system

Abstract

Provide a system. 【Solution means】 Voice sensor means for detecting the voice of a specific person, Recording means for recording the detected voice, Timestamp adding means for adding a timestamp to the voice file, Person recognition means for recognizing an object held by a person, Imaging means for imaging the recognized object, Timestamp adding means for adding a timestamp to the image file, Editing means for editing the recorded voice file and the imaged image file, Saving means for saving the edited file, Transmission means for sharing the saved file via a communication network, A system including.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, parents of the child-rearing generation and their grandparents are in a situation where it is difficult to appropriately record and share the moments of their children's growth due to their respective busy lives and physical distances. For this reason, important growth records may be lost, and the opportunity for all family members to enjoy the joy of their children's growth at the same time is decreasing. In order to solve this problem, it is necessary to create a mechanism that can easily record voices and videos in daily life and manage them centrally.

Means for Solving the Problems

[0005] This invention provides a system that includes means for recording audio using voice detection means for a specific person and for adding a timestamp to the recorded audio file. It also realizes a system that recognizes an object held in the hand using person recognition means, records that object as an image using a shooting means, and adds a timestamp to the image file. Furthermore, it improves the quality of recording and shooting by utilizing a generative AI model to remove audio noise and correct image color. It also includes a function to save edited files and automatically share the saved data via a communication network based on a schedule or event trigger. This allows for the efficient recording of important growth moments of children and their immediate sharing with family.

[0006] "Voice sensor means" refers to a device or function that can detect voices emitted from a specific person and process those signals.

[0007] "Recording means" refers to a device or program that has the function of recording detected sound as a digital file.

[0008] "Timestamping means" refers to a device or program that has the function of adding the date and time the file was created to a recorded audio file or a captured image file.

[0009] "Person recognition means" refers to an algorithm or technology for identifying a specific person and recognizing an object held in that person's hand.

[0010] "Means of capture" refers to a camera function or device for capturing an image of an object related to a person.

[0011] "Editing means" refers to a program or technology that has the function of processing recorded audio or captured images, such as noise reduction or color correction.

[0012] "Storage means" refers to a device or system that has the function of storing edited audio files or image files on a recording medium such as a database.

[0013] "Transmission means" refers to a device or program that has the function of transmitting stored audio files or image files to an external terminal or server via a communication network.

[0014] "Control means" refers to a program or system that has the function of setting a schedule or event trigger for file sharing and automatically sending the stored data at a specific time. [Brief explanation of the drawing]

[0015] [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] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in 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

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention relates to a system that automatically records a child's voice and the object they are holding, and shares this information with their family in real time. The operation of this system is described in detail below.

[0037] 1. Audio recording

[0038] First, the device constantly monitors ambient sounds and has a function to detect voices emitted by specific individuals. When the voice sensor detects a sound exceeding a certain volume level, the recording mechanism activates to record the voice in digital format, add a timestamp, and save the audio file. For example, when a child speaks their first word, the device automatically records that voice and saves it as a file.

[0039] 2. Taking the image

[0040] Next, the device has a function that uses person recognition to identify objects held by children. When a person or object is detected, the camera activates and captures an image. A timestamp is added to the image data, and it is saved as an image file. For example, it can automatically photograph and record a leaf a child is holding when they return from the park.

[0041] 3. Editing data

[0042] Recorded audio and captured images are edited on a server using a generated AI model. The editing process involves noise reduction and sound quality improvement for audio, and color correction for images. This editing improves the accuracy and quality of the recording.

[0043] 4. Sharing on social media

[0044] Finally, the server stores the edited data and uses a transmission method for sharing to automatically send it to family members via the internet through social networking platforms. Sharing is performed according to a specified schedule or event trigger, and the data is delivered to parents and grandparents in real time. For example, it is possible to set it up so that new records from the day are automatically distributed to an online group that grandparents participate in at 6 p.m. every day.

[0045] In this way, this system makes it possible to record important moments even amidst a busy daily life and share a child's growth with the whole family.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The device constantly monitors the surrounding sounds and uses a voice sensor to identify voices from specific individuals. If the voice is determined to exceed a set threshold, the recording process begins.

[0049] Step 2:

[0050] The device activates its recording mechanism and records the detected audio in digital format. A timestamp is obtained at the start of recording and added to the audio file. When recording is complete, this audio file is temporarily saved to storage.

[0051] Step 3:

[0052] The device uses a person recognition system and a camera to detect an object held by a child. Once the object is identified, it activates a camera to capture the object as an image, and saves it with a timestamp added.

[0053] Step 4:

[0054] The device uploads recorded audio files and captured image files to the server at regular intervals, attaching date and time information as metadata.

[0055] Step 5:

[0056] The server receives uploaded audio files and performs noise reduction and volume adjustment. It also processes image files with color correction and sharpening to improve data quality.

[0057] Step 6:

[0058] The server organizes the edited audio and image files and saves them to a dedicated database. Each file is tagged during saving to ensure efficient management.

[0059] Step 7:

[0060] The server automatically sends stored data to a designated user group via the SNS platform according to a predetermined schedule or event trigger conditions. After sending, a success notification is sent to the user to inform them that the data has been successfully shared.

[0061] (Example 1)

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

[0063] Modern families, with their busy daily lives, often find it difficult to capture and share their children's growth and precious moments with the whole family. Traditional methods require manually recording and editing audio and photos and sharing them individually, which is time-consuming and laborious. To solve this problem, a system is needed that automatically records, edits, and shares audio and images.

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

[0065] In this invention, the server includes acoustic detection means for sensing specific sounds, identification means for identifying objects held by a person, and processing means for removing unwanted sounds from acoustic data and performing color adjustments in image data using a generative AI model. This makes it possible for even busy families to automatically record important moments in high quality and easily share them among family members.

[0066] An "acoustic detection means" is a device that has the function of sensing ambient sounds and detecting specific sounds.

[0067] A "recording device" is a device that has the function of saving detected sounds in digital format.

[0068] A "time information assignment means" is a device that has the function of assigning time information to recorded acoustic data or image data, specifically the time when the data was generated or recorded.

[0069] An "identification device" is a device that recognizes an object held by a person and has the function of identifying that object.

[0070] An "imaging device" is a device that has the function of generating an image based on an identified object and saving it in digital format.

[0071] A "processing device" is a device that has the function of editing recorded acoustic and image data using a generation AI model or the like to improve its quality.

[0072] A "storage means" is a device that has the function of storing processed acoustic data and image data in a storage device.

[0073] A "distribution means" is a device that has the function of transmitting stored data to other devices or platforms via a communication channel.

[0074] A "generative AI model" is an artificial intelligence technique that learns by analyzing large amounts of data and then generates new data or edits existing data.

[0075] An "operating means" is a device that has the function of automatically transmitting stored data based on conditions that trigger the transmission of data.

[0076] This system automatically records a child's voice and the objects they hold, and shares this information with their family in real time. The system primarily operates through a terminal and a server.

[0077] The device constantly monitors ambient sounds using a microphone as an acoustic detection means. When sounds exceeding a certain volume or frequency are detected, the digital recorder, which is the recording means, activates and starts recording. The recorded audio data is given a timestamp function as a means of adding time information, indicating the time the sound was recorded. This makes it easy to manage the data chronologically later on.

[0078] Furthermore, the device's camera executes a person recognition algorithm as an identification tool, specifically identifying objects held by children. Once identification is complete, the camera activates as an imaging tool, capturing an image and saving it with time information added.

[0079] The server receives audio and image data transmitted from the terminal and uses a generative AI model for editing. Noise is removed from the audio data to improve sound quality. Color adjustments are applied to the image data to create a visually appealing and aesthetically pleasing record.

[0080] The edited data is stored on the server's storage system. It is then transmitted to the SNS platform via the internet using a distribution system. Distribution is automatically performed according to the schedule and events specified by the user.

[0081] For example, when a user enters a prompt such as, "Please record the first time my child says 'Mommy' and send it to Grandma in real time," these functions work together to generate and edit the necessary data, which is then quickly shared with the family. This makes it possible for even busy families to record and share important growth moments without missing them.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Sound monitoring and recording

[0085] The device uses a microphone as an acoustic detection method to constantly monitor ambient sounds. When a sound exceeding a certain volume or frequency is detected, the device activates its recording mechanism and records the sound in digital format. The input is ambient sound, and the output is acoustic data with temporal information attached. This data is temporarily stored within the device.

[0086] Step 2:

[0087] Person and object identification and image capture

[0088] The device acquires video using a camera and recognizes people and the objects they are holding using an identification mechanism. When a target is detected, the camera activates as an imaging mechanism and captures an image. The input is real-time video data, and the output is image data with time information. This image data is also stored on the device.

[0089] Step 3:

[0090] Transfer of audio and image data

[0091] The terminal transfers the stored audio and image data to the server. During this process, the data is sent to the server via the network, and the server's processing power is used to perform the next steps.

[0092] Step 4:

[0093] Editing data

[0094] The server uses a generative AI model to edit the transmitted audio and image data. Noise reduction is applied to the audio data, improving sound quality and increasing clarity. Color correction is applied to the image data, improving visibility. As a result of the editing, higher quality data is output.

[0095] Step 5:

[0096] Data storage

[0097] The server stores the edited audio and image data in its own storage system. This ensures that the data is securely stored and accessible when needed.

[0098] Step 6:

[0099] Data distribution

[0100] The server transmits the stored data to the designated SNS platform via a distribution method. The input is the stored, edited data, and the output is information shared among family members. Distribution is performed automatically according to the schedule and events set by the user. This ensures that the data is delivered to family members at the appropriate time.

[0101] (Application Example 1)

[0102] 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."

[0103] This invention aims to enhance customer engagement with families with children in physical stores, enabling the whole family to enjoy the shopping experience more. It also aims to strengthen in-store sales promotion activities and to more accurately understand customer behavior that leads to product purchases.

[0104] 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.

[0105] In this invention, the server includes voice detection means, object recognition means, and presentation means. This makes it possible to record in real time the products and objects that a child shows interest in within the store and present them to the customer through an information display device or online platform.

[0106] A "sound detection means" is a device for detecting and recognizing the sound of a specific object from surrounding sounds.

[0107] A "recording means" is a device for recording detected audio in digital format.

[0108] A "time information assignment means" is a device that assigns a timestamp to audio or image data to record the exact time of occurrence.

[0109] "Object recognition means" refers to technology for visually identifying the objects that an object possesses.

[0110] "Photography means" refers to a device used to capture an image of an identified object.

[0111] "Processing means" refers to technologies for analyzing and processing recorded audio data and captured image data to improve their quality.

[0112] "Storage means" refers to technology for securely storing processed data and retrieving it as needed.

[0113] "Sharing methods" refer to technologies for sharing information by transmitting stored data via other devices or networks.

[0114] "Presentation means" refers to technology for displaying data on an information display device within a store or on an online communication platform for visual presentation.

[0115] "Generative AI technology" refers to a technology that uses artificial intelligence to highly analyze audio and image data and perform processing such as noise reduction and color correction.

[0116] As a specific embodiment of this invention, a system is proposed to enhance customer engagement with customers accompanied by children in physical stores.

[0117] This system consists of hardware and software, including voice detection means, object recognition means, and presentation means. The server constantly monitors the sounds emitted by children using the voice detection means and records the identified sounds. At this time, time information is added to the voice data as a timestamp.

[0118] Next, object recognition means visually identify the object the child is holding, such as a toy or product. This identification utilizes a general-purpose camera module and AI technology. Subsequently, a shooting means is used to capture an image of the object, and this image is also stored as data with time information added. This contributes to sales promotion and improved customer experience.

[0119] The server analyzes and processes the recorded audio and image data using processing equipment. This process involves generative AI technology, performing noise reduction and color correction. AI models such as TENSORFLOW® are used. The processed data is managed by storage equipment and can be reused as needed.

[0120] The server further uses a sharing mechanism to display the stored data in real time via the communication network to store information display devices and online platforms. This display mechanism allows customers and staff to instantly receive information through in-store and external visual terminals.

[0121] For example, information about a specific stuffed animal that a child shows interest in can be visually provided through in-store displays and a store-specific application, allowing parents to use this information to make a purchase decision. Prompts such as "Please tell me how to edit this audio data clearly" or "Please provide a setting to automatically color-correct this image" are input into a generating AI model, which then performs data analysis and improvement.

[0122] This system allows physical stores to offer new customer experiences and effectively promote sales.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The device constantly monitors the surrounding sound environment using a voice sensor and detects specific sounds emitted by the child. The detected sounds are recorded digitally and time information is added. This generates "child's voice data" as voice input data, and its output is a digital audio file.

