system

Abstract

We provide the system. [Solution] A speech recognition means that converts speech data received from a speech input device into text data, Image recognition means that analyzes image data received from an image acquisition device and assigns labels based on specific events, A natural language generation means that automatically generates text based on converted text data and labeled image data, A database management system that organizes and saves generated text in a diary format, 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 the 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 modern times, there is a problem that it is difficult for parents to find time to write a diary to record their children's growth and daily events during their busy days of child-rearing, housework, and work. As a result, there is a risk of losing precious growth moments and family memories without recording them. To solve this problem, there is a need for a means to easily and automatically generate child-rearing records, reduce the burden on parents, and preserve precious memories.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that combines multiple technologies. First, a voice input device is used to receive conversations during childcare, and the voice data is converted into text data by a voice recognition means. In addition, image data captured by an image acquisition device is analyzed by an image recognition means, and event-based labels are assigned. Next, a natural language generation means automatically generates diary-style text based on the converted text data and labeled image data. Furthermore, the generated text is organized and stored by a database management means, and made easily viewable and editable by parents via a user interface. Since emotional nuances are also added to the text using sentiment analysis, a richer record is possible. In this way, an environment is realized in which parents can record important moments of their children without requiring any special operation.

[0006] A "voice input device" is a device that collects voice data and outputs it to a system in a format that can receive it.

[0007] "Speech recognition means" refers to the technology and process for analyzing speech data and converting it into text data.

[0008] An "image acquisition device" is a device that captures or acquires image data, such as still images and videos, and provides it to a system.

[0009] "Image recognition means" refers to a technology that analyzes image data, identifies specific objects or events, and assigns relevant labels to them.

[0010] A "natural language generation method" is a technology that automatically generates human-readable text based on input data.

[0011] A "database management system" is a technology that efficiently organizes and stores generated data, and enables searching and access as needed.

[0012] A "user interface" is an interface through which a system and a user exchange information, and a means by which a user operates the functions of the system.

[0013] "Sentiment analysis" is a technology that analyzes text and audio data to identify the emotional nuances contained within them. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention constructs a system that allows parents raising children to easily generate and view diaries by using a voice input device, an image acquisition device, a server, and a dedicated application or web interface. The functions and interactions of each element are described below in natural language.

[0036] 1. Voice input and speech recognition

[0037] First, smartphones and home AI devices, acting as terminals, receive voice data from parents. Because these devices are frequently used in home settings, parents can record voices naturally without needing any special preparation. This voice data is sent to a server, where it is converted into text data by speech recognition capabilities.

[0038] 2. Image Acquisition and Analysis

[0039] Next, using the image acquisition device provided by the terminal, users can capture everyday moments of childcare in photographs. The captured images are automatically sent to a server and analyzed using image recognition. The server identifies people and situations in the images and assigns appropriate event labels.

[0040] 3. Natural language generation

[0041] The server generates diary entries using natural language generation methods based on text data obtained through speech recognition and label data obtained through image recognition. During this generation process, sentiment analysis can be performed to add emotional depth to the content of the events.

[0042] 4. Data storage and management

[0043] The generated diary entries are organized chronologically and permanently stored by a database management system on the server. This process allows users to easily review past childcare records.

[0044] 5. Access via User Interface

[0045] Users can access their diaries, generated from their smartphones or computers, through a dedicated application or web interface. This interface is designed to be intuitive and easy to use, allowing for comfortable viewing, editing, and management of diaries.

[0046] For example, if a user wants to record their child's first words, the voice input device receives the conversation at that moment, and the server automatically generates a diary, allowing the user to preserve this valuable record without any effort. In this way, the present invention provides a system that helps busy parents keep records of their children's development.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The device receives voice data of the user's conversations and childcare-related information using a voice input device. It allows users to record natural, everyday conversations without pressing any special buttons.

[0050] Step 2:

[0051] The terminal sends the received audio data to the server. This initiates the necessary communication for processing the audio data.

[0052] Step 3:

[0053] The server converts the received audio data into text data using speech recognition. The speech recognition engine performs filtering to remove noise and generate accurate text.

[0054] Step 4:

[0055] The device automatically collects images taken by the user and sends these image data to a server. This makes it possible to process image data that captures moments of childcare.

[0056] Step 5:

[0057] The server applies image recognition to the received image data. It analyzes the people and situations contained in the image and generates event labels based on the content.

[0058] Step 6:

[0059] The server inputs the converted text data and labeled image data into a natural language generation system. This process automatically generates diary-style entries.

[0060] Step 7:

[0061] The server performs sentiment analysis and adds emotional nuances to the generated text. This makes the diary entries more relatable.

[0062] Step 8:

[0063] The server organizes and stores the generated diaries using a database management system. The data is properly organized and configured to facilitate future access.

[0064] Step 9:

[0065] Users can view their diaries, which are generated through a dedicated application or web interface. This allows them to review and reflect on their entries.

[0066] Step 10:

[0067] Users can manually edit their diary entries and add additional information as needed. This can be easily done through the user interface.

[0068] (Example 1)

[0069] 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."

[0070] Parents raising children face the challenge of not being able to easily record their child's growth and memories in a timely manner amidst their busy daily lives, and not being able to easily look back on them later. Furthermore, they are required to create emotionally rich and detailed records, but doing so manually is laborious. In addition, they are expected to imbue the resulting parenting records with emotional depth.

[0071] 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.

[0072] In this invention, the server includes speech recognition means for converting speech data received from a speech input device into text data, image recognition means for analyzing image data received from an image acquisition device and assigning labels based on specific activities, and natural language generation means for automatically generating written text based on the converted text data and labeled image data. This makes it possible for parents raising children to record everyday events and automatically generate emotionally rich diaries without any effort.

[0073] A "voice input device" is a device used to collect the voice spoken by a user as digital data.

[0074] "Speech recognition means" refers to a technology that analyzes digitized speech data from a speech input device and converts the speech into corresponding text data.

[0075] An "image acquisition device" is a device used to collect still images and videos as digital data.

[0076] "Image recognition means" refers to a technology that analyzes image data acquired from an image acquisition device, identifies objects and scenes within the image, and assigns labels based on this identification.

[0077] A "natural language generation method" is a technology that generates naturally readable text using human language structure based on input data.

[0078] "Emotion analysis techniques" are technologies that identify emotions from text data and contextual information, and add emotional nuances to written text.

[0079] "Data storage means" refers to technologies for systematically storing generated text and related information, and making them easily accessible as needed.

[0080] A "generative AI model" is a type of artificial intelligence that has the ability to learn from large amounts of data and generate natural language.

[0081] A "prompt" is text used to give instructions or conditions to a generative AI model regarding the content it should generate.

[0082] This invention is a system for parents raising children to automatically generate and save diaries using voice and images. The following describes a specific form of implementing this system.

[0083] Users can use smartphones or home AI devices as voice input devices. These devices blend seamlessly into the user's daily activities, enabling natural voice recording. For example, if a user says, "I want to record my son's first words today," the device sends the voice data to the server. The server uses speech recognition technology to convert the voice data into text data. A common speech recognition API can be used for this speech recognition.

[0084] Furthermore, users can use their smartphone camera as an image acquisition device to capture moments of childcare in photographs. For example, if a user takes a picture of their child playing in a park, the image data is automatically uploaded to the server. The server uses image recognition technology to analyze people and backgrounds, and applies a general-purpose image recognition API to assign labels to the images based on that analysis.

[0085] Once this audio and image data is collected on the server, the server uses a generative AI model to automatically generate natural-sounding diary entries based on this data. This process employs a natural language processing engine to construct sentences based on the user's experience. For example, it can generate sentences such as, "Today my son said 'Mom' for the first time."

[0086] This system performs sentiment analysis on generated text, adding emotional nuances to the writing. The resulting diary entries are organized chronologically in the server's data storage system and stored long-term. This allows users to easily search for specific memories and revisit them at any time.

[0087] Users can access this diary using a dedicated application or web interface. The interface is intuitively designed, making it easy to view, edit, and manage the generated diary entries. Specifically, users can select a particular date from a calendar-style screen and view or modify the entries for that day.

[0088] As a concrete example of a prompt, giving an AI model instructions such as, "Generate a touching parenting diary based on your experience of your child's birthday," can generate more emotionally profound text.

[0089] In this way, this system provides parents raising children with a means to easily and efficiently generate and save diaries.

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] The user records voice using a smartphone or home AI device. The recorded voice data is stored in the voice input device. The user might say, for example, "Record my child's growth today." This is recorded as voice data and sent from the device to the server.

[0093] Step 2:

[0094] The server uses speech recognition to convert received audio data into text data. During this process, a speech recognition API is used to analyze the audio waveform data and transcribe the words based on that analysis. The output is generated as text data, which is temporarily stored on the server.

[0095] Step 3:

[0096] A user takes an image using their smartphone camera. For example, they might take a picture of their child playing in a park. This image data is saved on the device and automatically uploaded to the server.

[0097] Step 4:

[0098] The server uses image recognition to analyze the received image data. This analysis utilizes an image recognition API to identify people and scenes and generate appropriate labels based on that identification. Labeled image data is then output and stored on the server.

[0099] Step 5:

[0100] The server uses natural language generation tools to integrate text data from speech recognition and label data from image recognition to generate text. This process is driven by a generative AI model that creates natural-sounding sentences based on the estimated context. The output is the generated text.

[0101] Step 6:

[0102] The server analyzes the generated text using sentiment analysis techniques, adding emotional nuances to the sentences. As a result of this analysis, an emotionally rich diary is generated. The output is emotionally charged text data, which is then saved.

[0103] Step 7:

[0104] The server uses data storage to organize the generated text in chronological order and save it to a database. During the saving process, the records are organized using date and time information as a key. This makes it easier for users to search for the text later.

[0105] Step 8:

[0106] Users access their generated diaries through a dedicated application or web interface. They can view and, if necessary, edit their diaries within the interface. A high-performance UI / UX design ensures intuitive operation.

[0107] (Application Example 1)

[0108] 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."

[0109] In modern society, many parents are overwhelmed with balancing childcare and work, and face the challenge of not having time to record important moments in their parenting lives. Furthermore, there is a need for a system that can easily manage the recorded information and effectively integrate with the various childcare support services provided within cities.

[0110] 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.

[0111] In this invention, the server includes speech recognition means for converting acoustic information collected from a speech acquisition device into text information, image recognition means for analyzing image information collected from an image acquisition device and assigning tags based on specific events, and natural language generation means for automatically generating text based on the converted text information and tagged image information. This makes it possible for busy parents to easily create childcare records and effectively link with childcare support networks within smart cities.

[0112] A "sound acquisition device" is a device that collects acoustic information and transmits it to a server.

[0113] "Speech recognition means" refers to technology that converts acoustic information into textual information.

[0114] An "image acquisition device" is a device used to capture image information and transmit it to a server.

[0115] "Image recognition means" refers to a technology that analyzes image information and assigns tags based on specific events.

[0116] "Natural language generation means" refers to technology that automatically generates text based on character information and tagged image information.

[0117] "Information management means" refers to the technology for organizing and storing generated text in a record format.

[0118] An "intracity network" refers to information systems and communication networks designed to support the lives of residents.

[0119] This invention provides a system for parents to easily record childcare activities. The system mainly consists of a voice acquisition device, an image collection device, and a server. The server is equipped with voice recognition means, image recognition means, natural language generation means, and information management means, and plays a role in supporting residents' childcare records by linking with the urban network within a smart city.

[0120] The user provides voice input via a smartphone or home AI device. A voice acquisition device records this voice and sends it to a server. The server uses voice recognition to convert the acoustic information into text. In parallel, the user takes pictures of everyday scenes with an image acquisition device and sends the image information to the server. The image recognition device analyzes this image information and assigns tags based on specific events.

[0121] Subsequently, the server automatically generates text based on the converted text information and tagged image information using natural language generation capabilities. This process also includes sentiment analysis, so the generated records are imbued with emotional nuances. Furthermore, the generated text is organized and stored in a record format by information management capabilities, and users can view and edit the records using a dedicated application or web interface. The entire system workflow is designed to allow busy parents to easily and accurately record important childcare moments without missing any.

[0122] One specific use case is when a user wants to record their child's first words. In this case, they input the audio at that moment using their smartphone, and the server converts it into text and collects an image of the scene. The generative AI model then applies prompt text to create an emotionally rich record such as, "On this day, our child said 'Mama' for the first time. The whole family was filled with surprise and joy at that moment."

[0123] Examples of prompts for generative AI models:

[0124] Please generate a childcare record based on the following events:

[0125] Audio: The sound of a child speaking their first words.

[0126] Image: Photo of children and family

[0127] Keywords: First words, joy, family

[0128] Emotion: Joy

[0129] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0130] Step 1:

[0131] The user inputs voice using a device. The device's microphone acquires acoustic information and sends it to the server as digital data. The input acoustic information is output as transmitted data.

[0132] Step 2:

[0133] The server receives acoustic information using speech recognition technology and converts the acoustic data into text data. The server applies a speech recognition algorithm to analyze the acoustic signal and generate corresponding character information. Through this process, the input is acoustic data and the output is converted character information.

[0134] Step 3:

[0135] The user takes an image using their device. The device's camera acquires image information and sends it to the server as digital image data. The input image information is output as transmitted data.

