System

Abstract

A system is provided.SOLUTION: A means for acquiring daily conversations of a family by using a voice microphone installed in a living room, a means for locally storing acquired voice data and performing format conversion, a means for uploading the converted voice data to a server through a network, a means for converting the voice data into text data by using a voice recognition engine in the server, and a means for analyzing the text data by using a natural language processing technology; A system comprising: means for extracting a characteristic conversation part; means for automatically generating a diary based on the extracted characteristic conversation part; means for storing the generated diary in a database in a searchable format; and means for providing an interface for a user to browse the diary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's busy lifestyles, it is very difficult to record family conversations and memories. Manual recording takes time and effort, and is difficult to do sustainably. As a result, in many homes, valuable conversations are not recorded, and family growth and important events are lost. To solve this problem, a system is needed that automatically records family conversations and allows for easy review later. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for capturing everyday conversations among family members using an audio microphone installed in the living room; means for locally saving the captured audio data and converting its format; means for uploading the converted audio data to a server via a network; means for converting the audio data into text data using a speech recognition engine in the server; means for analyzing the text data using natural language processing technology and extracting characteristic parts of the conversation; means for automatically generating a diary based on the extracted characteristic parts of the conversation; means for saving the generated diary in a database in a searchable format; and means for providing an interface for users to view the diary.

[0006] An "audio microphone" is a device that converts sound into an electrical signal and transmits that signal to another device.

[0007] "Audio data" is data that represents audio in digital form.

[0008] "Stored locally" means that the data is kept in the internal storage of the device that acquired it, rather than in any other external system.

[0009] "Format conversion" is the process of changing data from one format to another.

[0010] A "network" is a system in which multiple computers or devices are connected so that they can communicate with each other.

[0011] A "server" is a computer that provides services to other computers and devices over a network.

[0012] A "voice recognition engine" is software or hardware that analyzes speech and converts it into text data.

[0013] "Text data" is information expressed as characters.

[0014] "Natural language processing technology" is a technology that enables computers to understand, generate, and analyze human language.

[0015] A "characteristic part of a conversation" is a part of a normal conversation that is considered to be particularly important or interesting.

[0016] "Automatically generating a diary" means that the system creates diary-style text from specified data without human intervention.

[0017] A "database" is a system for efficiently storing, managing, and retrieving data.

[0018] An "interface" is the means by which a user and a system exchange information with each other. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a 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.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0033] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] This invention relates to a system that automatically records everyday conversations of a family and compiles them into a diary. The system is mainly composed of an audio microphone installed in the living room, a local terminal, and a server.

[0041] Audio data collection

[0042] A dedicated microphone is installed in the living room to capture daily family conversations in real time. This microphone is equipped with noise cancellation and echo cancellation functions, allowing for clear audio data to be collected.

[0043] The device periodically receives audio data from the microphone, stores it locally, converts it into a suitable digital format, and prepares it for upload to the server. The audio data is encrypted and transmitted to the server using a secure communication protocol.

[0044] Analysis of voice data and diary generation

[0045] The server receives the voice data sent from the device. After receiving it, it converts the voice data into text data using a voice recognition engine. The converted text data is then analyzed using natural language processing (NLP) technology.

[0046] The server's NLP system extracts characteristic conversational segments from the text data using techniques such as keyword extraction, sentiment analysis, and speaker identification. Based on these segments, the system automatically generates a diary, which is organized chronologically and designed to include important events and interesting conversations from the family's daily life.

[0047] Save and access your diary

[0048] The generated diary entries are stored in a searchable database by the server, and the diary entries are categorized by date, tags, etc., allowing users to easily search and access them later.

[0049] Users can view the created diaries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find conversations from a specific date or diary entries containing specific keywords.

[0050] Specific examples

[0051] Morning conversation transcript

[0052] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[0053] Parent: "Where do you want to go today?"

[0054] Child: "The zoo is great!"

[0055] The conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's speech recognition engine and NLP system analyze the conversation and generate a diary entry similar to the following:

[0056] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[0057] The diary created in this way is stored on a server, and users can view it using a dedicated app and look back on important events of the day.

[0058] The present invention provides a system that allows family conversations and memories to be easily recorded and looked back on later, even during busy daily life.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] A dedicated microphone picks up everyday conversations in the living room in real time, and the microphone features high-precision noise cancellation and echo cancellation to collect clear audio data.

[0062] Step 2:

[0063] The device periodically acquires audio data collected from a dedicated microphone and temporarily stores it in local storage. This data is encrypted to ensure security.

[0064] Step 3:

[0065] The device converts the locally stored audio data into an appropriate digital format (e.g., WAV or MP3). The conversion process compresses the audio data and prepares it for transmission.

[0066] Step 4:

[0067] The device uploads the converted audio data to the server. The upload is performed using a secure communication protocol (e.g., HTTPS or SFTP), ensuring data security.

[0068] Step 5:

[0069] The server receives the uploaded voice data and stores it in storage. This stored data is immediately used for voice recognition processing.

[0070] Step 6:

[0071] The server uses a speech recognition engine to convert the voice data into text data, using a highly accurate speech recognition model to accurately convert the content of the conversation into text.

[0072] Step 7:

[0073] The server passes the text data to a natural language processing (NLP) system for analysis, which performs processes such as keyword extraction, sentiment analysis, and speaker identification to extract characteristic conversational segments.

[0074] Step 8:

[0075] The server automatically generates a diary based on the extracted characteristic conversation segments. The diary is organized chronologically and is designed to include important events and interesting conversations in the family's daily life.

[0076] Step 9:

[0077] The server stores the created diary in a database, and the saved diary is managed in a format that makes it easy to search and view.

[0078] Step 10:

[0079] Users can view the created diary through a dedicated application or a web interface. The interface is intuitive and easy to use, allowing users to easily find conversations or interesting events from specific dates.

[0080] In this way, the system automatically records everyday family conversations and provides them as an easily accessible diary.

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] In today's busy lifestyles, it is difficult to easily record everyday family conversations and events and review them later. Manual recording is also a time-consuming and labor-intensive process. Furthermore, protecting privacy and ensuring data security are important, and a system that can achieve both is needed.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes a means for converting acoustic information into text information, a means for analyzing the text information using natural language processing technology, and a means for automatically generating a record based on the extracted dialogue portion. This allows for automatic and efficient recording of everyday family conversations, protecting privacy and making it easy to review later.

[0086] The "sound acquisition device" is a device that is installed to acquire human conversations, and includes an acoustic sensor and a microphone.

[0087] "Acoustic information" refers to captured human dialogue and other acoustic data.

[0088] An "electronic recording format" is a format for storing acoustic information as digital data, such as MP3 or WAV.

[0089] "Communication line" refers to the network infrastructure used to transmit acoustic information to a remote device, including the Internet, dedicated communication lines, etc.

[0090] A "remote device" is a server or cloud computing system for receiving and processing the acoustic information.

[0091] An "acoustic recognition mechanism" refers to technology or equipment for converting acoustic information into text information, including voice recognition engines.

[0092] "Text information" refers to text data generated based on acoustic information.

[0093] "Natural language processing technology" is a technology used to analyze textual information, and includes keyword extraction, sentiment analysis, and speaker identification.

[0094] "Characteristic dialogue parts" are parts of a dialogue that have important meaning or are interesting, extracted using natural language processing technology.

[0095] "Records" refer to diaries and memos automatically generated based on extracted characteristic parts of the dialogue.

[0096] "Storage device" refers to a database or storage system for storing the generated records.

[0097] "Interface" refers to the application, web page, or other operating means that a user uses to view a record.

[0098] The present invention relates to a system that automatically records everyday family conversations and compiles them into a diary. This system is composed of an audio capture device, a terminal, a server, and a user interface.

[0099] Audio data collection

[0100] The audio capture device is installed in the living room and captures people's conversations in real time. This audio capture device is equipped with noise cancellation and echo removal functions, allowing for clear audio data to be collected.

[0101] Local storage and format conversion of audio data

[0102] The device periodically receives audio data from the audio capture device, stores it locally, and converts it into an appropriate digital recording format (e.g., MP3, WAV). Existing libraries such as FFmpeg can be used for this format conversion.

[0103] Encrypting and uploading audio data

[0104] The device encrypts the converted audio data using an encryption algorithm such as AES, and the encrypted data is sent to the server over a secure communication line (e.g., HTTPS, TLS).

[0105] Analysis of audio data

[0106] The server receives the encrypted voice data sent from the device and converts it into text using an acoustic recognition mechanism (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech Services). The converted text is then analyzed using natural language processing technology (NLP system). This analysis process uses techniques such as keyword extraction, sentiment analysis, and speaker identification.

[0107] Generating diary data

[0108] The server's NLP system extracts characteristic dialogue segments from the analyzed text information. Based on these segments, the server automatically generates a record (diary). This diary is organized chronologically and is designed to include important events and interesting dialogues from the family's daily life.

[0109] Storage and access of diary data

[0110] The generated diary is stored in a searchable format on a storage device by the server. The diary data is categorized by date and tags, allowing users to easily search and access it later.

[0111] User Interface

[0112] Users can view the created diaries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diaries containing conversations from a specific date or specific keywords.

[0113] Specific examples

[0114] For example, in the morning, while a family is having breakfast in the living room, an audio capture device captures their conversation. The conversation goes something like this:

[0115] Parent: "Where do you want to go today?"

[0116] Child: "The zoo is great!"

[0117] This conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's acoustic recognition and NLP systems analyze the conversation and generate a diary entry like this:

[0118] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[0119] The diary created in this way is stored on a server, and users can view it using a dedicated app and look back on important events of the day.

[0120] This invention is a system realized by linking an audio capture device, a terminal, a server, and a user interface, which automatically records everyday family conversations and allows them to be easily reviewed later.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: Acquire audio data

[0123] A dedicated microphone is installed in the living room to capture people's conversations in real time, using noise cancellation and echo cancellation functions to collect clear audio data.

[0124] Input: Audio from living room

[0125] Output: Audio data (analog format)

[0126] Step 2: Save the audio data locally

[0127] The device receives audio data from the acoustic capture device at regular intervals and stores it locally. The audio data is divided into chunks of a fixed size (e.g., every 5 minutes) and stored.

[0128] Input: Audio data (analog format)

[0129] Output: Locally stored audio data (digital format)

[0130] Step 3: Convert the audio data format

[0131] The device converts the stored audio data into the appropriate digital recording format (e.g., MP3, WAV), using existing libraries such as FFmpeg.

[0132] Input: Locally stored audio data (digital format)

[0133] Output: Converted audio data (MP3, WAV format)

[0134] Step 4: Encrypt the audio data

[0135] The device then encrypts the converted audio data using an encryption algorithm such as AES, and the encrypted data is ready to be sent to the server via the communication line.

[0136] Input: Converted audio data (MP3, WAV format)

[0137] Output: Encrypted audio data

[0138] Step 5: Upload your audio data

[0139] The device sends encrypted audio data to the server using a secure communication line (e.g., HTTPS, TLS).

[0140] Input: Encrypted audio data

[0141] Output: Encrypted audio data sent to the server

[0142] Step 6: Receiving and analyzing audio data

[0143] The server receives the encrypted voice data sent from the device and converts the voice data into text information using an acoustic recognition mechanism (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech Services).

[0144] Input: Encrypted audio data

[0145] Output: Text data

[0146] Step 7: Natural Language Processing of Text Data

[0147] The server then analyzes the converted text data using natural language processing techniques, such as keyword extraction, sentiment analysis, and speaker identification.

[0148] Input: Text data

[0149] Output: Parsed text data

[0150] Step 8: Extracting characteristic dialogue parts

[0151] The server's NLP system extracts characteristic dialogue segments from the parsed text.

[0152] Input: Parsed text data

[0153] Output: Characteristic dialogue

[0154] Step 9: Automatic generation of diary data

[0155] The server automatically generates a diary based on the extracted characteristic dialogue. The diary is organized chronologically and includes important events and interesting conversations from the family's daily life.

[0156] Input: Characteristic dialogue

[0157] Output: Generated diary data

[0158] Step 10: Save diary data

[0159] The server stores the generated diary in a searchable format on a storage device. The diary data is categorized by date and tags, making it easy to search later.

[0160] Input: Generated diary data

[0161] Output: Saved diary data

[0162] Step 11: User Access

[0163] Users can view the created diary entries using a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily find diary entries containing conversations from specific dates or specific keywords.

[0164] Input: Search query (e.g. date, keyword)

[0165] Output: Diary data as search results

[0166] (Application example 1)

[0167] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0168] Efficiently recording everyday conversations within the home and automatically generating a diary is extremely important for looking back on family memories and important events. Food delivery services also need to record conversations with customers and use the results as feedback to improve service quality. However, current systems have difficulty automatically executing these processes, and manual recording and data organization are time-consuming and labor-intensive.

[0169] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0170] In this invention, the server includes: means for capturing everyday conversations between family members using an audio microphone installed in the living room; means for locally saving the captured audio data and converting its format; means for uploading the converted audio data to the server via a network; means for converting the audio data into text data using a speech recognition engine in the server; means for analyzing the text data using natural language processing technology and extracting characteristic portions of the conversation; means for automatically generating a diary based on the extracted characteristic portions of the conversation; means for saving the generated diary in a database in a searchable format; means for providing an interface for users to view the diary; means for collecting audio data to capture conversations between delivery staff in a delivery service; means for converting important conversations between customers and delivery staff into text data using a speech recognition engine; and means for analyzing the generated text data and saving it as feedback. This allows for efficient recording and review of memories and important events in the home, and automatically obtaining and analyzing important feedback to improve customer satisfaction in the food delivery service.

[0171] A "living room" is a living space where family members gather on a daily basis to talk and engage in activities.

[0172] An "audio microphone" is a device that picks up sound, converts it into a digital signal, and records it.

[0173] A "conversation" is the act of people communicating through the exchange of words.

[0174] "Local" refers to an area or range limited to an individual device or environment.

[0175] A "server" is a computer system that processes and stores data and provides services to other devices over a network.

[0176] A "voice recognition engine" is software for converting voice into text data.

[0177] "Text data" refers to information expressed as a string of characters.

[0178] "Natural language processing technology" is a technology that allows computers to understand and analyze the natural language used by humans.

[0179] A "characteristic part of a conversation" is a part of a conversation that indicates important or unique content.

[0180] A diary is a document that records everyday events and conversations.

