people face in their daily lives include managing their emotions and physical condition appropriately. However, conventional life support systems have struggled to fully understand users' emotions and intentions and provide appropriate support accordingly. Therefore, there is a need for more personalized and appropriate support systems that meet the individual needs of elderly people.

[0542] 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.

[0543] In this invention, the server includes means for identifying the user's intent from the voice and analyzing the user's emotions from the voice; means for acquiring relevant information based on the identified intent and emotions and adjusting the response; and means for generating and presenting a voice response to the user based on the acquired and adjusted information. This enables advanced support that responds to the user's evolving needs and emotional state.

[0544] A "speech recognition system" is a mechanism that acquires voice input from a user and converts it into text data.

[0545] "Emotion recognition means" refers to technology that analyzes voice data obtained from users and identifies the user's emotional state from that data.

[0546] "Analysis and information acquisition means" refers to a method for clarifying the user's intent from voice input and retrieving relevant information from a database or external source based on the identified intent and emotions.

[0547] "Speech synthesis and presentation means" refers to a process for generating new speech data based on acquired information and adjusted response content, and for presenting it to the user in an easily understandable manner.

[0548] "Profiling methods" are systems for recording and analyzing a user's personal preferences, lifestyle habits, and long-term changes in their emotions.

[0549] The "schedule management function" is a feature that allows users to set reminders and create notes in response to voice commands, thereby managing their daily schedule.

[0550] This invention is a system that supports the lives of elderly users, integrating voice recognition, emotion recognition, and information provision technologies. When a user provides voice input, the terminal uses voice recognition software to convert that input into text data. This process utilizes widely used voice recognition technology. The converted text data is then sent to a server.

[0551] The server analyzes the received text data using natural language processing techniques and identifies the user's intent based on the results. Natural language processing models such as BERT may be used here. Simultaneously, an emotion engine is used to analyze the user's emotional state. In this process, if the user inputs, for example, "I'm feeling a bit down today," the emotion engine recognizes this feeling of depression.

[0552] The server searches for relevant information from databases and external sources based on identified intentions and emotions, and generates an appropriate response. In particular, the tone and content of the response are adjusted to match the user's emotions. For example, if the user is feeling stressed, relaxing music or words of encouragement may be suggested.

[0553] This response is sent as text to the terminal, which then converts it back into speech using speech synthesis technology and outputs it to the user. At this stage, the speed and tone of the speech are adjusted to match the emotion.

[0554] Furthermore, this system incorporates profiling tools to accumulate and analyze users' emotions and reactions over the long term. This enables personalized lifestyle suggestions for each user. Based on past data, for example, activities that have previously improved the user's mood may be suggested again.

[0555] By utilizing generative AI models, the system aims to create rich conversational experiences tailored to individual user needs and emotions. An example of a prompt would be: "Recognize that the elderly user is experiencing stress and generate a response suggesting relaxing music." This system aims to enrich the lives of seniors by combining these technologies.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The device acquires the user's voice input via the microphone. When the user says, "What's the weather like today?", this voice data is treated as input. The device uses speech recognition software to convert the voice data into text data. This conversion outputs the user's speech as text data. Specifically, this involves operations that perform high-precision text conversion using noise filtering and acoustic modeling techniques.

[0559] Step 2:

[0560] Text data is sent from the terminal to the server. The server uses the received text data as input and interprets the user's intent using natural language processing technology. This process involves data analysis to extract keywords and context from the text and identify the content of the user's question. The output is an interpretation that the user is "seeking weather information." Furthermore, emotion recognition is also employed, analyzing the user's emotions from the tone and content of their voice, and outputting emotion data such as "curious."

[0561] Step 3:

[0562] The server retrieves relevant information from a database or external sources based on the interpretation results and sentiment data. The input consists of data interpreted as the user's intent and the results of the sentiment analysis. At this stage, it accesses a weather information database to obtain real-time weather information based on the user's location and date. The resulting weather data is further refined into a format that matches the user's sentiment.

[0563] Step 4:

[0564] The server then generates a voice response based on the acquired information. The inputs used are interpretation results, sentiment data, and the acquired information. The generated response is produced using a template-based language generation model to create a natural conversation. The output is a voice response text, such as "It's sunny today. It's a good day to go out."

[0565] Step 5:

[0566] The generated voice response text is sent from the server to the terminal. The terminal uses speech synthesis technology to convert the text into speech. This conversion adjusts the tone and pace of the voice to produce an emotionally responsive, easy-to-understand, and friendly voice. The output includes playing the synthesized voice for the user to hear.