[0136] Step 4:

[0137] The server receives image information using image recognition means, analyzes the image data, and assigns tags based on specific events. Using image analysis algorithms, it recognizes objects and scenes within the image and generates appropriate tags. The input is image data, and the output is tagged image information.

[0138] Step 5:

[0139] The server uses natural language generation tools to automatically generate text from converted character information and tagged image information as input. Combining prompts and a generation AI model, including sentiment analysis, it generates the final text data. The output of this step is text data for recording purposes.

[0140] Step 6:

[0141] The server uses information management tools to organize the generated text data and store it in a database in a record format. It defines the data structure, adds metadata as needed, and makes it easily accessible to users. The output is organized record data.

[0142] Step 7:

[0143] Users retrieve, view, and edit recorded data from the server using a dedicated application or web interface. The user interface allows for intuitive display and modification of records. The output is the edited recorded data.

[0144] 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.

[0145] This invention constructs a system for automatically recording and organizing important moments in childcare, and in particular, incorporates an emotion engine that recognizes the user's emotions and reflects them in the diary creation. This system uses a voice input device, an image acquisition device, a server, and a user interface to enable efficient childcare record creation in a way that is effortless for the user. The functions of each element and their interrelationships are described below in natural language.

[0146] First, the terminal, such as a smartphone or home AI device, is equipped with a voice input device to record everyday conversations and events, and a camera function to acquire images. While the user goes about their daily life without requiring any special operation, the terminal collects voice and image data in real time and periodically sends this data to a server.

[0147] Next, the server converts the received audio data into text data using speech recognition. In addition, image data is analyzed using image recognition, and labels based on specific events are assigned. Crucial to this process is the role of the emotion engine, which analyzes the user's emotional state obtained from the audio and image data and adds emotional nuances to the written expression. This makes the diary entries richer in content and more relatable.

[0148] The generated diary entries are organized and stored using a database management system on the server. Users can access these records through a dedicated application or a web interface. This interface is designed with usability in mind, making it easy to view, edit, and check emotional feedback on the diary entries.

[0149] For example, if a user feels happy during a bedtime conversation with their child, this moment is recorded by a voice input device, and the emotion is reflected in a text-based diary. Furthermore, image recognition labels smiling photos, supplementing visual memories. This allows users to maintain a rich, emotion-based record of their parenting, rather than just a collection of facts.

[0150] The following describes the processing flow.

[0151] Step 1:

[0152] The device receives everyday voice messages from the user via a voice input device. This data collection is performed automatically without any user intervention.

[0153] Step 2:

[0154] The device quantizes the acquired voice data and sends it to the server. The voice data is transferred via the internet or a home network.

[0155] Step 3:

[0156] The server analyzes the received audio data using speech recognition and converts it into text data. During this process, background noise is removed, and accurate text is generated based on a language model.

[0157] Step 4:

[0158] The device collects image data captured by the user and sends it to the server. The image data is automatically synchronized, enabling real-time processing.

[0159] Step 5:

[0160] The server analyzes the received image data using image recognition technology. This identifies objects within the image and assigns appropriate labels to them.

[0161] Step 6:

[0162] The server uses an emotion engine to analyze the user's emotions based on audio and image data. This emotion analysis helps identify the user's emotional state on that particular day.

[0163] Step 7:

[0164] The server combines the converted text data, labeled image data, and analyzed sentiment information to generate text using natural language generation tools. This text is then given nuances based on the user's emotions.

[0165] Step 8:

[0166] The server stores and organizes the generated text using a database management system. The data is organized chronologically to prepare for future searches and viewing.

[0167] Step 9:

[0168] Users view their generated diaries using a dedicated user interface. The interface is designed to be easy to use, allowing for simple viewing and editing of diaries.

[0169] Step 10:

[0170] Users can manually edit their diary entries as needed. These edits are immediately reflected in the database, and the record is updated.

[0171] (Example 2)

[0172] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0173] Efficiently recording everyday moments in childcare and automatically generating rich, emotionally resonant diaries can be time-consuming and burdensome for users. This invention aims to alleviate this burden by providing a system that generates emotionally-driven diaries while minimizing user interaction.

[0174] 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.

[0175] In this invention, the server includes an acoustic recognition means that converts acoustic data acquired from an acoustic input device into text data, an image recognition means that analyzes image data acquired from an image acquisition device and assigns identification information based on specific events, and a document creation means that automatically creates text based on the converted text data and the image data to which the identification information has been assigned. This makes it possible to automatically generate a rich diary with emotional nuances of important moments in childcare, and to easily save and view it.

[0176] An "acoustic input device" is a device that acquires sound from the environment and converts it into an electrical signal.

[0177] "Acoustic recognition means" refers to technology for converting acquired acoustic data into text data.

[0178] A "video acquisition device" is a device that acquires visual information and records it as image or video data.

[0179] "Image recognition means" refers to technology for analyzing video data and identifying and classifying the information contained therein.

[0180] "Identification information" refers to labels or tags assigned based on specific events or characteristics as a result of image recognition.

[0181] "Document creation means" refers to technology for generating text in natural language based on audio and video data.

[0182] "Memory management means" refers to technologies for structuring, storing, and managing generated data.

[0183] "Methods for adding emotion" refer to techniques for adding emotional elements to documents and reflecting subjective nuances.

[0184] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate text based on input instructions.

[0185] An "input prompt" refers to an instruction or question given to an AI model in order to perform a specific task.

[0186] This invention is a system for efficiently recording important moments and everyday events in childcare, and organizing and storing them in a diary format that reflects emotions. A detailed explanation of how to implement this system is provided below.

[0187] First, the terminals used will include smartphones and home AI devices. These terminals are equipped with an audio input device and a video acquisition device, allowing for the continuous collection of audio and image data from the user's daily life. The audio input device converts ambient sound into electrical signals to generate audio data. The video acquisition device uses a camera to acquire visual information as video data.

[0188] Next, this data is sent to a server, where acoustic recognition means are used to convert the acoustic data into text data. General speech recognition software is used for this acoustic recognition. The server then analyzes the video data using video recognition means and assigns identification information based on specific events and features. At this stage, identification information based on specific events or user actions is added as labels.

[0189] The server then uses the collected text data and identification information to generate text in natural language using document creation tools. During this process, a generative AI model is utilized, and emotional nuances are added to the document through empathy-adding tools. This process generates text that is more likely to evoke empathy.

[0190] The generated documents are structured into a record format on the server and organized and stored using memory management systems. Users can access, view, and edit this information through dedicated applications or web interfaces.

[0191] As a concrete example, consider a scenario where a user feels happy through a bedtime conversation with their child. In this case, an audio input device records the audio, and a video acquisition device records the conversation as video. Based on this data, the server can generate a diary entry such as, "I ended the day with a pleasant conversation with my child. I felt especially happy today."

[0192] An example of a prompt to input into the generation AI model is, "Based on a cheerful conversation the user had while playing with their child, please generate a diary entry that reflects their emotions."

[0193] In this way, the present invention provides a system that allows users to record important moments in their daily lives as emotionally rich diaries without actually having to expend much effort.

[0194] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0195] Step 1:

[0196] The device continuously collects audio and image data of the user's daily life using an audio input device and a video acquisition device. Inputs include audio data such as ambient sounds and conversations, and image data capturing everyday events. Audio data is recorded as digital signals, and video data is stored as images taken at fixed intervals. Outputs this data are temporarily stored in the device's storage.

[0197] Step 2:

[0198] The collected audio and image data is transmitted from the terminal to the server via the internet. At this time, the data is converted to a compressed format to reduce bandwidth usage. The input is compressed audio and image data, and the output is the raw data decompressed by the server. The server receives this data for processing.

[0199] Step 3:

[0200] The server converts audio data into text data using acoustic recognition. It analyzes the received audio data and generates corresponding text based on each acoustic feature. Here, an acoustic signal processing algorithm is used to extract significant keywords and phrases from the audio. The output is text data, where the audio has been converted into characters.

[0201] Step 4:

[0202] The server analyzes video data using image recognition and adds identification information based on specific events and facial expressions. The input is the image data to be analyzed; an image processing algorithm is executed to detect people and objects, and related event and emotion labels are added. The output is the image data with the added identification information.

[0203] Step 5:

[0204] Based on the analysis results, the server generates natural language text through a document creation tool. It integrates text data obtained from audio and identification information obtained from video as input, and uses a generative AI model to form text corresponding to the background information. The output is obtained as a diary-style text with added emotional nuances.

[0205] Step 6:

[0206] The generated text is organized and stored as individual document files in a structured database by the server's memory management system. The input is generated string data, which is tagged and categorized during saving. The output is a diary-style document stored in the database, which can be searched and viewed later.

[0207] Step 7:

[0208] Users access their generated diaries through a dedicated application or web interface, viewing and editing them as needed. When a user accesses the interface, the requested data is sent from the server and displayed on the screen. The output is the content of the screen the user is viewing.

[0209] (Application Example 2)

[0210] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0211] Traditional childcare record systems required users to manually input records, and it was difficult to reflect emotional elements in the documents. This made it challenging to accurately and emotionally record important moments in childcare. Furthermore, when reviewing childcare records later, it was difficult to effectively recreate the emotions of those moments.

[0212] 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.

[0213] In this invention, the server includes a speech recognition means that converts speech information acquired from a speech information processing device into text information, an image recognition means that analyzes video information acquired from a video information acquisition device and assigns identification information based on specific events, a natural language generation means that automatically generates a document based on the converted text information and identification information, and an emotion analysis means that performs emotion analysis and assigns emotional nuances to the generated document. This makes it possible for users to record important moments in childcare with rich emotion without any hassle and to easily look back on them.

[0214] A "voice information processing device" is a device that collects voice data and converts that data into other formats.

[0215] A "speech recognition means for converting into text information" is a means that has the function of analyzing speech information and representing its content as text data.

[0216] A "video information acquisition device" is a device for collecting visual information, primarily consisting of a camera function.

[0217] "Image recognition means" refers to means that analyze acquired video data and have the function of identifying specific events.

[0218] "Identification information" refers to labels or tags attached to image data to indicate a specific event.

[0219] A "natural language generation method" is a means of generating documents from data, and in particular, organizing information in a form that closely resembles human language.

[0220] "Emotional analysis methods" are techniques for analyzing emotional states from audio and video data and reflecting the results in documents.

[0221] A "consumer autonomous machine" is an automatically operating mechanical device designed for use in ordinary households.

[0222] "Information management means" refers to means that have the ability to organize and store data, and provide the function of making information accessible as needed.

[0223] The system for carrying out this invention includes a program that processes audio and video information in real time using a consumer-grade autonomous machine installed in a home. In the user's daily life, the terminal uses an audio information processing device and a video information acquisition device to record conversations and visual events. This data is transmitted to a server via the home network. The server utilizes speech recognition means to convert the received audio information into text information. As a specific example, the Google® Cloud Speech-to-Text API can be used.

[0224] Furthermore, the server uses image recognition to analyze the video information. This could involve using an image analysis service such as Amazon Rekognition. Identification information about specific events is generated from the image data and incorporated as part of the document structure.

[0225] Next, the server automatically generates documents from the obtained information using natural language generation tools. These documents are then subjected to sentiment analysis tools to reflect the user's emotional state. Sentiment analysis, based on audio and video data, adds emotional nuances to the documents. This function enables the recording of the user's emotionally rich experiences.

[0226] Ultimately, the generated documents are organized by information management systems and stored in a format accessible to users. Users can easily access, view, and edit the recorded documents via smartphones or personal computers. For example, a photo of a parent smiling while playing with their child could be saved the next day as a diary entry that captures the joyful atmosphere.

[0227] To realize such an invention, an example of a prompt statement could be as follows: "Please create specifications for a robot system that records enjoyable moments with children while analyzing their emotions." This prompt would allow the generating AI model to efficiently propose a system that reflects the content of the childcare records.

[0228] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0229] Step 1:

[0230] The terminal uses a voice information processing device to collect the user's everyday conversations. The input is voice data, which is transmitted to the server in real time. Specifically, this involves capturing voice through a microphone and converting it into a digital format.

[0231] Step 2:

[0232] The terminal uses a video information acquisition device to capture everyday events of the user. The input is video data, which is then transmitted to the server. Specifically, the system captures video using a camera and converts it to a digital format.

[0233] Step 3:

[0234] The server converts received audio data into text data using speech recognition technology. The input is audio data, and the output is text data. Data processing includes, for example, speech-to-text conversion using Google Cloud Speech-to-Text.

[0235] Step 4:

[0236] The server analyzes video data using image recognition technology and generates identification information for specific events. The input is video data, and the output is identification information. Specific processing includes image analysis and labeling using Amazon Rekognition.

[0237] Step 5:

[0238] The server generates documents from character data and identifiers converted using natural language generation methods. The input is character data and identifiers, and the output is the generated document. Data processing involves creating a human-readable document based on this information.

[0239] Step 6:

[0240] The server analyzes the user's emotions from audio and video data using emotion analysis tools and adds emotional nuances to the generated document. The input is audio and video data, and the output is a document with added emotional elements. Specifically, the system infers emotions from voice and facial expressions and reflects them in the document style.

[0241] Step 7:

[0242] The server stores documents generated by the information management system in a database. The input is document data, and the output is the documents stored in the database. The operation involves registering documents in the database and indexing them.