[0126] Step 2:

[0127] The device uses object recognition technology to identify the object the child is holding. This involves acquiring video data using a camera and analyzing the object's features based on AI technology. In this process, video data is input, and the identified object information is output.

[0128] Step 3:

[0129] The terminal captures an image of the identified object and adds time information to the image data. The captured image data is input, and a timestamped image file is output. This data is used for subsequent data processing.

[0130] Step 4:

[0131] The server uses a generative AI model to remove noise from recorded audio data and perform color correction on image data. This improves the quality of both audio and image data, making it easier to visually review. The input is raw audio and image data, and the output is the improved data.

[0132] Step 5:

[0133] The server stores the processed data and, at specific times or events, displays it on the store's information display devices via a communication network using a sharing mechanism. In this process, the stored data is treated as input, and the information displayed on the store's display or online platform is the output.

[0134] Step 6:

[0135] Based on the information presented, users can check product details and make purchases. The information received by the user is data output from the server. This step involves the specific actions the user actually takes when using the system.

[0136] 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.

[0137] This invention relates to a system that senses and records the voice of a specific person, records an object held in the hand as an image, and further recognizes the user's emotions and processes the data. The embodiments of this system are described in detail below.

[0138] 1. Recording of voice and emotions

[0139] The device constantly monitors surrounding sounds and recognizes voices emitted by specific individuals. The recognized voices are recorded digitally using a recording device. During this process, an emotion engine is used to analyze the emotions in the voice and determine the user's emotional state. For example, if it detects a child laughing happily, the emotion engine recognizes this as "joy."

[0140] 2. Capture of dynamic images

[0141] Based on the analysis results of the emotion engine, if a specific emotional state is detected, the device automatically attempts to photograph the object the person is holding. Person recognition is used, and the camera is activated at the appropriate time. The image is saved with a timestamp, and then an emotion tag added by the emotion engine is associated with it.

[0142] 3. Editing and saving data

[0143] The editing process utilizes a generative AI model to edit recorded audio files and captured image files. Specifically, it removes noise from the audio, optimizes sound quality, and performs color correction on images to improve clarity. The edited data is systematically stored in a database on a server.

[0144] 4. Emotion-based data sharing

[0145] Based on the emotion tag information contained in the stored data, the server prioritizes sending data recorded in specific emotional states to the social networking platform. For example, if a particular user sets their preferences to receive only data containing the emotion of "joy," audio files and images associated with "joy" will be sent preferentially.

[0146] This system makes it possible to more efficiently record and share children's touching moments and events that evoke special emotions. The use of an emotion engine enhances data personalization, enabling the provision of information best suited to the user.

[0147] The following describes the processing flow.

[0148] Step 1:

[0149] The device uses a voice sensor to continuously monitor ambient sounds. When voice from a specific person is detected, the emotion engine simultaneously activates, analyzing the voice to identify the user's emotional state.

[0150] Step 2:

[0151] Based on the detected user's emotional information, the device activates the recording mechanism and saves the audio as a digital file. The emotional data generated during recording is added to the audio file as metadata, and a timestamp is also added.

[0152] Step 3:

[0153] The device determines whether to photograph an object held in a person's hand based on the emotion recognition result. If a specific emotional state, such as "joy," is detected, the camera automatically activates and captures an image.

[0154] Step 4:

[0155] The captured images are saved as image files, containing emotional information and timestamps. At this time, the images are tagged based on emotions, making it possible to identify specific emotional states.

[0156] Step 5:

[0157] The device uploads recorded audio files and captured image files to the server. The server receives these files and performs editing to improve their quality, such as noise reduction and color correction.

[0158] Step 6:

[0159] Edited files are saved to a database on the server and managed efficiently. During saving, they are categorized based on sentiment tags, improving searchability.

[0160] Step 7:

[0161] If the server is configured to prioritize the transmission of data with a specific emotional state, it will automatically send that data to the SNS platform. After transmission, data that matches the user's set criteria will be shared with the user in real time.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] There is a challenge in efficiently recording important moments related to specific emotions or actions and sharing them in a more personalized way. This problem cannot be adequately addressed with traditional manual recording or photography, and the process of selecting and sharing data is cumbersome.

[0165] 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.

[0166] In this invention, the server includes emotion analysis means for analyzing emotions within speech, editing means for removing noise from speech and performing color correction on images using a generative model, and transmission means for transmitting stored data over a communication network. This enables the recording and personalized sharing of data based on emotions and specific situations.

[0167] "Sound sensor means" refers to a device or method for detecting ambient sounds, which has the function of identifying sounds from a specific sound source.

[0168] "Recording means" refers to a device or method for storing audio data or image data in digital format, such that the content is recorded in a way that allows for later playback.

[0169] "Emotional analysis means" refers to a device or method for analyzing emotions from acquired voice data, and for determining the user's emotional state by evaluating the characteristics of the voice.

[0170] "Person identification means" refers to a device or method for recognizing an object held in the hand of a specific person, and uses image processing technology to identify the person and their actions.

[0171] "Photography means" refers to a device or method for taking an image, which has the function of capturing a specific moment and saving it as digital data.

[0172] "Tagging means" refers to a device or method for adding emotional and temporal information related to audio data and image data.

[0173] "Generative modeling" refers to editing techniques that use machine learning methods to perform noise reduction and color correction, aiming to improve data quality through the model.

[0174] "Editing means" refers to a device or method for processing audio and image data to remove noise and correct color, thereby improving the quality of the data.

[0175] "Storage means" refers to a device or method for systematically accumulating edited data and keeping it accessible as needed.

[0176] "Transmission means" refers to a device or method for transmitting stored data to an external party via a communication network, enabling the effective sharing of data.

[0177] This invention is a system that analyzes emotions based on voice, records and edits images and audio according to those emotions, and selectively shares them. The configuration for implementing this system is described in detail below.

[0178] Voice detection and emotion analysis

[0179] The terminal constantly monitors surrounding sounds using a voice sensor. This voice sensor uses a voice input device such as a microphone. When the voice of a specific person is recognized, an emotion analysis means analyzes the voice and estimates the user's emotional state. For example, it analyzes the pitch, intonation, and speed of the voice to identify emotions such as "joy" or "surprise."

[0180] Image capture and tagging

[0181] When a specific emotion is detected, the device automatically activates the camera and uses person identification to photograph the object the person is holding. At this time, a timestamp and emotion tag are added to the captured image. The emotion tagging function works in conjunction with this data.

[0182] Editing and saving data

[0183] The server has editing capabilities that remove noise from recorded audio using a generative AI model and perform color correction on images. These editing processes utilize advanced audio processing algorithms and image processing techniques. The edited data is efficiently organized and stored in the server's storage.

[0184] Selective data sharing

[0185] Based on specific emotions set by the user, the server organizes the stored data and prioritizes sending it to the platform specified by the user. The transmission means enables this via a communication network.

[0186] Specific example

[0187] For example, if parents want to record their child's first birthday, the system analyzes the child's smiles and cheers emotionally and tags them as "joy." This data is then noise-free, color-corrected, and stored in the cloud, where it can be easily shared on social media as needed.

[0188] Example of a prompt

[0189] "How can we implement measures to record and share the touching moments of a child's birthday party on social media?"

[0190] This allows users to capture and selectively share special moments without missing a beat.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The device continuously monitors ambient sounds using voice sensors. The input is the entire ambient noise, from which the voice of a specific person is extracted. This audio data is processed by speech recognition software, which recognizes the speech of the specific person and records it as digital data. The output is the digital audio data of the specific person.

[0194] Step 2:

[0195] The server transmits the recorded audio data to an emotion analysis system. Here, the user's emotions are estimated by analyzing the intonation, speed, pitch, etc., of the voice. The input is digital audio data of a specific person, and the output is estimated emotion data based on that audio. For example, if the audio contains high pitch and laughter, it will be determined to represent "joy."

[0196] Step 3:

[0197] The terminal uses emotion data received from the server to capture images of scenes where those emotions are expressed, employing a person identification method. The input consists of estimated emotion data and subject information. The camera device activates and captures people or objects with an appropriate composition. The output is digital image data with a timestamp and emotion tag.

[0198] Step 4:

[0199] The server uses a generative AI model to denoise recorded audio data and color-correct captured image data. The input consists of noisy audio data and uncorrected image data. The audio and image processing models improve the quality of the data. The output consists of denoised audio data with improved sound quality and color-corrected, high-resolution image data.

[0200] Step 5:

[0201] The server systematically stores the edited data and transmits the appropriate data via the communication network based on user specifications. Inputs include edited audio and image data, and user-defined sharing criteria. The data management system within the server organizes them appropriately and selectively shares the specified data as needed. Output is the transmission of the selected data to the platform.

[0202] (Application Example 2)

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

[0204] Traditional food delivery services have struggled to provide suggestions based on individual user emotions and preferences. While recommendations based on past order history and reviews are offered, they fail to provide optimal suggestions that take into account the user's real-time emotional state. Therefore, there is a need for a system that automates the suggestion of meals best suited to the user's current mood, thereby improving user satisfaction.

[0205] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0206] In this invention, the server includes a voice detection device for detecting the utterance of a specific target, a recording device for recording the detected utterance, and a time information addition device for adding time information to the voice data. This makes it possible to determine the user's emotional state in real time and generate personalized recommendation information.

[0207] A "voice detection device" is a device used to detect the vocalizations of a target in real time.

[0208] A "recording device" is a device for saving detected vocalizations in digital format.

[0209] A "time information adding device" is a device used to add specific time information about the recording date to audio data or image data.

[0210] An "object recognition device" is a device used to recognize an object that a person is holding in their hand.

[0211] An "image acquisition device" is a device used to acquire recognized objects as image data.

[0212] An "editing device" is a device used to process and optimize recorded audio data or acquired image data.

[0213] A "memory device" is a device for systematically storing edited data.

[0214] A "transmission device" is a device that provides stored data to external parties via an information network.

[0215] A "recommendation device" is a device that generates personalized recommendation information based on the emotional information of the provided data, tailored to the user's preferences.

[0216] This invention provides a system that analyzes user emotions in real time and offers personalized recommendations as part of a food delivery service. This system mainly consists of a server and terminals.

[0217] The terminal constantly detects and records the user's speech through a voice detection device. The recorded audio is time-stamped by a time information device and sent to the server in real time. The server analyzes the received audio data and stores it digitally in a recording device. For sentiment analysis, "Google® Cloud Speech-to-Text API" and "Amazon Web Services Comprehend" are used to detect emotions from the user's speech.

[0218] Next, the server uses an object recognition device and an image acquisition device to record the object held by the user as an image. This data is optimized using an editing device, and noise reduction and color correction are performed using generative AI models such as "OpenCV," "Stable Diffusion," and "Hugging Face Transformers."

[0219] The edited data is stored in a memory device and then provided externally via an information network by a transmission device. At this time, a recommendation device generates food delivery recommendations optimized for the user based on emotional information.

[0220] As a concrete example, consider a scenario where a user is planning a party with friends over the weekend. If the device senses the user's enjoyment and excitement from their laughter and conversation, the server will automatically recommend dishes that have received high ratings in the past. The following is an example of a prompt message.

[0221] "What food delivery service would be best for our weekend party?"

[0222] "What restaurant would you recommend to recreate the fun we experienced together with our friends?"

[0223] "Based on my previous experience ordering a delicious pizza, please suggest a new dessert."

[0224] The above describes the detailed configuration and operating procedure for carrying out the present invention. This configuration makes it possible to suggest meals based on the user's current emotions, significantly improving user satisfaction.

[0225] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0226] Step 1:

[0227] The terminal uses a voice detection device to constantly monitor the sounds around the user. The voice signals received as input act as triggers for detecting specific vocalizations. The detected voice signals are sent to a recording device and stored in digital format. As output, a time information device generates digital voice data with a timestamp.

[0228] Step 2:

[0229] The server receives the audio data and converts it to text using the Google Cloud Speech-to-Text API. It extracts the necessary text information from the digital audio data received as input and performs sentiment analysis using Amazon Web Services' Comprehend. The output is tag information indicating the user's emotional state.

[0230] Step 3:

[0231] The server uses an object recognition device to identify the object the user is holding. Using emotion information obtained from emotion analysis as input, the object is photographed by an image acquisition device. As output, image data recorded with a timestamp and an emotion tag are generated.

[0232] Step 4:

[0233] The server uses an editing device to optimize image and audio data. Digital audio and image data are acquired as input, and color correction of the images is performed using "OpenCV," while noise reduction of the audio data is performed using "Stable Diffusion" and "Hugging Face Transformers." The output consists of optimized image and audio data after editing.

[0234] Step 5:

[0235] The server stores data optimized for storage. The input data consists of edited audio and image data. The stored data serves as the foundation for later delivery via a transmission device.