[0567] (Application Example 2)

[0568] 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."

[0569] In modern society, there is a need for advanced life support tailored to individual needs so that elderly people can live their daily lives safely and comfortably. However, conventional life support systems have the challenge of not considering the user's emotional state and having difficulty providing individualized responses and suggestions. As a result, there are few systems that can adequately alleviate the stress and anxiety that elderly people experience in their daily lives, leading to a problem where their quality of life cannot be improved.

[0570] 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.

[0571] In this invention, the server includes an analysis and information acquisition means for analyzing voice input to identify intent, an emotion analysis and response adjustment means for analyzing emotional state to adjust response, and a profiling means for storing and analyzing user information. This makes it possible to generate personalized responses according to the user's emotional state and to provide appropriate lifestyle suggestions based on the user's long-term emotional tendencies.

[0572] A "speech recognition means" is a technical device that acquires speech input from a user, analyzes that speech input, and converts it into text data.

[0573] "Analysis and information acquisition means" refers to a technical device for identifying the user's intent using text data and acquiring related information from a storage device or external information source.

[0574] "Speech synthesis and presentation means" refers to a technical device for generating a speech response based on acquired information and presenting that speech response to the user.

[0575] "Emotion analysis and response adjustment means" refers to a technical device for analyzing a user's emotional state and adjusting the response based on those emotions.

[0576] A "profiling tool" is a technological device used to store and analyze information based on a user's individual preferences and lifestyle.

[0577] "Emotional profiling tools" are technological devices used to analyze a user's long-term emotional tendencies.

[0578] "Assistance support devices" are technological devices that provide appropriate information and support according to the user's emotions.

[0579] This invention is a life support system that acquires user voice input, analyzes emotions, and generates personalized responses. This system consists of a terminal and a server, each playing a specific role.

[0580] First, the device acquires voice input from the user. It captures the voice using a microphone and converts it into text data using speech recognition software. This converted text data is then sent to the server.

[0581] The server uses natural language processing (NLP) techniques to identify the user's intent from the received text data. It then runs an emotion analysis engine to analyze the user's emotional state. Based on the analyzed emotional state and user intent, the server retrieves relevant information from a database or external sources. Furthermore, based on the retrieved information, it generates an appropriate voice response, adjusting the content and tone of the response according to the user's emotions.

[0582] The generated voice response is returned to the device. The device uses speech synthesis technology to convert the text response into speech and plays it back to the user. The tone and speed of the voice are also adjusted to take the user's emotional state into consideration.

[0583] For example, if a user voice-inputs "I'm feeling a little down today," that input is converted to text, and the emotion engine recognizes the emotion of "feeling down." The server then generates encouraging messages that resonate with the user's feelings, as well as suggestions for relaxing music. This allows the user to receive care tailored to their needs.

[0584] An example of a prompt message is, "How have you been feeling lately? Please tell me in voice." This allows the invention to more accurately understand the user's condition and provide appropriate support.

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0586] Step 1:

[0587] The user speaks into the device's microphone. The device uses speech recognition software to convert this speech into text data.

[0588] The input is the user's voice, and the output is converted text data. In this step, the voice data is digitized and phoneme analysis is performed.

[0589] Step 2:

[0590] The terminal sends the converted text data to the server. The server then uses natural language processing techniques to analyze the text data and identify the user's intent.

[0591] The input is text data, and the output is an analysis result that includes the user's intent. This step involves contextual analysis and keyword extraction.

[0592] Step 3:

[0593] The server uses an emotion analysis engine to determine the user's emotional state from text data.

[0594] The input is text data, and the output is an identified emotional state. This step involves detecting elements that indicate emotion (e.g., interjections, adjectives).

[0595] Step 4:

[0596] Based on the analyzed intent and emotional state, the server retrieves relevant information from storage or external sources.

[0597] The input is the user's intent and emotional state, and the output is the relevant information retrieved. This step involves database queries and API calls.

[0598] Step 5:

[0599] Based on the acquired information, the server generates a voice response using natural language generation technology, adjusting the content and tone according to the emotional state.

[0600] The input is the acquired information, and the output is the text of the generated voice response. In this step, the response is structured and refined.

[0601] Step 6:

[0602] The terminal receives the text of the voice response sent from the server, converts it into speech using speech synthesis technology, and plays it back to the user.

[0603] The input is the text of the voice response, and the output is the audio presented to the user. In this step, synthesized speech is generated and output.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] [Fourth Embodiment]

[0608] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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".