[0243] Step 8:

[0244] Users access, view, or edit saved documents via a smartphone app. Input is the user's request, and output is viewable or editable document data. Specifically, this includes data display and editing functions through the user interface within the app.

[0245] 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.

[0246] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0247] 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.

[0248] [Second Embodiment]

[0249] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0250] 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.

[0251] 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).

[0252] 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.

[0253] 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.

[0254] 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).

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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".

[0261] This invention constructs a system that allows parents raising children to easily generate and view diaries by using a voice input device, an image acquisition device, a server, and a dedicated application or web interface. The functions and interactions of each element are described below in natural language.

[0262] 1. Voice input and speech recognition

[0263] First, smartphones and home AI devices, acting as terminals, receive voice data from parents. Because these devices are frequently used in home settings, parents can record voices naturally without needing any special preparation. This voice data is sent to a server, where it is converted into text data by speech recognition capabilities.

[0264] 2. Image Acquisition and Analysis

[0265] Next, using the image acquisition device provided by the terminal, users can capture everyday moments of childcare in photographs. The captured images are automatically sent to a server and analyzed using image recognition. The server identifies people and situations in the images and assigns appropriate event labels.

[0266] 3. Natural language generation

[0267] The server generates diary entries using natural language generation methods based on text data obtained through speech recognition and label data obtained through image recognition. During this generation process, sentiment analysis can be performed to add emotional depth to the content of the events.

[0268] 4. Data storage and management

[0269] The generated diary entries are organized chronologically and permanently stored by a database management system on the server. This process allows users to easily review past childcare records.

[0270] 5. Access via User Interface

[0271] Users can access their diaries, generated from their smartphones or computers, through a dedicated application or web interface. This interface is designed to be intuitive and easy to use, allowing for comfortable viewing, editing, and management of diaries.

[0272] For example, if a user wants to record their child's first words, the voice input device receives the conversation at that moment, and the server automatically generates a diary, allowing the user to preserve this valuable record without any effort. In this way, the present invention provides a system that helps busy parents keep records of their children's development.

[0273] The following describes the processing flow.

[0274] Step 1:

[0275] The device receives voice data of the user's conversations and childcare-related information using a voice input device. It allows users to record natural, everyday conversations without pressing any special buttons.

[0276] Step 2:

[0277] The terminal sends the received audio data to the server. This initiates the necessary communication for processing the audio data.

[0278] Step 3:

[0279] The server converts the received audio data into text data using speech recognition. The speech recognition engine performs filtering to remove noise and generate accurate text.

[0280] Step 4:

[0281] The device automatically collects images taken by the user and sends these image data to a server. This makes it possible to process image data that captures moments of childcare.

[0282] Step 5:

[0283] The server applies image recognition to the received image data. It analyzes the people and situations contained in the image and generates event labels based on the content.

[0284] Step 6:

[0285] The server inputs the converted text data and the labeled image data into the natural language generation means. In this process, a diary-style sentence is automatically generated.

[0286] Step 7:

[0287] The server performs sentiment analysis and assigns a sentimental nuance to the generated sentence. As a result, the diary content becomes more friendly.

[0288] Step 8:

[0289] The server organizes and stores the generated diary using the database management means. The data is appropriately organized and configured to facilitate future access.

[0290] Step 9:

[0291] The user views the generated diary via a dedicated application or web interface. As a result, the diary can be confirmed and reviewed.

[0292] Step 10:

[0293] The user can manually edit the content of the diary and add additional information as needed. This operation can be easily performed through the user interface.

[0294] (Example 1)

[0295] Next, 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".

[0296] Parents raising children face the challenge of not being able to easily record their child's growth and memories in a timely manner amidst their busy daily lives, and not being able to easily look back on them later. Furthermore, they are required to create emotionally rich and detailed records, but doing so manually is laborious. In addition, they are expected to imbue the resulting parenting records with emotional depth.

[0297] 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.

[0298] In this invention, the server includes speech recognition means for converting speech data received from a speech input device into text data, image recognition means for analyzing image data received from an image acquisition device and assigning labels based on specific activities, and natural language generation means for automatically generating written text based on the converted text data and labeled image data. This makes it possible for parents raising children to record everyday events and automatically generate emotionally rich diaries without any effort.

[0299] A "voice input device" is a device used to collect the voice spoken by a user as digital data.

[0300] "Speech recognition means" refers to a technology that analyzes digitized speech data from a speech input device and converts the speech into corresponding text data.

[0301] An "image acquisition device" is a device used to collect still images and videos as digital data.

[0302] "Image recognition means" refers to a technology that analyzes image data acquired from an image acquisition device, identifies objects and scenes within the image, and assigns labels based on this identification.

[0303] A "natural language generation method" is a technology that generates naturally readable text using human language structure based on input data.

[0304] The "emotion analysis means" is a technology that identifies emotions from text data and context information and imparts emotional nuances to the text.

[0305] The "data storage means" is a technology for systematically storing the generated text and related information and making it easily accessible as needed.

[0306] The "generative AI model" is an artificial intelligence mechanism that has the ability to generate natural language by learning a large amount of data.

[0307] The "prompt text" is text for giving instructions and conditions on what to generate to the generative AI model.

[0308] This invention is a system for automatically generating and storing a diary using voice and images by parents during child-rearing. Specific embodiments for implementing this system will be described below.

[0309] The user can use a smartphone or a home AI device as a voice input device. These terminals blend into the user's daily activities and enable natural voice recording. When the user says, for example, "I want to record my son's first words today," the voice data is sent by the terminal to the server. The server converts the voice data into text data using voice recognition technology. For this voice recognition, a voice recognition API can be used as a general technology.

[0310] Also, the user can use the camera of a smartphone as an image acquisition device to capture moments of child-rearing in photos. As an example, when the user takes a picture of a child playing in the park, the image data is automatically uploaded to the server. The server analyzes people and the background using image recognition technology and applies a general-purpose image recognition API to assign labels based on that to the image.

[0311] Once this audio and image data is collected on the server, the server uses a generative AI model to automatically generate natural-sounding diary entries based on this data. This process employs a natural language processing engine to construct sentences based on the user's experience. For example, it can generate sentences such as, "Today my son said 'Mom' for the first time."

[0312] This system performs sentiment analysis on generated text, adding emotional nuances to the writing. The resulting diary entries are organized chronologically in the server's data storage system and stored long-term. This allows users to easily search for specific memories and revisit them at any time.

[0313] Users can access this diary using a dedicated application or web interface. The interface is intuitively designed, making it easy to view, edit, and manage the generated diary entries. Specifically, users can select a particular date from a calendar-style screen and view or modify the entries for that day.

[0314] As a concrete example of a prompt, giving an AI model instructions such as, "Generate a touching parenting diary based on your experience of your child's birthday," can generate more emotionally profound text.

[0315] In this way, this system provides parents raising children with a means to easily and efficiently generate and save diaries.

[0316] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0317] Step 1:

[0318] The user records voice using a smartphone or home AI device. The recorded voice data is stored in the voice input device. The user might say, for example, "Record my child's growth today." This is recorded as voice data and sent from the device to the server.

[0319] Step 2:

[0320] The server uses speech recognition to convert received audio data into text data. During this process, a speech recognition API is used to analyze the audio waveform data and transcribe the words based on that analysis. The output is generated as text data, which is temporarily stored on the server.

[0321] Step 3:

[0322] A user takes an image using their smartphone camera. For example, they might take a picture of their child playing in a park. This image data is saved on the device and automatically uploaded to the server.

[0323] Step 4:

[0324] The server uses image recognition to analyze the received image data. This analysis utilizes an image recognition API to identify people and scenes and generate appropriate labels based on that identification. Labeled image data is then output and stored on the server.

[0325] Step 5:

[0326] The server uses natural language generation tools to integrate text data from speech recognition and label data from image recognition to generate text. This process is driven by a generative AI model that creates natural-sounding sentences based on the estimated context. The output is the generated text.

[0327] Step 6:

[0328] The server analyzes the generated text using sentiment analysis techniques, adding emotional nuances to the sentences. As a result of this analysis, an emotionally rich diary is generated. The output is emotionally charged text data, which is then saved.

[0329] Step 7:

[0330] The server uses data storage to organize the generated text in chronological order and save it to a database. During the saving process, the records are organized using date and time information as a key. This makes it easier for users to search for the text later.

[0331] Step 8:

[0332] Users access their generated diaries through a dedicated application or web interface. They can view and, if necessary, edit their diaries within the interface. A high-performance UI / UX design ensures intuitive operation.

[0333] (Application Example 1)

[0334] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0335] In modern society, many parents are overwhelmed with balancing childcare and work, and face the challenge of not having time to record important moments in their parenting lives. Furthermore, there is a need for a system that can easily manage the recorded information and effectively integrate with the various childcare support services provided within cities.

[0336] 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.

[0337] In this invention, the server includes speech recognition means for converting acoustic information collected from a speech acquisition device into text information, image recognition means for analyzing image information collected from an image acquisition device and assigning tags based on specific events, and natural language generation means for automatically generating text based on the converted text information and tagged image information. This makes it possible for busy parents to easily create childcare records and effectively link with childcare support networks within smart cities.

[0338] A "sound acquisition device" is a device that collects acoustic information and transmits it to a server.

[0339] "Speech recognition means" refers to technology that converts acoustic information into textual information.

[0340] An "image acquisition device" is a device used to capture image information and transmit it to a server.

[0341] "Image recognition means" refers to a technology that analyzes image information and assigns tags based on specific events.

[0342] "Natural language generation means" refers to technology that automatically generates text based on character information and tagged image information.

[0343] "Information management means" refers to the technology for organizing and storing generated text in a record format.

[0344] An "intracity network" refers to information systems and communication networks designed to support the lives of residents.

[0345] This invention provides a system for parents to easily record childcare activities. The system mainly consists of a voice acquisition device, an image collection device, and a server. The server is equipped with voice recognition means, image recognition means, natural language generation means, and information management means, and plays a role in supporting residents' childcare records by linking with the urban network within a smart city.

[0346] The user provides voice input via a smartphone or home AI device. A voice acquisition device records this voice and sends it to a server. The server uses voice recognition to convert the acoustic information into text. In parallel, the user takes pictures of everyday scenes with an image acquisition device and sends the image information to the server. The image recognition device analyzes this image information and assigns tags based on specific events.

[0347] Subsequently, the server automatically generates text based on the converted text information and tagged image information using natural language generation capabilities. This process also includes sentiment analysis, so the generated records are imbued with emotional nuances. Furthermore, the generated text is organized and stored in a record format by information management capabilities, and users can view and edit the records using a dedicated application or web interface. The entire system workflow is designed to allow busy parents to easily and accurately record important childcare moments without missing any.

[0348] One specific use case is when a user wants to record their child's first words. In this case, they input the audio at that moment using their smartphone, and the server converts it into text and collects an image of the scene. The generative AI model then applies prompt text to create an emotionally rich record such as, "On this day, our child said 'Mama' for the first time. The whole family was filled with surprise and joy at that moment."

[0349] Examples of prompts for generative AI models:

[0350] Please generate a childcare record based on the following events:

[0351] Audio: The sound of a child speaking their first words.

[0352] Image: Photo of children and family

[0353] Keywords: First words, joy, family

[0354] Emotion: Joy

[0355] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0356] Step 1:

[0357] The user inputs voice using a device. The device's microphone acquires acoustic information and sends it to the server as digital data. The input acoustic information is output as transmitted data.

[0358] Step 2:

[0359] The server receives acoustic information using speech recognition technology and converts the acoustic data into text data. The server applies a speech recognition algorithm to analyze the acoustic signal and generate corresponding character information. Through this process, the input is acoustic data and the output is converted character information.

[0360] Step 3:

[0361] The user takes an image using their device. The device's camera acquires image information and sends it to the server as digital image data. The input image information is output as transmitted data.

[0362] Step 4:

[0363] The server receives image information using image recognition means, analyzes the image data, and assigns tags based on specific events. Using image analysis algorithms, it recognizes objects and scenes within the image and generates appropriate tags. The input is image data, and the output is tagged image information.

[0364] Step 5:

[0365] The server uses natural language generation tools to automatically generate text from converted character information and tagged image information as input. Combining prompts and a generation AI model, including sentiment analysis, it generates the final text data. The output of this step is text data for recording purposes.

[0366] Step 6:

[0367] The server uses information management tools to organize the generated text data and store it in a database in a record format. It defines the data structure, adds metadata as needed, and makes it easily accessible to users. The output is organized record data.

[0368] Step 7:

[0369] Users retrieve, view, and edit recorded data from the server using a dedicated application or web interface. The user interface allows for intuitive display and modification of records. The output is the edited recorded data.

[0370] 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.

[0371] This invention constructs a system for automatically recording and organizing important moments in childcare, and in particular, incorporates an emotion engine that recognizes the user's emotions and reflects them in the diary creation. This system uses a voice input device, an image acquisition device, a server, and a user interface to enable efficient childcare record creation in a way that is effortless for the user. The functions of each element and their interrelationships are described below in natural language.

[0372] First, the terminal, such as a smartphone or home AI device, is equipped with a voice input device to record everyday conversations and events, and a camera function to acquire images. While the user goes about their daily life without requiring any special operation, the terminal collects voice and image data in real time and periodically sends this data to a server.