[0181] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0182] An "interface" is the means or device by which a user operates a system and inputs and displays information.

[0183] "Delivery service" refers to a service that delivers products or goods to a location designated by a customer.

[0184] A "delivery person" is a person who is responsible for delivering goods or items to customers.

[0185] "Audio collection means" means any device or technology used to record speech or audio.

[0186] "Customer" means a person who purchases or uses a product or service.

[0187] "Feedback" refers to opinions and evaluations received about a particular action or product.

[0188] System program generation

[0189] As an embodiment of the present invention, we will create a program to realize a system that records conversations at home and customer interactions in delivery services. The system is composed of a dedicated audio microphone, a local terminal, and a server.

[0190] Explanation of program processing

[0191] The server processes and calculates the data in the following steps:

[0192] 1. Audio data collection:

[0193] A dedicated audio microphone is used to collect voices from within the home and from the delivery person. The audio microphone is equipped with noise cancellation and echo removal functions, allowing for clear audio data to be collected. The data collected by the audio microphone is stored on the local device.

[0194] 2. Audio data format conversion and upload:

[0195] The local device converts the collected audio data into the appropriate format and uploads it to the server using a secure communication protocol in an encrypted format, using a standard audio format conversion library.

[0196] 3. Audio to text conversion:

[0197] The server's speech recognition engine (e.g., Google Speech Recognition API) is used to convert the uploaded voice data into text data, which will serve as the basis for further analysis.

[0198] 4. Natural Language Processing Analysis:

[0199] The text data is analyzed using a server-based natural language processing system (e.g., spaCy, NLTK), which extracts characteristic conversational segments from the text and utilizes techniques including keyword extraction and sentiment analysis.

[0200] 5. Automatic generation and saving of diary entries:

[0201] Based on the extracted characteristic conversation segments, the system automatically generates a diary, which is then stored in a database in an easy-to-read and search format.

[0202] 6. User View:

[0203] Users can view their created diaries through a dedicated application or web interface, which is designed to be intuitive and easy to use, and offers search functions for specific dates and keywords.

[0204] Specific examples

[0205] For example, a conversation at home while having breakfast is recorded:

[0206] Parent: "Where do you want to go today?"

[0207] Child: "The zoo is great!"

[0208] This conversation is generated by the system as a diary entry:

[0209] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[0210] Or, in the food delivery service example, the conversations between delivery people and customers are recorded:

[0211] Staff: "This is a delivery. Are you sure you want a pizza and salad?"

[0212] Customer: "Yes, that's correct. Thank you. I'm glad it arrived on time."

[0213] The conversation is recorded as follows:

[0214] 10:15 Conversation at delivery destination: The delivery consisted of a pizza and salad, and the customer seemed pleased. The delivery was on time and was appreciated.

[0215] Prompt Sentence Examples

[0216] Please record and save the following conversation in diary format:

[0217] Staff: "This is a delivery. Are you sure you want a pizza and salad?"

[0218] Customer: "Yes, that's correct. Thank you. I'm glad you got it on time."

[0219] Example of a diary entry:

[0220] 10:15 Conversation at delivery destination: The delivery consisted of a pizza and salad, and the customer seemed pleased. The delivery was on time and was appreciated.

[0221] In this way, users can efficiently record everyday conversations at home or conversations during deliveries and review them later.

[0222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0223] Step 1:

[0224] Audio data collection: The device activates the audio microphones used by the home and the delivery person to capture conversations in real time. Ambient sounds are included as input. Clear audio data is collected using noise cancellation and echo cancellation, and stored locally.

[0225] Step 2:

[0226] Audio data format conversion and upload: The device converts the collected audio data into a standard audio format. The input in this step is the audio data collected in step 1, and the output is the converted audio file. The converted audio data is encrypted and uploaded to the server using a secure communication protocol (e.g., HTTPS).

[0227] Step 3:

[0228] Voice data to text conversion: Using the server's speech recognition engine (e.g., Google Speech Recognition API), the uploaded voice data is converted into text data. The output is a text representation of the conversation. This text data is used in the subsequent analysis process.

[0229] Step 4:

[0230] Analysis by natural language processing: The server analyzes the text data using a natural language processing system (e.g., spaCy, NLTK). The input is the text data converted in step 3, and the output is the analysis results after keyword extraction and sentiment analysis. This identifies characteristic parts of the conversation.

[0231] Step 5:

[0232] Automatic generation and storage of diary entries: The server automatically generates a diary entry based on the analysis results. The input is the analysis results from step 4, and the output is diary-format text data. This diary entry data is stored in a database and converted into a searchable format.

[0233] Step 6:

[0234] User viewing: Users can view the created diary through a dedicated application or a web interface. As input, they specify dates and keywords. As output, the corresponding diary entries are displayed. The interface is designed to be intuitive and easy to use.

[0235] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0236] This invention relates to a system that automatically records everyday family conversations and compiles them into a diary, and also combines it with an emotion engine that recognizes the user's emotions. The system is mainly composed of an audio microphone installed in the living room, a local terminal, a server, and an emotion engine.

[0237] Audio data collection

[0238] A dedicated microphone is installed in the living room to capture daily family conversations in real time. The microphone is equipped with noise cancellation and echo cancellation functions to collect clear audio data.

[0239] The device periodically receives audio data from the microphone, stores it locally, converts it into a suitable digital format, and prepares it for upload to the server. The audio data is encrypted and transmitted to the server using a secure communication protocol.

[0240] Analysis of voice data and diary generation

[0241] The server receives the voice data sent from the device. After receiving it, it converts the voice data into text data using a voice recognition engine. The converted text data is then analyzed using natural language processing (NLP) technology.

[0242] The server's NLP system extracts characteristic conversational segments from the text data, utilizing techniques such as keyword extraction, sentiment analysis, and speaker identification. Furthermore, an emotion engine recognizes the user's emotions from the text data and incorporates the results into the analysis. Based on the characteristic conversational segments and the user's emotions, the system automatically generates a diary. This diary is organized chronologically and includes emotional elements, providing a richer portrayal of important family events and interesting conversations.

[0243] Save and access your diary

[0244] The generated diary entries are stored in a searchable database by the server, and the diary data is categorized by date, tag, emotion type, etc., allowing users to easily search and access them later.

[0245] Users can view the created diary entries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diary entries based on a specific date or emotion type.

[0246] Specific examples

[0247] Emotion recognition and recording process

[0248] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[0249] Parent: "Where do you want to go today?"

[0250] Child: "The zoo is great!"

[0251] The conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's speech recognition engine and NLP system analyze the conversation and generate a diary entry similar to the following:

[0252] 8:00 At breakfast today, the family planned to go to the zoo. The children looked very excited.

[0253] Furthermore, the emotion engine recognizes the child's emotion of "it looks like they're having fun" and reflects that in the diary. The diary created in this way is stored on a server, and users can view it using a dedicated app to look back on their emotions and important events at the time.

[0254] The present invention provides a system that automatically records family conversations and memories, including emotions, even in busy daily life, allowing for rich reflection later.

[0255] The processing flow will be explained below.

[0256] Step 1:

[0257] A dedicated microphone picks up everyday conversations between family members in the living room in real time. The dedicated microphone is equipped with high-precision noise cancellation and echo cancellation functions, allowing for clear audio data to be collected.

[0258] Step 2:

[0259] The device periodically acquires audio data collected from a dedicated microphone and temporarily stores it in local storage. The audio data is encrypted during storage to ensure security.

[0260] Step 3:

[0261] The device converts the audio data stored in its local storage into an appropriate digital format (e.g., WAV, MP3, etc.). The conversion process compresses the data and prepares it for transmission.

[0262] Step 4:

[0263] The terminal uploads the converted voice data to a server using a secure communication protocol (e.g., HTTPS or SFTP), thereby ensuring secure data transfer.

[0264] Step 5:

[0265] The server receives the uploaded voice data and stores it in storage, where it is immediately used for voice recognition processing.

[0266] Step 6:

[0267] The server uses a speech recognition engine to convert the voice data into text data, using a highly accurate speech recognition model to accurately convert the conversation into text.

[0268] Step 7:

[0269] The server passes the text data to a natural language processing (NLP) system for analysis, which performs keyword extraction, sentiment analysis, speaker identification, and more.

[0270] Step 8:

[0271] The emotion engine uses the analyzed text data to recognize and identify the user's emotions expressed in the conversation using emotion recognition algorithms.

[0272] Step 9:

[0273] The server extracts characteristic conversational parts based on the analysis results, including the identified emotional information, and automatically generates a diary by combining the extracted conversational content and emotional information.

[0274] Step 10:

[0275] The server stores the generated diary in a searchable database. When saving, the diary data is categorized by date, emotion type, etc.

[0276] Step 11:

[0277] Users can view the created diary entries through a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily find entries based on specific dates or emotions.

[0278] Specific examples

[0279] Emotion recognition and recording process

[0280] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[0281] Parent: "Where do you want to go today?"

[0282] Child: "The zoo is great!"

[0283] The conversations are collected by the devices and uploaded to a server after format conversion. The server's speech recognition engine and NLP system analyze the conversation, and the emotion engine recognizes the children's emotion of "it looks like they're having fun" and reflects that in the diary. The following diary entry is then generated:

[0284] 8:00 At breakfast today, the family planned to go to the zoo. The children looked very excited.

[0285] The generated diary is stored on a server, and users can view it at any time using a dedicated app to recall their emotions and important events at the time.

[0286] In this way, this system automatically records everyday conversations of family members, including their emotions, allowing for richer reflection later.

[0287] Example 2

[0288] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0289] It is difficult for many households to effectively record everyday conversations at home and save them in a format that can be reviewed later. There is also a demand for recording not only the content of conversations but also the emotions expressed at the time, but doing this manually is extremely time-consuming. Furthermore, ensuring the security of collected data is also an important issue.

[0290] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring household conversations using a voice collecting device, means for locally storing the acquired voice data and converting it into a digital format, means for transmitting the converted voice data to a central processing unit over a network, means for converting the voice data into text data using a voice recognition model in the central processing unit, means for analyzing the text data using natural language processing technology and extracting characteristic parts of the conversation, means for automatically generating a diary based on the extracted characteristic parts of the conversation and user emotion data, means for storing the generated diary in a searchable format in a data storage device, and means for providing a user interface for the user to view the diary. This makes it possible to effectively and safely record and store everyday household conversations and the emotions felt at the time, and easily review them later.

[0291] An "audio collection device" is a device installed in a home, such as a living room, to capture conversations, and is equipped with noise cancellation and echo removal functions.

[0292] "Local" refers to the storage area within the device where audio data acquired from a dedicated microphone is temporarily saved.

[0293] "Converting to digital format" refers to the process of converting captured audio data into an appropriate digital format.

[0294] "Central Processing Unit" refers to a server or another powerful computer for receiving voice data and processing voice recognition models, etc.

[0295] "Speech recognition model" refers to an algorithm or system for converting speech data into text data.

[0296] "Natural language processing technology" refers to technology for analyzing text data and performing keyword extraction, speaker identification, sentiment analysis, etc.

[0297] "Characteristic conversational parts" refer to important conversational content extracted from text data using natural language processing technology.

[0298] "Automatic diary generation" refers to the process in which the system automatically creates diary-style records based on characteristic conversation segments and the user's emotional data.

[0299] "Data repository" refers to a database or other storage medium for storing generated diary data.

[0300] "User interface" refers to the application or web interface that allows a user to view the diary they have created.

[0301] The present invention is a system that automatically records everyday conversations at home and compiles them into a diary. This system consists of a user, a terminal, and a server. The specific configuration and operation procedure are described in detail below.

[0302] First, the device captures household conversations in real time using a dedicated microphone installed in the living room. This microphone is equipped with noise cancellation and echo cancellation functions, allowing for clear audio data to be collected. The collected audio data is stored locally on the device and converted into an appropriate digital format. The converted audio data is then encrypted and transmitted to a central processing unit (server) using a secure communication protocol.

[0303] The server receives the voice data sent from the device. The received voice data is converted into text data using a voice recognition model. The text data is then analyzed using natural language processing technology. This includes processes such as keyword extraction, speaker identification, and sentiment analysis. Characteristic parts of the conversation are extracted from the analyzed text data. In addition, an emotion engine recognizes the user's emotional data and reflects it in the analysis results.

[0304] The server then automatically generates a diary based on the extracted characteristic conversation segments and the user's emotional data. This automatically generated diary is organized chronologically and includes emotional elements, allowing it to richly capture important events and interesting conversations that occur daily at home. The generated diary is then stored in a searchable format in a data storage device (database).

[0305] Users can view their diaries stored on the server through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diaries based on a specific date or emotion type.

[0306] Specific examples

[0307] In the morning, while a family is having breakfast in the living room, a dedicated microphone captures their conversation. For example, a parent might ask, "Where do you want to go today?" and the child might reply, "The zoo!" This conversation is collected by the device, converted into an appropriate format, encrypted, and sent to a server. The server's speech recognition model and natural language processing technology analyze the conversation and generate a diary entry like the one below.

[0308] "08:00 At breakfast today, the family planned to go to the zoo. The children looked very excited."

[0309] Furthermore, the emotion engine recognizes the child's emotion of "it looks like they're having fun" and reflects that in the diary. The diary created in this way is saved on a server, and users can view it using a dedicated app to look back on their emotions and important events at the time.

[0310] Prompt Sentence Examples

[0311] Examples of prompts to input to a generative AI model include:

[0312] "Please explain a system that records everyday household conversations in real time and compiles them into a diary. The system collects audio using a dedicated microphone, converts the data on the device, and sends it to a server. The server analyzes the audio to generate text data and also recognizes emotions using an emotion engine. Finally, it automatically generates a diary that the user can access."

[0313] Using this prompt, the generative AI model highlights detailed descriptions of the system in question, helping to clarify specific processing steps and technologies used.

[0314] This invention provides a system that can effectively and safely record and store everyday conversations at home and the emotions felt during those conversations, allowing for easy review later.

[0315] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0316] Step 1:

[0317] The device captures household conversations in real time using a voice collection device (dedicated microphone) installed in the living room. The input is the voice data collected by the microphone, and the output is clear voice data with noise cancellation and echo removal. Specifically, the microphone sensor detects sound waves in the space and converts them into a digital signal. Next, the built-in noise cancellation technology removes background noise, and the echo removal function suppresses voice reverberation.