[0236] Step 6:

[0237] The server uses a transmission device to prepare to send the stored data to the information network. At this time, it utilizes a "generative AI model" based on sentiment tags to generate personalized recommendation information and create prompt messages to provide to the user. The output is personalized recommendation information presented to the user.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] [Second Embodiment]

[0242] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0243] 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.

[0244] 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).

[0245] 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.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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".

[0254] This invention relates to a system that automatically records a child's voice and the object they are holding, and shares this information with their family in real time. The operation of this system is described in detail below.

[0255] 1. Audio recording

[0256] First, the device constantly monitors ambient sounds and has a function to detect voices emitted by specific individuals. When the voice sensor detects a sound exceeding a certain volume level, the recording mechanism activates to record the voice in digital format, add a timestamp, and save the audio file. For example, when a child speaks their first word, the device automatically records that voice and saves it as a file.

[0257] 2. Taking the image

[0258] Next, the device has a function that uses person recognition to identify objects held by children. When a person or object is detected, the camera activates and captures an image. A timestamp is added to the image data, and it is saved as an image file. For example, it can automatically photograph and record a leaf a child is holding when they return from the park.

[0259] 3. Editing data

[0260] Recorded audio and captured images are edited on a server using a generated AI model. The editing process involves noise reduction and sound quality improvement for audio, and color correction for images. This editing improves the accuracy and quality of the recording.

[0261] 4. Sharing on social media

[0262] Finally, the server stores the edited data and uses a transmission method for sharing to automatically send it to family members via the internet through social networking platforms. Sharing is performed according to a specified schedule or event trigger, and the data is delivered to parents and grandparents in real time. For example, it is possible to set it up so that new records from the day are automatically distributed to an online group that grandparents participate in at 6 p.m. every day.

[0263] In this way, this system makes it possible to record important moments even amidst a busy daily life and share a child's growth with the whole family.

[0264] The following describes the processing flow.

[0265] Step 1:

[0266] The device constantly monitors the surrounding sounds and uses a voice sensor to identify voices from specific individuals. If the voice is determined to exceed a set threshold, the recording process begins.

[0267] Step 2:

[0268] The device activates its recording mechanism and records the detected audio in digital format. A timestamp is obtained at the start of recording and added to the audio file. When recording is complete, this audio file is temporarily saved to storage.

[0269] Step 3:

[0270] The device uses a person recognition system and a camera to detect an object held by a child. Once the object is identified, it activates a camera to capture the object as an image, and saves it with a timestamp added.

[0271] Step 4:

[0272] The device uploads recorded audio files and captured image files to the server at regular intervals, attaching date and time information as metadata.

[0273] Step 5:

[0274] The server receives uploaded audio files and performs noise reduction and volume adjustment. It also processes image files with color correction and sharpening to improve data quality.

[0275] Step 6:

[0276] The server sorts out the edited voice and image files and saves them in a dedicated database. When saving, each file is tagged to make it efficiently manageable.

[0277] Step 7:

[0278] The server automatically sends the saved data to a specified user group via the SNS platform according to a pre - determined schedule or event trigger condition. After sending, a success notification is sent to the user to convey that the data has been successfully shared.

[0279] (Example 1)

[0280] 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".

[0281] In modern families' busy daily lives, it is difficult to record and share the growth and precious moments of children without missing them among all family members. In the conventional method, it is necessary to manually record and edit voices and photos and share them individually, which takes a lot of time and effort. To solve such problems, a system that automatically records, edits, and shares voices and images is needed.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0283] In this invention, the server includes an acoustic detection means for detecting specific acoustics, an identification means for identifying an object held by a person, and a processing means for removing unnecessary sounds from acoustic data and performing color adjustment in image data using a generation AI model. Thereby, even busy families can automatically record important moments with high quality and easily share them among family members.

[0284] The "acoustic detection means" is a device having a function of sensing ambient acoustics and detecting specific sounds.

[0285] The "recording means" is a device having a function of storing the detected sound in digital form.

[0286] The "time information adding means" is a device having a function of adding time information of the time of generation or recording to the recorded sound data or image data.

[0287] The "identification means" is a device having a function of recognizing an object held by a person and specifying the object.

[0288] The "imaging means" is a device having a function of generating an image based on the identified object and storing it in digital form.

[0289] The "processing means" is a device having a function of editing the recorded sound data and image data using a generation AI model or the like to improve the quality.

[0290] The "storage means" is a device having a function of storing the processed sound data and image data in a storage device.

[0291] The "distribution means" is a device having a function of transmitting the stored data to other devices or platforms via a communication path.

[0292] The "generation AI model" is an artificial intelligence method that learns by analyzing a large amount of data and generates new data or edits existing data.

[0293] The "operation means" is a device having a function of automatically transmitting the stored data based on conditions that trigger the transmission of data.

[0294] This system is for automatically recording a child's voice and the object in their hand and sharing it with the family in real time. The system mainly functions through a terminal and a server.

[0295] The device constantly monitors ambient sounds using a microphone as an acoustic detection means. When sounds exceeding a certain volume or frequency are detected, the digital recorder, which is the recording means, activates and starts recording. The recorded audio data is given a timestamp function as a means of adding time information, indicating the time the sound was recorded. This makes it easy to manage the data chronologically later on.

[0296] Furthermore, the device's camera executes a person recognition algorithm as an identification tool, specifically identifying objects held by children. Once identification is complete, the camera activates as an imaging tool, capturing an image and saving it with time information added.

[0297] The server receives audio and image data transmitted from the terminal and uses a generative AI model for editing. Noise is removed from the audio data to improve sound quality. Color adjustments are applied to the image data to create a visually appealing and aesthetically pleasing record.

[0298] The edited data is stored on the server's storage system. It is then transmitted to the SNS platform via the internet using a distribution system. Distribution is automatically performed according to the schedule and events specified by the user.

[0299] For example, when a user enters a prompt such as, "Please record the first time my child says 'Mommy' and send it to Grandma in real time," these functions work together to generate and edit the necessary data, which is then quickly shared with the family. This makes it possible for even busy families to record and share important growth moments without missing them.

[0300] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0301] Step 1:

[0302] Sound monitoring and recording

[0303] The terminal uses a microphone as the acoustic detection means and constantly monitors the surrounding sounds. When a sound exceeding a specific volume or frequency is detected, the terminal activates the recording means and records the sound in digital format. The input is the surrounding acoustics, and the output is the acoustic data with time information attached. This data is temporarily stored in the terminal.

[0304] Step 2:

[0305] Identification of people and objects and image capture

[0306] The terminal uses a camera to acquire video and uses the identification means to recognize people and the objects they hold. When the target is detected, the camera operates as the imaging means and captures an image. The input is real-time video data, and the output is the image data with time information. This image data is also stored in the terminal.

[0307] Step 3:

[0308] Transfer of recorded data and image data

[0309] The terminal transfers the stored acoustic data and image data to the server. At this time, the data is sent to the server via the network, and the processing of the next step is performed using the processing capabilities of the server.

[0310] Step 4:

[0311] Data editing

[0312] The server edits the transferred acoustic data and image data using the generated AI model. Noise removal is performed on the acoustic data, improving the sound quality and enabling the clarification of the voice. Color tone correction is applied to the image data, improving the visibility. As a result of the editing, higher-quality data is output.

[0313] Step 5:

[0314] Data storage

[0315] The server stores the edited audio and image data in its own storage system. This ensures that the data is securely stored and accessible when needed.

[0316] Step 6:

[0317] Data distribution

[0318] The server transmits the stored data to the designated SNS platform via a distribution method. The input is the stored, edited data, and the output is information shared among family members. Distribution is performed automatically according to the schedule and events set by the user. This ensures that the data is delivered to family members at the appropriate time.

[0319] (Application Example 1)

[0320] 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."

[0321] This invention aims to enhance customer engagement with families with children in physical stores, enabling the whole family to enjoy the shopping experience more. It also aims to strengthen in-store sales promotion activities and to more accurately understand customer behavior that leads to product purchases.

[0322] 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.

[0323] In this invention, the server includes voice detection means, object recognition means, and presentation means. This makes it possible to record in real time the products and objects that a child shows interest in within the store and present them to the customer through an information display device or online platform.

[0324] A "sound detection means" is a device for detecting and recognizing the sound of a specific object from surrounding sounds.

[0325] A "recording means" is a device for recording detected audio in digital format.

[0326] A "time information assignment means" is a device that assigns a timestamp to audio or image data to record the exact time of occurrence.

[0327] "Object recognition means" refers to technology for visually identifying the objects that an object possesses.

[0328] "Photography means" refers to a device used to capture an image of an identified object.

[0329] "Processing means" refers to technologies for analyzing and processing recorded audio data and captured image data to improve their quality.

[0330] "Storage means" refers to technology for securely storing processed data and retrieving it as needed.

[0331] "Sharing methods" refer to technologies for sharing information by transmitting stored data via other devices or networks.

[0332] "Presentation means" refers to technology for displaying data on an information display device within a store or on an online communication platform for visual presentation.

[0333] "Generative AI technology" refers to a technology that uses artificial intelligence to highly analyze audio and image data and perform processing such as noise reduction and color correction.

[0334] As a specific embodiment of this invention, a system is proposed to enhance customer engagement with customers accompanied by children in physical stores.

[0335] This system consists of hardware and software, including voice detection means, object recognition means, and presentation means. The server constantly monitors the sounds emitted by children using the voice detection means and records the identified sounds. At this time, time information is added to the voice data as a timestamp.

[0336] Next, object recognition means visually identify the object the child is holding, such as a toy or product. This identification utilizes a general-purpose camera module and AI technology. Subsequently, a shooting means is used to capture an image of the object, and this image is also stored as data with time information added. This contributes to sales promotion and improved customer experience.

[0337] The server analyzes and processes the recorded audio and image data using processing tools. This process involves generative AI technology, performing noise reduction and color correction. TensorFlow and similar AI models are used. The processed data is managed by storage tools and can be reused as needed.

[0338] The server further uses a sharing mechanism to display the stored data in real time via the communication network to store information display devices and online platforms. This display mechanism allows customers and staff to instantly receive information through in-store and external visual terminals.

[0339] For example, information about a specific stuffed animal that a child shows interest in can be visually provided through in-store displays and a store-specific application, allowing parents to use this information to make a purchase decision. Prompts such as "Please tell me how to edit this audio data clearly" or "Please provide a setting to automatically color-correct this image" are input into a generating AI model, which then performs data analysis and improvement.

[0340] This system allows physical stores to offer new customer experiences and effectively promote sales.

[0341] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0342] Step 1:

[0343] The device constantly monitors the surrounding sound environment using a voice sensor and detects specific sounds emitted by the child. The detected sounds are recorded digitally and time information is added. This generates "child's voice data" as voice input data, and its output is a digital audio file.

[0344] Step 2:

[0345] The device uses object recognition technology to identify the object the child is holding. This involves acquiring video data using a camera and analyzing the object's features based on AI technology. In this process, video data is input, and the identified object information is output.

[0346] Step 3:

[0347] The terminal captures an image of the identified object and adds time information to the image data. The captured image data is input, and a timestamped image file is output. This data is used for subsequent data processing.

[0348] Step 4:

[0349] The server uses a generative AI model to remove noise from recorded audio data and perform color correction on image data. This improves the quality of both audio and image data, making it easier to visually review. The input is raw audio and image data, and the output is the improved data.

[0350] Step 5:

[0351] The server stores the processed data and, at specific times or events, displays it on the store's information display devices via a communication network using a sharing mechanism. In this process, the stored data is treated as input, and the information displayed on the store's display or online platform is the output.

[0352] Step 6:

[0353] Based on the information presented, users can check product details and make purchases. The information received by the user is data output from the server. This step involves the specific actions the user actually takes when using the system.

[0354] 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.

[0355] This invention relates to a system that senses and records the voice of a specific person, records an object held in the hand as an image, and further recognizes the user's emotions and processes the data. The embodiments of this system are described in detail below.

[0356] 1. Recording of voice and emotions

[0357] The device constantly monitors surrounding sounds and recognizes voices emitted by specific individuals. The recognized voices are recorded digitally using a recording device. During this process, an emotion engine is used to analyze the emotions in the voice and determine the user's emotional state. For example, if it detects a child laughing happily, the emotion engine recognizes this as "joy."

[0358] 2. Capture of dynamic images

[0359] Based on the analysis results of the emotion engine, if a specific emotional state is detected, the device automatically attempts to photograph the object the person is holding. Person recognition is used, and the camera is activated at the appropriate time. The image is saved with a timestamp, and then an emotion tag added by the emotion engine is associated with it.