[0621] This invention is a system designed to allow elderly people to easily receive support for their daily lives. In this system, the user, terminal, and server communicate with each other to provide the user with the information and support they need in their daily lives.

[0622] To implement this system, the terminal is equipped with a microphone and speech recognition software to receive user voice input. The speech recognition software analyzes the user's voice in real time and converts it into text data. For example, if a user says, "I want to know tomorrow's weather," that voice is instantly converted into text.

[0623] Text data sent from the device is sent to the server. The server analyzes the text data and uses natural language processing (NLP) techniques to understand the user's intent. After the intent is interpreted, the server retrieves the necessary information from the user's personal database or via the internet. For example, it might retrieve the latest weather forecast from a weather information API.

[0624] The information collected by the server is then sent back to the terminal. The terminal uses speech synthesis technology to play the text data as speech and provide the information to the user. If the user asked for weather information, it can say, "Tomorrow will be sunny, and the high temperature will be 25 degrees Celsius."

[0625] The system also features profiling capabilities, providing personalized suggestions based on user preferences and past usage history. For example, it can periodically notify users about local events they have previously shown interest in.

[0626] Furthermore, the scheduling function allows users to set reminders and create notes using voice commands. For example, if a user says, "Set a reminder to take my medicine at 10 AM," the device sends the instruction to the server and sets a reminder alarm at the appropriate time.

[0627] Thus, the system of the present invention can support the user's lifestyle through voice communication and comprehensively provide services ranging from information provision to daily support.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The device receives voice input from the user. The user speaks a voice command into the device's microphone.

[0631] Step 2:

[0632] The terminal converts the acquired audio into text data using speech recognition software. The converted text data is then prepared for the next processing step.

[0633] Step 3:

[0634] The device sends the converted text data to the server. The transmitted data serves as the basis for analyzing the user's intent.

[0635] Step 4:

[0636] The server analyzes the received text data. It uses natural language processing (NLP) techniques to identify user requests and questions.

[0637] Step 5:

[0638] The server retrieves information from a database or external API based on the specified request. The information retrieved is related to what the user has instructed.

[0639] Step 6:

[0640] The server generates an audio response to the user based on the acquired information. The response content is created in text format.

[0641] Step 7:

[0642] The server sends the generated voice response as text data to the terminal. The terminal receives this data.

[0643] Step 8:

[0644] The device converts the received voice response text into speech using speech synthesis technology. It then plays the generated speech to the user, providing them with information.

[0645] Step 9:

[0646] Based on the information provided, users can ask additional questions or make new requests. This causes the process to repeat from step 1.

[0647] (Example 1)

[0648] 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".

[0649] A key challenge is ensuring that elderly individuals can effectively access the information and support they need in their daily lives. In particular, there is a need for systems that allow elderly individuals to easily obtain information and improve their quality of life. Such systems require a means for users to obtain necessary information and receive personalized suggestions simply by issuing voice commands.

[0650] 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.

[0651] In this invention, the server includes acoustic recognition means, analysis and information acquisition means, and acoustic synthesis and presentation means. This enables users to efficiently obtain information via acoustic input and receive support based on their individual preferences and habits.

[0652] A "user" is an individual who uses a system to obtain information or receive support.

[0653] "Audio input" refers to audio data generated when a user gives instructions or requests information to the system using their voice.

[0654] "Character data" refers to text-formatted data obtained by analyzing and converting acoustic input using acoustic recognition means.

[0655] "Acoustic recognition means" refers to technologies and devices that receive acoustic input and convert it into text data in real time.

[0656] "Analysis and information acquisition means" refers to a function that analyzes converted character data to identify the user's intent and acquires relevant information from storage or external sources.

[0657] "Sound synthesis and presentation means" refers to technologies and devices for generating and presenting voice responses to a user based on acquired information.

[0658] "Profiling tools" are functions that record a user's past usage history and preferences, and provide personalized information and suggestions based on that information.

[0659] "Time management features" refer to functions that allow users to set schedules and reminders using sound commands, enabling efficient time management.

[0660] This system is designed to enable elderly individuals to easily obtain information and receive personalized support through voice communication. It primarily utilizes a combination of acoustic recognition, natural language processing, and sound synthesis technologies.

[0661] The device is equipped with acoustic recognition software to receive acoustic input from the user. This software can utilize a general-purpose speech recognition engine. When the user speaks a specific command, the device receives the acoustic input and converts it into text data in real time. For example, if the user says, "Tell me the weather tomorrow," this acoustic information is converted into text data.