[0318] Step 2:

[0319] The device stores the collected voice data in local storage and converts it into an appropriate digital format. The input is clear voice data with noise and echo removed, and the output is voice data converted into a digital format. Specifically, the device converts the stored voice data into a digital format (e.g., WAV or MP3) at regular intervals using a digital signal processor.

[0320] Step 3:

[0321] The device encrypts the converted voice data and sends it to the server using a secure communication protocol. The input is the voice data converted to digital format, and the output is the encrypted data sent to the server. Specifically, the device securely converts the digital format voice data using an encryption algorithm such as AES, and then sends the encrypted data to the server using a secure protocol such as HTTPS or TLS.

[0322] Step 4:

[0323] The server receives and decodes the encrypted voice data sent from the terminal. The input is encrypted voice data, and the output is decoded clear voice data. Specifically, the server's communication module receives the encrypted data packet and decrypts the voice data using a built-in decoding algorithm.

[0324] Step 5:

[0325] The server converts the received voice data into text data using a voice recognition model. The input is decoded clear voice data, and the output is text data converted from the voice data. Specifically, the voice recognition engine in the server analyzes the characteristics of the voice waveform and generates the corresponding text.

[0326] Step 6:

[0327] The server analyzes the text data using natural language processing (NLP) technology, extracting keywords and analyzing emotions, and extracting characteristic parts of the conversation. The input is text data generated by a speech recognition model, and the output is the analyzed characteristic parts of the conversation and emotional data. Specifically, the NLP engine analyzes the text data and automatically extracts important keywords and the speaker's emotions.

[0328] Step 7:

[0329] The server automatically generates a diary based on the extracted characteristic conversational parts and the user's emotional data. The input is the analyzed characteristic conversational parts and emotional data, and the output is the generated diary data. Specifically, the diary generation algorithm integrates the characteristic conversational parts and the emotion analysis results, and automatically creates a diary-style record organized chronologically.

[0330] Step 8:

[0331] The server stores the created diary in a searchable format in a data storage device (database). The input is the created diary data, and the output is searchable diary data stored in the database. Specifically, the database management system classifies and stores the created diary data with appropriate tags (e.g., date, emotion type).

[0332] Step 9:

[0333] Users can view the diaries stored on the server through a dedicated application or a web interface. The input is a search query by the user, and the output is diary data extracted based on the query. Specifically, the user interface receives the search query, extracts relevant diary data from the database, and displays it.

[0334] (Application example 2)

[0335] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0336] Conventional systems can record conversations in a living room, compile them into a diary, and even recognize emotions. However, it is difficult to record conversations between customers and staff in commercial facilities and provide information useful for improving services and customer satisfaction. Furthermore, there is a lack of concrete methods for improving customer satisfaction and evaluating service quality. Furthermore, there is a need for a method to evaluate smooth communication between customers and staff by automatically analyzing the emotions in conversations and generating diary-style reports.

[0337] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0338] In this invention, the server includes means for converting voice data into text data using a voice recognition engine, means for analyzing the text data using natural language processing technology to extract characteristic conversation parts, and means for automatically generating a diary based on the extracted characteristic conversation parts and the user's emotions. This makes it possible to record and analyze conversations between customers and staff and provide information useful for improving services and increasing customer satisfaction.

[0339] An "audio microphone" is a device that converts sound waves into electrical signals and collects sounds such as conversations.

[0340] "Local storage" means that acquired data is temporarily stored on a local terminal or device rather than on a central server.

[0341] "Format conversion" refers to converting an original data format into another format, including converting audio data into an appropriate digital format.

[0342] "Uploading via a network" means transferring data to another device or server via the Internet or other communication means.

[0343] A "voice recognition engine" is a software system for converting voice data into text data.

[0344] "Natural language processing technology" is a set of algorithms and techniques for analyzing text data and understanding its meaning.

[0345] "Characteristic conversational segments" are specific important keywords, phrases, emotions, etc. extracted from the analyzed text data.

[0346] "Automatic diary generation" means automatically creating diary-style documents based on collected and analyzed data.

[0347] A "searchable format" is a format in which stored data is structured so that it can be easily searched and accessed by users.

[0348] "Providing an interface" means providing a means for users to operate the system and access data.

[0349] A "commercial facility" is an environment where customers and staff communicate face-to-face, including shopping malls and stores.

[0350] "Sentiment analysis" is the process of detecting emotions and intent from text data and determining the type and intensity of those emotions.

[0351] "Service improvement" refers to efforts to improve the quality of services provided in order to enhance customer experience and satisfaction.

[0352] "Improving customer satisfaction" refers to efforts to increase customer satisfaction with the services they receive.

[0353] MODE FOR CARRYING OUT THE INVENTION

[0354] The embodiments of the present invention will be described in detail below. The system is realized by using an audio microphone, a local terminal, a server, natural language processing (NLP) technology, and a sentiment analysis engine.

[0355] Hardware and software used

[0356] The following hardware and software are used to build this system. The Shure MV7, which has noise cancellation functionality, is suitable as an audio microphone. A Raspberry Pi can be used as the local terminal. Amazon Web Services (AWS) is used as the server, and Flask is used to build the API. The Google Speech Recognition API is used as the speech recognition engine, and the TextBlob library is used for natural language processing. TextBlob is also used as a sentiment analysis engine, and detailed analysis is performed based on the data obtained from it.

[0357] Overall system flow and data processing

[0358] Audio data collection

[0359] The server uses audio microphones installed in commercial facilities to capture conversations between customers and staff in real time. The audio microphones are equipped with noise cancellation functionality, enabling clear audio data to be collected. The collected audio data is temporarily stored on a local device and converted into an appropriate digital format. The converted audio data is then encrypted and securely uploaded to the server via a network.

[0360] Analysis of voice data and diary generation

[0361] When the server receives the uploaded voice data, it first converts it into text using a speech recognition engine. It then uses NLP technology to analyze the text, extracting keywords and conducting sentiment analysis. It then extracts characteristic parts of the conversation and automatically generates a diary based on them. The diary also contains emotional elements, allowing it to richly express the content of interactions between users and staff at commercial facilities.

[0362] Save and access your diary

[0363] The generated diary entries are stored in a searchable database by the server. Users can access this database and view their entries through a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily search and find entries based on specific dates or emotional types.

[0364] Specific examples

[0365] Recording conversations in commercial facilities

[0366] Imagine a scenario in which a customer and a staff member are talking in a retail establishment about a new campaign. The conversation goes something like this:

[0367] Staff: "What do you think about the new campaign?"

[0368] Customer: "Very interested!"

[0369] This conversation is recorded by a microphone and temporarily saved on the local device before being uploaded to a server, where it is analyzed using a speech recognition engine and NLP technology to generate a diary entry like this:

[0370] 14:00 Customer response to the new campaign has been very positive. Customers have shown great interest.

[0371] Furthermore, the sentiment analysis engine recognizes the customer's positive emotion of "interested" and reflects this in the diary. The diary generated in this way is saved on the server and can be viewed by the user through a dedicated app. Specifically, the following prompt sentences can be used to help improve services, for example:

[0372] "Record customer reactions to new campaigns. Find out their opinions, feelings and areas for improvement regarding the desired service."

[0373] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0374] Step 1:

[0375] The server uses audio microphones installed in commercial facilities to capture conversations between customers and staff in real time. The audio microphones are equipped with a noise cancellation function to collect clear audio data, which is then used as input data.

[0376] Step 2:

[0377] The device periodically stores audio data received from the audio microphone locally, converts the audio data into a suitable digital format, encrypts it, and uploads the converted audio data to a server using a secure communication protocol. The output of this step includes the encrypted audio data.

[0378] Step 3:

[0379] The server receives and decrypts the uploaded encrypted audio data. The received and decrypted audio data is converted into text data using the Google Speech Recognition API. The input of this process is the encrypted audio data, and the output is text data.

[0380] Step 4:

[0381] The server performs natural language processing (NLP) on the converted text data using the TextBlob library. The NLP process involves keyword extraction and sentiment analysis, which specifically extracts customer sentiment and important keywords. The input is text data, and the output is analyzed keywords and sentiment data. Specifically, keywords and sentiment are extracted using TextBlob's NLP functions.

[0382] Step 5:

[0383] The server automatically generates a diary-style report based on the extracted keywords and emotion data. This diary richly reflects the conversations and emotions between customers and staff. The input is the analyzed keywords and emotion data, and the output is a diary-style text report.

[0384] Step 6:

[0385] The server stores the generated diary-style reports in a searchable database. The database is tagged with dates, types of emotions, and other information, and organized for easy later search and access. The input is a diary-style text report, and the output is diary data saved in a searchable format.

[0386] Step 7:

[0387] Users can access the database and view the created diary entries through a dedicated application or web interface. The interface is intuitive and easy to use, and allows searches for specific dates or types of emotions. The input is search criteria, and the output is the corresponding diary data. Specifically, related diary data is retrieved from the database based on the user's input and displayed.

[0388] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0389] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0390] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0391] [Second embodiment]

[0392] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0393] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0394] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0395] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0396] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0397] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0398] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0399] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0400] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0401] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0402] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0403] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0404] This invention relates to a system that automatically records everyday conversations of a family and compiles them into a diary. The system is mainly composed of an audio microphone installed in the living room, a local terminal, and a server.

[0405] Audio data collection

[0406] A dedicated microphone is installed in the living room to capture daily family conversations in real time. This microphone is equipped with noise cancellation and echo cancellation functions, allowing for clear audio data to be collected.

[0407] The device periodically receives audio data from the microphone, stores it locally, converts it into a suitable digital format, and prepares it for upload to the server. The audio data is encrypted and transmitted to the server using a secure communication protocol.

[0408] Analysis of voice data and diary generation

[0409] The server receives the voice data sent from the device. After receiving it, it converts the voice data into text data using a voice recognition engine. The converted text data is then analyzed using natural language processing (NLP) technology.

[0410] The server's NLP system extracts characteristic conversational segments from the text data using techniques such as keyword extraction, sentiment analysis, and speaker identification. Based on these segments, the system automatically generates a diary, which is organized chronologically and designed to include important events and interesting conversations from the family's daily life.

[0411] Save and access your diary

[0412] The generated diary entries are stored in a searchable database by the server, and the diary entries are categorized by date, tags, etc., allowing users to easily search and access them later.

[0413] Users can view the created diaries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find conversations from a specific date or diary entries containing specific keywords.

[0414] Specific examples

[0415] Morning conversation transcript

[0416] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[0417] Parent: "Where do you want to go today?"

[0418] Child: "The zoo is great!"

[0419] The conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's speech recognition engine and NLP system analyze the conversation and generate a diary entry similar to the following:

[0420] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[0421] The diary created in this way is stored on a server, and users can view it using a dedicated app and look back on important events of the day.

[0422] The present invention provides a system that allows family conversations and memories to be easily recorded and looked back on later, even during busy daily life.

[0423] The processing flow will be explained below.

[0424] Step 1:

[0425] A dedicated microphone picks up everyday conversations in the living room in real time, and the microphone features high-precision noise cancellation and echo cancellation to collect clear audio data.

[0426] Step 2:

[0427] The device periodically acquires audio data collected from a dedicated microphone and temporarily stores it in local storage. This data is encrypted to ensure security.

[0428] Step 3:

[0429] The device converts the locally stored audio data into an appropriate digital format (e.g., WAV or MP3). The conversion process compresses the audio data and prepares it for transmission.

[0430] Step 4:

[0431] The device uploads the converted audio data to the server. The upload is performed using a secure communication protocol (e.g., HTTPS or SFTP), ensuring data security.

[0432] Step 5:

[0433] The server receives the uploaded voice data and stores it in storage. This stored data is immediately used for voice recognition processing.

[0434] Step 6:

[0435] The server uses a speech recognition engine to convert the voice data into text data, using a highly accurate speech recognition model to accurately convert the content of the conversation into text.

[0436] Step 7:

[0437] The server passes the text data to a natural language processing (NLP) system for analysis, which performs processes such as keyword extraction, sentiment analysis, and speaker identification to extract characteristic conversational segments.

[0438] Step 8:

[0439] The server automatically generates a diary based on the extracted characteristic conversation segments. The diary is organized chronologically and is designed to include important events and interesting conversations in the family's daily life.

[0440] Step 9:

[0441] The server stores the created diary in a database, and the saved diary is managed in a format that makes it easy to search and view.

[0442] Step 10:

[0443] Users can view the created diary through a dedicated application or a web interface. The interface is intuitive and easy to use, allowing users to easily find conversations or interesting events from specific dates.

[0444] In this way, the system automatically records everyday family conversations and provides them as an easily accessible diary.

[0445] Example 1

[0446] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0447] In today's busy lifestyles, it is difficult to easily record everyday family conversations and events and review them later. Manual recording is also a time-consuming and labor-intensive process. Furthermore, protecting privacy and ensuring data security are important, and a system that can achieve both is needed.

[0448] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0449] In this invention, the server includes a means for converting acoustic information into text information, a means for analyzing the text information using natural language processing technology, and a means for automatically generating a record based on the extracted dialogue portion. This allows for automatic and efficient recording of everyday family conversations, protecting privacy and making it easy to review later.

[0450] The "sound acquisition device" is a device that is installed to acquire human conversations, and includes an acoustic sensor and a microphone.

[0451] "Acoustic information" refers to captured human dialogue and other acoustic data.

[0452] An "electronic recording format" is a format for storing acoustic information as digital data, such as MP3 or WAV.

[0453] "Communication line" refers to the network infrastructure used to transmit acoustic information to a remote device, including the Internet, dedicated communication lines, etc.

[0454] A "remote device" is a server or cloud computing system for receiving and processing the acoustic information.

[0455] An "acoustic recognition mechanism" refers to technology or equipment for converting acoustic information into text information, including voice recognition engines.

[0456] "Text information" refers to text data generated based on acoustic information.

[0457] "Natural language processing technology" is a technology used to analyze textual information, and includes keyword extraction, sentiment analysis, and speaker identification.

[0458] "Characteristic dialogue parts" are parts of a dialogue that have important meaning or are interesting, extracted using natural language processing technology.

[0459] "Records" refer to diaries and memos automatically generated based on extracted characteristic parts of the dialogue.

[0460] "Storage device" refers to a database or storage system for storing the generated records.

[0461] "Interface" refers to the application, web page, or other operating means that a user uses to view a record.