[0373] Next, the server converts the received audio data into text data using speech recognition. In addition, image data is analyzed using image recognition, and labels based on specific events are assigned. Crucial to this process is the role of the emotion engine, which analyzes the user's emotional state obtained from the audio and image data and adds emotional nuances to the written expression. This makes the diary entries richer in content and more relatable.

[0374] The generated diary entries are organized and stored using a database management system on the server. Users can access these records through a dedicated application or a web interface. This interface is designed with usability in mind, making it easy to view, edit, and check emotional feedback on the diary entries.

[0375] For example, if a user feels happy during a bedtime conversation with their child, this moment is recorded by a voice input device, and the emotion is reflected in a text-based diary. Furthermore, image recognition labels smiling photos, supplementing visual memories. This allows users to maintain a rich, emotion-based record of their parenting, rather than just a collection of facts.

[0376] The following describes the processing flow.

[0377] Step 1:

[0378] The device receives everyday voice messages from the user via a voice input device. This data collection is performed automatically without any user intervention.

[0379] Step 2:

[0380] The device quantizes the acquired voice data and sends it to the server. The voice data is transferred via the internet or a home network.

[0381] Step 3:

[0382] The server analyzes the received audio data using speech recognition and converts it into text data. During this process, background noise is removed, and accurate text is generated based on a language model.

[0383] Step 4:

[0384] The device collects image data captured by the user and sends it to the server. The image data is automatically synchronized, enabling real-time processing.

[0385] Step 5:

[0386] The server analyzes the received image data using image recognition technology. This identifies objects within the image and assigns appropriate labels to them.

[0387] Step 6:

[0388] The server uses an emotion engine to analyze the user's emotions based on audio and image data. This emotion analysis helps identify the user's emotional state on that particular day.

[0389] Step 7:

[0390] The server combines the converted text data, labeled image data, and analyzed sentiment information to generate text using natural language generation tools. This text is then given nuances based on the user's emotions.

[0391] Step 8:

[0392] The server stores and organizes the generated text using a database management system. The data is organized chronologically to prepare for future searches and viewing.

[0393] Step 9:

[0394] Users view their generated diaries using a dedicated user interface. The interface is designed to be easy to use, allowing for simple viewing and editing of diaries.

[0395] Step 10:

[0396] Users can manually edit their diary entries as needed. These edits are immediately reflected in the database, and the record is updated.

[0397] (Example 2)

[0398] 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".

[0399] Efficiently recording everyday moments in childcare and automatically generating rich, emotionally resonant diaries can be time-consuming and burdensome for users. This invention aims to alleviate this burden by providing a system that generates emotionally-driven diaries while minimizing user interaction.

[0400] 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.

[0401] In this invention, the server includes an acoustic recognition means that converts acoustic data acquired from an acoustic input device into text data, an image recognition means that analyzes image data acquired from an image acquisition device and assigns identification information based on specific events, and a document creation means that automatically creates text based on the converted text data and the image data to which the identification information has been assigned. This makes it possible to automatically generate a rich diary with emotional nuances of important moments in childcare, and to easily save and view it.

[0402] An "acoustic input device" is a device that acquires sound from the environment and converts it into an electrical signal.

[0403] "Acoustic recognition means" refers to technology for converting acquired acoustic data into text data.

[0404] A "video acquisition device" is a device that acquires visual information and records it as image or video data.

[0405] "Image recognition means" refers to technology for analyzing video data and identifying and classifying the information contained therein.

[0406] "Identification information" refers to labels or tags assigned based on specific events or characteristics as a result of image recognition.

[0407] "Document creation means" refers to technology for generating text in natural language based on audio and video data.

[0408] "Memory management means" refers to technologies for structuring, storing, and managing generated data.

[0409] "Methods for adding emotion" refer to techniques for adding emotional elements to documents and reflecting subjective nuances.

[0410] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate text based on input instructions.

[0411] An "input prompt" refers to an instruction or question given to an AI model in order to perform a specific task.

[0412] This invention is a system for efficiently recording important moments and everyday events in childcare, and organizing and storing them in a diary format that reflects emotions. A detailed explanation of how to implement this system is provided below.

[0413] First, the terminals used will include smartphones and home AI devices. These terminals are equipped with an audio input device and a video acquisition device, allowing for the continuous collection of audio and image data from the user's daily life. The audio input device converts ambient sound into electrical signals to generate audio data. The video acquisition device uses a camera to acquire visual information as video data.

[0414] Next, this data is sent to a server, where acoustic recognition means are used to convert the acoustic data into text data. General speech recognition software is used for this acoustic recognition. The server then analyzes the video data using video recognition means and assigns identification information based on specific events and features. At this stage, identification information based on specific events or user actions is added as labels.

[0415] The server then uses the collected text data and identification information to generate text in natural language using document creation tools. During this process, a generative AI model is utilized, and emotional nuances are added to the document through empathy-adding tools. This process generates text that is more likely to evoke empathy.

[0416] The generated documents are structured into a record format on the server and organized and stored using memory management systems. Users can access, view, and edit this information through dedicated applications or web interfaces.

[0417] As a concrete example, consider a scenario where a user feels happy through a bedtime conversation with their child. In this case, an audio input device records the audio, and a video acquisition device records the conversation as video. Based on this data, the server can generate a diary entry such as, "I ended the day with a pleasant conversation with my child. I felt especially happy today."

[0418] An example of a prompt to input into the generation AI model is, "Based on a cheerful conversation the user had while playing with their child, please generate a diary entry that reflects their emotions."

[0419] In this way, the present invention provides a system that allows users to record important moments in their daily lives as emotionally rich diaries without actually having to expend much effort.

[0420] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0421] Step 1:

[0422] The device continuously collects audio and image data of the user's daily life using an audio input device and a video acquisition device. Inputs include audio data such as ambient sounds and conversations, and image data capturing everyday events. Audio data is recorded as digital signals, and video data is stored as images taken at fixed intervals. Outputs this data are temporarily stored in the device's storage.

[0423] Step 2:

[0424] The collected audio and image data is transmitted from the terminal to the server via the internet. At this time, the data is converted to a compressed format to reduce bandwidth usage. The input is compressed audio and image data, and the output is the raw data decompressed by the server. The server receives this data for processing.

[0425] Step 3:

[0426] The server converts audio data into text data using acoustic recognition. It analyzes the received audio data and generates corresponding text based on each acoustic feature. Here, an acoustic signal processing algorithm is used to extract significant keywords and phrases from the audio. The output is text data, where the audio has been converted into characters.

[0427] Step 4:

[0428] The server analyzes video data using image recognition and adds identification information based on specific events and facial expressions. The input is the image data to be analyzed; an image processing algorithm is executed to detect people and objects, and related event and emotion labels are added. The output is the image data with the added identification information.

[0429] Step 5:

[0430] Based on the analysis results, the server generates natural language text through a document creation tool. It integrates text data obtained from audio and identification information obtained from video as input, and uses a generative AI model to form text corresponding to the background information. The output is obtained as a diary-style text with added emotional nuances.

[0431] Step 6:

[0432] The generated text is organized and stored as individual document files in a structured database by the server's memory management system. The input is generated string data, which is tagged and categorized during saving. The output is a diary-style document stored in the database, which can be searched and viewed later.

[0433] Step 7:

[0434] Users access their generated diaries through a dedicated application or web interface, viewing and editing them as needed. When a user accesses the interface, the requested data is sent from the server and displayed on the screen. The output is the content of the screen the user is viewing.

[0435] (Application Example 2)

[0436] 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."

[0437] Traditional childcare record systems required users to manually input records, and it was difficult to reflect emotional elements in the documents. This made it challenging to accurately and emotionally record important moments in childcare. Furthermore, when reviewing childcare records later, it was difficult to effectively recreate the emotions of those moments.

[0438] 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.

[0439] In this invention, the server includes a speech recognition means that converts speech information acquired from a speech information processing device into text information, an image recognition means that analyzes video information acquired from a video information acquisition device and assigns identification information based on specific events, a natural language generation means that automatically generates a document based on the converted text information and identification information, and an emotion analysis means that performs emotion analysis and assigns emotional nuances to the generated document. This makes it possible for users to record important moments in childcare with rich emotion without any hassle and to easily look back on them.

[0440] A "voice information processing device" is a device that collects voice data and converts that data into other formats.

[0441] A "speech recognition means for converting into text information" is a means that has the function of analyzing speech information and representing its content as text data.

[0442] A "video information acquisition device" is a device for collecting visual information, primarily consisting of a camera function.

[0443] "Image recognition means" refers to means that analyze acquired video data and have the function of identifying specific events.

[0444] "Identification information" refers to labels or tags attached to image data to indicate a specific event.

[0445] A "natural language generation method" is a means of generating documents from data, and in particular, organizing information in a form that closely resembles human language.

[0446] "Emotional analysis methods" are techniques for analyzing emotional states from audio and video data and reflecting the results in documents.

[0447] A "consumer autonomous machine" is an automatically operating mechanical device designed for use in ordinary households.

[0448] "Information management means" refers to means that have the ability to organize and store data, and provide the function of making information accessible as needed.

[0449] The system for carrying out this invention includes a program that processes audio and video information in real time using a consumer-grade autonomous machine installed in a home. In the user's daily life, the terminal uses an audio information processing device and a video information acquisition device to record conversations and visual events. This data is transmitted to a server via the home network. The server utilizes speech recognition means to convert the received audio information into text information. As a specific example, the Google Cloud Speech-to-Text API can be used.

[0450] Furthermore, the server uses image recognition to analyze the video information. This could involve using an image analysis service such as Amazon Rekognition. Identification information about specific events is generated from the image data and incorporated as part of the document structure.

[0451] Next, the server automatically generates documents from the obtained information using natural language generation tools. These documents are then subjected to sentiment analysis tools to reflect the user's emotional state. Sentiment analysis, based on audio and video data, adds emotional nuances to the documents. This function enables the recording of the user's emotionally rich experiences.

[0452] Ultimately, the generated documents are organized by information management systems and stored in a format accessible to users. Users can easily access, view, and edit the recorded documents via smartphones or personal computers. For example, a photo of a parent smiling while playing with their child could be saved the next day as a diary entry that captures the joyful atmosphere.

[0453] To realize such an invention, an example of a prompt statement could be as follows: "Please create specifications for a robot system that records enjoyable moments with children while analyzing their emotions." This prompt would allow the generating AI model to efficiently propose a system that reflects the content of the childcare records.

[0454] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0455] Step 1:

[0456] The terminal uses a voice information processing device to collect the user's everyday conversations. The input is voice data, which is transmitted to the server in real time. Specifically, this involves capturing voice through a microphone and converting it into a digital format.

[0457] Step 2:

[0458] The terminal uses a video information acquisition device to capture everyday events of the user. The input is video data, which is then transmitted to the server. Specifically, the system captures video using a camera and converts it to a digital format.

[0459] Step 3:

[0460] The server converts received audio data into text data using speech recognition technology. The input is audio data, and the output is text data. Data processing includes, for example, speech-to-text conversion using Google Cloud Speech-to-Text.

[0461] Step 4:

[0462] The server analyzes video data using image recognition technology and generates identification information for specific events. The input is video data, and the output is identification information. Specific processing includes image analysis and labeling using Amazon Rekognition.

[0463] Step 5:

[0464] The server generates documents from character data and identifiers converted using natural language generation methods. The input is character data and identifiers, and the output is the generated document. Data processing involves creating a human-readable document based on this information.

[0465] Step 6:

[0466] The server analyzes the user's emotions from audio and video data using emotion analysis tools and adds emotional nuances to the generated document. The input is audio and video data, and the output is a document with added emotional elements. Specifically, the system infers emotions from voice and facial expressions and reflects them in the document style.

[0467] Step 7:

[0468] The server stores documents generated by the information management system in a database. The input is document data, and the output is the documents stored in the database. The operation involves registering documents in the database and indexing them.

[0469] Step 8:

[0470] Users access, view, or edit saved documents via a smartphone app. Input is the user's request, and output is viewable or editable document data. Specifically, this includes data display and editing functions through the user interface within the app.

[0471] 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.

[0472] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0473] 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.

[0474] [Third Embodiment]

[0475] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0476] 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.

[0477] 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).

[0478] 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.

[0479] 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.

[0480] 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).

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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.

[0486] 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".

[0487] This invention constructs a system that allows parents raising children to easily generate and view diaries by using a voice input device, an image acquisition device, a server, and a dedicated application or web interface. The functions and interactions of each element are described below in natural language.

[0488] 1. Voice input and speech recognition

[0489] First, smartphones and home AI devices, acting as terminals, receive voice data from parents. Because these devices are frequently used in home settings, parents can record voices naturally without needing any special preparation. This voice data is sent to a server, where it is converted into text data by speech recognition capabilities.

[0490] 2. Image Acquisition and Analysis

[0491] Next, using the image acquisition device provided by the terminal, users can capture everyday moments of childcare in photographs. The captured images are automatically sent to a server and analyzed using image recognition. The server identifies people and situations in the images and assigns appropriate event labels.

[0492] 3. Natural language generation

[0493] The server generates diary entries using natural language generation methods based on text data obtained through speech recognition and label data obtained through image recognition. During this generation process, sentiment analysis can be performed to add emotional depth to the content of the events.