[0662] Next, the text data sent from the terminal to the server is parsed by the server's natural language processing engine. The server utilizes general-purpose natural language processing techniques to accurately understand the user's intent. This allows, for example, access to a weather information API and retrieve the necessary information.

[0663] The acquired information is returned to the terminal, which uses sound synthesis software to convert the text data back into sound. This sound information is then presented directly to the user. For example, in response to a user's question, it can generate an audio response such as, "Tomorrow will be sunny, and the maximum temperature will be 25 degrees Celsius."

[0664] Furthermore, the system includes profiling capabilities, providing personalized information based on the user's past behavior and preferences. This allows users to manage their schedules and set reminders using voice commands. Specific prompts are expected to include instructions such as, "Set a reminder to take my medicine at 10 AM."

[0665] This system is designed with the aim of supporting the daily lives of the elderly through voice communication, utilizing generative AI models.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] The user provides audio input to the device. The user gives a specific voice command, such as "Tell me the weather tomorrow." This audio input is received by the device's microphone and sent to speech recognition software. The speech recognition software analyzes the audio data and converts it into text data. In this case, the input is audio data, and the output is text data.

[0669] Step 2:

[0670] The terminal sends text data to the server. The server receives the text data and processes it using a natural language processing engine to analyze the user's intent. The input is text data, and the output is a data structure that represents the user's intent. The server uses this data structure to access appropriate external information sources (e.g., weather information API) and retrieve the requested information.

[0671] Step 3:

[0672] When the server retrieves information, it organizes it and sends it back to the terminal. The input is external information retrieved based on the user's intent, and the output is information structured in a format that is easy for the user to understand. This structured information is then sent back to the terminal.

[0673] Step 4:

[0674] The terminal passes the received information to the sound synthesis engine, converting the text data into speech data. This speech data is then played back to the user, providing information such as, "Tomorrow will be sunny, with a high of 25 degrees Celsius." The input is structured information, and the output is speech data.

[0675] Step 5:

[0676] User behavior and preferences are recorded on the server and used for profiling. This allows for personalized suggestions during subsequent inquiries. The input here is the user's past usage history, and the output is profile data.

[0677] (Application Example 1)

[0678] 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".

[0679] In situations where elderly people require assistance with daily living, it is necessary to address the challenges of obtaining necessary information without complex operations and maintaining independent living. In particular, there is a need to improve the quality of life by easily obtaining information through voice and setting reminders for daily activities.

[0680] 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.

[0681] In this invention, the server includes a speech recognition means that analyzes voice input and converts it into text data; an analysis and information acquisition means that identifies the user's intent using the text data and acquires relevant information; a speech synthesis and presentation means that generates a voice response based on the acquired information and presents it to the user; and a management means for setting reminders to assist daily activities in a care support environment. This makes it possible for elderly people to acquire necessary information through simple voice operations and to use reminders to maintain their daily routines.

[0682] "Speech recognition means" refers to technology that acquires voice input from a user, analyzes that voice, and converts it into text data.

[0683] "Analysis and information acquisition means" refers to functions that use text data to identify user intent and acquire relevant information from a database or external information source.

[0684] "Speech synthesis and presentation means" refers to a technology for generating a speech response based on acquired information and presenting that speech response to the user.

[0685] "Management means" refers to functions for setting and managing reminders to assist with daily activities in a care support environment.

[0686] The system implementing this invention is designed to allow users to obtain information through voice and to support their daily lives. The system mainly consists of a terminal used by the user and a server that processes the data.

[0687] The device has a built-in microphone for receiving voice input and uses the Google Speech-to-Text API for speech recognition. This process allows for real-time conversion of voice input into text data. The text data is then sent to a server on Amazon Web Services (AWS) via an internet connection.

[0688] The server analyzes the received text data using the Google Cloud Natural Language API to identify the user's intent. Based on this intent, it retrieves the necessary information from various data sources on the internet. For example, if it's a weather forecast, it retrieves the latest information from the relevant API.

[0689] The acquired information is synthesized into speech using Amazon Polly and presented to the user as an audio response. The device can play the generated audio and communicate the answer to the user. Furthermore, through the management system, reminders related to daily activities can be set using the user's voice commands. This ensures that elderly individuals can easily receive timely notifications to avoid forgetting medication or important appointments.

[0690] As a concrete example, if an elderly user A says, "Tell me the weather in my area today," the system immediately transcribes it into text, retrieves the weather information from the server, and returns it. The terminal can then respond by voice, "Today's weather is sunny with a high of 20 degrees Celsius."