[0462] The present invention relates to a system that automatically records everyday family conversations and compiles them into a diary. This system is composed of an audio capture device, a terminal, a server, and a user interface.

[0463] Audio data collection

[0464] The audio capture device is installed in the living room and captures people's conversations in real time. This audio capture device is equipped with noise cancellation and echo removal functions, allowing for clear audio data to be collected.

[0465] Local storage and format conversion of audio data

[0466] The device periodically receives audio data from the audio capture device, stores it locally, and converts it into an appropriate digital recording format (e.g., MP3, WAV). Existing libraries such as FFmpeg can be used for this format conversion.

[0467] Encrypting and uploading audio data

[0468] The device encrypts the converted audio data using an encryption algorithm such as AES, and the encrypted data is sent to the server over a secure communication line (e.g., HTTPS, TLS).

[0469] Analysis of audio data

[0470] The server receives the encrypted voice data sent from the device and converts it into text using an acoustic recognition mechanism (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech Services). The converted text is then analyzed using natural language processing technology (NLP system). This analysis process uses techniques such as keyword extraction, sentiment analysis, and speaker identification.

[0471] Generating diary data

[0472] The server's NLP system extracts characteristic dialogue segments from the analyzed text information. Based on these segments, the server automatically generates a record (diary). This diary is organized chronologically and is designed to include important events and interesting dialogues from the family's daily life.

[0473] Storage and access of diary data

[0474] The generated diary is stored in a searchable format on a storage device by the server. The diary data is categorized by date and tags, allowing users to easily search and access it later.

[0475] User Interface

[0476] Users can view the created diaries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diaries containing conversations from a specific date or specific keywords.

[0477] Specific examples

[0478] For example, in the morning, while a family is having breakfast in the living room, an audio capture device captures their conversation. The conversation goes something like this:

[0479] Parent: "Where do you want to go today?"

[0480] Child: "The zoo is great!"

[0481] This conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's acoustic recognition and NLP systems analyze the conversation and generate a diary entry like this:

[0482] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[0483] The diary created in this way is stored on a server, and users can view it using a dedicated app and look back on important events of the day.

[0484] This invention is a system realized by linking an audio capture device, a terminal, a server, and a user interface, which automatically records everyday family conversations and allows them to be easily reviewed later.

[0485] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0486] Step 1: Acquire audio data

[0487] A dedicated microphone is installed in the living room to capture people's conversations in real time, using noise cancellation and echo cancellation functions to collect clear audio data.

[0488] Input: Audio from living room

[0489] Output: Audio data (analog format)

[0490] Step 2: Save the audio data locally

[0491] The device receives audio data from the acoustic capture device at regular intervals and stores it locally. The audio data is divided into chunks of a fixed size (e.g., every 5 minutes) and stored.

[0492] Input: Audio data (analog format)

[0493] Output: Locally stored audio data (digital format)

[0494] Step 3: Convert the audio data format

[0495] The device converts the stored audio data into the appropriate digital recording format (e.g., MP3, WAV), using existing libraries such as FFmpeg.

[0496] Input: Locally stored audio data (digital format)

[0497] Output: Converted audio data (MP3, WAV format)

[0498] Step 4: Encrypt the audio data

[0499] The device then encrypts the converted audio data using an encryption algorithm such as AES, and the encrypted data is ready to be sent to the server via the communication line.

[0500] Input: Converted audio data (MP3, WAV format)

[0501] Output: Encrypted audio data

[0502] Step 5: Upload your audio data

[0503] The device sends encrypted audio data to the server using a secure communication line (e.g., HTTPS, TLS).

[0504] Input: Encrypted audio data

[0505] Output: Encrypted audio data sent to the server

[0506] Step 6: Receiving and analyzing audio data

[0507] The server receives the encrypted voice data sent from the device and converts the voice data into text information using an acoustic recognition mechanism (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech Services).

[0508] Input: Encrypted audio data

[0509] Output: Text data

[0510] Step 7: Natural Language Processing of Text Data

[0511] The server then analyzes the converted text data using natural language processing techniques, such as keyword extraction, sentiment analysis, and speaker identification.

[0512] Input: Text data

[0513] Output: Parsed text data

[0514] Step 8: Extracting characteristic dialogue parts

[0515] The server's NLP system extracts characteristic dialogue segments from the parsed text.

[0516] Input: Parsed text data

[0517] Output: Characteristic dialogue

[0518] Step 9: Automatic generation of diary data

[0519] The server automatically generates a diary based on the extracted characteristic dialogue. The diary is organized chronologically and includes important events and interesting conversations from the family's daily life.

[0520] Input: Characteristic dialogue

[0521] Output: Generated diary data

[0522] Step 10: Save diary data

[0523] The server stores the generated diary in a searchable format on a storage device. The diary data is categorized by date and tags, making it easy to search later.

[0524] Input: Generated diary data

[0525] Output: Saved diary data

[0526] Step 11: User Access

[0527] Users can view the created diary entries using a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily find diary entries containing conversations from specific dates or specific keywords.

[0528] Input: Search query (e.g. date, keyword)

[0529] Output: Diary data as search results

[0530] (Application example 1)

[0531] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0532] Efficiently recording everyday conversations within the home and automatically generating a diary is extremely important for looking back on family memories and important events. Food delivery services also need to record conversations with customers and use the results as feedback to improve service quality. However, current systems have difficulty automatically executing these processes, and manual recording and data organization are time-consuming and labor-intensive.

[0533] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0534] In this invention, the server includes: means for capturing everyday conversations between family members using an audio microphone installed in the living room; means for locally saving the captured audio data and converting its format; means for uploading the converted audio data to the server via a network; means for converting the audio data into text data using a speech recognition engine in the server; means for analyzing the text data using natural language processing technology and extracting characteristic portions of the conversation; means for automatically generating a diary based on the extracted characteristic portions of the conversation; means for saving the generated diary in a database in a searchable format; means for providing an interface for users to view the diary; means for collecting audio data to capture conversations between delivery staff in a delivery service; means for converting important conversations between customers and delivery staff into text data using a speech recognition engine; and means for analyzing the generated text data and saving it as feedback. This allows for efficient recording and review of memories and important events in the home, and automatically obtaining and analyzing important feedback to improve customer satisfaction in the food delivery service.

[0535] A "living room" is a living space where family members gather on a daily basis to talk and engage in activities.

[0536] An "audio microphone" is a device that picks up sound, converts it into a digital signal, and records it.

[0537] A "conversation" is the act of people communicating through the exchange of words.

[0538] "Local" refers to an area or range limited to an individual device or environment.

[0539] A "server" is a computer system that processes and stores data and provides services to other devices over a network.

[0540] A "voice recognition engine" is software for converting voice into text data.

[0541] "Text data" refers to information expressed as a string of characters.

[0542] "Natural language processing technology" is a technology that allows computers to understand and analyze the natural language used by humans.

[0543] A "characteristic part of a conversation" is a part of a conversation that indicates important or unique content.

[0544] A diary is a document that records everyday events and conversations.

[0545] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0546] An "interface" is the means or device by which a user operates a system and inputs and displays information.

[0547] "Delivery service" refers to a service that delivers products or goods to a location designated by a customer.

[0548] A "delivery person" is a person who is responsible for delivering goods or items to customers.

[0549] "Audio collection means" means any device or technology used to record speech or audio.

[0550] "Customer" means a person who purchases or uses a product or service.

[0551] "Feedback" refers to opinions and evaluations received about a particular action or product.

[0552] System program generation

[0553] As an embodiment of the present invention, we will create a program to realize a system that records conversations at home and customer interactions in delivery services. The system is composed of a dedicated audio microphone, a local terminal, and a server.

[0554] Explanation of program processing

[0555] The server processes and calculates the data in the following steps:

[0556] 1. Audio data collection:

[0557] A dedicated audio microphone is used to collect voices from within the home and from the delivery person. The audio microphone is equipped with noise cancellation and echo removal functions, allowing for clear audio data to be collected. The data collected by the audio microphone is stored on the local device.

[0558] 2. Audio data format conversion and upload:

[0559] The local device converts the collected audio data into the appropriate format and uploads it to the server using a secure communication protocol in an encrypted format, using a standard audio format conversion library.

[0560] 3. Audio to text conversion:

[0561] The server's speech recognition engine (e.g., Google Speech Recognition API) is used to convert the uploaded voice data into text data, which will serve as the basis for further analysis.

[0562] 4. Natural Language Processing Analysis:

[0563] The text data is analyzed using a server-based natural language processing system (e.g., spaCy, NLTK), which extracts characteristic conversational segments from the text and utilizes techniques including keyword extraction and sentiment analysis.

[0564] 5. Automatic generation and saving of diary entries:

[0565] Based on the extracted characteristic conversation segments, the system automatically generates a diary, which is then stored in a database in an easy-to-read and search format.

[0566] 6. User View:

[0567] Users can view their created diaries through a dedicated application or web interface, which is designed to be intuitive and easy to use, and offers search functions for specific dates and keywords.

[0568] Specific examples

[0569] For example, a conversation at home while having breakfast is recorded:

[0570] Parent: "Where do you want to go today?"

[0571] Child: "The zoo is great!"

[0572] This conversation is generated by the system as a diary entry:

[0573] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[0574] Or, in the food delivery service example, the conversations between delivery people and customers are recorded:

[0575] Staff: "This is a delivery. Are you sure you want a pizza and salad?"

[0576] Customer: "Yes, that's correct. Thank you. I'm glad it arrived on time."

[0577] The conversation is recorded as follows:

[0578] 10:15 Conversation at delivery destination: The delivery consisted of a pizza and salad, and the customer seemed pleased. The delivery was on time and was appreciated.

[0579] Prompt Sentence Examples

[0580] Please record and save the following conversation in diary format:

[0581] Staff: "This is a delivery. Are you sure you want a pizza and salad?"

[0582] Customer: "Yes, that's correct. Thank you. I'm glad you got it on time."

[0583] Example of a diary entry:

[0584] 10:15 Conversation at delivery destination: The delivery consisted of a pizza and salad, and the customer seemed pleased. The delivery was on time and was appreciated.

[0585] In this way, users can efficiently record everyday conversations at home or conversations during deliveries and review them later.

[0586] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0587] Step 1:

[0588] Audio data collection: The device activates the audio microphones used by the home and the delivery person to capture conversations in real time. Ambient sounds are included as input. Clear audio data is collected using noise cancellation and echo cancellation, and stored locally.

[0589] Step 2:

[0590] Audio data format conversion and upload: The device converts the collected audio data into a standard audio format. The input in this step is the audio data collected in step 1, and the output is the converted audio file. The converted audio data is encrypted and uploaded to the server using a secure communication protocol (e.g., HTTPS).

[0591] Step 3:

[0592] Voice data to text conversion: Using the server's speech recognition engine (e.g., Google Speech Recognition API), the uploaded voice data is converted into text data. The output is a text representation of the conversation. This text data is used in the subsequent analysis process.

[0593] Step 4:

[0594] Analysis by natural language processing: The server analyzes the text data using a natural language processing system (e.g., spaCy, NLTK). The input is the text data converted in step 3, and the output is the analysis results after keyword extraction and sentiment analysis. This identifies characteristic parts of the conversation.

[0595] Step 5:

[0596] Automatic generation and storage of diary entries: The server automatically generates a diary entry based on the analysis results. The input is the analysis results from step 4, and the output is diary-format text data. This diary entry data is stored in a database and converted into a searchable format.

[0597] Step 6:

[0598] User viewing: Users can view the created diary through a dedicated application or a web interface. As input, they specify dates and keywords. As output, the corresponding diary entries are displayed. The interface is designed to be intuitive and easy to use.

[0599] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0600] This invention relates to a system that automatically records everyday family conversations and compiles them into a diary, and also combines it with an emotion engine that recognizes the user's emotions. The system is mainly composed of an audio microphone installed in the living room, a local terminal, a server, and an emotion engine.

[0601] Audio data collection

[0602] A dedicated microphone is installed in the living room to capture daily family conversations in real time. The microphone is equipped with noise cancellation and echo cancellation functions to collect clear audio data.

[0603] The device periodically receives audio data from the microphone, stores it locally, converts it into a suitable digital format, and prepares it for upload to the server. The audio data is encrypted and transmitted to the server using a secure communication protocol.

[0604] Analysis of voice data and diary generation

[0605] The server receives the voice data sent from the device. After receiving it, it converts the voice data into text data using a voice recognition engine. The converted text data is then analyzed using natural language processing (NLP) technology.

[0606] The server's NLP system extracts characteristic conversational segments from the text data, utilizing techniques such as keyword extraction, sentiment analysis, and speaker identification. Furthermore, an emotion engine recognizes the user's emotions from the text data and incorporates the results into the analysis. Based on the characteristic conversational segments and the user's emotions, the system automatically generates a diary. This diary is organized chronologically and includes emotional elements, providing a richer portrayal of important family events and interesting conversations.

[0607] Save and access your diary

[0608] The generated diary entries are stored in a searchable database by the server, and the diary data is categorized by date, tag, emotion type, etc., allowing users to easily search and access them later.

[0609] Users can view the created diary entries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diary entries based on a specific date or emotion type.

[0610] Specific examples

[0611] Emotion recognition and recording process

[0612] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[0613] Parent: "Where do you want to go today?"

[0614] Child: "The zoo is great!"

[0615] The conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's speech recognition engine and NLP system analyze the conversation and generate a diary entry similar to the following:

[0616] 8:00 At breakfast today, the family planned to go to the zoo. The children looked very excited.

[0617] Furthermore, the emotion engine recognizes the child's emotion of "it looks like they're having fun" and reflects that in the diary. The diary created in this way is stored on a server, and users can view it using a dedicated app to look back on their emotions and important events at the time.

[0618] The present invention provides a system that automatically records family conversations and memories, including emotions, even in busy daily life, allowing for rich reflection later.

[0619] The processing flow will be explained below.

[0620] Step 1:

[0621] A dedicated microphone picks up everyday conversations between family members in the living room in real time. The dedicated microphone is equipped with high-precision noise cancellation and echo cancellation functions, allowing for clear audio data to be collected.

[0622] Step 2:

[0623] The device periodically acquires audio data collected from a dedicated microphone and temporarily stores it in local storage. The audio data is encrypted during storage to ensure security.

[0624] Step 3:

[0625] The device converts the audio data stored in its local storage into an appropriate digital format (e.g., WAV, MP3, etc.). The conversion process compresses the data and prepares it for transmission.