[0494] 4. Data storage and management

[0495] The generated diary entries are organized chronologically and permanently stored by a database management system on the server. This process allows users to easily review past childcare records.

[0496] 5. Access via User Interface

[0497] Users can access their diaries, generated from their smartphones or computers, through a dedicated application or web interface. This interface is designed to be intuitive and easy to use, allowing for comfortable viewing, editing, and management of diaries.

[0498] For example, if a user wants to record their child's first words, the voice input device receives the conversation at that moment, and the server automatically generates a diary, allowing the user to preserve this valuable record without any effort. In this way, the present invention provides a system that helps busy parents keep records of their children's development.

[0499] The following describes the processing flow.

[0500] Step 1:

[0501] The device receives voice data of the user's conversations and childcare-related information using a voice input device. It allows users to record natural, everyday conversations without pressing any special buttons.

[0502] Step 2:

[0503] The terminal sends the received audio data to the server. This initiates the necessary communication for processing the audio data.

[0504] Step 3:

[0505] The server converts the received audio data into text data using speech recognition. The speech recognition engine performs filtering to remove noise and generate accurate text.

[0506] Step 4:

[0507] The device automatically collects images taken by the user and sends these image data to a server. This makes it possible to process image data that captures moments of childcare.

[0508] Step 5:

[0509] The server applies image recognition to the received image data. It analyzes the people and situations contained in the image and generates event labels based on the content.

[0510] Step 6:

[0511] The server inputs the converted text data and labeled image data into a natural language generation system. This process automatically generates diary-style entries.

[0512] Step 7:

[0513] The server performs sentiment analysis and adds emotional nuances to the generated text. This makes the diary entries more relatable.

[0514] Step 8:

[0515] The server organizes and stores the generated diaries using a database management system. The data is properly organized and configured to facilitate future access.

[0516] Step 9:

[0517] Users can view their diaries, which are generated through a dedicated application or web interface. This allows them to review and reflect on their entries.

[0518] Step 10:

[0519] Users can manually edit their diary entries and add additional information as needed. This can be easily done through the user interface.

[0520] (Example 1)

[0521] 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."

[0522] Parents raising children face the challenge of not being able to easily record their child's growth and memories in a timely manner amidst their busy daily lives, and not being able to easily look back on them later. Furthermore, they are required to create emotionally rich and detailed records, but doing so manually is laborious. In addition, they are expected to imbue the resulting parenting records with emotional depth.

[0523] 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.

[0524] In this invention, the server includes speech recognition means for converting speech data received from a speech input device into text data, image recognition means for analyzing image data received from an image acquisition device and assigning labels based on specific activities, and natural language generation means for automatically generating written text based on the converted text data and labeled image data. This makes it possible for parents raising children to record everyday events and automatically generate emotionally rich diaries without any effort.

[0525] A "voice input device" is a device used to collect the voice spoken by a user as digital data.

[0526] "Speech recognition means" refers to a technology that analyzes digitized speech data from a speech input device and converts the speech into corresponding text data.

[0527] An "image acquisition device" is a device used to collect still images and videos as digital data.

[0528] "Image recognition means" refers to a technology that analyzes image data acquired from an image acquisition device, identifies objects and scenes within the image, and assigns labels based on this identification.

[0529] A "natural language generation method" is a technology that generates naturally readable text using human language structure based on input data.

[0530] "Emotion analysis techniques" are technologies that identify emotions from text data and contextual information, and add emotional nuances to written text.

[0531] "Data storage means" refers to technologies for systematically storing generated text and related information, and making them easily accessible as needed.

[0532] A "generative AI model" is a type of artificial intelligence that has the ability to learn from large amounts of data and generate natural language.

[0533] A "prompt" is text used to give instructions or conditions to a generative AI model regarding the content it should generate.

[0534] This invention is a system for parents raising children to automatically generate and save diaries using voice and images. The following describes a specific form of implementing this system.

[0535] Users can use smartphones or home AI devices as voice input devices. These devices blend seamlessly into the user's daily activities, enabling natural voice recording. For example, if a user says, "I want to record my son's first words today," the device sends the voice data to the server. The server uses speech recognition technology to convert the voice data into text data. A common speech recognition API can be used for this speech recognition.

[0536] Furthermore, users can use their smartphone camera as an image acquisition device to capture moments of childcare in photographs. For example, if a user takes a picture of their child playing in a park, the image data is automatically uploaded to the server. The server uses image recognition technology to analyze people and backgrounds, and applies a general-purpose image recognition API to assign labels to the images based on that analysis.

[0537] Once this audio and image data is collected on the server, the server uses a generative AI model to automatically generate natural-sounding diary entries based on this data. This process employs a natural language processing engine to construct sentences based on the user's experience. For example, it can generate sentences such as, "Today my son said 'Mom' for the first time."

[0538] This system performs sentiment analysis on generated text, adding emotional nuances to the writing. The resulting diary entries are organized chronologically in the server's data storage system and stored long-term. This allows users to easily search for specific memories and revisit them at any time.

[0539] Users can access this diary using a dedicated application or web interface. The interface is intuitively designed, making it easy to view, edit, and manage the generated diary entries. Specifically, users can select a particular date from a calendar-style screen and view or modify the entries for that day.

[0540] As a concrete example of a prompt, giving an AI model instructions such as, "Generate a touching parenting diary based on your experience of your child's birthday," can generate more emotionally profound text.

[0541] In this way, this system provides parents raising children with a means to easily and efficiently generate and save diaries.

[0542] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0543] Step 1:

[0544] The user records voice using a smartphone or home AI device. The recorded voice data is stored in the voice input device. The user might say, for example, "Record my child's growth today." This is recorded as voice data and sent from the device to the server.

[0545] Step 2:

[0546] The server uses speech recognition to convert received audio data into text data. During this process, a speech recognition API is used to analyze the audio waveform data and transcribe the words based on that analysis. The output is generated as text data, which is temporarily stored on the server.

[0547] Step 3:

[0548] A user takes an image using their smartphone camera. For example, they might take a picture of their child playing in a park. This image data is saved on the device and automatically uploaded to the server.

[0549] Step 4:

[0550] The server uses image recognition to analyze the received image data. This analysis utilizes an image recognition API to identify people and scenes and generate appropriate labels based on that identification. Labeled image data is then output and stored on the server.

[0551] Step 5:

[0552] The server uses natural language generation tools to integrate text data from speech recognition and label data from image recognition to generate text. This process is driven by a generative AI model that creates natural-sounding sentences based on the estimated context. The output is the generated text.

[0553] Step 6:

[0554] The server analyzes the generated text using sentiment analysis techniques, adding emotional nuances to the sentences. As a result of this analysis, an emotionally rich diary is generated. The output is emotionally charged text data, which is then saved.

[0555] Step 7:

[0556] The server uses data storage to organize the generated text in chronological order and save it to a database. During the saving process, the records are organized using date and time information as a key. This makes it easier for users to search for the text later.

[0557] Step 8:

[0558] Users access their generated diaries through a dedicated application or web interface. They can view and, if necessary, edit their diaries within the interface. A high-performance UI / UX design ensures intuitive operation.

[0559] (Application Example 1)

[0560] 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."

[0561] In modern society, many parents are overwhelmed with balancing childcare and work, and face the challenge of not having time to record important moments in their parenting lives. Furthermore, there is a need for a system that can easily manage the recorded information and effectively integrate with the various childcare support services provided within cities.

[0562] 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.

[0563] In this invention, the server includes speech recognition means for converting acoustic information collected from a speech acquisition device into text information, image recognition means for analyzing image information collected from an image acquisition device and assigning tags based on specific events, and natural language generation means for automatically generating text based on the converted text information and tagged image information. This makes it possible for busy parents to easily create childcare records and effectively link with childcare support networks within smart cities.

[0564] A "sound acquisition device" is a device that collects acoustic information and transmits it to a server.

[0565] "Speech recognition means" refers to technology that converts acoustic information into textual information.

[0566] An "image acquisition device" is a device used to capture image information and transmit it to a server.

[0567] "Image recognition means" refers to a technology that analyzes image information and assigns tags based on specific events.

[0568] "Natural language generation means" refers to technology that automatically generates text based on character information and tagged image information.

[0569] "Information management means" refers to the technology for organizing and storing generated text in a record format.

[0570] An "intracity network" refers to information systems and communication networks designed to support the lives of residents.

[0571] This invention provides a system for parents to easily record childcare activities. The system mainly consists of a voice acquisition device, an image collection device, and a server. The server is equipped with voice recognition means, image recognition means, natural language generation means, and information management means, and plays a role in supporting residents' childcare records by linking with the urban network within a smart city.

[0572] The user provides voice input via a smartphone or home AI device. A voice acquisition device records this voice and sends it to a server. The server uses voice recognition to convert the acoustic information into text. In parallel, the user takes pictures of everyday scenes with an image acquisition device and sends the image information to the server. The image recognition device analyzes this image information and assigns tags based on specific events.

[0573] Subsequently, the server automatically generates text based on the converted text information and tagged image information using natural language generation capabilities. This process also includes sentiment analysis, so the generated records are imbued with emotional nuances. Furthermore, the generated text is organized and stored in a record format by information management capabilities, and users can view and edit the records using a dedicated application or web interface. The entire system workflow is designed to allow busy parents to easily and accurately record important childcare moments without missing any.

[0574] One specific use case is when a user wants to record their child's first words. In this case, they input the audio at that moment using their smartphone, and the server converts it into text and collects an image of the scene. The generative AI model then applies prompt text to create an emotionally rich record such as, "On this day, our child said 'Mama' for the first time. The whole family was filled with surprise and joy at that moment."

[0575] Examples of prompts for generative AI models:

[0576] Please generate a childcare record based on the following events:

[0577] Audio: The sound of a child speaking their first words.

[0578] Image: Photo of children and family

[0579] Keywords: First words, joy, family

[0580] Emotion: Joy

[0581] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0582] Step 1:

[0583] The user inputs voice using a device. The device's microphone acquires acoustic information and sends it to the server as digital data. The input acoustic information is output as transmitted data.

[0584] Step 2:

[0585] The server receives acoustic information using speech recognition technology and converts the acoustic data into text data. The server applies a speech recognition algorithm to analyze the acoustic signal and generate corresponding character information. Through this process, the input is acoustic data and the output is converted character information.

[0586] Step 3:

[0587] The user takes an image using their device. The device's camera acquires image information and sends it to the server as digital image data. The input image information is output as transmitted data.

[0588] Step 4:

[0589] The server receives image information using image recognition means, analyzes the image data, and assigns tags based on specific events. Using image analysis algorithms, it recognizes objects and scenes within the image and generates appropriate tags. The input is image data, and the output is tagged image information.

[0590] Step 5:

[0591] The server uses natural language generation tools to automatically generate text from converted character information and tagged image information as input. Combining prompts and a generation AI model, including sentiment analysis, it generates the final text data. The output of this step is text data for recording purposes.

[0592] Step 6:

[0593] The server uses information management tools to organize the generated text data and store it in a database in a record format. It defines the data structure, adds metadata as needed, and makes it easily accessible to users. The output is organized record data.

[0594] Step 7:

[0595] Users retrieve, view, and edit recorded data from the server using a dedicated application or web interface. The user interface allows for intuitive display and modification of records. The output is the edited recorded data.

[0596] 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.

[0597] This invention constructs a system for automatically recording and organizing important moments in childcare, and in particular, incorporates an emotion engine that recognizes the user's emotions and reflects them in the diary creation. This system uses a voice input device, an image acquisition device, a server, and a user interface to enable efficient childcare record creation in a way that is effortless for the user. The functions of each element and their interrelationships are described below in natural language.

[0598] First, the terminal, such as a smartphone or home AI device, is equipped with a voice input device to record everyday conversations and events, and a camera function to acquire images. While the user goes about their daily life without requiring any special operation, the terminal collects voice and image data in real time and periodically sends this data to a server.

[0599] Next, the server converts the received audio data into text data using speech recognition. In addition, image data is analyzed using image recognition, and labels based on specific events are assigned. Crucial to this process is the role of the emotion engine, which analyzes the user's emotional state obtained from the audio and image data and adds emotional nuances to the written expression. This makes the diary entries richer in content and more relatable.

[0600] The generated diary entries are organized and stored using a database management system on the server. Users can access these records through a dedicated application or a web interface. This interface is designed with usability in mind, making it easy to view, edit, and check emotional feedback on the diary entries.

[0601] For example, if a user feels happy during a bedtime conversation with their child, this moment is recorded by a voice input device, and the emotion is reflected in a text-based diary. Furthermore, image recognition labels smiling photos, supplementing visual memories. This allows users to maintain a rich, emotion-based record of their parenting, rather than just a collection of facts.

[0602] The following describes the processing flow.

[0603] Step 1:

[0604] The device receives everyday voice messages from the user via a voice input device. This data collection is performed automatically without any user intervention.

[0605] Step 2:

[0606] The device quantizes the acquired voice data and sends it to the server. The voice data is transferred via the internet or a home network.

[0607] Step 3:

[0608] The server analyzes the received audio data using speech recognition and converts it into text data. During this process, background noise is removed, and accurate text is generated based on a language model.

[0609] Step 4:

[0610] The device collects image data captured by the user and sends it to the server. The image data is automatically synchronized, enabling real-time processing.