[0691] Example of a prompt:

[0692] "Develop an application that allows users to easily obtain everyday information using voice. Design a system where voice recognition and information provision work together seamlessly."

[0693] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0694] Step 1:

[0695] The device receives the user's voice input. The microphone captures the user's voice, and this audio data is sent as input to the Google Speech-to-Text API. This API analyzes the audio data, converts it into corresponding text data, and outputs it.

[0696] Step 2:

[0697] The device sends the converted text data to the server. Upon receiving the text data, the server uses the Google Cloud Natural Language API to analyze it and identify the user's intent. The input is text data, and the output is the user's query intent.

[0698] Step 3:

[0699] The server retrieves the necessary information from various data sources and APIs based on the identified user's intent. In this step, requests are sent to databases and external APIs to obtain the required information. The input is the user's intent, and the output is the retrieved information.

[0700] Step 4:

[0701] Based on the information acquired by the server, Amazon Polly is used to generate a voice response. The server takes text-based information as input, converts it into voice data, and outputs it.

[0702] Step 5:

[0703] The device receives the generated audio data and presents it to the user. It plays the audio through the device's speaker, providing information to the user. This allows the user to receive an audio response.

[0704] Step 6:

[0705] Based on the user's voice command, the device uses its management mechanisms to set reminders. The input is the user's voice command, which is analyzed and then the device outputs reminder information set to send notifications at the appropriate time.

[0706] 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.

[0707] This invention is a life support system equipped with a function to recognize the user's emotions in order to enrich the lives of elderly users. This system combines the functions of speech recognition, data analysis, speech synthesis, and emotion recognition.

[0708] The terminal is equipped with a microphone to acquire user voice input and speech recognition software to convert speech into text. When the user inputs a question or command by voice, the terminal converts the voice into text data and sends it to the server.

[0709] The server analyzes voice input by using natural language processing (NLP) techniques to analyze the text and identify the user's intent. Furthermore, the server uses an emotion engine to recognize the user's emotions from the voice. For example, if the user is feeling stressed, that emotion will be recognized.

[0710] Based on identified intentions and emotions, the server retrieves necessary information from databases and external sources. Simultaneously, it considers the perceived emotions and adjusts the tone and content of its responses. For example, a user experiencing stress will receive a calmer, more reassuring response.

[0711] The voice response text prepared by the server is sent to the terminal. The terminal uses speech synthesis technology to convert this text into speech and play it back to the user. At this stage, the tone and speed of the voice are also adjusted according to the emotion.

[0712] For example, if a user voice-inputs "I'm feeling a bit down today," the device converts the voice into text, and the emotion engine recognizes that the user is feeling down. The server then provides encouraging messages or suggests relaxing music tailored to this emotion.

[0713] Furthermore, this system can collect user emotional data using profiling techniques and analyze long-term emotional trends to provide personalized lifestyle suggestions. For example, it can learn what activities or information have improved a user's mood in the past and make similar suggestions again.

[0714] Thus, by incorporating emotion recognition functionality, the present invention realizes richer and more personalized life support.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The user issues a voice command to the device. For example, they might say, "I'm feeling a bit down today."

[0718] Step 2:

[0719] The device captures the user's voice and converts it into text data using speech recognition software.

[0720] Step 3:

[0721] The device sends the converted text data to the server. This text data serves as the basis for analyzing the user's intent and emotions.

[0722] Step 4:

[0723] The server analyzes the received text data using natural language processing techniques. This is where the user's requests and questions are identified.

[0724] Step 5:

[0725] The server utilizes an emotion engine to recognize the user's emotions from voice input. In this example, the emotion engine recognizes that the user is "depressed."

[0726] Step 6:

[0727] The server selects appropriate information and actions based on the recognized user's intentions and emotions. For example, it might retrieve information suggesting cheerful music or videos to brighten the user's mood.

[0728] Step 7:

[0729] The server uses the acquired information to generate a voice response to the user. The tone and content of the response are adjusted according to the user's emotions.

[0730] Step 8:

[0731] The server sends the generated voice response to the terminal. The terminal receives this data.

[0732] Step 9:

[0733] The device converts the received voice response text into speech using speech synthesis technology and plays it back to the user. The speech is played back in an emotionally appropriate tone.

[0734] Step 10:

[0735] Based on the information provided, the user can make additional requests or ask new questions. This will cause the process to resume from step 1.

[0736] (Example 2)

[0737] 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".