[0626] Step 4:

[0627] The terminal uploads the converted voice data to a server using a secure communication protocol (e.g., HTTPS or SFTP), thereby ensuring secure data transfer.

[0628] Step 5:

[0629] The server receives the uploaded voice data and stores it in storage, where it is immediately used for voice recognition processing.

[0630] Step 6:

[0631] The server uses a speech recognition engine to convert the voice data into text data, using a highly accurate speech recognition model to accurately convert the conversation into text.

[0632] Step 7:

[0633] The server passes the text data to a natural language processing (NLP) system for analysis, which performs keyword extraction, sentiment analysis, speaker identification, and more.

[0634] Step 8:

[0635] The emotion engine uses the analyzed text data to recognize and identify the user's emotions expressed in the conversation using emotion recognition algorithms.

[0636] Step 9:

[0637] The server extracts characteristic conversational parts based on the analysis results, including the identified emotional information, and automatically generates a diary by combining the extracted conversational content and emotional information.

[0638] Step 10:

[0639] The server stores the generated diary in a searchable database. When saving, the diary data is categorized by date, emotion type, etc.

[0640] Step 11:

[0641] Users can view the created diary entries through a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily find entries based on specific dates or emotions.

[0642] Specific examples

[0643] Emotion recognition and recording process

[0644] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[0645] Parent: "Where do you want to go today?"

[0646] Child: "The zoo is great!"

[0647] The conversations are collected by the devices and uploaded to a server after format conversion. The server's speech recognition engine and NLP system analyze the conversation, and the emotion engine recognizes the children's emotion of "it looks like they're having fun" and reflects that in the diary. The following diary entry is then generated:

[0648] 8:00 At breakfast today, the family planned to go to the zoo. The children looked very excited.

[0649] The generated diary is stored on a server, and users can view it at any time using a dedicated app to recall their emotions and important events at the time.

[0650] In this way, this system automatically records everyday conversations of family members, including their emotions, allowing for richer reflection later.

[0651] Example 2

[0652] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0653] It is difficult for many households to effectively record everyday conversations at home and save them in a format that can be reviewed later. There is also a demand for recording not only the content of conversations but also the emotions expressed at the time, but doing this manually is extremely time-consuming. Furthermore, ensuring the security of collected data is also an important issue.

[0654] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring household conversations using a voice collecting device, means for locally storing the acquired voice data and converting it into a digital format, means for transmitting the converted voice data to a central processing unit over a network, means for converting the voice data into text data using a voice recognition model in the central processing unit, means for analyzing the text data using natural language processing technology and extracting characteristic parts of the conversation, means for automatically generating a diary based on the extracted characteristic parts of the conversation and user emotion data, means for storing the generated diary in a searchable format in a data storage device, and means for providing a user interface for the user to view the diary. This makes it possible to effectively and safely record and store everyday household conversations and the emotions felt at the time, and easily review them later.

[0655] An "audio collection device" is a device installed in a home, such as a living room, to capture conversations, and is equipped with noise cancellation and echo removal functions.

[0656] "Local" refers to the storage area within the device where audio data acquired from a dedicated microphone is temporarily saved.

[0657] "Converting to digital format" refers to the process of converting captured audio data into an appropriate digital format.

[0658] "Central Processing Unit" refers to a server or another powerful computer for receiving voice data and processing voice recognition models, etc.

[0659] "Speech recognition model" refers to an algorithm or system for converting speech data into text data.

[0660] "Natural language processing technology" refers to technology for analyzing text data and performing keyword extraction, speaker identification, sentiment analysis, etc.

[0661] "Characteristic conversational parts" refer to important conversational content extracted from text data using natural language processing technology.

[0662] "Automatic diary generation" refers to the process in which the system automatically creates diary-style records based on characteristic conversation segments and the user's emotional data.

[0663] "Data repository" refers to a database or other storage medium for storing generated diary data.

[0664] "User interface" refers to the application or web interface that allows a user to view the diary they have created.

[0665] The present invention is a system that automatically records everyday conversations at home and compiles them into a diary. This system consists of a user, a terminal, and a server. The specific configuration and operation procedure are described in detail below.

[0666] First, the device captures household conversations in real time using a dedicated microphone installed in the living room. This microphone is equipped with noise cancellation and echo cancellation functions, allowing for clear audio data to be collected. The collected audio data is stored locally on the device and converted into an appropriate digital format. The converted audio data is then encrypted and transmitted to a central processing unit (server) using a secure communication protocol.

[0667] The server receives the voice data sent from the device. The received voice data is converted into text data using a voice recognition model. The text data is then analyzed using natural language processing technology. This includes processes such as keyword extraction, speaker identification, and sentiment analysis. Characteristic parts of the conversation are extracted from the analyzed text data. In addition, an emotion engine recognizes the user's emotional data and reflects it in the analysis results.

[0668] The server then automatically generates a diary based on the extracted characteristic conversation segments and the user's emotional data. This automatically generated diary is organized chronologically and includes emotional elements, allowing it to richly capture important events and interesting conversations that occur daily at home. The generated diary is then stored in a searchable format in a data storage device (database).

[0669] Users can view their diaries stored on the server through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diaries based on a specific date or emotion type.

[0670] Specific examples

[0671] In the morning, while a family is having breakfast in the living room, a dedicated microphone captures their conversation. For example, a parent might ask, "Where do you want to go today?" and the child might reply, "The zoo!" This conversation is collected by the device, converted into an appropriate format, encrypted, and sent to a server. The server's speech recognition model and natural language processing technology analyze the conversation and generate a diary entry like the one below.

[0672] "08:00 At breakfast today, the family planned to go to the zoo. The children looked very excited."

[0673] Furthermore, the emotion engine recognizes the child's emotion of "it looks like they're having fun" and reflects that in the diary. The diary created in this way is saved on a server, and users can view it using a dedicated app to look back on their emotions and important events at the time.

[0674] Prompt Sentence Examples

[0675] Examples of prompts to input to a generative AI model include:

[0676] "Please explain a system that records everyday household conversations in real time and compiles them into a diary. The system collects audio using a dedicated microphone, converts the data on the device, and sends it to a server. The server analyzes the audio to generate text data and also recognizes emotions using an emotion engine. Finally, it automatically generates a diary that the user can access."

[0677] Using this prompt, the generative AI model highlights detailed descriptions of the system in question, helping to clarify specific processing steps and technologies used.

[0678] This invention provides a system that can effectively and safely record and store everyday conversations at home and the emotions felt during those conversations, allowing for easy review later.

[0679] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0680] Step 1:

[0681] The device captures household conversations in real time using a voice collection device (dedicated microphone) installed in the living room. The input is the voice data collected by the microphone, and the output is clear voice data with noise cancellation and echo removal. Specifically, the microphone sensor detects sound waves in the space and converts them into a digital signal. Next, the built-in noise cancellation technology removes background noise, and the echo removal function suppresses voice reverberation.

[0682] Step 2:

[0683] The device stores the collected voice data in local storage and converts it into an appropriate digital format. The input is clear voice data with noise and echo removed, and the output is voice data converted into a digital format. Specifically, the device converts the stored voice data into a digital format (e.g., WAV or MP3) at regular intervals using a digital signal processor.

[0684] Step 3:

[0685] The device encrypts the converted voice data and sends it to the server using a secure communication protocol. The input is the voice data converted to digital format, and the output is the encrypted data sent to the server. Specifically, the device securely converts the digital format voice data using an encryption algorithm such as AES, and then sends the encrypted data to the server using a secure protocol such as HTTPS or TLS.

[0686] Step 4:

[0687] The server receives and decodes the encrypted voice data sent from the terminal. The input is encrypted voice data, and the output is decoded clear voice data. Specifically, the server's communication module receives the encrypted data packet and decrypts the voice data using a built-in decoding algorithm.

[0688] Step 5:

[0689] The server converts the received voice data into text data using a voice recognition model. The input is decoded clear voice data, and the output is text data converted from the voice data. Specifically, the voice recognition engine in the server analyzes the characteristics of the voice waveform and generates the corresponding text.

[0690] Step 6:

[0691] The server analyzes the text data using natural language processing (NLP) technology, extracting keywords and analyzing emotions, and extracting characteristic parts of the conversation. The input is text data generated by a speech recognition model, and the output is the analyzed characteristic parts of the conversation and emotional data. Specifically, the NLP engine analyzes the text data and automatically extracts important keywords and the speaker's emotions.

[0692] Step 7:

[0693] The server automatically generates a diary based on the extracted characteristic conversational parts and the user's emotional data. The input is the analyzed characteristic conversational parts and emotional data, and the output is the generated diary data. Specifically, the diary generation algorithm integrates the characteristic conversational parts and the emotion analysis results, and automatically creates a diary-style record organized chronologically.

[0694] Step 8:

[0695] The server stores the created diary in a searchable format in a data storage device (database). The input is the created diary data, and the output is searchable diary data stored in the database. Specifically, the database management system classifies and stores the created diary data with appropriate tags (e.g., date, emotion type).

[0696] Step 9:

[0697] Users can view the diaries stored on the server through a dedicated application or a web interface. The input is a search query by the user, and the output is diary data extracted based on the query. Specifically, the user interface receives the search query, extracts relevant diary data from the database, and displays it.

[0698] (Application example 2)

[0699] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0700] Conventional systems can record conversations in a living room, compile them into a diary, and even recognize emotions. However, it is difficult to record conversations between customers and staff in commercial facilities and provide information useful for improving services and customer satisfaction. Furthermore, there is a lack of concrete methods for improving customer satisfaction and evaluating service quality. Furthermore, there is a need for a method to evaluate smooth communication between customers and staff by automatically analyzing the emotions in conversations and generating diary-style reports.

[0701] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0702] In this invention, the server includes means for converting voice data into text data using a voice recognition engine, means for analyzing the text data using natural language processing technology to extract characteristic conversation parts, and means for automatically generating a diary based on the extracted characteristic conversation parts and the user's emotions. This makes it possible to record and analyze conversations between customers and staff and provide information useful for improving services and increasing customer satisfaction.

[0703] An "audio microphone" is a device that converts sound waves into electrical signals and collects sounds such as conversations.

[0704] "Local storage" means that acquired data is temporarily stored on a local terminal or device rather than on a central server.

[0705] "Format conversion" refers to converting an original data format into another format, including converting audio data into an appropriate digital format.

[0706] "Uploading via a network" means transferring data to another device or server via the Internet or other communication means.

[0707] A "voice recognition engine" is a software system for converting voice data into text data.

[0708] "Natural language processing technology" is a set of algorithms and techniques for analyzing text data and understanding its meaning.

[0709] "Characteristic conversational segments" are specific important keywords, phrases, emotions, etc. extracted from the analyzed text data.

[0710] "Automatic diary generation" means automatically creating diary-style documents based on collected and analyzed data.

[0711] A "searchable format" is a format in which stored data is structured so that it can be easily searched and accessed by users.

[0712] "Providing an interface" means providing a means for users to operate the system and access data.

[0713] A "commercial facility" is an environment where customers and staff communicate face-to-face, including shopping malls and stores.

[0714] "Sentiment analysis" is the process of detecting emotions and intent from text data and determining the type and intensity of those emotions.

[0715] "Service improvement" refers to efforts to improve the quality of services provided in order to enhance customer experience and satisfaction.

[0716] "Improving customer satisfaction" refers to efforts to increase customer satisfaction with the services they receive.

[0717] MODE FOR CARRYING OUT THE INVENTION

[0718] The embodiments of the present invention will be described in detail below. The system is realized by using an audio microphone, a local terminal, a server, natural language processing (NLP) technology, and a sentiment analysis engine.

[0719] Hardware and software used

[0720] The following hardware and software are used to build this system. The Shure MV7, which has noise cancellation functionality, is suitable as an audio microphone. A Raspberry Pi can be used as the local terminal. Amazon Web Services (AWS) is used as the server, and Flask is used to build the API. The Google Speech Recognition API is used as the speech recognition engine, and the TextBlob library is used for natural language processing. TextBlob is also used as a sentiment analysis engine, and detailed analysis is performed based on the data obtained from it.

[0721] Overall system flow and data processing

[0722] Audio data collection

[0723] The server uses audio microphones installed in commercial facilities to capture conversations between customers and staff in real time. The audio microphones are equipped with noise cancellation functionality, enabling clear audio data to be collected. The collected audio data is temporarily stored on a local device and converted into an appropriate digital format. The converted audio data is then encrypted and securely uploaded to the server via a network.

[0724] Analysis of voice data and diary generation

[0725] When the server receives the uploaded voice data, it first converts it into text using a speech recognition engine. It then uses NLP technology to analyze the text, extracting keywords and conducting sentiment analysis. It then extracts characteristic parts of the conversation and automatically generates a diary based on them. The diary also contains emotional elements, allowing it to richly express the content of interactions between users and staff at commercial facilities.

[0726] Save and access your diary

[0727] The generated diary entries are stored in a searchable database by the server. Users can access this database and view their entries through a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily search and find entries based on specific dates or emotional types.

[0728] Specific examples

[0729] Recording conversations in commercial facilities

[0730] Imagine a scenario in which a customer and a staff member are talking in a retail establishment about a new campaign. The conversation goes something like this:

[0731] Staff: "What do you think about the new campaign?"

[0732] Customer: "Very interested!"

[0733] This conversation is recorded by a microphone and temporarily saved on the local device before being uploaded to a server, where it is analyzed using a speech recognition engine and NLP technology to generate a diary entry like this:

[0734] 14:00 Customer response to the new campaign has been very positive. Customers have shown great interest.

[0735] Furthermore, the sentiment analysis engine recognizes the customer's positive emotion of "interested" and reflects this in the diary. The diary generated in this way is saved on the server and can be viewed by the user through a dedicated app. Specifically, the following prompt sentences can be used to help improve services, for example:

[0736] "Record customer reactions to new campaigns. Find out their opinions, feelings and areas for improvement regarding the desired service."

[0737] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0738] Step 1:

[0739] The server uses audio microphones installed in commercial facilities to capture conversations between customers and staff in real time. The audio microphones are equipped with a noise cancellation function to collect clear audio data, which is then used as input data.

[0740] Step 2:

[0741] The device periodically stores audio data received from the audio microphone locally, converts the audio data into a suitable digital format, encrypts it, and uploads the converted audio data to a server using a secure communication protocol. The output of this step includes the encrypted audio data.