[0360] 3. Editing and saving data

[0361] The editing process utilizes a generative AI model to edit recorded audio files and captured image files. Specifically, it removes noise from the audio, optimizes sound quality, and performs color correction on images to improve clarity. The edited data is systematically stored in a database on a server.

[0362] 4. Emotion-based data sharing

[0363] Based on the emotion tag information contained in the stored data, the server prioritizes sending data recorded in specific emotional states to the social networking platform. For example, if a particular user sets their preferences to receive only data containing the emotion of "joy," audio files and images associated with "joy" will be sent preferentially.

[0364] This system makes it possible to more efficiently record and share children's touching moments and events that evoke special emotions. The use of an emotion engine enhances data personalization, enabling the provision of information best suited to the user.

[0365] The following describes the processing flow.

[0366] Step 1:

[0367] The device uses a voice sensor to continuously monitor ambient sounds. When voice from a specific person is detected, the emotion engine simultaneously activates, analyzing the voice to identify the user's emotional state.

[0368] Step 2:

[0369] Based on the detected user's emotional information, the device activates the recording mechanism and saves the audio as a digital file. The emotional data generated during recording is added to the audio file as metadata, and a timestamp is also added.

[0370] Step 3:

[0371] The device determines whether to photograph an object held in a person's hand based on the emotion recognition result. If a specific emotional state, such as "joy," is detected, the camera automatically activates and captures an image.

[0372] Step 4:

[0373] The captured images are saved as image files, containing emotional information and timestamps. At this time, the images are tagged based on emotions, making it possible to identify specific emotional states.

[0374] Step 5:

[0375] The device uploads recorded audio files and captured image files to the server. The server receives these files and performs editing to improve their quality, such as noise reduction and color correction.

[0376] Step 6:

[0377] Edited files are saved to a database on the server and managed efficiently. During saving, they are categorized based on sentiment tags, improving searchability.

[0378] Step 7:

[0379] If the server is configured to prioritize the transmission of data with a specific emotional state, it will automatically send that data to the SNS platform. After transmission, data that matches the user's set criteria will be shared with the user in real time.

[0380] (Example 2)

[0381] 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".

[0382] There is a challenge in efficiently recording important moments related to specific emotions or actions and sharing them in a more personalized way. This problem cannot be adequately addressed with traditional manual recording or photography, and the process of selecting and sharing data is cumbersome.

[0383] 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.

[0384] In this invention, the server includes emotion analysis means for analyzing emotions within speech, editing means for removing noise from speech and performing color correction on images using a generative model, and transmission means for transmitting stored data over a communication network. This enables the recording and personalized sharing of data based on emotions and specific situations.

[0385] "Sound sensor means" refers to a device or method for detecting ambient sounds, which has the function of identifying sounds from a specific sound source.

[0386] "Recording means" refers to a device or method for storing audio data or image data in digital format, such that the content is recorded in a way that allows for later playback.

[0387] "Emotional analysis means" refers to a device or method for analyzing emotions from acquired voice data, and for determining the user's emotional state by evaluating the characteristics of the voice.

[0388] "Person identification means" refers to a device or method for recognizing an object held in the hand of a specific person, and uses image processing technology to identify the person and their actions.

[0389] "Photography means" refers to a device or method for taking an image, which has the function of capturing a specific moment and saving it as digital data.

[0390] "Tagging means" refers to a device or method for adding emotional and temporal information related to audio data and image data.

[0391] "Generative modeling" refers to editing techniques that use machine learning methods to perform noise reduction and color correction, aiming to improve data quality through the model.

[0392] "Editing means" refers to a device or method for processing audio and image data to remove noise and correct color, thereby improving the quality of the data.

[0393] "Storage means" refers to a device or method for systematically accumulating edited data and keeping it accessible as needed.

[0394] "Transmission means" refers to a device or method for transmitting stored data to an external party via a communication network, enabling the effective sharing of data.

[0395] This invention is a system that analyzes emotions based on voice, records and edits images and audio according to those emotions, and selectively shares them. The configuration for implementing this system is described in detail below.

[0396] Voice detection and emotion analysis

[0397] The terminal constantly monitors surrounding sounds using a voice sensor. This voice sensor uses a voice input device such as a microphone. When the voice of a specific person is recognized, an emotion analysis means analyzes the voice and estimates the user's emotional state. For example, it analyzes the pitch, intonation, and speed of the voice to identify emotions such as "joy" or "surprise."

[0398] Image capture and tagging

[0399] When a specific emotion is detected, the device automatically activates the camera and uses person identification to photograph the object the person is holding. At this time, a timestamp and emotion tag are added to the captured image. The emotion tagging function works in conjunction with this data.

[0400] Editing and saving data

[0401] The server has editing capabilities that remove noise from recorded audio using a generative AI model and perform color correction on images. These editing processes utilize advanced audio processing algorithms and image processing techniques. The edited data is efficiently organized and stored in the server's storage.

[0402] Selective data sharing

[0403] Based on specific emotions set by the user, the server organizes the stored data and prioritizes sending it to the platform specified by the user. The transmission means enables this via a communication network.

[0404] Specific example

[0405] For example, if parents want to record their child's first birthday, the system analyzes the child's smiles and cheers emotionally and tags them as "joy." This data is then noise-free, color-corrected, and stored in the cloud, where it can be easily shared on social media as needed.

[0406] Example of a prompt

[0407] "How can we implement measures to record and share the touching moments of a child's birthday party on social media?"

[0408] This allows users to capture and selectively share special moments without missing a beat.

[0409] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0410] Step 1:

[0411] The device continuously monitors ambient sounds using voice sensors. The input is the entire ambient noise, from which the voice of a specific person is extracted. This audio data is processed by speech recognition software, which recognizes the speech of the specific person and records it as digital data. The output is the digital audio data of the specific person.

[0412] Step 2:

[0413] The server transmits the recorded audio data to an emotion analysis system. Here, the user's emotions are estimated by analyzing the intonation, speed, pitch, etc., of the voice. The input is digital audio data of a specific person, and the output is estimated emotion data based on that audio. For example, if the audio contains high pitch and laughter, it will be determined to represent "joy."

[0414] Step 3:

[0415] The terminal uses emotion data received from the server to capture images of scenes where those emotions are expressed, employing a person identification method. The input consists of estimated emotion data and subject information. The camera device activates and captures people or objects with an appropriate composition. The output is digital image data with a timestamp and emotion tag.

[0416] Step 4:

[0417] The server uses a generative AI model to denoise recorded audio data and color-correct captured image data. The input consists of noisy audio data and uncorrected image data. The audio and image processing models improve the quality of the data. The output consists of denoised audio data with improved sound quality and color-corrected, high-resolution image data.

[0418] Step 5:

[0419] The server systematically stores the edited data and transmits the appropriate data via the communication network based on user specifications. Inputs include edited audio and image data, and user-defined sharing criteria. The data management system within the server organizes them appropriately and selectively shares the specified data as needed. Output is the transmission of the selected data to the platform.

[0420] (Application Example 2)

[0421] 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."

[0422] Traditional food delivery services have struggled to provide suggestions based on individual user emotions and preferences. While recommendations based on past order history and reviews are offered, they fail to provide optimal suggestions that take into account the user's real-time emotional state. Therefore, there is a need for a system that automates the suggestion of meals best suited to the user's current mood, thereby improving user satisfaction.

[0423] 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.

[0424] In this invention, the server includes a voice detection device for detecting the utterance of a specific target, a recording device for recording the detected utterance, and a time information addition device for adding time information to the voice data. This makes it possible to determine the user's emotional state in real time and generate personalized recommendation information.

[0425] A "voice detection device" is a device used to detect the vocalizations of a target in real time.

[0426] A "recording device" is a device for saving detected vocalizations in digital format.

[0427] A "time information adding device" is a device used to add specific time information about the recording date to audio data or image data.

[0428] An "object recognition device" is a device used to recognize an object that a person is holding in their hand.

[0429] An "image acquisition device" is a device used to acquire recognized objects as image data.

[0430] An "editing device" is a device used to process and optimize recorded audio data or acquired image data.

[0431] A "memory device" is a device for systematically storing edited data.

[0432] A "transmission device" is a device that provides stored data to external parties via an information network.

[0433] A "recommendation device" is a device that generates personalized recommendation information based on the emotional information of the provided data, tailored to the user's preferences.

[0434] This invention provides a system that analyzes user emotions in real time and offers personalized recommendations as part of a food delivery service. This system mainly consists of a server and terminals.

[0435] The terminal constantly detects and records the user's speech through a voice detection device. The recorded audio is time-stamped by a time information device and sent to the server in real time. The server analyzes the received audio data and stores it digitally in a recording device. For sentiment analysis, "Google Cloud Speech-to-Text API" and "Amazon Web Services Comprehend" are used to detect emotions from the user's speech.

[0436] Next, the server uses an object recognition device and an image acquisition device to record the object held by the user as an image. This data is optimized using an editing device, and noise reduction and color correction are performed using generative AI models such as "OpenCV," "Stable Diffusion," and "Hugging Face Transformers."

[0437] The edited data is stored in a memory device and then provided externally via an information network by a transmission device. At this time, a recommendation device generates food delivery recommendations optimized for the user based on emotional information.

[0438] As a concrete example, consider a scenario where a user is planning a party with friends over the weekend. If the device senses the user's enjoyment and excitement from their laughter and conversation, the server will automatically recommend dishes that have received high ratings in the past. The following is an example of a prompt message.

[0439] "What food delivery service would be best for our weekend party?"

[0440] "What restaurant would you recommend to recreate the fun we experienced together with our friends?"

[0441] "Based on my previous experience ordering a delicious pizza, please suggest a new dessert."

[0442] The above describes the detailed configuration and operating procedure for carrying out the present invention. This configuration makes it possible to suggest meals based on the user's current emotions, significantly improving user satisfaction.

[0443] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0444] Step 1:

[0445] The terminal uses a voice detection device to constantly monitor the sounds around the user. The voice signals received as input act as triggers for detecting specific vocalizations. The detected voice signals are sent to a recording device and stored in digital format. As output, a time information device generates digital voice data with a timestamp.

[0446] Step 2:

[0447] The server receives the audio data and converts it to text using the Google Cloud Speech-to-Text API. It extracts the necessary text information from the digital audio data received as input and performs sentiment analysis using Amazon Web Services' Comprehend. The output is tag information indicating the user's emotional state.

[0448] Step 3:

[0449] The server uses an object recognition device to identify the object the user is holding. Using emotion information obtained from emotion analysis as input, the object is photographed by an image acquisition device. As output, image data recorded with a timestamp and an emotion tag are generated.

[0450] Step 4:

[0451] The server uses an editing device to optimize image and audio data. Digital audio and image data are acquired as input, and color correction of the images is performed using "OpenCV," while noise reduction of the audio data is performed using "Stable Diffusion" and "Hugging Face Transformers." The output consists of optimized image and audio data after editing.

[0452] Step 5:

[0453] The server stores data optimized for storage. The input data consists of edited audio and image data. The stored data serves as the foundation for later delivery via a transmission device.

[0454] Step 6:

[0455] The server uses a transmission device to prepare to send the stored data to the information network. At this time, it utilizes a "generative AI model" based on sentiment tags to generate personalized recommendation information and create prompt messages to provide to the user. The output is personalized recommendation information presented to the user.

[0456] 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.

[0457] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0458] 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.

[0459] [Third Embodiment]

[0460] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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".

[0472] This invention relates to a system that automatically records a child's voice and the object they are holding, and shares this information with their family in real time. The operation of this system is described in detail below.

[0473] 1. Audio recording

[0474] First, the device constantly monitors ambient sounds and has a function to detect voices emitted by specific individuals. When the voice sensor detects a sound exceeding a certain volume level, the recording mechanism activates to record the voice in digital format, add a timestamp, and save the audio file. For example, when a child speaks their first word, the device automatically records that voice and saves it as a file.

[0475] 2. Taking the image

[0476] Next, the device has a function that uses person recognition to identify objects held by children. When a person or object is detected, the camera activates and captures an image. A timestamp is added to the image data, and it is saved as an image file. For example, it can automatically photograph and record a leaf a child is holding when they return from the park.

[0477] 3. Editing data

[0478] Recorded audio and captured images are edited on a server using a generated AI model. The editing process involves noise reduction and sound quality improvement for audio, and color correction for images. This editing improves the accuracy and quality of the recording.

[0479] 4. Sharing on social media

[0480] Finally, the server stores the edited data and uses a transmission method for sharing to automatically send it to family members via the internet through social networking platforms. Sharing is performed according to a specified schedule or event trigger, and the data is delivered to parents and grandparents in real time. For example, it is possible to set it up so that new records from the day are automatically distributed to an online group that grandparents participate in at 6 p.m. every day.