[0738] The challenges that elderly people face in their daily lives include managing their emotions and physical condition appropriately. However, conventional life support systems have struggled to fully understand users' emotions and intentions and provide appropriate support accordingly. Therefore, there is a need for more personalized and appropriate support systems that meet the individual needs of elderly people.

[0739] 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.

[0740] In this invention, the server includes means for identifying the user's intent from the voice and analyzing the user's emotions from the voice; means for acquiring relevant information based on the identified intent and emotions and adjusting the response; and means for generating and presenting a voice response to the user based on the acquired and adjusted information. This enables advanced support that responds to the user's evolving needs and emotional state.

[0741] A "speech recognition system" is a mechanism that acquires voice input from a user and converts it into text data.

[0742] "Emotion recognition means" refers to technology that analyzes voice data obtained from users and identifies the user's emotional state from that data.

[0743] "Analysis and information acquisition means" refers to a method for clarifying the user's intent from voice input and retrieving relevant information from a database or external source based on the identified intent and emotions.

[0744] "Speech synthesis and presentation means" refers to a process for generating new speech data based on acquired information and adjusted response content, and for presenting it to the user in an easily understandable manner.

[0745] "Profiling methods" are systems for recording and analyzing a user's personal preferences, lifestyle habits, and long-term changes in their emotions.

[0746] The "schedule management function" is a feature that allows users to set reminders and create notes in response to voice commands, thereby managing their daily schedule.

[0747] This invention is a system that supports the lives of elderly users, integrating voice recognition, emotion recognition, and information provision technologies. When a user provides voice input, the terminal uses voice recognition software to convert that input into text data. This process utilizes widely used voice recognition technology. The converted text data is then sent to a server.

[0748] The server analyzes the received text data using natural language processing techniques and identifies the user's intent based on the results. Natural language processing models such as BERT may be used here. Simultaneously, an emotion engine is used to analyze the user's emotional state. In this process, if the user inputs, for example, "I'm feeling a bit down today," the emotion engine recognizes this feeling of depression.

[0749] The server searches for relevant information from databases and external sources based on identified intentions and emotions, and generates an appropriate response. In particular, the tone and content of the response are adjusted to match the user's emotions. For example, if the user is feeling stressed, relaxing music or words of encouragement may be suggested.

[0750] This response is sent as text to the terminal, which then converts it back into speech using speech synthesis technology and outputs it to the user. At this stage, the speed and tone of the speech are adjusted to match the emotion.

[0751] Furthermore, this system incorporates profiling tools to accumulate and analyze users' emotions and reactions over the long term. This enables personalized lifestyle suggestions for each user. Based on past data, for example, activities that have previously improved the user's mood may be suggested again.

[0752] By utilizing generative AI models, the system aims to create rich conversational experiences tailored to individual user needs and emotions. An example of a prompt would be: "Recognize that the elderly user is experiencing stress and generate a response suggesting relaxing music." This system aims to enrich the lives of seniors by combining these technologies.

[0753] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0754] Step 1:

[0755] The device acquires the user's voice input via the microphone. When the user says, "What's the weather like today?", this voice data is treated as input. The device uses speech recognition software to convert the voice data into text data. This conversion outputs the user's speech as text data. Specifically, this involves operations that perform high-precision text conversion using noise filtering and acoustic modeling techniques.

[0756] Step 2:

[0757] Text data is sent from the terminal to the server. The server uses the received text data as input and interprets the user's intent using natural language processing technology. This process involves data analysis to extract keywords and context from the text and identify the content of the user's question. The output is an interpretation that the user is "seeking weather information." Furthermore, emotion recognition is also employed, analyzing the user's emotions from the tone and content of their voice, and outputting emotion data such as "curious."

[0758] Step 3:

[0759] The server retrieves relevant information from a database or external sources based on the interpretation results and sentiment data. The input consists of data interpreted as the user's intent and the results of the sentiment analysis. At this stage, it accesses a weather information database to obtain real-time weather information based on the user's location and date. The resulting weather data is further refined into a format that matches the user's sentiment.

[0760] Step 4:

[0761] The server then generates a voice response based on the acquired information. The inputs used are interpretation results, sentiment data, and the acquired information. The generated response is produced using a template-based language generation model to create a natural conversation. The output is a voice response text, such as "It's sunny today. It's a good day to go out."

[0762] Step 5:

[0763] The generated voice response text is sent from the server to the terminal. The terminal uses speech synthesis technology to convert the text into speech. This conversion adjusts the tone and pace of the voice to produce an emotionally responsive, easy-to-understand, and friendly voice. The output includes playing the synthesized voice for the user to hear.