[0742] Step 3:

[0743] The server receives and decrypts the uploaded encrypted audio data. The received and decrypted audio data is converted into text data using the Google Speech Recognition API. The input of this process is the encrypted audio data, and the output is text data.

[0744] Step 4:

[0745] The server performs natural language processing (NLP) on the converted text data using the TextBlob library. The NLP process involves keyword extraction and sentiment analysis, which specifically extracts customer sentiment and important keywords. The input is text data, and the output is analyzed keywords and sentiment data. Specifically, keywords and sentiment are extracted using TextBlob's NLP functions.

[0746] Step 5:

[0747] The server automatically generates a diary-style report based on the extracted keywords and emotion data. This diary richly reflects the conversations and emotions between customers and staff. The input is the analyzed keywords and emotion data, and the output is a diary-style text report.

[0748] Step 6:

[0749] The server stores the generated diary-style reports in a searchable database. The database is tagged with dates, types of emotions, and other information, and organized for easy later search and access. The input is a diary-style text report, and the output is diary data saved in a searchable format.

[0750] Step 7:

[0751] Users can access the database and view the created diary entries through a dedicated application or web interface. The interface is intuitive and easy to use, and allows searches for specific dates or types of emotions. The input is search criteria, and the output is the corresponding diary data. Specifically, related diary data is retrieved from the database based on the user's input and displayed.

[0752] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0753] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0754] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0755] [Third embodiment]

[0756] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0757] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0758] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0759] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0760] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0761] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0762] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0763] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0764] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0765] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0766] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0767] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0768] This invention relates to a system that automatically records everyday conversations of a family and compiles them into a diary. The system is mainly composed of an audio microphone installed in the living room, a local terminal, and a server.

[0769] Audio data collection

[0770] A dedicated microphone is installed in the living room to capture daily family conversations in real time. This microphone is equipped with noise cancellation and echo cancellation functions, allowing for clear audio data to be collected.

[0771] The device periodically receives audio data from the microphone, stores it locally, converts it into a suitable digital format, and prepares it for upload to the server. The audio data is encrypted and transmitted to the server using a secure communication protocol.

[0772] Analysis of voice data and diary generation

[0773] The server receives the voice data sent from the device. After receiving it, it converts the voice data into text data using a voice recognition engine. The converted text data is then analyzed using natural language processing (NLP) technology.

[0774] The server's NLP system extracts characteristic conversational segments from the text data using techniques such as keyword extraction, sentiment analysis, and speaker identification. Based on these segments, the system automatically generates a diary, which is organized chronologically and designed to include important events and interesting conversations from the family's daily life.

[0775] Save and access your diary

[0776] The generated diary entries are stored in a searchable database by the server, and the diary entries are categorized by date, tags, etc., allowing users to easily search and access them later.

[0777] Users can view the created diaries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find conversations from a specific date or diary entries containing specific keywords.

[0778] Specific examples

[0779] Morning conversation transcript

[0780] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[0781] Parent: "Where do you want to go today?"

[0782] Child: "The zoo is great!"

[0783] The conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's speech recognition engine and NLP system analyze the conversation and generate a diary entry similar to the following:

[0784] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[0785] The diary created in this way is stored on a server, and users can view it using a dedicated app and look back on important events of the day.

[0786] The present invention provides a system that allows family conversations and memories to be easily recorded and looked back on later, even during busy daily life.

[0787] The processing flow will be explained below.

[0788] Step 1:

[0789] A dedicated microphone picks up everyday conversations in the living room in real time, and the microphone features high-precision noise cancellation and echo cancellation to collect clear audio data.

[0790] Step 2:

[0791] The device periodically acquires audio data collected from a dedicated microphone and temporarily stores it in local storage. This data is encrypted to ensure security.

[0792] Step 3:

[0793] The device converts the locally stored audio data into an appropriate digital format (e.g., WAV or MP3). The conversion process compresses the audio data and prepares it for transmission.

[0794] Step 4:

[0795] The device uploads the converted audio data to the server. The upload is performed using a secure communication protocol (e.g., HTTPS or SFTP), ensuring data security.

[0796] Step 5:

[0797] The server receives the uploaded voice data and stores it in storage. This stored data is immediately used for voice recognition processing.

[0798] Step 6:

[0799] The server uses a speech recognition engine to convert the voice data into text data, using a highly accurate speech recognition model to accurately convert the content of the conversation into text.

[0800] Step 7:

[0801] The server passes the text data to a natural language processing (NLP) system for analysis, which performs processes such as keyword extraction, sentiment analysis, and speaker identification to extract characteristic conversational segments.

[0802] Step 8:

[0803] The server automatically generates a diary based on the extracted characteristic conversation segments. The diary is organized chronologically and is designed to include important events and interesting conversations in the family's daily life.

[0804] Step 9:

[0805] The server stores the created diary in a database, and the saved diary is managed in a format that makes it easy to search and view.

[0806] Step 10:

[0807] Users can view the created diary through a dedicated application or a web interface. The interface is intuitive and easy to use, allowing users to easily find conversations or interesting events from specific dates.

[0808] In this way, the system automatically records everyday family conversations and provides them as an easily accessible diary.

[0809] Example 1

[0810] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0811] In today's busy lifestyles, it is difficult to easily record everyday family conversations and events and review them later. Manual recording is also a time-consuming and labor-intensive process. Furthermore, protecting privacy and ensuring data security are important, and a system that can achieve both is needed.

[0812] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0813] In this invention, the server includes a means for converting acoustic information into text information, a means for analyzing the text information using natural language processing technology, and a means for automatically generating a record based on the extracted dialogue portion. This allows for automatic and efficient recording of everyday family conversations, protecting privacy and making it easy to review later.

[0814] The "sound acquisition device" is a device that is installed to acquire human conversations, and includes an acoustic sensor and a microphone.

[0815] "Acoustic information" refers to captured human dialogue and other acoustic data.

[0816] An "electronic recording format" is a format for storing acoustic information as digital data, such as MP3 or WAV.

[0817] "Communication line" refers to the network infrastructure used to transmit acoustic information to a remote device, including the Internet, dedicated communication lines, etc.

[0818] A "remote device" is a server or cloud computing system for receiving and processing the acoustic information.

[0819] An "acoustic recognition mechanism" refers to technology or equipment for converting acoustic information into text information, including voice recognition engines.

[0820] "Text information" refers to text data generated based on acoustic information.

[0821] "Natural language processing technology" is a technology used to analyze textual information, and includes keyword extraction, sentiment analysis, and speaker identification.

[0822] "Characteristic dialogue parts" are parts of a dialogue that have important meaning or are interesting, extracted using natural language processing technology.

[0823] "Records" refer to diaries and memos automatically generated based on extracted characteristic parts of the dialogue.

[0824] "Storage device" refers to a database or storage system for storing the generated records.

[0825] "Interface" refers to the application, web page, or other operating means that a user uses to view a record.

[0826] The present invention relates to a system that automatically records everyday family conversations and compiles them into a diary. This system is composed of an audio capture device, a terminal, a server, and a user interface.

[0827] Audio data collection

[0828] The audio capture device is installed in the living room and captures people's conversations in real time. This audio capture device is equipped with noise cancellation and echo removal functions, allowing for clear audio data to be collected.

[0829] Local storage and format conversion of audio data

[0830] The device periodically receives audio data from the audio capture device, stores it locally, and converts it into an appropriate digital recording format (e.g., MP3, WAV). Existing libraries such as FFmpeg can be used for this format conversion.

[0831] Encrypting and uploading audio data

[0832] The device encrypts the converted audio data using an encryption algorithm such as AES, and the encrypted data is sent to the server over a secure communication line (e.g., HTTPS, TLS).

[0833] Analysis of audio data

[0834] The server receives the encrypted voice data sent from the device and converts it into text using an acoustic recognition mechanism (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech Services). The converted text is then analyzed using natural language processing technology (NLP system). This analysis process uses techniques such as keyword extraction, sentiment analysis, and speaker identification.

[0835] Generating diary data

[0836] The server's NLP system extracts characteristic dialogue segments from the analyzed text information. Based on these segments, the server automatically generates a record (diary). This diary is organized chronologically and is designed to include important events and interesting dialogues from the family's daily life.

[0837] Storage and access of diary data

[0838] The generated diary is stored in a searchable format on a storage device by the server. The diary data is categorized by date and tags, allowing users to easily search and access it later.

[0839] User Interface

[0840] Users can view the created diaries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diaries containing conversations from a specific date or specific keywords.

[0841] Specific examples

[0842] For example, in the morning, while a family is having breakfast in the living room, an audio capture device captures their conversation. The conversation goes something like this:

[0843] Parent: "Where do you want to go today?"

[0844] Child: "The zoo is great!"

[0845] This conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's acoustic recognition and NLP systems analyze the conversation and generate a diary entry like this:

[0846] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[0847] The diary created in this way is stored on a server, and users can view it using a dedicated app and look back on important events of the day.

[0848] This invention is a system realized by linking an audio capture device, a terminal, a server, and a user interface, which automatically records everyday family conversations and allows them to be easily reviewed later.

[0849] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0850] Step 1: Acquire audio data

[0851] A dedicated microphone is installed in the living room to capture people's conversations in real time, using noise cancellation and echo cancellation functions to collect clear audio data.

[0852] Input: Audio from living room

[0853] Output: Audio data (analog format)

[0854] Step 2: Save the audio data locally

[0855] The device receives audio data from the acoustic capture device at regular intervals and stores it locally. The audio data is divided into chunks of a fixed size (e.g., every 5 minutes) and stored.

[0856] Input: Audio data (analog format)

[0857] Output: Locally stored audio data (digital format)

[0858] Step 3: Convert the audio data format

[0859] The device converts the stored audio data into the appropriate digital recording format (e.g., MP3, WAV), using existing libraries such as FFmpeg.

[0860] Input: Locally stored audio data (digital format)

[0861] Output: Converted audio data (MP3, WAV format)

[0862] Step 4: Encrypt the audio data

[0863] The device then encrypts the converted audio data using an encryption algorithm such as AES, and the encrypted data is ready to be sent to the server via the communication line.

[0864] Input: Converted audio data (MP3, WAV format)

[0865] Output: Encrypted audio data

[0866] Step 5: Upload your audio data

[0867] The device sends encrypted audio data to the server using a secure communication line (e.g., HTTPS, TLS).

[0868] Input: Encrypted audio data

[0869] Output: Encrypted audio data sent to the server

[0870] Step 6: Receiving and analyzing audio data

[0871] The server receives the encrypted voice data sent from the device and converts the voice data into text information using an acoustic recognition mechanism (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech Services).

[0872] Input: Encrypted audio data

[0873] Output: Text data

[0874] Step 7: Natural Language Processing of Text Data

[0875] The server then analyzes the converted text data using natural language processing techniques, such as keyword extraction, sentiment analysis, and speaker identification.

[0876] Input: Text data

[0877] Output: Parsed text data

[0878] Step 8: Extracting characteristic dialogue parts

[0879] The server's NLP system extracts characteristic dialogue segments from the parsed text.

[0880] Input: Parsed text data

[0881] Output: Characteristic dialogue

[0882] Step 9: Automatic generation of diary data

[0883] The server automatically generates a diary based on the extracted characteristic dialogue. The diary is organized chronologically and includes important events and interesting conversations from the family's daily life.

[0884] Input: Characteristic dialogue

[0885] Output: Generated diary data

[0886] Step 10: Save diary data

[0887] The server stores the generated diary in a searchable format on a storage device. The diary data is categorized by date and tags, making it easy to search later.

[0888] Input: Generated diary data

[0889] Output: Saved diary data

[0890] Step 11: User Access

[0891] Users can view the created diary entries using a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily find diary entries containing conversations from specific dates or specific keywords.

[0892] Input: Search query (e.g. date, keyword)

[0893] Output: Diary data as search results

[0894] (Application example 1)

[0895] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0896] Efficiently recording everyday conversations within the home and automatically generating a diary is extremely important for looking back on family memories and important events. Food delivery services also need to record conversations with customers and use the results as feedback to improve service quality. However, current systems have difficulty automatically executing these processes, and manual recording and data organization are time-consuming and labor-intensive.

[0897] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0898] In this invention, the server includes: means for capturing everyday conversations between family members using an audio microphone installed in the living room; means for locally saving the captured audio data and converting its format; means for uploading the converted audio data to the server via a network; means for converting the audio data into text data using a speech recognition engine in the server; means for analyzing the text data using natural language processing technology and extracting characteristic portions of the conversation; means for automatically generating a diary based on the extracted characteristic portions of the conversation; means for saving the generated diary in a database in a searchable format; means for providing an interface for users to view the diary; means for collecting audio data to capture conversations between delivery staff in a delivery service; means for converting important conversations between customers and delivery staff into text data using a speech recognition engine; and means for analyzing the generated text data and saving it as feedback. This allows for efficient recording and review of memories and important events in the home, and automatically obtaining and analyzing important feedback to improve customer satisfaction in the food delivery service.

[0899] A "living room" is a living space where family members gather on a daily basis to talk and engage in activities.

[0900] An "audio microphone" is a device that picks up sound, converts it into a digital signal, and records it.

[0901] A "conversation" is the act of people communicating through the exchange of words.

[0902] "Local" refers to an area or range limited to an individual device or environment.

[0903] A "server" is a computer system that processes and stores data and provides services to other devices over a network.

[0904] A "voice recognition engine" is software for converting voice into text data.

[0905] "Text data" refers to information expressed as a string of characters.

[0906] "Natural language processing technology" is a technology that allows computers to understand and analyze the natural language used by humans.

[0907] A "characteristic part of a conversation" is a part of a conversation that indicates important or unique content.

[0908] A diary is a document that records everyday events and conversations.

[0909] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0910] An "interface" is the means or device by which a user operates a system and inputs and displays information.

[0911] "Delivery service" refers to a service that delivers products or goods to a location designated by a customer.

[0912] A "delivery person" is a person who is responsible for delivering goods or items to customers.

[0913] "Audio collection means" means any device or technology used to record speech or audio.

[0914] "Customer" means a person who purchases or uses a product or service.

[0915] "Feedback" refers to opinions and evaluations received about a particular action or product.

[0916] System program generation

[0917] As an embodiment of the present invention, we will create a program to realize a system that records conversations at home and customer interactions in delivery services. The system is composed of a dedicated audio microphone, a local terminal, and a server.