[0481] In this way, this system makes it possible to record important moments even amidst a busy daily life and share a child's growth with the whole family.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] The device constantly monitors the surrounding sounds and uses a voice sensor to identify voices from specific individuals. If the voice is determined to exceed a set threshold, the recording process begins.

[0485] Step 2:

[0486] The device activates its recording mechanism and records the detected audio in digital format. A timestamp is obtained at the start of recording and added to the audio file. When recording is complete, this audio file is temporarily saved to storage.

[0487] Step 3:

[0488] The device uses a person recognition system and a camera to detect an object held by a child. Once the object is identified, it activates a camera to capture the object as an image, and saves it with a timestamp added.

[0489] Step 4:

[0490] The device uploads recorded audio files and captured image files to the server at regular intervals, attaching date and time information as metadata.

[0491] Step 5:

[0492] The server receives uploaded audio files and performs noise reduction and volume adjustment. It also processes image files with color correction and sharpening to improve data quality.

[0493] Step 6:

[0494] The server organizes the edited audio and image files and saves them to a dedicated database. Each file is tagged during saving to ensure efficient management.

[0495] Step 7:

[0496] The server automatically sends stored data to a designated user group via the SNS platform according to a predetermined schedule or event trigger conditions. After sending, a success notification is sent to the user to inform them that the data has been successfully shared.

[0497] (Example 1)

[0498] 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."

[0499] Modern families, with their busy daily lives, often find it difficult to capture and share their children's growth and precious moments with the whole family. Traditional methods require manually recording and editing audio and photos and sharing them individually, which is time-consuming and laborious. To solve this problem, a system is needed that automatically records, edits, and shares audio and images.

[0500] 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.

[0501] In this invention, the server includes acoustic detection means for sensing specific sounds, identification means for identifying objects held by a person, and processing means for removing unwanted sounds from acoustic data and performing color adjustments in image data using a generative AI model. This makes it possible for even busy families to automatically record important moments in high quality and easily share them among family members.

[0502] An "acoustic detection means" is a device that has the function of sensing ambient sounds and detecting specific sounds.

[0503] A "recording device" is a device that has the function of saving detected sounds in digital format.

[0504] A "time information assignment means" is a device that has the function of assigning time information to recorded acoustic data or image data, specifically the time when the data was generated or recorded.

[0505] An "identification device" is a device that recognizes an object held by a person and has the function of identifying that object.

[0506] An "imaging device" is a device that has the function of generating an image based on an identified object and saving it in digital format.

[0507] A "processing device" is a device that has the function of editing recorded acoustic and image data using a generation AI model or the like to improve its quality.

[0508] A "storage means" is a device that has the function of storing processed acoustic data and image data in a storage device.

[0509] A "distribution means" is a device that has the function of transmitting stored data to other devices or platforms via a communication channel.

[0510] A "generative AI model" is an artificial intelligence technique that learns by analyzing large amounts of data and then generates new data or edits existing data.

[0511] An "operating means" is a device that has the function of automatically transmitting stored data based on conditions that trigger the transmission of data.

[0512] This system automatically records a child's voice and the objects they hold, and shares this information with their family in real time. The system primarily operates through a terminal and a server.

[0513] The device constantly monitors ambient sounds using a microphone as an acoustic detection means. When sounds exceeding a certain volume or frequency are detected, the digital recorder, which is the recording means, activates and starts recording. The recorded audio data is given a timestamp function as a means of adding time information, indicating the time the sound was recorded. This makes it easy to manage the data chronologically later on.

[0514] Furthermore, the device's camera executes a person recognition algorithm as an identification tool, specifically identifying objects held by children. Once identification is complete, the camera activates as an imaging tool, capturing an image and saving it with time information added.

[0515] The server receives audio and image data transmitted from the terminal and uses a generative AI model for editing. Noise is removed from the audio data to improve sound quality. Color adjustments are applied to the image data to create a visually appealing and aesthetically pleasing record.

[0516] The edited data is stored on the server's storage system. It is then transmitted to the SNS platform via the internet using a distribution system. Distribution is automatically performed according to the schedule and events specified by the user.

[0517] For example, when a user enters a prompt such as, "Please record the first time my child says 'Mommy' and send it to Grandma in real time," these functions work together to generate and edit the necessary data, which is then quickly shared with the family. This makes it possible for even busy families to record and share important growth moments without missing them.

[0518] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0519] Step 1:

[0520] Sound monitoring and recording

[0521] The device uses a microphone as an acoustic detection method to constantly monitor ambient sounds. When a sound exceeding a certain volume or frequency is detected, the device activates its recording mechanism and records the sound in digital format. The input is ambient sound, and the output is acoustic data with temporal information attached. This data is temporarily stored within the device.

[0522] Step 2:

[0523] Person and object identification and image capture

[0524] The device acquires video using a camera and recognizes people and the objects they are holding using an identification mechanism. When a target is detected, the camera activates as an imaging mechanism and captures an image. The input is real-time video data, and the output is image data with time information. This image data is also stored on the device.

[0525] Step 3:

[0526] Transfer of audio and image data

[0527] The terminal transfers the stored audio and image data to the server. During this process, the data is sent to the server via the network, and the server's processing power is used to perform the next steps.

[0528] Step 4:

[0529] Editing data

[0530] The server uses a generative AI model to edit the transmitted audio and image data. Noise reduction is applied to the audio data, improving sound quality and increasing clarity. Color correction is applied to the image data, improving visibility. As a result of the editing, higher quality data is output.

[0531] Step 5:

[0532] Data storage

[0533] The server stores the edited audio and image data in its own storage system. This ensures that the data is securely stored and accessible when needed.

[0534] Step 6:

[0535] Data distribution

[0536] The server transmits the stored data to the designated SNS platform via a distribution method. The input is the stored, edited data, and the output is information shared among family members. Distribution is performed automatically according to the schedule and events set by the user. This ensures that the data is delivered to family members at the appropriate time.

[0537] (Application Example 1)

[0538] 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."

[0539] This invention aims to enhance customer engagement with families with children in physical stores, enabling the whole family to enjoy the shopping experience more. It also aims to strengthen in-store sales promotion activities and to more accurately understand customer behavior that leads to product purchases.

[0540] 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.

[0541] In this invention, the server includes voice detection means, object recognition means, and presentation means. This makes it possible to record in real time the products and objects that a child shows interest in within the store and present them to the customer through an information display device or online platform.

[0542] A "sound detection means" is a device for detecting and recognizing the sound of a specific object from surrounding sounds.

[0543] A "recording means" is a device for recording detected audio in digital format.

[0544] A "time information assignment means" is a device that assigns a timestamp to audio or image data to record the exact time of occurrence.

[0545] "Object recognition means" refers to technology for visually identifying the objects that an object possesses.

[0546] "Photography means" refers to a device used to capture an image of an identified object.

[0547] "Processing means" refers to technologies for analyzing and processing recorded audio data and captured image data to improve their quality.

[0548] "Storage means" refers to technology for securely storing processed data and retrieving it as needed.

[0549] "Sharing methods" refer to technologies for sharing information by transmitting stored data via other devices or networks.

[0550] "Presentation means" refers to technology for displaying data on an information display device within a store or on an online communication platform for visual presentation.

[0551] "Generative AI technology" refers to a technology that uses artificial intelligence to highly analyze audio and image data and perform processing such as noise reduction and color correction.

[0552] As a specific embodiment of this invention, a system is proposed to enhance customer engagement with customers accompanied by children in physical stores.

[0553] This system consists of hardware and software, including voice detection means, object recognition means, and presentation means. The server constantly monitors the sounds emitted by children using the voice detection means and records the identified sounds. At this time, time information is added to the voice data as a timestamp.

[0554] Next, object recognition means visually identify the object the child is holding, such as a toy or product. This identification utilizes a general-purpose camera module and AI technology. Subsequently, a shooting means is used to capture an image of the object, and this image is also stored as data with time information added. This contributes to sales promotion and improved customer experience.

[0555] The server analyzes and processes the recorded audio and image data using processing tools. This process involves generative AI technology, performing noise reduction and color correction. TensorFlow and similar AI models are used. The processed data is managed by storage tools and can be reused as needed.

[0556] The server further uses a sharing mechanism to display the stored data in real time via the communication network to store information display devices and online platforms. This display mechanism allows customers and staff to instantly receive information through in-store and external visual terminals.

[0557] For example, information about a specific stuffed animal that a child shows interest in can be visually provided through in-store displays and a store-specific application, allowing parents to use this information to make a purchase decision. Prompts such as "Please tell me how to edit this audio data clearly" or "Please provide a setting to automatically color-correct this image" are input into a generating AI model, which then performs data analysis and improvement.

[0558] This system allows physical stores to offer new customer experiences and effectively promote sales.

[0559] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0560] Step 1:

[0561] The device constantly monitors the surrounding sound environment using a voice sensor and detects specific sounds emitted by the child. The detected sounds are recorded digitally and time information is added. This generates "child's voice data" as voice input data, and its output is a digital audio file.

[0562] Step 2:

[0563] The device uses object recognition technology to identify the object the child is holding. This involves acquiring video data using a camera and analyzing the object's features based on AI technology. In this process, video data is input, and the identified object information is output.

[0564] Step 3:

[0565] The terminal captures an image of the identified object and adds time information to the image data. The captured image data is input, and a timestamped image file is output. This data is used for subsequent data processing.

[0566] Step 4:

[0567] The server uses a generative AI model to remove noise from recorded audio data and perform color correction on image data. This improves the quality of both audio and image data, making it easier to visually review. The input is raw audio and image data, and the output is the improved data.

[0568] Step 5:

[0569] The server stores the processed data and, at specific times or events, displays it on the store's information display devices via a communication network using a sharing mechanism. In this process, the stored data is treated as input, and the information displayed on the store's display or online platform is the output.

[0570] Step 6:

[0571] Based on the information presented, users can check product details and make purchases. The information received by the user is data output from the server. This step involves the specific actions the user actually takes when using the system.

[0572] 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.

[0573] This invention relates to a system that senses and records the voice of a specific person, records an object held in the hand as an image, and further recognizes the user's emotions and processes the data. The embodiments of this system are described in detail below.

[0574] 1. Recording of voice and emotions

[0575] The device constantly monitors surrounding sounds and recognizes voices emitted by specific individuals. The recognized voices are recorded digitally using a recording device. During this process, an emotion engine is used to analyze the emotions in the voice and determine the user's emotional state. For example, if it detects a child laughing happily, the emotion engine recognizes this as "joy."

[0576] 2. Capture of dynamic images

[0577] Based on the analysis results of the emotion engine, if a specific emotional state is detected, the device automatically attempts to photograph the object the person is holding. Person recognition is used, and the camera is activated at the appropriate time. The image is saved with a timestamp, and then an emotion tag added by the emotion engine is associated with it.

[0578] 3. Editing and saving data

[0579] The editing process utilizes a generative AI model to edit recorded audio files and captured image files. Specifically, it removes noise from the audio, optimizes sound quality, and performs color correction on images to improve clarity. The edited data is systematically stored in a database on a server.

[0580] 4. Emotion-based data sharing

[0581] Based on the emotion tag information contained in the stored data, the server prioritizes sending data recorded in specific emotional states to the social networking platform. For example, if a particular user sets their preferences to receive only data containing the emotion of "joy," audio files and images associated with "joy" will be sent preferentially.

[0582] This system makes it possible to more efficiently record and share children's touching moments and events that evoke special emotions. The use of an emotion engine enhances data personalization, enabling the provision of information best suited to the user.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The device uses a voice sensor to continuously monitor ambient sounds. When voice from a specific person is detected, the emotion engine simultaneously activates, analyzing the voice to identify the user's emotional state.

[0586] Step 2:

[0587] Based on the detected user's emotional information, the device activates the recording mechanism and saves the audio as a digital file. The emotional data generated during recording is added to the audio file as metadata, and a timestamp is also added.

[0588] Step 3:

[0589] The device determines whether to photograph an object held in a person's hand based on the emotion recognition result. If a specific emotional state, such as "joy," is detected, the camera automatically activates and captures an image.

[0590] Step 4:

[0591] The captured images are saved as image files, containing emotional information and timestamps. At this time, the images are tagged based on emotions, making it possible to identify specific emotional states.

[0592] Step 5:

[0593] The device uploads recorded audio files and captured image files to the server. The server receives these files and performs editing to improve their quality, such as noise reduction and color correction.

[0594] Step 6:

[0595] Edited files are saved to a database on the server and managed efficiently. During saving, they are categorized based on sentiment tags, improving searchability.