[0611] Step 5:

[0612] The server analyzes the received image data using image recognition technology. This identifies objects within the image and assigns appropriate labels to them.

[0613] Step 6:

[0614] The server uses an emotion engine to analyze the user's emotions based on audio and image data. This emotion analysis helps identify the user's emotional state on that particular day.

[0615] Step 7:

[0616] The server combines the converted text data, labeled image data, and analyzed sentiment information to generate text using natural language generation tools. This text is then given nuances based on the user's emotions.

[0617] Step 8:

[0618] The server stores and organizes the generated text using a database management system. The data is organized chronologically to prepare for future searches and viewing.

[0619] Step 9:

[0620] Users view their generated diaries using a dedicated user interface. The interface is designed to be easy to use, allowing for simple viewing and editing of diaries.

[0621] Step 10:

[0622] Users can manually edit their diary entries as needed. These edits are immediately reflected in the database, and the record is updated.

[0623] (Example 2)

[0624] 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."

[0625] Efficiently recording everyday moments in childcare and automatically generating rich, emotionally resonant diaries can be time-consuming and burdensome for users. This invention aims to alleviate this burden by providing a system that generates emotionally-driven diaries while minimizing user interaction.

[0626] 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.

[0627] In this invention, the server includes an acoustic recognition means that converts acoustic data acquired from an acoustic input device into text data, an image recognition means that analyzes image data acquired from an image acquisition device and assigns identification information based on specific events, and a document creation means that automatically creates text based on the converted text data and the image data to which the identification information has been assigned. This makes it possible to automatically generate a rich diary with emotional nuances of important moments in childcare, and to easily save and view it.

[0628] An "acoustic input device" is a device that acquires sound from the environment and converts it into an electrical signal.

[0629] "Acoustic recognition means" refers to technology for converting acquired acoustic data into text data.

[0630] A "video acquisition device" is a device that acquires visual information and records it as image or video data.

[0631] "Image recognition means" refers to technology for analyzing video data and identifying and classifying the information contained therein.

[0632] "Identification information" refers to labels or tags assigned based on specific events or characteristics as a result of image recognition.

[0633] "Document creation means" refers to technology for generating text in natural language based on audio and video data.

[0634] "Memory management means" refers to technologies for structuring, storing, and managing generated data.

[0635] "Methods for adding emotion" refer to techniques for adding emotional elements to documents and reflecting subjective nuances.

[0636] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate text based on input instructions.

[0637] An "input prompt" refers to an instruction or question given to an AI model in order to perform a specific task.

[0638] This invention is a system for efficiently recording important moments and everyday events in childcare, and organizing and storing them in a diary format that reflects emotions. A detailed explanation of how to implement this system is provided below.

[0639] First, the terminals used will include smartphones and home AI devices. These terminals are equipped with an audio input device and a video acquisition device, allowing for the continuous collection of audio and image data from the user's daily life. The audio input device converts ambient sound into electrical signals to generate audio data. The video acquisition device uses a camera to acquire visual information as video data.

[0640] Next, this data is sent to a server, where acoustic recognition means are used to convert the acoustic data into text data. General speech recognition software is used for this acoustic recognition. The server then analyzes the video data using video recognition means and assigns identification information based on specific events and features. At this stage, identification information based on specific events or user actions is added as labels.

[0641] The server then uses the collected text data and identification information to generate text in natural language using document creation tools. During this process, a generative AI model is utilized, and emotional nuances are added to the document through empathy-adding tools. This process generates text that is more likely to evoke empathy.

[0642] The generated documents are structured into a record format on the server and organized and stored using memory management systems. Users can access, view, and edit this information through dedicated applications or web interfaces.

[0643] As a concrete example, consider a scenario where a user feels happy through a bedtime conversation with their child. In this case, an audio input device records the audio, and a video acquisition device records the conversation as video. Based on this data, the server can generate a diary entry such as, "I ended the day with a pleasant conversation with my child. I felt especially happy today."

[0644] An example of a prompt to input into the generation AI model is, "Based on a cheerful conversation the user had while playing with their child, please generate a diary entry that reflects their emotions."

[0645] In this way, the present invention provides a system that allows users to record important moments in their daily lives as emotionally rich diaries without actually having to expend much effort.

[0646] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0647] Step 1:

[0648] The device continuously collects audio and image data of the user's daily life using an audio input device and a video acquisition device. Inputs include audio data such as ambient sounds and conversations, and image data capturing everyday events. Audio data is recorded as digital signals, and video data is stored as images taken at fixed intervals. Outputs this data are temporarily stored in the device's storage.

[0649] Step 2:

[0650] The collected audio and image data is transmitted from the terminal to the server via the internet. At this time, the data is converted to a compressed format to reduce bandwidth usage. The input is compressed audio and image data, and the output is the raw data decompressed by the server. The server receives this data for processing.

[0651] Step 3:

[0652] The server converts audio data into text data using acoustic recognition. It analyzes the received audio data and generates corresponding text based on each acoustic feature. Here, an acoustic signal processing algorithm is used to extract significant keywords and phrases from the audio. The output is text data, where the audio has been converted into characters.

[0653] Step 4:

[0654] The server analyzes video data using image recognition and adds identification information based on specific events and facial expressions. The input is the image data to be analyzed; an image processing algorithm is executed to detect people and objects, and related event and emotion labels are added. The output is the image data with the added identification information.

[0655] Step 5:

[0656] Based on the analysis results, the server generates natural language text through a document creation tool. It integrates text data obtained from audio and identification information obtained from video as input, and uses a generative AI model to form text corresponding to the background information. The output is obtained as a diary-style text with added emotional nuances.

[0657] Step 6:

[0658] The generated text is organized and stored as individual document files in a structured database by the server's memory management system. The input is generated string data, which is tagged and categorized during saving. The output is a diary-style document stored in the database, which can be searched and viewed later.

[0659] Step 7:

[0660] Users access their generated diaries through a dedicated application or web interface, viewing and editing them as needed. When a user accesses the interface, the requested data is sent from the server and displayed on the screen. The output is the content of the screen the user is viewing.

[0661] (Application Example 2)

[0662] 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."

[0663] Traditional childcare record systems required users to manually input records, and it was difficult to reflect emotional elements in the documents. This made it challenging to accurately and emotionally record important moments in childcare. Furthermore, when reviewing childcare records later, it was difficult to effectively recreate the emotions of those moments.

[0664] 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.

[0665] In this invention, the server includes a speech recognition means that converts speech information acquired from a speech information processing device into text information, an image recognition means that analyzes video information acquired from a video information acquisition device and assigns identification information based on specific events, a natural language generation means that automatically generates a document based on the converted text information and identification information, and an emotion analysis means that performs emotion analysis and assigns emotional nuances to the generated document. This makes it possible for users to record important moments in childcare with rich emotion without any hassle and to easily look back on them.

[0666] A "voice information processing device" is a device that collects voice data and converts that data into other formats.

[0667] A "speech recognition means for converting into text information" is a means that has the function of analyzing speech information and representing its content as text data.

[0668] A "video information acquisition device" is a device for collecting visual information, primarily consisting of a camera function.

[0669] "Image recognition means" refers to means that analyze acquired video data and have the function of identifying specific events.

[0670] "Identification information" refers to labels or tags attached to image data to indicate a specific event.

[0671] A "natural language generation method" is a means of generating documents from data, and in particular, organizing information in a form that closely resembles human language.

[0672] "Emotional analysis methods" are techniques for analyzing emotional states from audio and video data and reflecting the results in documents.

[0673] A "consumer autonomous machine" is an automatically operating mechanical device designed for use in ordinary households.

[0674] "Information management means" refers to means that have the ability to organize and store data, and provide the function of making information accessible as needed.

[0675] The system for carrying out this invention includes a program that processes audio and video information in real time using a consumer-grade autonomous machine installed in a home. In the user's daily life, the terminal uses an audio information processing device and a video information acquisition device to record conversations and visual events. This data is transmitted to a server via the home network. The server utilizes speech recognition means to convert the received audio information into text information. As a specific example, the Google Cloud Speech-to-Text API can be used.

[0676] Furthermore, the server uses image recognition to analyze the video information. This could involve using an image analysis service such as Amazon Rekognition. Identification information about specific events is generated from the image data and incorporated as part of the document structure.

[0677] Next, the server automatically generates documents from the obtained information using natural language generation tools. These documents are then subjected to sentiment analysis tools to reflect the user's emotional state. Sentiment analysis, based on audio and video data, adds emotional nuances to the documents. This function enables the recording of the user's emotionally rich experiences.

[0678] Ultimately, the generated documents are organized by information management systems and stored in a format accessible to users. Users can easily access, view, and edit the recorded documents via smartphones or personal computers. For example, a photo of a parent smiling while playing with their child could be saved the next day as a diary entry that captures the joyful atmosphere.

[0679] To realize such an invention, an example of a prompt statement could be as follows: "Please create specifications for a robot system that records enjoyable moments with children while analyzing their emotions." This prompt would allow the generating AI model to efficiently propose a system that reflects the content of the childcare records.

[0680] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0681] Step 1:

[0682] The terminal uses a voice information processing device to collect the user's everyday conversations. The input is voice data, which is transmitted to the server in real time. Specifically, this involves capturing voice through a microphone and converting it into a digital format.

[0683] Step 2:

[0684] The terminal uses a video information acquisition device to capture everyday events of the user. The input is video data, which is then transmitted to the server. Specifically, the system captures video using a camera and converts it to a digital format.

[0685] Step 3:

[0686] The server converts received audio data into text data using speech recognition technology. The input is audio data, and the output is text data. Data processing includes, for example, speech-to-text conversion using Google Cloud Speech-to-Text.

[0687] Step 4:

[0688] The server analyzes video data using image recognition technology and generates identification information for specific events. The input is video data, and the output is identification information. Specific processing includes image analysis and labeling using Amazon Rekognition.

[0689] Step 5:

[0690] The server generates documents from character data and identifiers converted using natural language generation methods. The input is character data and identifiers, and the output is the generated document. Data processing involves creating a human-readable document based on this information.

[0691] Step 6:

[0692] The server analyzes the user's emotions from audio and video data using emotion analysis tools and adds emotional nuances to the generated document. The input is audio and video data, and the output is a document with added emotional elements. Specifically, the system infers emotions from voice and facial expressions and reflects them in the document style.

[0693] Step 7:

[0694] The server stores documents generated by the information management system in a database. The input is document data, and the output is the documents stored in the database. The operation involves registering documents in the database and indexing them.

[0695] Step 8:

[0696] Users access, view, or edit saved documents via a smartphone app. Input is the user's request, and output is viewable or editable document data. Specifically, this includes data display and editing functions through the user interface within the app.

[0697] 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.

[0698] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0699] 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.

[0700] [Fourth Embodiment]

[0701] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0702] 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.

[0703] 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).

[0704] 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.

[0705] 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.

[0706] 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).

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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".

[0714] This invention constructs a system that allows parents raising children to easily generate and view diaries by using a voice input device, an image acquisition device, a server, and a dedicated application or web interface. The functions and interactions of each element are described below in natural language.

[0715] 1. Voice input and speech recognition

[0716] First, smartphones and home AI devices, acting as terminals, receive voice data from parents. Because these devices are frequently used in home settings, parents can record voices naturally without needing any special preparation. This voice data is sent to a server, where it is converted into text data by speech recognition capabilities.

[0717] 2. Image Acquisition and Analysis

[0718] Next, using the image acquisition device provided by the terminal, users can capture everyday moments of childcare in photographs. The captured images are automatically sent to a server and analyzed using image recognition. The server identifies people and situations in the images and assigns appropriate event labels.

[0719] 3. Natural language generation

[0720] The server generates diary entries using natural language generation methods based on text data obtained through speech recognition and label data obtained through image recognition. During this generation process, sentiment analysis can be performed to add emotional depth to the content of the events.

[0721] 4. Data storage and management

[0722] The generated diary entries are organized chronologically and permanently stored by a database management system on the server. This process allows users to easily review past childcare records.

[0723] 5. Access via User Interface

[0724] Users can access their diaries, generated from their smartphones or computers, through a dedicated application or web interface. This interface is designed to be intuitive and easy to use, allowing for comfortable viewing, editing, and management of diaries.

[0725] For example, if a user wants to record their child's first words, the voice input device receives the conversation at that moment, and the server automatically generates a diary, allowing the user to preserve this valuable record without any effort. In this way, the present invention provides a system that helps busy parents keep records of their children's development.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The device receives voice data of the user's conversations and childcare-related information using a voice input device. It allows users to record natural, everyday conversations without pressing any special buttons.

[0729] Step 2:

[0730] The terminal sends the received audio data to the server. This initiates the necessary communication for processing the audio data.

[0731] Step 3:

[0732] The server converts the received audio data into text data using speech recognition. The speech recognition engine performs filtering to remove noise and generate accurate text.

[0733] Step 4:

[0734] The device automatically collects images taken by the user and sends these image data to a server. This makes it possible to process image data that captures moments of childcare.

[0735] Step 5:

[0736] The server applies image recognition to the received image data. It analyzes the people and situations contained in the image and generates event labels based on the content.

[0737] Step 6:

[0738] The server inputs the converted text data and labeled image data into a natural language generation system. This process automatically generates diary-style entries.