[0918] Explanation of program processing

[0919] The server processes and calculates the data in the following steps:

[0920] 1. Audio data collection:

[0921] A dedicated audio microphone is used to collect voices from within the home and from the delivery person. The audio microphone is equipped with noise cancellation and echo removal functions, allowing for clear audio data to be collected. The data collected by the audio microphone is stored on the local device.

[0922] 2. Audio data format conversion and upload:

[0923] The local device converts the collected audio data into the appropriate format and uploads it to the server using a secure communication protocol in an encrypted format, using a standard audio format conversion library.

[0924] 3. Audio to text conversion:

[0925] The server's speech recognition engine (e.g., Google Speech Recognition API) is used to convert the uploaded voice data into text data, which will serve as the basis for further analysis.

[0926] 4. Natural Language Processing Analysis:

[0927] The text data is analyzed using a server-based natural language processing system (e.g., spaCy, NLTK), which extracts characteristic conversational segments from the text and utilizes techniques including keyword extraction and sentiment analysis.

[0928] 5. Automatic generation and saving of diary entries:

[0929] Based on the extracted characteristic conversation segments, the system automatically generates a diary, which is then stored in a database in an easy-to-read and search format.

[0930] 6. User View:

[0931] Users can view their created diaries through a dedicated application or web interface, which is designed to be intuitive and easy to use, and offers search functions for specific dates and keywords.

[0932] Specific examples

[0933] For example, a conversation at home while having breakfast is recorded:

[0934] Parent: "Where do you want to go today?"

[0935] Child: "The zoo is great!"

[0936] This conversation is generated by the system as a diary entry:

[0937] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[0938] Or, in the food delivery service example, the conversations between delivery people and customers are recorded:

[0939] Staff: "This is a delivery. Are you sure you want a pizza and salad?"

[0940] Customer: "Yes, that's correct. Thank you. I'm glad it arrived on time."

[0941] The conversation is recorded as follows:

[0942] 10:15 Conversation at delivery destination: The delivery consisted of a pizza and salad, and the customer seemed pleased. The delivery was on time and was appreciated.

[0943] Prompt Sentence Examples

[0944] Please record and save the following conversation in diary format:

[0945] Staff: "This is a delivery. Are you sure you want a pizza and salad?"

[0946] Customer: "Yes, that's correct. Thank you. I'm glad you got it on time."

[0947] Example of a diary entry:

[0948] 10:15 Conversation at delivery destination: The delivery consisted of a pizza and salad, and the customer seemed pleased. The delivery was on time and was appreciated.

[0949] In this way, users can efficiently record everyday conversations at home or conversations during deliveries and review them later.

[0950] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0951] Step 1:

[0952] Audio data collection: The device activates the audio microphones used by the home and the delivery person to capture conversations in real time. Ambient sounds are included as input. Clear audio data is collected using noise cancellation and echo cancellation, and stored locally.

[0953] Step 2:

[0954] Audio data format conversion and upload: The device converts the collected audio data into a standard audio format. The input in this step is the audio data collected in step 1, and the output is the converted audio file. The converted audio data is encrypted and uploaded to the server using a secure communication protocol (e.g., HTTPS).

[0955] Step 3:

[0956] Voice data to text conversion: Using the server's speech recognition engine (e.g., Google Speech Recognition API), the uploaded voice data is converted into text data. The output is a text representation of the conversation. This text data is used in the subsequent analysis process.

[0957] Step 4:

[0958] Analysis by natural language processing: The server analyzes the text data using a natural language processing system (e.g., spaCy, NLTK). The input is the text data converted in step 3, and the output is the analysis results after keyword extraction and sentiment analysis. This identifies characteristic parts of the conversation.

[0959] Step 5:

[0960] Automatic generation and storage of diary entries: The server automatically generates a diary entry based on the analysis results. The input is the analysis results from step 4, and the output is diary-format text data. This diary entry data is stored in a database and converted into a searchable format.

[0961] Step 6:

[0962] User viewing: Users can view the created diary through a dedicated application or a web interface. As input, they specify dates and keywords. As output, the corresponding diary entries are displayed. The interface is designed to be intuitive and easy to use.

[0963] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0964] This invention relates to a system that automatically records everyday family conversations and compiles them into a diary, and also combines it with an emotion engine that recognizes the user's emotions. The system is mainly composed of an audio microphone installed in the living room, a local terminal, a server, and an emotion engine.

[0965] Audio data collection

[0966] A dedicated microphone is installed in the living room to capture daily family conversations in real time. The microphone is equipped with noise cancellation and echo cancellation functions to collect clear audio data.

[0967] The device periodically receives audio data from the microphone, stores it locally, converts it into a suitable digital format, and prepares it for upload to the server. The audio data is encrypted and transmitted to the server using a secure communication protocol.

[0968] Analysis of voice data and diary generation

[0969] The server receives the voice data sent from the device. After receiving it, it converts the voice data into text data using a voice recognition engine. The converted text data is then analyzed using natural language processing (NLP) technology.

[0970] The server's NLP system extracts characteristic conversational segments from the text data, utilizing techniques such as keyword extraction, sentiment analysis, and speaker identification. Furthermore, an emotion engine recognizes the user's emotions from the text data and incorporates the results into the analysis. Based on the characteristic conversational segments and the user's emotions, the system automatically generates a diary. This diary is organized chronologically and includes emotional elements, providing a richer portrayal of important family events and interesting conversations.

[0971] Save and access your diary

[0972] The generated diary entries are stored in a searchable database by the server, and the diary data is categorized by date, tag, emotion type, etc., allowing users to easily search and access them later.

[0973] Users can view the created diary entries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diary entries based on a specific date or emotion type.

[0974] Specific examples

[0975] Emotion recognition and recording process

[0976] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[0977] Parent: "Where do you want to go today?"

[0978] Child: "The zoo is great!"

[0979] The conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's speech recognition engine and NLP system analyze the conversation and generate a diary entry similar to the following:

[0980] 8:00 At breakfast today, the family planned to go to the zoo. The children looked very excited.

[0981] Furthermore, the emotion engine recognizes the child's emotion of "it looks like they're having fun" and reflects that in the diary. The diary created in this way is stored on a server, and users can view it using a dedicated app to look back on their emotions and important events at the time.

[0982] The present invention provides a system that automatically records family conversations and memories, including emotions, even during busy daily life, allowing for rich reflection later.

[0983] The processing flow will be explained below.

[0984] Step 1:

[0985] A dedicated microphone picks up everyday conversations between family members in the living room in real time. The dedicated microphone is equipped with high-precision noise cancellation and echo cancellation functions, allowing clear audio data to be collected.

[0986] Step 2:

[0987] The device periodically acquires audio data collected from a dedicated microphone and temporarily stores it in local storage. The audio data is encrypted during storage to ensure security.

[0988] Step 3:

[0989] The device converts the audio data stored in its local storage into an appropriate digital format (e.g., WAV, MP3, etc.). The conversion process compresses the data and prepares it for transmission.

[0990] Step 4:

[0991] The terminal uploads the converted voice data to a server using a secure communication protocol (e.g., HTTPS or SFTP), thereby ensuring secure data transfer.

[0992] Step 5:

[0993] The server receives the uploaded voice data and stores it in storage, where it is immediately used for voice recognition processing.

[0994] Step 6:

[0995] The server uses a speech recognition engine to convert the voice data into text data, using a highly accurate speech recognition model to accurately convert the conversation into text.

[0996] Step 7:

[0997] The server passes the text data to a natural language processing (NLP) system for analysis, which performs keyword extraction, sentiment analysis, speaker identification, and more.

[0998] Step 8:

[0999] The emotion engine uses the analyzed text data to recognize and identify the user's emotions expressed in the conversation using emotion recognition algorithms.

[1000] Step 9:

[1001] The server extracts characteristic conversational parts based on the analysis results, including the identified emotional information, and automatically generates a diary by combining the extracted conversational content and emotional information.

[1002] Step 10:

[1003] The server stores the generated diary in a searchable database. When saving, the diary data is categorized by date, emotion type, etc.

[1004] Step 11:

[1005] Users can view the created diary entries through a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily find entries based on specific dates or emotions.

[1006] Specific examples

[1007] Emotion recognition and recording process

[1008] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[1009] Parent: "Where do you want to go today?"

[1010] Child: "The zoo is great!"

[1011] The conversations are collected by the devices and uploaded to a server after format conversion. The server's speech recognition engine and NLP system analyze the conversation, and the emotion engine recognizes the children's emotion of "it looks like they're having fun" and reflects that in the diary. The following diary entry is then generated:

[1012] 8:00 At breakfast today, the family planned to go to the zoo. The children looked very excited.

[1013] The generated diary is stored on a server, and users can view it at any time using a dedicated app to recall their emotions and important events at the time.

[1014] In this way, this system automatically records everyday conversations of family members, including their emotions, allowing for richer reflection later.

[1015] Example 2

[1016] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1017] It is difficult for many households to effectively record everyday conversations at home and save them in a format that can be reviewed later. There is also a demand for recording not only the content of conversations but also the emotions expressed at the time, but doing this manually is extremely time-consuming. Furthermore, ensuring the security of collected data is also an important issue.

[1018] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring household conversations using a voice collecting device, means for locally storing the acquired voice data and converting it into a digital format, means for transmitting the converted voice data to a central processing unit over a network, means for converting the voice data into text data using a voice recognition model in the central processing unit, means for analyzing the text data using natural language processing technology and extracting characteristic parts of the conversation, means for automatically generating a diary based on the extracted characteristic parts of the conversation and user emotion data, means for storing the generated diary in a searchable format in a data storage device, and means for providing a user interface for the user to view the diary. This makes it possible to effectively and safely record and store everyday household conversations and the emotions felt at the time, and easily review them later.

[1019] An "audio collection device" is a device installed in a home, such as a living room, to capture conversations, and is equipped with noise cancellation and echo removal functions.

[1020] "Local" refers to the storage area within the device where audio data acquired from a dedicated microphone is temporarily saved.

[1021] "Converting to digital format" refers to the process of converting captured audio data into an appropriate digital format.

[1022] "Central Processing Unit" refers to a server or another powerful computer for receiving voice data and processing voice recognition models, etc.

[1023] "Speech recognition model" refers to an algorithm or system for converting speech data into text data.

[1024] "Natural language processing technology" refers to technology for analyzing text data and performing keyword extraction, speaker identification, sentiment analysis, etc.

[1025] "Characteristic conversational parts" refer to important conversational content extracted from text data using natural language processing technology.

[1026] "Automatic diary generation" refers to the process in which the system automatically creates diary-style records based on characteristic conversation segments and the user's emotional data.

[1027] "Data repository" refers to a database or other storage medium for storing generated diary data.

[1028] "User interface" refers to the application or web interface that allows a user to view the diary they have created.

[1029] The present invention is a system that automatically records everyday conversations at home and compiles them into a diary. This system consists of a user, a terminal, and a server. The specific configuration and operation procedure are described in detail below.

[1030] First, the device captures household conversations in real time using a dedicated microphone installed in the living room. This microphone is equipped with noise cancellation and echo cancellation functions, allowing for clear audio data to be collected. The collected audio data is stored locally on the device and converted into an appropriate digital format. The converted audio data is then encrypted and transmitted to a central processing unit (server) using a secure communication protocol.

[1031] The server receives the voice data sent from the device. The received voice data is converted into text data using a voice recognition model. The text data is then analyzed using natural language processing technology. This includes processes such as keyword extraction, speaker identification, and sentiment analysis. Characteristic parts of the conversation are extracted from the analyzed text data. In addition, an emotion engine recognizes the user's emotional data and reflects it in the analysis results.

[1032] The server then automatically generates a diary based on the extracted characteristic conversation segments and the user's emotional data. This automatically generated diary is organized chronologically and includes emotional elements, allowing it to richly capture important events and interesting conversations that occur daily at home. The generated diary is then stored in a searchable format in a data storage device (database).

[1033] Users can view their diaries stored on the server through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diaries based on a specific date or emotion type.

[1034] Specific examples

[1035] In the morning, while a family is having breakfast in the living room, a dedicated microphone captures their conversation. For example, a parent might ask, "Where do you want to go today?" and the child might reply, "The zoo!" This conversation is collected by the device, converted into an appropriate format, encrypted, and sent to a server. The server's speech recognition model and natural language processing technology analyze the conversation and generate a diary entry like the one below.

[1036] "08:00 At breakfast today, the family planned to go to the zoo. The children looked very excited."

[1037] Furthermore, the emotion engine recognizes the child's emotion of "it looks like they're having fun" and reflects that in the diary. The diary created in this way is saved on a server, and users can view it using a dedicated app to look back on their emotions and important events at the time.

[1038] Prompt Sentence Examples

[1039] Examples of prompts to input to a generative AI model include:

[1040] "Please explain a system that records everyday household conversations in real time and compiles them into a diary. The system collects audio using a dedicated microphone, converts the data on the device, and sends it to a server. The server analyzes the audio to generate text data and also recognizes emotions using an emotion engine. Finally, it automatically generates a diary that the user can access."

[1041] Using this prompt, the generative AI model highlights detailed descriptions of the system in question, helping to clarify specific processing steps and technologies used.

[1042] This invention provides a system that can effectively and safely record and store everyday conversations at home and the emotions felt during those conversations, allowing for easy review later.

[1043] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1044] Step 1:

[1045] The device captures household conversations in real time using a voice collection device (dedicated microphone) installed in the living room. The input is the voice data collected by the microphone, and the output is clear voice data with noise cancellation and echo removal. Specifically, the microphone sensor detects sound waves in the space and converts them into a digital signal. Next, the built-in noise cancellation technology removes background noise, and the echo removal function suppresses voice reverberation.

[1046] Step 2:

[1047] The device stores the collected voice data in local storage and converts it into an appropriate digital format. The input is clear voice data with noise and echo removed, and the output is voice data converted into a digital format. Specifically, the device converts the stored voice data into a digital format (e.g., WAV or MP3) at regular intervals using a digital signal processor.

[1048] Step 3:

[1049] The device encrypts the converted voice data and sends it to the server using a secure communication protocol. The input is the voice data converted to digital format, and the output is the encrypted data sent to the server. Specifically, the device securely converts the digital format voice data using an encryption algorithm such as AES, and then sends the encrypted data to the server using a secure protocol such as HTTPS or TLS.