[0596] Step 7:

[0597] If the server is configured to prioritize the transmission of data with a specific emotional state, it will automatically send that data to the SNS platform. After transmission, data that matches the user's set criteria will be shared with the user in real time.

[0598] (Example 2)

[0599] 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."

[0600] There is a challenge in efficiently recording important moments related to specific emotions or actions and sharing them in a more personalized way. This problem cannot be adequately addressed with traditional manual recording or photography, and the process of selecting and sharing data is cumbersome.

[0601] 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.

[0602] In this invention, the server includes emotion analysis means for analyzing emotions within speech, editing means for removing noise from speech and performing color correction on images using a generative model, and transmission means for transmitting stored data over a communication network. This enables the recording and personalized sharing of data based on emotions and specific situations.

[0603] "Sound sensor means" refers to a device or method for detecting ambient sounds, which has the function of identifying sounds from a specific sound source.

[0604] "Recording means" refers to a device or method for storing audio data or image data in digital format, such that the content is recorded in a way that allows for later playback.

[0605] "Emotional analysis means" refers to a device or method for analyzing emotions from acquired voice data, and for determining the user's emotional state by evaluating the characteristics of the voice.

[0606] "Person identification means" refers to a device or method for recognizing an object held in the hand of a specific person, and uses image processing technology to identify the person and their actions.

[0607] "Photography means" refers to a device or method for taking an image, which has the function of capturing a specific moment and saving it as digital data.

[0608] "Tagging means" refers to a device or method for adding emotional and temporal information related to audio data and image data.

[0609] "Generative modeling" refers to editing techniques that use machine learning methods to perform noise reduction and color correction, aiming to improve data quality through the model.

[0610] "Editing means" refers to a device or method for processing audio and image data to remove noise and correct color, thereby improving the quality of the data.

[0611] "Storage means" refers to a device or method for systematically accumulating edited data and keeping it accessible as needed.

[0612] "Transmission means" refers to a device or method for transmitting stored data to an external party via a communication network, enabling the effective sharing of data.

[0613] This invention is a system that analyzes emotions based on voice, records and edits images and audio according to those emotions, and selectively shares them. The configuration for implementing this system is described in detail below.

[0614] Voice detection and emotion analysis

[0615] The terminal constantly monitors surrounding sounds using a voice sensor. This voice sensor uses a voice input device such as a microphone. When the voice of a specific person is recognized, an emotion analysis means analyzes the voice and estimates the user's emotional state. For example, it analyzes the pitch, intonation, and speed of the voice to identify emotions such as "joy" or "surprise."

[0616] Image capture and tagging

[0617] When a specific emotion is detected, the device automatically activates the camera and uses person identification to photograph the object the person is holding. At this time, a timestamp and emotion tag are added to the captured image. The emotion tagging function works in conjunction with this data.

[0618] Editing and saving data

[0619] The server has editing capabilities that remove noise from recorded audio using a generative AI model and perform color correction on images. These editing processes utilize advanced audio processing algorithms and image processing techniques. The edited data is efficiently organized and stored in the server's storage.

[0620] Selective data sharing

[0621] Based on specific emotions set by the user, the server organizes the stored data and prioritizes sending it to the platform specified by the user. The transmission means enables this via a communication network.

[0622] Specific example

[0623] For example, if parents want to record their child's first birthday, the system analyzes the child's smiles and cheers emotionally and tags them as "joy." This data is then noise-free, color-corrected, and stored in the cloud, where it can be easily shared on social media as needed.

[0624] Example of a prompt

[0625] "How can we implement measures to record and share the touching moments of a child's birthday party on social media?"

[0626] This allows users to capture and selectively share special moments without missing a beat.

[0627] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0628] Step 1:

[0629] The device continuously monitors ambient sounds using voice sensors. The input is the entire ambient noise, from which the voice of a specific person is extracted. This audio data is processed by speech recognition software, which recognizes the speech of the specific person and records it as digital data. The output is the digital audio data of the specific person.

[0630] Step 2:

[0631] The server transmits the recorded audio data to an emotion analysis system. Here, the user's emotions are estimated by analyzing the intonation, speed, pitch, etc., of the voice. The input is digital audio data of a specific person, and the output is estimated emotion data based on that audio. For example, if the audio contains high pitch and laughter, it will be determined to represent "joy."

[0632] Step 3:

[0633] The terminal uses emotion data received from the server to capture images of scenes where those emotions are expressed, employing a person identification method. The input consists of estimated emotion data and subject information. The camera device activates and captures people or objects with an appropriate composition. The output is digital image data with a timestamp and emotion tag.

[0634] Step 4:

[0635] The server uses a generative AI model to denoise recorded audio data and color-correct captured image data. The input consists of noisy audio data and uncorrected image data. The audio and image processing models improve the quality of the data. The output consists of denoised audio data with improved sound quality and color-corrected, high-resolution image data.

[0636] Step 5:

[0637] The server systematically stores the edited data and transmits the appropriate data via the communication network based on user specifications. Inputs include edited audio and image data, and user-defined sharing criteria. The data management system within the server organizes them appropriately and selectively shares the specified data as needed. Output is the transmission of the selected data to the platform.

[0638] (Application Example 2)

[0639] 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."

[0640] Traditional food delivery services have struggled to provide suggestions based on individual user emotions and preferences. While recommendations based on past order history and reviews are offered, they fail to provide optimal suggestions that take into account the user's real-time emotional state. Therefore, there is a need for a system that automates the suggestion of meals best suited to the user's current mood, thereby improving user satisfaction.

[0641] 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.

[0642] In this invention, the server includes a voice detection device for detecting the utterance of a specific target, a recording device for recording the detected utterance, and a time information addition device for adding time information to the voice data. This makes it possible to determine the user's emotional state in real time and generate personalized recommendation information.

[0643] A "voice detection device" is a device used to detect the vocalizations of a target in real time.

[0644] A "recording device" is a device for saving detected vocalizations in digital format.

[0645] A "time information adding device" is a device used to add specific time information about the recording date to audio data or image data.

[0646] An "object recognition device" is a device used to recognize an object that a person is holding in their hand.

[0647] An "image acquisition device" is a device used to acquire recognized objects as image data.

[0648] An "editing device" is a device used to process and optimize recorded audio data or acquired image data.

[0649] A "memory device" is a device for systematically storing edited data.

[0650] A "transmission device" is a device that provides stored data to external parties via an information network.

[0651] A "recommendation device" is a device that generates personalized recommendation information based on the emotional information of the provided data, tailored to the user's preferences.

[0652] This invention provides a system that analyzes user emotions in real time and offers personalized recommendations as part of a food delivery service. This system mainly consists of a server and terminals.

[0653] The terminal constantly detects and records the user's speech through a voice detection device. The recorded audio is time-stamped by a time information device and sent to the server in real time. The server analyzes the received audio data and stores it digitally in a recording device. For sentiment analysis, "Google Cloud Speech-to-Text API" and "Amazon Web Services Comprehend" are used to detect emotions from the user's speech.

[0654] Next, the server uses an object recognition device and an image acquisition device to record the object held by the user as an image. This data is optimized using an editing device, and noise reduction and color correction are performed using generative AI models such as "OpenCV," "Stable Diffusion," and "Hugging Face Transformers."

[0655] The edited data is stored in a memory device and then provided externally via an information network by a transmission device. At this time, a recommendation device generates food delivery recommendations optimized for the user based on emotional information.

[0656] As a concrete example, consider a scenario where a user is planning a party with friends over the weekend. If the device senses the user's enjoyment and excitement from their laughter and conversation, the server will automatically recommend dishes that have received high ratings in the past. The following is an example of a prompt message.

[0657] "What food delivery service would be best for our weekend party?"

[0658] "What restaurant would you recommend to recreate the fun we experienced together with our friends?"

[0659] "Based on my previous experience ordering a delicious pizza, please suggest a new dessert."

[0660] The above describes the detailed configuration and operating procedure for carrying out the present invention. This configuration makes it possible to suggest meals based on the user's current emotions, significantly improving user satisfaction.

[0661] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0662] Step 1:

[0663] The terminal uses a voice detection device to constantly monitor the sounds around the user. The voice signals received as input act as triggers for detecting specific vocalizations. The detected voice signals are sent to a recording device and stored in digital format. As output, a time information device generates digital voice data with a timestamp.

[0664] Step 2:

[0665] The server receives the audio data and converts it to text using the Google Cloud Speech-to-Text API. It extracts the necessary text information from the digital audio data received as input and performs sentiment analysis using Amazon Web Services' Comprehend. The output is tag information indicating the user's emotional state.

[0666] Step 3:

[0667] The server uses an object recognition device to identify the object the user is holding. Using emotion information obtained from emotion analysis as input, the object is photographed by an image acquisition device. As output, image data recorded with a timestamp and an emotion tag are generated.

[0668] Step 4:

[0669] The server uses an editing device to optimize image and audio data. Digital audio and image data are acquired as input, and color correction of the images is performed using "OpenCV," while noise reduction of the audio data is performed using "Stable Diffusion" and "Hugging Face Transformers." The output consists of optimized image and audio data after editing.

[0670] Step 5:

[0671] The server stores data optimized for storage. The input data consists of edited audio and image data. The stored data serves as the foundation for later delivery via a transmission device.

[0672] Step 6:

[0673] The server uses a transmission device to prepare to send the stored data to the information network. At this time, it utilizes a "generative AI model" based on sentiment tags to generate personalized recommendation information and create prompt messages to provide to the user. The output is personalized recommendation information presented to the user.

[0674] 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.

[0675] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0676] 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.

[0677] [Fourth Embodiment]

[0678] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0679] 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.

[0680] 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).

[0681] 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.

[0682] 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.

[0683] 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).

[0684] 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.

[0685] 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.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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".

[0691] This invention relates to a system that automatically records a child's voice and the object they are holding, and shares this information with their family in real time. The operation of this system is described in detail below.

[0692] 1. Audio recording

[0693] First, the device constantly monitors ambient sounds and has a function to detect voices emitted by specific individuals. When the voice sensor detects a sound exceeding a certain volume level, the recording mechanism activates to record the voice in digital format, add a timestamp, and save the audio file. For example, when a child speaks their first word, the device automatically records that voice and saves it as a file.

[0694] 2. Taking the image

[0695] Next, the device has a function that uses person recognition to identify objects held by children. When a person or object is detected, the camera activates and captures an image. A timestamp is added to the image data, and it is saved as an image file. For example, it can automatically photograph and record a leaf a child is holding when they return from the park.

[0696] 3. Editing data

[0697] Recorded audio and captured images are edited on a server using a generated AI model. The editing process involves noise reduction and sound quality improvement for audio, and color correction for images. This editing improves the accuracy and quality of the recording.

[0698] 4. Sharing on social media

[0699] Finally, the server stores the edited data and uses a transmission method for sharing to automatically send it to family members via the internet through social networking platforms. Sharing is performed according to a specified schedule or event trigger, and the data is delivered to parents and grandparents in real time. For example, it is possible to set it up so that new records from the day are automatically distributed to an online group that grandparents participate in at 6 p.m. every day.

[0700] In this way, this system makes it possible to record important moments even amidst a busy daily life and share a child's growth with the whole family.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] The device constantly monitors the surrounding sounds and uses a voice sensor to identify voices from specific individuals. If the voice is determined to exceed a set threshold, the recording process begins.

[0704] Step 2:

[0705] The device activates its recording mechanism and records the detected audio in digital format. A timestamp is obtained at the start of recording and added to the audio file. When recording is complete, this audio file is temporarily saved to storage.

[0706] Step 3:

[0707] The device uses a person recognition system and a camera to detect an object held by a child. Once the object is identified, it activates a camera to capture the object as an image, and saves it with a timestamp added.

[0708] Step 4:

[0709] The device uploads recorded audio files and captured image files to the server at regular intervals, attaching date and time information as metadata.

[0710] Step 5:

[0711] The server receives uploaded audio files and performs noise reduction and volume adjustment. It also processes image files with color correction and sharpening to improve data quality.

[0712] Step 6:

[0713] The server organizes the edited audio and image files and saves them to a dedicated database. Each file is tagged during saving to ensure efficient management.

[0714] Step 7:

[0715] The server automatically sends stored data to a designated user group via the SNS platform according to a predetermined schedule or event trigger conditions. After sending, a success notification is sent to the user to inform them that the data has been successfully shared.

[0716] (Example 1)

[0717] 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".

[0718] Modern families, with their busy daily lives, often find it difficult to capture and share their children's growth and precious moments with the whole family. Traditional methods require manually recording and editing audio and photos and sharing them individually, which is time-consuming and laborious. To solve this problem, a system is needed that automatically records, edits, and shares audio and images.

[0719] 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.