[0739] Step 7:

[0740] The server performs sentiment analysis and adds emotional nuances to the generated text. This makes the diary entries more relatable.

[0741] Step 8:

[0742] The server organizes and stores the generated diaries using a database management system. The data is properly organized and configured to facilitate future access.

[0743] Step 9:

[0744] Users can view their diaries, which are generated through a dedicated application or web interface. This allows them to review and reflect on their entries.

[0745] Step 10:

[0746] Users can manually edit their diary entries and add additional information as needed. This can be easily done through the user interface.

[0747] (Example 1)

[0748] 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".

[0749] Parents raising children face the challenge of not being able to easily record their child's growth and memories in a timely manner amidst their busy daily lives, and not being able to easily look back on them later. Furthermore, they are required to create emotionally rich and detailed records, but doing so manually is laborious. In addition, they are expected to imbue the resulting parenting records with emotional depth.

[0750] 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.

[0751] In this invention, the server includes speech recognition means for converting speech data received from a speech input device into text data, image recognition means for analyzing image data received from an image acquisition device and assigning labels based on specific activities, and natural language generation means for automatically generating written text based on the converted text data and labeled image data. This makes it possible for parents raising children to record everyday events and automatically generate emotionally rich diaries without any effort.

[0752] A "voice input device" is a device used to collect the voice spoken by a user as digital data.

[0753] "Speech recognition means" refers to a technology that analyzes digitized speech data from a speech input device and converts the speech into corresponding text data.

[0754] An "image acquisition device" is a device used to collect still images and videos as digital data.

[0755] "Image recognition means" refers to a technology that analyzes image data acquired from an image acquisition device, identifies objects and scenes within the image, and assigns labels based on this identification.

[0756] A "natural language generation method" is a technology that generates naturally readable text using human language structure based on input data.

[0757] "Emotion analysis techniques" are technologies that identify emotions from text data and contextual information, and add emotional nuances to written text.

[0758] "Data storage means" refers to technologies for systematically storing generated text and related information, and making them easily accessible as needed.

[0759] A "generative AI model" is a type of artificial intelligence that has the ability to learn from large amounts of data and generate natural language.

[0760] A "prompt" is text used to give instructions or conditions to a generative AI model regarding the content it should generate.

[0761] This invention is a system for parents raising children to automatically generate and save diaries using voice and images. The following describes a specific form of implementing this system.

[0762] Users can use smartphones or home AI devices as voice input devices. These devices blend seamlessly into the user's daily activities, enabling natural voice recording. For example, if a user says, "I want to record my son's first words today," the device sends the voice data to the server. The server uses speech recognition technology to convert the voice data into text data. A common speech recognition API can be used for this speech recognition.

[0763] Furthermore, users can use their smartphone camera as an image acquisition device to capture moments of childcare in photographs. For example, if a user takes a picture of their child playing in a park, the image data is automatically uploaded to the server. The server uses image recognition technology to analyze people and backgrounds, and applies a general-purpose image recognition API to assign labels to the images based on that analysis.

[0764] Once this audio and image data is collected on the server, the server uses a generative AI model to automatically generate natural-sounding diary entries based on this data. This process employs a natural language processing engine to construct sentences based on the user's experience. For example, it can generate sentences such as, "Today my son said 'Mom' for the first time."

[0765] This system performs sentiment analysis on generated text, adding emotional nuances to the writing. The resulting diary entries are organized chronologically in the server's data storage system and stored long-term. This allows users to easily search for specific memories and revisit them at any time.

[0766] Users can access this diary using a dedicated application or web interface. The interface is intuitively designed, making it easy to view, edit, and manage the generated diary entries. Specifically, users can select a particular date from a calendar-style screen and view or modify the entries for that day.

[0767] As a concrete example of a prompt, giving an AI model instructions such as, "Generate a touching parenting diary based on your experience of your child's birthday," can generate more emotionally profound text.

[0768] In this way, this system provides parents raising children with a means to easily and efficiently generate and save diaries.

[0769] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0770] Step 1:

[0771] The user records voice using a smartphone or home AI device. The recorded voice data is stored in the voice input device. The user might say, for example, "Record my child's growth today." This is recorded as voice data and sent from the device to the server.

[0772] Step 2:

[0773] The server uses speech recognition to convert received audio data into text data. During this process, a speech recognition API is used to analyze the audio waveform data and transcribe the words based on that analysis. The output is generated as text data, which is temporarily stored on the server.

[0774] Step 3:

[0775] A user takes an image using their smartphone camera. For example, they might take a picture of their child playing in a park. This image data is saved on the device and automatically uploaded to the server.

[0776] Step 4:

[0777] The server uses image recognition to analyze the received image data. This analysis utilizes an image recognition API to identify people and scenes and generate appropriate labels based on that identification. Labeled image data is then output and stored on the server.

[0778] Step 5:

[0779] The server uses natural language generation tools to integrate text data from speech recognition and label data from image recognition to generate text. This process is driven by a generative AI model that creates natural-sounding sentences based on the estimated context. The output is the generated text.

[0780] Step 6:

[0781] The server analyzes the generated text using sentiment analysis techniques, adding emotional nuances to the sentences. As a result of this analysis, an emotionally rich diary is generated. The output is emotionally charged text data, which is then saved.

[0782] Step 7:

[0783] The server uses data storage to organize the generated text in chronological order and save it to a database. During the saving process, the records are organized using date and time information as a key. This makes it easier for users to search for the text later.

[0784] Step 8:

[0785] Users access their generated diaries through a dedicated application or web interface. They can view and, if necessary, edit their diaries within the interface. A high-performance UI / UX design ensures intuitive operation.

[0786] (Application Example 1)

[0787] 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".

[0788] In modern society, many parents are overwhelmed with balancing childcare and work, and face the challenge of not having time to record important moments in their parenting lives. Furthermore, there is a need for a system that can easily manage the recorded information and effectively integrate with the various childcare support services provided within cities.

[0789] 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.

[0790] In this invention, the server includes speech recognition means for converting acoustic information collected from a speech acquisition device into text information, image recognition means for analyzing image information collected from an image acquisition device and assigning tags based on specific events, and natural language generation means for automatically generating text based on the converted text information and tagged image information. This makes it possible for busy parents to easily create childcare records and effectively link with childcare support networks within smart cities.

[0791] A "sound acquisition device" is a device that collects acoustic information and transmits it to a server.

[0792] "Speech recognition means" refers to technology that converts acoustic information into textual information.

[0793] An "image acquisition device" is a device used to capture image information and transmit it to a server.

[0794] "Image recognition means" refers to a technology that analyzes image information and assigns tags based on specific events.

[0795] "Natural language generation means" refers to technology that automatically generates text based on character information and tagged image information.

[0796] "Information management means" refers to the technology for organizing and storing generated text in a record format.

[0797] An "intracity network" refers to information systems and communication networks designed to support the lives of residents.

[0798] This invention provides a system for parents to easily record childcare activities. The system mainly consists of a voice acquisition device, an image collection device, and a server. The server is equipped with voice recognition means, image recognition means, natural language generation means, and information management means, and plays a role in supporting residents' childcare records by linking with the urban network within a smart city.

[0799] The user provides voice input via a smartphone or home AI device. A voice acquisition device records this voice and sends it to a server. The server uses voice recognition to convert the acoustic information into text. In parallel, the user takes pictures of everyday scenes with an image acquisition device and sends the image information to the server. The image recognition device analyzes this image information and assigns tags based on specific events.

[0800] Subsequently, the server automatically generates text based on the converted text information and tagged image information using natural language generation capabilities. This process also includes sentiment analysis, so the generated records are imbued with emotional nuances. Furthermore, the generated text is organized and stored in a record format by information management capabilities, and users can view and edit the records using a dedicated application or web interface. The entire system workflow is designed to allow busy parents to easily and accurately record important childcare moments without missing any.

[0801] One specific use case is when a user wants to record their child's first words. In this case, they input the audio at that moment using their smartphone, and the server converts it into text and collects an image of the scene. The generative AI model then applies prompt text to create an emotionally rich record such as, "On this day, our child said 'Mama' for the first time. The whole family was filled with surprise and joy at that moment."

[0802] Examples of prompts for generative AI models:

[0803] Please generate a childcare record based on the following events:

[0804] Audio: The sound of a child speaking their first words.

[0805] Image: Photo of children and family

[0806] Keywords: First words, joy, family

[0807] Emotion: Joy

[0808] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0809] Step 1:

[0810] The user inputs voice using a device. The device's microphone acquires acoustic information and sends it to the server as digital data. The input acoustic information is output as transmitted data.

[0811] Step 2:

[0812] The server receives acoustic information using speech recognition technology and converts the acoustic data into text data. The server applies a speech recognition algorithm to analyze the acoustic signal and generate corresponding character information. Through this process, the input is acoustic data and the output is converted character information.

[0813] Step 3:

[0814] The user takes an image using their device. The device's camera acquires image information and sends it to the server as digital image data. The input image information is output as transmitted data.

[0815] Step 4:

[0816] The server receives image information using image recognition means, analyzes the image data, and assigns tags based on specific events. Using image analysis algorithms, it recognizes objects and scenes within the image and generates appropriate tags. The input is image data, and the output is tagged image information.

[0817] Step 5:

[0818] The server uses natural language generation tools to automatically generate text from converted character information and tagged image information as input. Combining prompts and a generation AI model, including sentiment analysis, it generates the final text data. The output of this step is text data for recording purposes.

[0819] Step 6:

[0820] The server uses information management tools to organize the generated text data and store it in a database in a record format. It defines the data structure, adds metadata as needed, and makes it easily accessible to users. The output is organized record data.

[0821] Step 7:

[0822] Users retrieve, view, and edit recorded data from the server using a dedicated application or web interface. The user interface allows for intuitive display and modification of records. The output is the edited recorded data.

[0823] 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.

[0824] This invention constructs a system for automatically recording and organizing important moments in childcare, and in particular, incorporates an emotion engine that recognizes the user's emotions and reflects them in the diary creation. This system uses a voice input device, an image acquisition device, a server, and a user interface to enable efficient childcare record creation in a way that is effortless for the user. The functions of each element and their interrelationships are described below in natural language.

[0825] First, the terminal, such as a smartphone or home AI device, is equipped with a voice input device to record everyday conversations and events, and a camera function to acquire images. While the user goes about their daily life without requiring any special operation, the terminal collects voice and image data in real time and periodically sends this data to a server.

[0826] Next, the server converts the received audio data into text data using speech recognition. In addition, image data is analyzed using image recognition, and labels based on specific events are assigned. Crucial to this process is the role of the emotion engine, which analyzes the user's emotional state obtained from the audio and image data and adds emotional nuances to the written expression. This makes the diary entries richer in content and more relatable.

[0827] The generated diary entries are organized and stored using a database management system on the server. Users can access these records through a dedicated application or a web interface. This interface is designed with usability in mind, making it easy to view, edit, and check emotional feedback on the diary entries.

[0828] For example, if a user feels happy during a bedtime conversation with their child, this moment is recorded by a voice input device, and the emotion is reflected in a text-based diary. Furthermore, image recognition labels smiling photos, supplementing visual memories. This allows users to maintain a rich, emotion-based record of their parenting, rather than just a collection of facts.

[0829] The following describes the processing flow.

[0830] Step 1:

[0831] The device receives everyday voice messages from the user via a voice input device. This data collection is performed automatically without any user intervention.

[0832] Step 2:

[0833] The device quantizes the acquired voice data and sends it to the server. The voice data is transferred via the internet or a home network.

[0834] Step 3:

[0835] The server analyzes the received audio data using speech recognition and converts it into text data. During this process, background noise is removed, and accurate text is generated based on a language model.

[0836] Step 4:

[0837] The device collects image data captured by the user and sends it to the server. The image data is automatically synchronized, enabling real-time processing.

[0838] Step 5:

[0839] The server analyzes the received image data using image recognition technology. This identifies objects within the image and assigns appropriate labels to them.

[0840] Step 6:

[0841] The server uses an emotion engine to analyze the user's emotions based on audio and image data. This emotion analysis helps identify the user's emotional state on that particular day.

[0842] Step 7:

[0843] The server combines the converted text data, labeled image data, and analyzed sentiment information to generate text using natural language generation tools. This text is then given nuances based on the user's emotions.

[0844] Step 8:

[0845] The server stores and organizes the generated text using a database management system. The data is organized chronologically to prepare for future searches and viewing.

[0846] Step 9:

[0847] Users view their generated diaries using a dedicated user interface. The interface is designed to be easy to use, allowing for simple viewing and editing of diaries.

[0848] Step 10:

[0849] Users can manually edit their diary entries as needed. These edits are immediately reflected in the database, and the record is updated.

[0850] (Example 2)

[0851] 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".

[0852] Efficiently recording everyday moments in childcare and automatically generating rich, emotionally resonant diaries can be time-consuming and burdensome for users. This invention aims to alleviate this burden by providing a system that generates emotionally-driven diaries while minimizing user interaction.

[0853] 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.

[0854] In this invention, the server includes an acoustic recognition means that converts acoustic data acquired from an acoustic input device into text data, an image recognition means that analyzes image data acquired from an image acquisition device and assigns identification information based on specific events, and a document creation means that automatically creates text based on the converted text data and the image data to which the identification information has been assigned. This makes it possible to automatically generate a rich diary with emotional nuances of important moments in childcare, and to easily save and view it.