[0764] (Application Example 2)

[0765] 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".

[0766] In modern society, there is a need for advanced life support tailored to individual needs so that elderly people can live their daily lives safely and comfortably. However, conventional life support systems have the challenge of not considering the user's emotional state and having difficulty providing individualized responses and suggestions. As a result, there are few systems that can adequately alleviate the stress and anxiety that elderly people experience in their daily lives, leading to a problem where their quality of life cannot be improved.

[0767] 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.

[0768] In this invention, the server includes an analysis and information acquisition means for analyzing voice input to identify intent, an emotion analysis and response adjustment means for analyzing emotional state to adjust response, and a profiling means for storing and analyzing user information. This makes it possible to generate personalized responses according to the user's emotional state and to provide appropriate lifestyle suggestions based on the user's long-term emotional tendencies.

[0769] A "speech recognition means" is a technical device that acquires speech input from a user, analyzes that speech input, and converts it into text data.

[0770] "Analysis and information acquisition means" refers to a technical device for identifying the user's intent using text data and acquiring related information from a storage device or external information source.

[0771] "Speech synthesis and presentation means" refers to a technical device for generating a speech response based on acquired information and presenting that speech response to the user.

[0772] "Emotion analysis and response adjustment means" refers to a technical device for analyzing a user's emotional state and adjusting the response based on those emotions.

[0773] A "profiling tool" is a technological device used to store and analyze information based on a user's individual preferences and lifestyle.

[0774] "Emotional profiling tools" are technological devices used to analyze a user's long-term emotional tendencies.

[0775] "Assistance support devices" are technological devices that provide appropriate information and support according to the user's emotions.

[0776] This invention is a life support system that acquires user voice input, analyzes emotions, and generates personalized responses. This system consists of a terminal and a server, each playing a specific role.

[0777] First, the device acquires voice input from the user. It captures the voice using a microphone and converts it into text data using speech recognition software. This converted text data is then sent to the server.

[0778] The server uses natural language processing (NLP) techniques to identify the user's intent from the received text data. It then runs an emotion analysis engine to analyze the user's emotional state. Based on the analyzed emotional state and user intent, the server retrieves relevant information from a database or external sources. Furthermore, based on the retrieved information, it generates an appropriate voice response, adjusting the content and tone of the response according to the user's emotions.

[0779] The generated voice response is returned to the device. The device uses speech synthesis technology to convert the text response into speech and plays it back to the user. The tone and speed of the voice are also adjusted to take the user's emotional state into consideration.

[0780] For example, if a user voice-inputs "I'm feeling a little down today," that input is converted to text, and the emotion engine recognizes the emotion of "feeling down." The server then generates encouraging messages that resonate with the user's feelings, as well as suggestions for relaxing music. This allows the user to receive care tailored to their needs.

[0781] An example of a prompt message is, "How have you been feeling lately? Please tell me in voice." This allows the invention to more accurately understand the user's condition and provide appropriate support.

[0782] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0783] Step 1:

[0784] The user speaks into the device's microphone. The device uses speech recognition software to convert this speech into text data.

[0785] The input is the user's voice, and the output is converted text data. In this step, the voice data is digitized and phoneme analysis is performed.

[0786] Step 2:

[0787] The terminal sends the converted text data to the server. The server then uses natural language processing techniques to analyze the text data and identify the user's intent.

[0788] The input is text data, and the output is an analysis result that includes the user's intent. This step involves contextual analysis and keyword extraction.

[0789] Step 3:

[0790] The server uses an emotion analysis engine to determine the user's emotional state from text data.

[0791] The input is text data, and the output is an identified emotional state. This step involves detecting elements that indicate emotion (e.g., interjections, adjectives).

[0792] Step 4:

[0793] Based on the analyzed intent and emotional state, the server retrieves relevant information from storage or external sources.

[0794] The input is the user's intent and emotional state, and the output is the relevant information retrieved. This step involves database queries and API calls.

[0795] Step 5:

[0796] Based on the acquired information, the server generates a voice response using natural language generation technology, adjusting the content and tone according to the emotional state.

[0797] The input is the acquired information, and the output is the text of the generated voice response. In this step, the response is structured and refined.

[0798] Step 6:

[0799] The terminal receives the text of the voice response sent from the server, converts it into speech using speech synthesis technology, and plays it back to the user.

[0800] The input is the text of the voice response, and the output is the audio presented to the user. In this step, synthesized speech is generated and output.