[0720] In this invention, the server includes acoustic detection means for sensing specific sounds, identification means for identifying objects held by a person, and processing means for removing unwanted sounds from acoustic data and performing color adjustments in image data using a generative AI model. This makes it possible for even busy families to automatically record important moments in high quality and easily share them among family members.

[0721] An "acoustic detection means" is a device that has the function of sensing ambient sounds and detecting specific sounds.

[0722] A "recording device" is a device that has the function of saving detected sounds in digital format.

[0723] A "time information assignment means" is a device that has the function of assigning time information to recorded acoustic data or image data, specifically the time when the data was generated or recorded.

[0724] An "identification device" is a device that recognizes an object held by a person and has the function of identifying that object.

[0725] An "imaging device" is a device that has the function of generating an image based on an identified object and saving it in digital format.

[0726] A "processing device" is a device that has the function of editing recorded acoustic and image data using a generation AI model or the like to improve its quality.

[0727] A "storage means" is a device that has the function of storing processed acoustic data and image data in a storage device.

[0728] A "distribution means" is a device that has the function of transmitting stored data to other devices or platforms via a communication channel.

[0729] A "generative AI model" is an artificial intelligence technique that learns by analyzing large amounts of data and then generates new data or edits existing data.

[0730] An "operating means" is a device that has the function of automatically transmitting stored data based on conditions that trigger the transmission of data.

[0731] This system automatically records a child's voice and the objects they hold, and shares this information with their family in real time. The system primarily operates through a terminal and a server.

[0732] The device constantly monitors ambient sounds using a microphone as an acoustic detection means. When sounds exceeding a certain volume or frequency are detected, the digital recorder, which is the recording means, activates and starts recording. The recorded audio data is given a timestamp function as a means of adding time information, indicating the time the sound was recorded. This makes it easy to manage the data chronologically later on.

[0733] Furthermore, the device's camera executes a person recognition algorithm as an identification tool, specifically identifying objects held by children. Once identification is complete, the camera activates as an imaging tool, capturing an image and saving it with time information added.

[0734] The server receives audio and image data transmitted from the terminal and uses a generative AI model for editing. Noise is removed from the audio data to improve sound quality. Color adjustments are applied to the image data to create a visually appealing and aesthetically pleasing record.

[0735] The edited data is stored on the server's storage system. It is then transmitted to the SNS platform via the internet using a distribution system. Distribution is automatically performed according to the schedule and events specified by the user.

[0736] For example, when a user enters a prompt such as, "Please record the first time my child says 'Mommy' and send it to Grandma in real time," these functions work together to generate and edit the necessary data, which is then quickly shared with the family. This makes it possible for even busy families to record and share important growth moments without missing them.

[0737] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0738] Step 1:

[0739] Sound monitoring and recording

[0740] The device uses a microphone as an acoustic detection method to constantly monitor ambient sounds. When a sound exceeding a certain volume or frequency is detected, the device activates its recording mechanism and records the sound in digital format. The input is ambient sound, and the output is acoustic data with temporal information attached. This data is temporarily stored within the device.

[0741] Step 2:

[0742] Person and object identification and image capture

[0743] The device acquires video using a camera and recognizes people and the objects they are holding using an identification mechanism. When a target is detected, the camera activates as an imaging mechanism and captures an image. The input is real-time video data, and the output is image data with time information. This image data is also stored on the device.

[0744] Step 3:

[0745] Transfer of audio and image data

[0746] The terminal transfers the stored audio and image data to the server. During this process, the data is sent to the server via the network, and the server's processing power is used to perform the next steps.

[0747] Step 4:

[0748] Editing data

[0749] The server uses a generative AI model to edit the transmitted audio and image data. Noise reduction is applied to the audio data, improving sound quality and increasing clarity. Color correction is applied to the image data, improving visibility. As a result of the editing, higher quality data is output.

[0750] Step 5:

[0751] Data storage

[0752] The server stores the edited audio and image data in its own storage system. This ensures that the data is securely stored and accessible when needed.

[0753] Step 6:

[0754] Data distribution

[0755] The server transmits the stored data to the designated SNS platform via a distribution method. The input is the stored, edited data, and the output is information shared among family members. Distribution is performed automatically according to the schedule and events set by the user. This ensures that the data is delivered to family members at the appropriate time.

[0756] (Application Example 1)

[0757] 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".

[0758] This invention aims to enhance customer engagement with families with children in physical stores, enabling the whole family to enjoy the shopping experience more. It also aims to strengthen in-store sales promotion activities and to more accurately understand customer behavior that leads to product purchases.

[0759] 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.

[0760] In this invention, the server includes voice detection means, object recognition means, and presentation means. This makes it possible to record in real time the products and objects that a child shows interest in within the store and present them to the customer through an information display device or online platform.

[0761] A "sound detection means" is a device for detecting and recognizing the sound of a specific object from surrounding sounds.

[0762] A "recording means" is a device for recording detected audio in digital format.

[0763] A "time information assignment means" is a device that assigns a timestamp to audio or image data to record the exact time of occurrence.

[0764] "Object recognition means" refers to technology for visually identifying the objects that an object possesses.

[0765] "Photography means" refers to a device used to capture an image of an identified object.

[0766] "Processing means" refers to technologies for analyzing and processing recorded audio data and captured image data to improve their quality.

[0767] "Storage means" refers to technology for securely storing processed data and retrieving it as needed.

[0768] "Sharing methods" refer to technologies for sharing information by transmitting stored data via other devices or networks.

[0769] "Presentation means" refers to technology for displaying data on an information display device within a store or on an online communication platform for visual presentation.

[0770] "Generative AI technology" refers to a technology that uses artificial intelligence to highly analyze audio and image data and perform processing such as noise reduction and color correction.

[0771] As a specific embodiment of this invention, a system is proposed to enhance customer engagement with customers accompanied by children in physical stores.

[0772] This system consists of hardware and software, including voice detection means, object recognition means, and presentation means. The server constantly monitors the sounds emitted by children using the voice detection means and records the identified sounds. At this time, time information is added to the voice data as a timestamp.

[0773] Next, object recognition means visually identify the object the child is holding, such as a toy or product. This identification utilizes a general-purpose camera module and AI technology. Subsequently, a shooting means is used to capture an image of the object, and this image is also stored as data with time information added. This contributes to sales promotion and improved customer experience.

[0774] The server analyzes and processes the recorded audio and image data using processing tools. This process involves generative AI technology, performing noise reduction and color correction. TensorFlow and similar AI models are used. The processed data is managed by storage tools and can be reused as needed.

[0775] The server further uses a sharing mechanism to display the stored data in real time via the communication network to store information display devices and online platforms. This display mechanism allows customers and staff to instantly receive information through in-store and external visual terminals.

[0776] For example, information about a specific stuffed animal that a child shows interest in can be visually provided through in-store displays and a store-specific application, allowing parents to use this information to make a purchase decision. Prompts such as "Please tell me how to edit this audio data clearly" or "Please provide a setting to automatically color-correct this image" are input into a generating AI model, which then performs data analysis and improvement.

[0777] This system allows physical stores to offer new customer experiences and effectively promote sales.

[0778] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0779] Step 1:

[0780] The device constantly monitors the surrounding sound environment using a voice sensor and detects specific sounds emitted by the child. The detected sounds are recorded digitally and time information is added. This generates "child's voice data" as voice input data, and its output is a digital audio file.

[0781] Step 2:

[0782] The device uses object recognition technology to identify the object the child is holding. This involves acquiring video data using a camera and analyzing the object's features based on AI technology. In this process, video data is input, and the identified object information is output.

[0783] Step 3:

[0784] The terminal captures an image of the identified object and adds time information to the image data. The captured image data is input, and a timestamped image file is output. This data is used for subsequent data processing.

[0785] Step 4:

[0786] The server uses a generative AI model to remove noise from recorded audio data and perform color correction on image data. This improves the quality of both audio and image data, making it easier to visually review. The input is raw audio and image data, and the output is the improved data.

[0787] Step 5:

[0788] The server stores the processed data and, at specific times or events, displays it on the store's information display devices via a communication network using a sharing mechanism. In this process, the stored data is treated as input, and the information displayed on the store's display or online platform is the output.

[0789] Step 6:

[0790] Based on the information presented, users can check product details and make purchases. The information received by the user is data output from the server. This step involves the specific actions the user actually takes when using the system.

[0791] 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.

[0792] This invention relates to a system that senses and records the voice of a specific person, records an object held in the hand as an image, and further recognizes the user's emotions and processes the data. The embodiments of this system are described in detail below.

[0793] 1. Recording of voice and emotions

[0794] The device constantly monitors surrounding sounds and recognizes voices emitted by specific individuals. The recognized voices are recorded digitally using a recording device. During this process, an emotion engine is used to analyze the emotions in the voice and determine the user's emotional state. For example, if it detects a child laughing happily, the emotion engine recognizes this as "joy."

[0795] 2. Capture of dynamic images

[0796] Based on the analysis results of the emotion engine, if a specific emotional state is detected, the device automatically attempts to photograph the object the person is holding. Person recognition is used, and the camera is activated at the appropriate time. The image is saved with a timestamp, and then an emotion tag added by the emotion engine is associated with it.

[0797] 3. Editing and saving data

[0798] The editing process utilizes a generative AI model to edit recorded audio files and captured image files. Specifically, it removes noise from the audio, optimizes sound quality, and performs color correction on images to improve clarity. The edited data is systematically stored in a database on a server.

[0799] 4. Emotion-based data sharing

[0800] Based on the emotion tag information contained in the stored data, the server prioritizes sending data recorded in specific emotional states to the social networking platform. For example, if a particular user sets their preferences to receive only data containing the emotion of "joy," audio files and images associated with "joy" will be sent preferentially.

[0801] This system makes it possible to more efficiently record and share children's touching moments and events that evoke special emotions. The use of an emotion engine enhances data personalization, enabling the provision of information best suited to the user.

[0802] The following describes the processing flow.

[0803] Step 1:

[0804] The device uses a voice sensor to continuously monitor ambient sounds. When voice from a specific person is detected, the emotion engine simultaneously activates, analyzing the voice to identify the user's emotional state.

[0805] Step 2:

[0806] Based on the detected user's emotional information, the device activates the recording mechanism and saves the audio as a digital file. The emotional data generated during recording is added to the audio file as metadata, and a timestamp is also added.

[0807] Step 3:

[0808] The device determines whether to photograph an object held in a person's hand based on the emotion recognition result. If a specific emotional state, such as "joy," is detected, the camera automatically activates and captures an image.

[0809] Step 4:

[0810] The captured images are saved as image files, containing emotional information and timestamps. At this time, the images are tagged based on emotions, making it possible to identify specific emotional states.

[0811] Step 5:

[0812] The device uploads recorded audio files and captured image files to the server. The server receives these files and performs editing to improve their quality, such as noise reduction and color correction.

[0813] Step 6:

[0814] Edited files are saved to a database on the server and managed efficiently. During saving, they are categorized based on sentiment tags, improving searchability.

[0815] Step 7:

[0816] If the server is configured to prioritize the transmission of data with a specific emotional state, it will automatically send that data to the SNS platform. After transmission, data that matches the user's set criteria will be shared with the user in real time.

[0817] (Example 2)

[0818] 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".

[0819] There is a challenge in efficiently recording important moments related to specific emotions or actions and sharing them in a more personalized way. This problem cannot be adequately addressed with traditional manual recording or photography, and the process of selecting and sharing data is cumbersome.

[0820] 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.

[0821] In this invention, the server includes emotion analysis means for analyzing emotions within speech, editing means for removing noise from speech and performing color correction on images using a generative model, and transmission means for transmitting stored data over a communication network. This enables the recording and personalized sharing of data based on emotions and specific situations.

[0822] "Sound sensor means" refers to a device or method for detecting ambient sounds, which has the function of identifying sounds from a specific sound source.

[0823] "Recording means" refers to a device or method for storing audio data or image data in digital format, such that the content is recorded in a way that allows for later playback.

[0824] "Emotional analysis means" refers to a device or method for analyzing emotions from acquired voice data, and for determining the user's emotional state by evaluating the characteristics of the voice.

[0825] "Person identification means" refers to a device or method for recognizing an object held in the hand of a specific person, and uses image processing technology to identify the person and their actions.

[0826] "Photography means" refers to a device or method for taking an image, which has the function of capturing a specific moment and saving it as digital data.

[0827] "Tagging means" refers to a device or method for adding emotional and temporal information related to audio data and image data.

[0828] "Generative modeling" refers to editing techniques that use machine learning methods to perform noise reduction and color correction, aiming to improve data quality through the model.

[0829] "Editing means" refers to a device or method for processing audio and image data to remove noise and correct color, thereby improving the quality of the data.