[0855] An "acoustic input device" is a device that acquires sound from the environment and converts it into an electrical signal.

[0856] "Acoustic recognition means" refers to technology for converting acquired acoustic data into text data.

[0857] A "video acquisition device" is a device that acquires visual information and records it as image or video data.

[0858] "Image recognition means" refers to technology for analyzing video data and identifying and classifying the information contained therein.

[0859] "Identification information" refers to labels or tags assigned based on specific events or characteristics as a result of image recognition.

[0860] "Document creation means" refers to technology for generating text in natural language based on audio and video data.

[0861] "Memory management means" refers to technologies for structuring, storing, and managing generated data.

[0862] "Methods for adding emotion" refer to techniques for adding emotional elements to documents and reflecting subjective nuances.

[0863] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate text based on input instructions.

[0864] An "input prompt" refers to an instruction or question given to an AI model in order to perform a specific task.

[0865] This invention is a system for efficiently recording important moments and everyday events in childcare, and organizing and storing them in a diary format that reflects emotions. A detailed explanation of how to implement this system is provided below.

[0866] First, the terminals used will include smartphones and home AI devices. These terminals are equipped with an audio input device and a video acquisition device, allowing for the continuous collection of audio and image data from the user's daily life. The audio input device converts ambient sound into electrical signals to generate audio data. The video acquisition device uses a camera to acquire visual information as video data.

[0867] Next, this data is sent to a server, where acoustic recognition means are used to convert the acoustic data into text data. General speech recognition software is used for this acoustic recognition. The server then analyzes the video data using video recognition means and assigns identification information based on specific events and features. At this stage, identification information based on specific events or user actions is added as labels.

[0868] The server then uses the collected text data and identification information to generate text in natural language using document creation tools. During this process, a generative AI model is utilized, and emotional nuances are added to the document through empathy-adding tools. This process generates text that is more likely to evoke empathy.

[0869] The generated documents are structured into a record format on the server and organized and stored using memory management systems. Users can access, view, and edit this information through dedicated applications or web interfaces.

[0870] As a concrete example, consider a scenario where a user feels happy through a bedtime conversation with their child. In this case, an audio input device records the audio, and a video acquisition device records the conversation as video. Based on this data, the server can generate a diary entry such as, "I ended the day with a pleasant conversation with my child. I felt especially happy today."

[0871] An example of a prompt to input into the generation AI model is, "Based on a cheerful conversation the user had while playing with their child, please generate a diary entry that reflects their emotions."

[0872] In this way, the present invention provides a system that allows users to record important moments in their daily lives as emotionally rich diaries without actually having to expend much effort.

[0873] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0874] Step 1:

[0875] The device continuously collects audio and image data of the user's daily life using an audio input device and a video acquisition device. Inputs include audio data such as ambient sounds and conversations, and image data capturing everyday events. Audio data is recorded as digital signals, and video data is stored as images taken at fixed intervals. Outputs this data are temporarily stored in the device's storage.

[0876] Step 2:

[0877] The collected audio and image data is transmitted from the terminal to the server via the internet. At this time, the data is converted to a compressed format to reduce bandwidth usage. The input is compressed audio and image data, and the output is the raw data decompressed by the server. The server receives this data for processing.

[0878] Step 3:

[0879] The server converts audio data into text data using acoustic recognition. It analyzes the received audio data and generates corresponding text based on each acoustic feature. Here, an acoustic signal processing algorithm is used to extract significant keywords and phrases from the audio. The output is text data, where the audio has been converted into characters.

[0880] Step 4:

[0881] The server analyzes video data using image recognition and adds identification information based on specific events and facial expressions. The input is the image data to be analyzed; an image processing algorithm is executed to detect people and objects, and related event and emotion labels are added. The output is the image data with the added identification information.

[0882] Step 5:

[0883] Based on the analysis results, the server generates natural language text through a document creation tool. It integrates text data obtained from audio and identification information obtained from video as input, and uses a generative AI model to form text corresponding to the background information. The output is obtained as a diary-style text with added emotional nuances.

[0884] Step 6:

[0885] The generated text is organized and stored as individual document files in a structured database by the server's memory management system. The input is generated string data, which is tagged and categorized during saving. The output is a diary-style document stored in the database, which can be searched and viewed later.

[0886] Step 7:

[0887] Users access their generated diaries through a dedicated application or web interface, viewing and editing them as needed. When a user accesses the interface, the requested data is sent from the server and displayed on the screen. The output is the content of the screen the user is viewing.

[0888] (Application Example 2)

[0889] 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".

[0890] Traditional childcare record systems required users to manually input records, and it was difficult to reflect emotional elements in the documents. This made it challenging to accurately and emotionally record important moments in childcare. Furthermore, when reviewing childcare records later, it was difficult to effectively recreate the emotions of those moments.

[0891] 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.

[0892] In this invention, the server includes a speech recognition means that converts speech information acquired from a speech information processing device into text information, an image recognition means that analyzes video information acquired from a video information acquisition device and assigns identification information based on specific events, a natural language generation means that automatically generates a document based on the converted text information and identification information, and an emotion analysis means that performs emotion analysis and assigns emotional nuances to the generated document. This makes it possible for users to record important moments in childcare with rich emotion without any hassle and to easily look back on them.

[0893] A "voice information processing device" is a device that collects voice data and converts that data into other formats.

[0894] A "speech recognition means for converting into text information" is a means that has the function of analyzing speech information and representing its content as text data.

[0895] A "video information acquisition device" is a device for collecting visual information, primarily consisting of a camera function.

[0896] "Image recognition means" refers to means that analyze acquired video data and have the function of identifying specific events.

[0897] "Identification information" refers to labels or tags attached to image data to indicate a specific event.

[0898] A "natural language generation method" is a means of generating documents from data, and in particular, organizing information in a form that closely resembles human language.

[0899] "Emotional analysis methods" are techniques for analyzing emotional states from audio and video data and reflecting the results in documents.

[0900] A "consumer autonomous machine" is an automatically operating mechanical device designed for use in ordinary households.

[0901] "Information management means" refers to means that have the ability to organize and store data, and provide the function of making information accessible as needed.

[0902] The system for carrying out this invention includes a program that processes audio and video information in real time using a consumer-grade autonomous machine installed in a home. In the user's daily life, the terminal uses an audio information processing device and a video information acquisition device to record conversations and visual events. This data is transmitted to a server via the home network. The server utilizes speech recognition means to convert the received audio information into text information. As a specific example, the Google Cloud Speech-to-Text API can be used.

[0903] Furthermore, the server uses image recognition to analyze the video information. This could involve using an image analysis service such as Amazon Rekognition. Identification information about specific events is generated from the image data and incorporated as part of the document structure.

[0904] Next, the server automatically generates documents from the obtained information using natural language generation tools. These documents are then subjected to sentiment analysis tools to reflect the user's emotional state. Sentiment analysis, based on audio and video data, adds emotional nuances to the documents. This function enables the recording of the user's emotionally rich experiences.

[0905] Ultimately, the generated documents are organized by information management systems and stored in a format accessible to users. Users can easily access, view, and edit the recorded documents via smartphones or personal computers. For example, a photo of a parent smiling while playing with their child could be saved the next day as a diary entry that captures the joyful atmosphere.

[0906] To realize such an invention, an example of a prompt statement could be as follows: "Please create specifications for a robot system that records enjoyable moments with children while analyzing their emotions." This prompt would allow the generating AI model to efficiently propose a system that reflects the content of the childcare records.

[0907] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0908] Step 1:

[0909] The terminal uses a voice information processing device to collect the user's everyday conversations. The input is voice data, which is transmitted to the server in real time. Specifically, this involves capturing voice through a microphone and converting it into a digital format.

[0910] Step 2:

[0911] The terminal uses a video information acquisition device to capture everyday events of the user. The input is video data, which is then transmitted to the server. Specifically, the system captures video using a camera and converts it to a digital format.

[0912] Step 3:

[0913] The server converts received audio data into text data using speech recognition technology. The input is audio data, and the output is text data. Data processing includes, for example, speech-to-text conversion using Google Cloud Speech-to-Text.

[0914] Step 4:

[0915] The server analyzes video data using image recognition technology and generates identification information for specific events. The input is video data, and the output is identification information. Specific processing includes image analysis and labeling using Amazon Rekognition.

[0916] Step 5:

[0917] The server generates documents from character data and identifiers converted using natural language generation methods. The input is character data and identifiers, and the output is the generated document. Data processing involves creating a human-readable document based on this information.

[0918] Step 6:

[0919] The server analyzes the user's emotions from audio and video data using emotion analysis tools and adds emotional nuances to the generated document. The input is audio and video data, and the output is a document with added emotional elements. Specifically, the system infers emotions from voice and facial expressions and reflects them in the document style.

[0920] Step 7:

[0921] The server stores documents generated by the information management system in a database. The input is document data, and the output is the documents stored in the database. The operation involves registering documents in the database and indexing them.

[0922] Step 8:

[0923] Users access, view, or edit saved documents via a smartphone app. Input is the user's request, and output is viewable or editable document data. Specifically, this includes data display and editing functions through the user interface within the app.

[0924] 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.

[0925] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0926] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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."

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] The following is further disclosed regarding the embodiments described above.

[0946] (Claim 1)

[0947] A speech recognition means that converts speech data received from a speech input device into text data,

[0948] Image recognition means that analyzes image data received from an image acquisition device and assigns labels based on specific events,

[0949] A natural language generation means that automatically generates text based on converted text data and labeled image data,

[0950] A database management system that organizes and saves generated text in a diary format,

[0951] A system that includes this.

[0952] (Claim 2)

[0953] The system according to claim 1, which allows users to view and modify a diary generated via a user interface.

[0954] (Claim 3)

[0955] The system according to claim 1, characterized by performing sentiment analysis and adding emotional nuances to the generated text.

[0956] "Example 1"

[0957] (Claim 1)

[0958] A speech recognition means that converts speech data received from a speech input device into text data,

[0959] Image recognition means that analyzes image data received from an image acquisition device and assigns labels based on specific activities,

[0960] A natural language generation means that automatically generates written text based on converted text data and labeled image data,

[0961] An analytical tool that performs sentiment analysis to add emotional depth to the generated text,

[0962] A data storage method for organizing and saving generated text in a diary format,

[0963] A system that includes this.

[0964] (Claim 2)

[0965] The system according to claim 1, wherein the generated diary can be viewed and modified via a display device.

[0966] (Claim 3)

[0967] The system according to claim 1, characterized in that it generates generated text based on instructions input to a generation AI model.

[0968] "Application Example 1"

[0969] (Claim 1)

[0970] A speech recognition means that converts acoustic information collected from a speech acquisition device into text information,

[0971] An image recognition means that analyzes image information collected from an image acquisition device and assigns tags based on specific events,

[0972] A natural language generation means that automatically generates text based on converted character information and tagged image information,

[0973] An information management system that organizes and stores the generated text in a record format,

[0974] A means of collaboration that connects with urban networks that support the lives of residents,

[0975] A system that includes this.

[0976] (Claim 2)

[0977] The system according to claim 1, which allows residents to access and edit records.

[0978] (Claim 3)

[0979] The system according to claim 1, characterized by performing sentiment analysis and adding emotional elements to the generated text.

[0980] "Example 2 of combining an emotion engine"

[0981] (Claim 1)

[0982] An acoustic recognition means that converts acoustic data acquired from an acoustic input device into character data,

[0983] A video recognition means that analyzes video data acquired from a video acquisition device and assigns identification information based on a specific event,

[0984] A document creation method that automatically creates text based on converted text data and video data to which identification information has been added,

[0985] A memory management system that structures and saves the created documents in a record format,

[0986] An emotion-adding means that analyzes emotions obtained from audio and video data and adds emotional elements to the created text,

[0987] A system that includes this.

[0988] (Claim 2)

[0989] The system according to claim 1, which displays and allows modification of records created via a user information display means.

[0990] (Claim 3)

[0991] The system according to claim 1, characterized in that it generates text based on an input prompt using a generative AI model.

[0992] "Application example 2 when combining with an emotional engine"

[0993] (Claim 1)

[0994] A speech recognition means that converts speech information acquired from a speech information processing device into text information,

[0995] An image recognition means that analyzes video information acquired from a video information acquisition device and assigns identification information based on a specific event,

[0996] A natural language generation means that automatically generates a document based on converted character information and identification information,

[0997] An information management system for organizing and storing generated documents in a record format,

[0998] A sentiment analysis means that performs sentiment analysis and adds emotional nuances to the generated document,

[0999] A system that includes this.

[1000] (Claim 2)

[1001] The system according to claim 1, which allows users to view and modify records generated via a user interface.

[1002] (Claim 3)

[1003] The system according to claim 1, characterized in that it is operated by a civilian autonomous machine. [Explanation of symbols]

[1004] 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 converts speech data received from a speech input device into text data, Image recognition means that analyzes image data received from an image acquisition device and assigns labels based on specific events, A natural language generation means that automatically generates text based on converted text data and labeled image data, A database management system that organizes and saves generated text in a diary format, A system that includes this.

2. The system according to claim 1, which allows users to view and modify a diary generated via a user interface.

3. The system according to claim 1, characterized by performing sentiment analysis and adding emotional nuances to the generated text.

Images