[0801] 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.

[0802] 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.

[0803] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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."

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0822] The following is further disclosed regarding the embodiments described above.

[0823] (Claim 1)

[0824] A speech recognition means that acquires voice input from a user, analyzes the voice input, and converts it into text data,

[0825] An analysis and information acquisition means that uses the text data to identify the user's intent and obtains related information from a database or external information source,

[0826] A speech synthesis and presentation means that generates a voice response based on acquired information and presents the voice response to the user,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, further comprising profiling means for storing and analyzing user information in order to make suggestions based on the user's individual preferences and lifestyle.

[0830] (Claim 3)

[0831] The system according to claim 1, further comprising a schedule management means for the user to set reminders and create notes using voice commands.

[0832] "Example 1"

[0833] (Claim 1)

[0834] Acoustic recognition means that acquires acoustic input from the user, analyzes the said input and converts it into text data,

[0835] An analysis and information acquisition means that uses the character data to identify the user's intent and obtains related information from a storage area or an external information source,

[0836] An acoustic synthesis and presentation means that generates an acoustic response based on acquired information and presents the acoustic response to the user,

[0837] A profiling method that provides personalized information based on the user's past behavior,

[0838] A time management means for users to manage their schedules and generate records using acoustic commands,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, which stores and analyzes user information in order to make suggestions based on the individual user's tendencies and habits.

[0842] (Claim 3)

[0843] The system according to claim 1, further comprising a planning management means for a user to set up notifications and create records using audible commands.

[0844] "Application Example 1"

[0845] (Claim 1)

[0846] A speech recognition means that acquires voice input from a user, analyzes the voice input, and converts it into text data,

[0847] An analysis and information acquisition means that uses the text data to identify the user's intent and obtains related information from a database or external information source,

[0848] A speech synthesis and presentation means that generates a voice response based on acquired information and supplies the voice response to the user,

[0849] A management system for setting reminders to assist with daily activities in a care support environment,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, further comprising profiling means for storing and analyzing user information in order to make suggestions based on the user's individual preferences and lifestyle.

[0853] (Claim 3)

[0854] The system according to claim 1, further comprising control means for a user to contactlessly set and receive reminders using voice commands.

[0855] "Example 2 of combining an emotion engine"

[0856] (Claim 1)

[0857] A speech recognition means that acquires voice input from a user, analyzes the voice input, and converts it into text data,

[0858] An emotion recognition means that identifies the user's intent using the text data and analyzes the user's emotions from the voice,

[0859] An analysis and information acquisition means that retrieves relevant information from a database or external source based on identified intentions and emotions, and adjusts the response accordingly.

[0860] A speech synthesis and presentation means that generates a voice response based on acquired information and a refined response, and presents the voice response to the user,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising profiling means for storing and analyzing user information and emotional data in order to make suggestions based on the user's individual preferences, lifestyle habits, and long-term emotional trends.

[0864] (Claim 3)

[0865] The system according to claim 1, further comprising a schedule management means for the user to set reminders and create notes using voice commands.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] A speech recognition means that acquires voice input from a user, analyzes the voice input, and converts it into text data,

[0869] An analysis and information acquisition means that uses the text data to identify the user's intent and obtains related information from a storage device or external information source,

[0870] A speech synthesis and presentation means that generates a voice response based on acquired information and presents the voice response to the user,

[0871] An emotion analysis and response adjustment means for analyzing the user's emotional state and generating a response based on that emotion,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, further comprising a profiling means for storing and analyzing user information and an emotional profiling means for analyzing the user's long-term emotional tendencies, in order to make suggestions based on the user's individual preferences and lifestyle.

[0875] (Claim 3)

[0876] The system according to claim 1, further comprising a schedule management means for the user to set reminders and create notes using voice commands, and an assistance support means for providing appropriate information and support according to emotions. [Explanation of Symbols]

[0877] 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 speech recognition means that acquires voice input from a user, analyzes the voice input, and converts it into text data, An analysis and information acquisition means that uses the text data to identify the user's intent and obtains related information from a database or external information source, A speech synthesis and presentation means that generates a voice response based on acquired information and supplies the voice response to the user, A management system for setting reminders to assist with daily activities in a care support environment, A system that includes this.

2. The system according to claim 1, further comprising profiling means for storing and analyzing user information in order to make suggestions based on the user's individual preferences and lifestyle.

3. The system according to claim 1, further comprising control means for a user to set and receive reminders contactlessly using voice commands.

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