[0830] "Storage means" refers to a device or method for systematically accumulating edited data and keeping it accessible as needed.

[0831] "Transmission means" refers to a device or method for transmitting stored data to an external party via a communication network, enabling the effective sharing of data.

[0832] This invention is a system that analyzes emotions based on voice, records and edits images and audio according to those emotions, and selectively shares them. The configuration for implementing this system is described in detail below.

[0833] Voice detection and emotion analysis

[0834] The terminal constantly monitors surrounding sounds using a voice sensor. This voice sensor uses a voice input device such as a microphone. When the voice of a specific person is recognized, an emotion analysis means analyzes the voice and estimates the user's emotional state. For example, it analyzes the pitch, intonation, and speed of the voice to identify emotions such as "joy" or "surprise."

[0835] Image capture and tagging

[0836] When a specific emotion is detected, the device automatically activates the camera and uses person identification to photograph the object the person is holding. At this time, a timestamp and emotion tag are added to the captured image. The emotion tagging function works in conjunction with this data.

[0837] Editing and saving data

[0838] The server has editing capabilities that remove noise from recorded audio using a generative AI model and perform color correction on images. These editing processes utilize advanced audio processing algorithms and image processing techniques. The edited data is efficiently organized and stored in the server's storage.

[0839] Selective data sharing

[0840] Based on specific emotions set by the user, the server organizes the stored data and prioritizes sending it to the platform specified by the user. The transmission means enables this via a communication network.

[0841] Specific example

[0842] For example, if parents want to record their child's first birthday, the system analyzes the child's smiles and cheers emotionally and tags them as "joy." This data is then noise-free, color-corrected, and stored in the cloud, where it can be easily shared on social media as needed.

[0843] Example of a prompt

[0844] "How can we implement measures to record and share the touching moments of a child's birthday party on social media?"

[0845] This allows users to capture and selectively share special moments without missing a beat.

[0846] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0847] Step 1:

[0848] The device continuously monitors ambient sounds using voice sensors. The input is the entire ambient noise, from which the voice of a specific person is extracted. This audio data is processed by speech recognition software, which recognizes the speech of the specific person and records it as digital data. The output is the digital audio data of the specific person.

[0849] Step 2:

[0850] The server transmits the recorded audio data to an emotion analysis system. Here, the user's emotions are estimated by analyzing the intonation, speed, pitch, etc., of the voice. The input is digital audio data of a specific person, and the output is estimated emotion data based on that audio. For example, if the audio contains high pitch and laughter, it will be determined to represent "joy."

[0851] Step 3:

[0852] The terminal uses emotion data received from the server to capture images of scenes where those emotions are expressed, employing a person identification method. The input consists of estimated emotion data and subject information. The camera device activates and captures people or objects with an appropriate composition. The output is digital image data with a timestamp and emotion tag.

[0853] Step 4:

[0854] The server uses a generative AI model to denoise recorded audio data and color-correct captured image data. The input consists of noisy audio data and uncorrected image data. The audio and image processing models improve the quality of the data. The output consists of denoised audio data with improved sound quality and color-corrected, high-resolution image data.

[0855] Step 5:

[0856] The server systematically stores the edited data and transmits the appropriate data via the communication network based on user specifications. Inputs include edited audio and image data, and user-defined sharing criteria. The data management system within the server organizes them appropriately and selectively shares the specified data as needed. Output is the transmission of the selected data to the platform.

[0857] (Application Example 2)

[0858] 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".

[0859] Traditional food delivery services have struggled to provide suggestions based on individual user emotions and preferences. While recommendations based on past order history and reviews are offered, they fail to provide optimal suggestions that take into account the user's real-time emotional state. Therefore, there is a need for a system that automates the suggestion of meals best suited to the user's current mood, thereby improving user satisfaction.

[0860] 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.

[0861] In this invention, the server includes a voice detection device for detecting the utterance of a specific target, a recording device for recording the detected utterance, and a time information addition device for adding time information to the voice data. This makes it possible to determine the user's emotional state in real time and generate personalized recommendation information.

[0862] A "voice detection device" is a device used to detect the vocalizations of a target in real time.

[0863] A "recording device" is a device for saving detected vocalizations in digital format.

[0864] A "time information adding device" is a device used to add specific time information about the recording date to audio data or image data.

[0865] An "object recognition device" is a device used to recognize an object that a person is holding in their hand.

[0866] An "image acquisition device" is a device used to acquire recognized objects as image data.

[0867] An "editing device" is a device used to process and optimize recorded audio data or acquired image data.

[0868] A "memory device" is a device for systematically storing edited data.

[0869] A "transmission device" is a device that provides stored data to external parties via an information network.

[0870] A "recommendation device" is a device that generates personalized recommendation information based on the emotional information of the provided data, tailored to the user's preferences.

[0871] This invention provides a system that analyzes user emotions in real time and offers personalized recommendations as part of a food delivery service. This system mainly consists of a server and terminals.

[0872] The terminal constantly detects and records the user's speech through a voice detection device. The recorded audio is time-stamped by a time information device and sent to the server in real time. The server analyzes the received audio data and stores it digitally in a recording device. For sentiment analysis, "Google Cloud Speech-to-Text API" and "Amazon Web Services Comprehend" are used to detect emotions from the user's speech.

[0873] Next, the server uses an object recognition device and an image acquisition device to record the object held by the user as an image. This data is optimized using an editing device, and noise reduction and color correction are performed using generative AI models such as "OpenCV," "Stable Diffusion," and "Hugging Face Transformers."

[0874] The edited data is stored in a memory device and then provided externally via an information network by a transmission device. At this time, a recommendation device generates food delivery recommendations optimized for the user based on emotional information.

[0875] As a concrete example, consider a scenario where a user is planning a party with friends over the weekend. If the device senses the user's enjoyment and excitement from their laughter and conversation, the server will automatically recommend dishes that have received high ratings in the past. The following is an example of a prompt message.

[0876] "What food delivery service would be best for our weekend party?"

[0877] "What restaurant would you recommend to recreate the fun we experienced together with our friends?"

[0878] "Based on my previous experience ordering a delicious pizza, please suggest a new dessert."

[0879] The above describes the detailed configuration and operating procedure for carrying out the present invention. This configuration makes it possible to suggest meals based on the user's current emotions, significantly improving user satisfaction.

[0880] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0881] Step 1:

[0882] The terminal uses a voice detection device to constantly monitor the sounds around the user. The voice signals received as input act as triggers for detecting specific vocalizations. The detected voice signals are sent to a recording device and stored in digital format. As output, a time information device generates digital voice data with a timestamp.

[0883] Step 2:

[0884] The server receives the audio data and converts it to text using the Google Cloud Speech-to-Text API. It extracts the necessary text information from the digital audio data received as input and performs sentiment analysis using Amazon Web Services' Comprehend. The output is tag information indicating the user's emotional state.

[0885] Step 3:

[0886] The server uses an object recognition device to identify the object the user is holding. Using emotion information obtained from emotion analysis as input, the object is photographed by an image acquisition device. As output, image data recorded with a timestamp and an emotion tag are generated.

[0887] Step 4:

[0888] The server uses an editing device to optimize image and audio data. Digital audio and image data are acquired as input, and color correction of the images is performed using "OpenCV," while noise reduction of the audio data is performed using "Stable Diffusion" and "Hugging Face Transformers." The output consists of optimized image and audio data after editing.

[0889] Step 5:

[0890] The server stores data optimized for storage. The input data consists of edited audio and image data. The stored data serves as the foundation for later delivery via a transmission device.

[0891] Step 6:

[0892] The server uses a transmission device to prepare to send the stored data to the information network. At this time, it utilizes a "generative AI model" based on sentiment tags to generate personalized recommendation information and create prompt messages to provide to the user. The output is personalized recommendation information presented to the user.

[0893] 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.

[0894] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0895] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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."

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0914] The following is further disclosed regarding the embodiments described above.

[0915] (Claim 1)

[0916] A voice sensor means for detecting the voice of a specific person,

[0917] A recording means for recording the detected sound,

[0918] A means for adding a timestamp to an audio file,

[0919] A person recognition method for recognizing an object held in a person's hand,

[0920] A means of photographing the recognized object,

[0921] A means for adding a timestamp to an image file,

[0922] An editing method for editing recorded audio files and captured image files,

[0923] A means of saving the edited file,

[0924] A transmission means for sharing saved files over a communication network,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, further comprising editing means for removing noise from recorded audio files and performing color correction in image files using a generative AI model.

[0928] (Claim 3)

[0929] The system according to claim 1, further comprising control means for automatically transmitting saved files over a communication network at a specific time or when new data is added.

[0930] "Example 1"

[0931] (Claim 1)

[0932] Acoustic detection means for sensing specific sounds,

[0933] A recording means for recording the detected sound,

[0934] A means for adding time information to acoustic data,

[0935] An identification means for identifying an object held by a person,

[0936] An imaging means for imaging an identified object,

[0937] A means for adding time information to image data,

[0938] A processing means for processing recorded acoustic data and image data,

[0939] A storage means for storing processed data,

[0940] A distribution method for distributing accumulated data via a communication channel,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, further comprising processing means for removing unwanted sounds from recorded acoustic data and performing color adjustments in image data using a generative AI model.

[0944] (Claim 3)

[0945] The system according to claim 1, further comprising operating means for automatically transmitting stored data via a communication channel at a specific time or when new data is added.

[0946] "Application Example 1"

[0947] (Claim 1)

[0948] A voice detection means for detecting the voice of a specific target,

[0949] A recording means for recording the detected sound,

[0950] A means for adding time information to audio data,

[0951] A means for recognizing objects possessed by an object,

[0952] A means of photographing an identified object,

[0953] A means for adding time information to image data,

[0954] A processing means for processing recorded audio data and captured image data,

[0955] A storage means for storing processed data,

[0956] A sharing means for sharing stored data via a communication channel,

[0957] A means of displaying data on an in-store information display device or online communication platform,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] The system according to claim 1, further comprising processing means for removing unwanted sounds from recorded audio data and performing visual adjustments in image data using generation AI technology.

[0961] (Claim 3)

[0962] The system according to claim 1, further comprising a transmission means for automatically sending stored data via a communication channel at a specific time or when new data is added.

[0963] "Example 2 of combining an emotion engine"

[0964] (Claim 1)

[0965] A sensor means for detecting sound,

[0966] A recording means for recording the detected sound,

[0967] A means of analyzing emotions within voice,

[0968] A means of identifying a person to recognize an object held in a person's hand,

[0969] A means of photographing recognized objects,

[0970] A tagging means for tagging audio data and image data,

[0971] An editing method that uses a generative model to remove noise from audio and perform color correction on images,

[0972] A means of saving the edited data,

[0973] A transmission means for transmitting stored data via a communication network,

[0974] A system that includes this.

[0975] (Claim 2)

[0976] The system according to claim 1, further comprising means for selectively sharing stored data based on the user's emotions.

[0977] (Claim 3)

[0978] The system according to claim 1, further comprising control means for automatically distributing data based on emotion tags when new data is added.

[0979] "Application example 2 of combining emotional engines"

[0980] (Claim 1)

[0981] A voice detection device for detecting the vocalizations of a specific subject,

[0982] A recording device that records the detected vocalizations,

[0983] A time information addition device that adds time information to audio data,

[0984] A device for recognizing an object held in the hand of a subject,

[0985] An image acquisition device for recording recognized objects,

[0986] A time information addition device that adds time information to image data,

[0987] An editing device for editing recorded audio data and acquired image data,

[0988] A memory device that stores the edited data,

[0989] A transmission device that provides stored data via an information network,

[0990] A recommendation device that generates personalized recommendation information based on the sentiment information of the provided data,

[0991] A system that includes this.

[0992] (Claim 2)

[0993] The system according to claim 1, further comprising an editing device that uses a generative model to remove noise from recorded audio data and perform color correction in image data.

[0994] (Claim 3)

[0995] The system according to claim 1, further comprising a control device that automatically provides stored data via an information network at a specific time or when new data is available, and a control function that provides recommendation information based on sentiment information. [Explanation of Symbols]

[0996] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A voice sensor means for detecting the voice of a specific person, A recording means for recording the detected sound, A means for adding a timestamp to an audio file, A person recognition method for recognizing an object held in a person's hand, A means of photographing the recognized object, A means for adding a timestamp to an image file, An editing method for editing recorded audio files and captured image files, A means of saving the edited file, A transmission means for sharing saved files over a communication network, A system that includes this.

2. The system according to claim 1, further comprising editing means for removing noise from recorded audio files and performing color correction in image files using a generative AI model.

3. The system according to claim 1, further comprising control means for automatically transmitting saved files over a communication network at a specific time or when new data is added.

Images