[1050] Step 4:

[1051] The server receives and decodes the encrypted voice data sent from the terminal. The input is encrypted voice data, and the output is decoded clear voice data. Specifically, the server's communication module receives the encrypted data packet and decrypts the voice data using a built-in decoding algorithm.

[1052] Step 5:

[1053] The server converts the received voice data into text data using a voice recognition model. The input is decoded clear voice data, and the output is text data converted from the voice data. Specifically, the voice recognition engine in the server analyzes the characteristics of the voice waveform and generates the corresponding text.

[1054] Step 6:

[1055] The server analyzes the text data using natural language processing (NLP) technology, extracting keywords and analyzing emotions, and extracting characteristic parts of the conversation. The input is text data generated by a speech recognition model, and the output is the analyzed characteristic parts of the conversation and emotional data. Specifically, the NLP engine analyzes the text data and automatically extracts important keywords and the speaker's emotions.

[1056] Step 7:

[1057] The server automatically generates a diary based on the extracted characteristic conversational parts and the user's emotional data. The input is the analyzed characteristic conversational parts and emotional data, and the output is the generated diary data. Specifically, the diary generation algorithm integrates the characteristic conversational parts and the emotion analysis results, and automatically creates a diary-style record organized chronologically.

[1058] Step 8:

[1059] The server stores the created diary in a searchable format in a data storage device (database). The input is the created diary data, and the output is searchable diary data stored in the database. Specifically, the database management system classifies and stores the created diary data with appropriate tags (e.g., date, emotion type).

[1060] Step 9:

[1061] Users can view the diaries stored on the server through a dedicated application or a web interface. The input is a search query by the user, and the output is diary data extracted based on the query. Specifically, the user interface receives the search query, extracts relevant diary data from the database, and displays it.

[1062] (Application example 2)

[1063] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1064] Conventional systems can record conversations in a living room, compile them into a diary, and even recognize emotions. However, it is difficult to record conversations between customers and staff in commercial facilities and provide information useful for improving services and customer satisfaction. Furthermore, there is a lack of concrete methods for improving customer satisfaction and evaluating service quality. Furthermore, there is a need for a method to evaluate smooth communication between customers and staff by automatically analyzing the emotions in conversations and generating diary-style reports.

[1065] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1066] In this invention, the server includes means for converting voice data into text data using a voice recognition engine, means for analyzing the text data using natural language processing technology to extract characteristic conversation parts, and means for automatically generating a diary based on the extracted characteristic conversation parts and the user's emotions. This makes it possible to record and analyze conversations between customers and staff and provide information useful for improving services and increasing customer satisfaction.

[1067] An "audio microphone" is a device that converts sound waves into electrical signals and collects sounds such as conversations.

[1068] "Local storage" means that acquired data is temporarily stored on a local terminal or device rather than on a central server.

[1069] "Format conversion" refers to converting an original data format into another format, including converting audio data into an appropriate digital format.

[1070] "Uploading via a network" means transferring data to another device or server via the Internet or other communication means.

[1071] A "voice recognition engine" is a software system for converting voice data into text data.

[1072] "Natural language processing technology" is a set of algorithms and techniques for analyzing text data and understanding its meaning.

[1073] "Characteristic conversational segments" are specific important keywords, phrases, emotions, etc. extracted from the analyzed text data.

[1074] "Automatic diary generation" means automatically creating diary-style documents based on collected and analyzed data.

[1075] A "searchable format" is a format in which stored data is structured so that it can be easily searched and accessed by users.

[1076] "Providing an interface" means providing a means for users to operate the system and access data.

[1077] A "commercial facility" is an environment where customers and staff communicate face-to-face, including shopping malls and stores.

[1078] "Sentiment analysis" is the process of detecting emotions and intent from text data and determining the type and intensity of those emotions.

[1079] "Service improvement" refers to efforts to improve the quality of services provided in order to enhance customer experience and satisfaction.

[1080] "Improving customer satisfaction" refers to efforts to increase customer satisfaction with the services they receive.

[1081] MODE FOR CARRYING OUT THE INVENTION

[1082] The embodiments of the present invention will be described in detail below. The system is realized by using an audio microphone, a local terminal, a server, natural language processing (NLP) technology, and a sentiment analysis engine.

[1083] Hardware and software used

[1084] The following hardware and software are used to build this system. The Shure MV7, which has noise cancellation functionality, is suitable as an audio microphone. A Raspberry Pi can be used as the local terminal. Amazon Web Services (AWS) is used as the server, and Flask is used to build the API. The Google Speech Recognition API is used as the speech recognition engine, and the TextBlob library is used for natural language processing. TextBlob is also used as a sentiment analysis engine, and detailed analysis is performed based on the data obtained from it.

[1085] Overall system flow and data processing

[1086] Audio data collection

[1087] The server uses audio microphones installed in commercial facilities to capture conversations between customers and staff in real time. The audio microphones are equipped with noise cancellation functionality, enabling clear audio data to be collected. The collected audio data is temporarily stored on a local device and converted into an appropriate digital format. The converted audio data is then encrypted and securely uploaded to the server via a network.

[1088] Analysis of voice data and diary generation

[1089] When the server receives the uploaded voice data, it first converts it into text using a speech recognition engine. It then uses NLP technology to analyze the text, extracting keywords and conducting sentiment analysis. It then extracts characteristic parts of the conversation and automatically generates a diary based on them. The diary also contains emotional elements, allowing it to richly express the content of interactions between users and staff at commercial facilities.

[1090] Save and access your diary

[1091] The generated diary entries are stored in a searchable database by the server. Users can access this database and view their entries through a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily search and find entries based on specific dates or emotional types.

[1092] Specific examples

[1093] Recording conversations in commercial facilities

[1094] Imagine a scenario in which a customer and a staff member are talking in a retail establishment about a new campaign. The conversation goes something like this:

[1095] Staff: "What do you think about the new campaign?"

[1096] Customer: "Very interested!"

[1097] This conversation is recorded by a microphone and temporarily saved on the local device before being uploaded to a server, where it is analyzed using a speech recognition engine and NLP technology to generate a diary entry like this:

[1098] 14:00 Customer response to the new campaign has been very positive. Customers have shown great interest.

[1099] Furthermore, the sentiment analysis engine recognizes the customer's positive emotion of "interested" and reflects this in the diary. The diary generated in this way is saved on the server and can be viewed by the user through a dedicated app. Specifically, the following prompt sentences can be used to help improve services, for example:

[1100] "Record customer reactions to new campaigns. Find out their opinions, feelings and areas for improvement regarding the desired service."

[1101] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1102] Step 1:

[1103] The server uses audio microphones installed in commercial facilities to capture conversations between customers and staff in real time. The audio microphones are equipped with a noise cancellation function to collect clear audio data, which is then used as input data.

[1104] Step 2:

[1105] The device periodically stores audio data received from the audio microphone locally, converts the audio data into a suitable digital format, encrypts it, and uploads the converted audio data to a server using a secure communication protocol. The output of this step includes the encrypted audio data.

[1106] Step 3:

[1107] The server receives and decrypts the uploaded encrypted audio data. The received and decrypted audio data is converted into text data using the Google Speech Recognition API. The input of this process is the encrypted audio data, and the output is text data.

[1108] Step 4:

[1109] The server performs natural language processing (NLP) on the converted text data using the TextBlob library. The NLP process involves keyword extraction and sentiment analysis, which specifically extracts customer sentiment and important keywords. The input is text data, and the output is analyzed keywords and sentiment data. Specifically, keywords and sentiment are extracted using TextBlob's NLP functions.

[1110] Step 5:

[1111] The server automatically generates a diary-style report based on the extracted keywords and emotion data. This diary richly reflects the conversations and emotions between customers and staff. The input is the analyzed keywords and emotion data, and the output is a diary-style text report.

[1112] Step 6:

[1113] The server stores the generated diary-style reports in a searchable database. The database is tagged with dates, types of emotions, and other information, and organized for easy later search and access. The input is a diary-style text report, and the output is diary data saved in a searchable format.

[1114] Step 7:

[1115] Users can access the database and view the created diary entries through a dedicated application or web interface. The interface is intuitive and easy to use, and allows searches for specific dates or types of emotions. The input is search criteria, and the output is the corresponding diary data. Specifically, related diary data is retrieved from the database based on the user's input and displayed.

[1116] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1118] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1119] [Fourth embodiment]

[1120] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1121] 7, a 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.

[1122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1123] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1124] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1127] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1128] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1129] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1131] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1132] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1133] This invention relates to a system that automatically records everyday conversations of a family and compiles them into a diary. The system is mainly composed of an audio microphone installed in the living room, a local terminal, and a server.

[1134] Audio data collection

[1135] A dedicated microphone is installed in the living room to capture daily family conversations in real time. This microphone is equipped with noise cancellation and echo cancellation functions, allowing for clear audio data to be collected.

[1136] The device periodically receives audio data from the microphone, stores it locally, converts it into a suitable digital format, and prepares it for upload to the server. The audio data is encrypted and transmitted to the server using a secure communication protocol.

[1137] Analysis of voice data and diary generation

[1138] The server receives the voice data sent from the device. After receiving it, it converts the voice data into text data using a voice recognition engine. The converted text data is then analyzed using natural language processing (NLP) technology.

[1139] The server's NLP system extracts characteristic conversational segments from the text data using techniques such as keyword extraction, sentiment analysis, and speaker identification. Based on these segments, the system automatically generates a diary, which is organized chronologically and designed to include important events and interesting conversations from the family's daily life.

[1140] Save and access your diary

[1141] The generated diary entries are stored in a searchable database by the server, and the diary entries are categorized by date, tags, etc., allowing users to easily search and access them later.

[1142] Users can view the created diaries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find conversations from a specific date or diary entries containing specific keywords.

[1143] Specific examples

[1144] Morning conversation transcript

[1145] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[1146] Parent: "Where do you want to go today?"

[1147] Child: "The zoo is great!"

[1148] The conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's speech recognition engine and NLP system analyze the conversation and generate a diary entry similar to the following:

[1149] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[1150] The diary created in this way is stored on a server, and users can view it using a dedicated app and look back on important events of the day.

[1151] The present invention provides a system that allows family conversations and memories to be easily recorded and looked back on later, even during busy daily life.

[1152] The processing flow will be explained below.

[1153] Step 1:

[1154] A dedicated microphone picks up everyday conversations in the living room in real time, and the microphone features high-precision noise cancellation and echo cancellation to collect clear audio data.

[1155] Step 2:

[1156] The device periodically acquires audio data collected from a dedicated microphone and temporarily stores it in local storage. This data is encrypted to ensure security.

[1157] Step 3:

[1158] The device converts the locally stored audio data into an appropriate digital format (e.g., WAV or MP3). The conversion process compresses the audio data and prepares it for transmission.

[1159] Step 4:

[1160] The device uploads the converted audio data to the server. The upload is performed using a secure communication protocol (e.g., HTTPS or SFTP), ensuring data security.

[1161] Step 5:

[1162] The server receives the uploaded voice data and stores it in storage. This stored data is immediately used for voice recognition processing.

[1163] Step 6:

[1164] The server uses a speech recognition engine to convert the voice data into text data, using a highly accurate speech recognition model to accurately convert the content of the conversation into text.

[1165] Step 7:

[1166] The server passes the text data to a natural language processing (NLP) system for analysis, which performs processes such as keyword extraction, sentiment analysis, and speaker identification to extract characteristic conversational segments.

[1167] Step 8:

[1168] The server automatically generates a diary based on the extracted characteristic conversation segments. The diary is organized chronologically and is designed to include important events and interesting conversations in the family's daily life.

[1169] Step 9:

[1170] The server stores the created diary in a database, and the saved diary is managed in a format that makes it easy to search and view.

[1171] Step 10:

[1172] Users can view the created diary through a dedicated application or a web interface. The interface is intuitive and easy to use, allowing users to easily find conversations or interesting events from specific dates.

[1173] In this way, the system automatically records everyday family conversations and provides them as an easily accessible diary.

[1174] Example 1

[1175] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1176] In today's busy lifestyles, it is difficult to easily record everyday family conversations and events and review them later. Manual recording is also a time-consuming and labor-intensive process. Furthermore, protecting privacy and ensuring data security are important, and a system that can achieve both is needed.

[1177] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1178] In this invention, the server includes a means for converting acoustic information into text information, a means for analyzing the text information using natural language processing technology, and a means for automatically generating a record based on the extracted dialogue portion. This allows for automatic and efficient recording of everyday family conversations, protecting privacy and making it easy to review later.

[1179] The "sound acquisition device" is a device that is installed to acquire human conversations, and includes an acoustic sensor and a microphone.

[1180] "Acoustic information" refers to captured human dialogue and other acoustic data.

[1181] An "electronic recording format" is a format for storing acoustic information as digital data, such as MP3 or WAV.

[1182] "Communication line" refers to the network infrastructure used to transmit acoustic information to a remote device, including the Internet, dedicated communication lines, etc.

[1183] A "remote device" is a server or cloud computing system for receiving and processing the acoustic information.

[1184] An "acoustic recognition mechanism" refers to technology or equipment for converting acoustic information into text information, including voice recognition engines.

[1185] "Text information" refers to text data generated based on acoustic information.

[1186] "Natural language processing technology" is a technology used to analyze textual information, and includes keyword extraction, sentiment analysis, and speaker identification.

[1187] "Characteristic dialogue parts" are parts of a dialogue that have important meaning or are interesting, extracted using natural language processing technology.

[1188] "Records" refer to diaries and memos automatically generated based on extracted characteristic parts of the dialogue.

[1189] "Storage device" refers to a database or storage system for storing the generated records.

[1190] "Interface" refers to the application, web page, or other operating means that a user uses to view a record.

[1191] The present invention relates to a system that automatically records everyday family conversations and compiles them into a diary. This system is composed of an audio capture device, a terminal, a server, and a user interface.

[1192] Audio data collection

[1193] The audio capture device is installed in the living room and captures people's conversations in real time. This audio capture device is equipped with noise cancellation and echo removal functions, allowing for clear audio data to be collected.

[1194] Local storage and format conversion of audio data

[1195] The device periodically receives audio data from the audio capture device, stores it locally, and converts it into an appropriate digital recording format (e.g., MP3, WAV). Existing libraries such as FFmpeg can be used for this format conversion.

[1196] Encrypting and uploading audio data

[1197] The device encrypts the converted audio data using an encryption algorithm such as AES, and the encrypted data is sent to the server over a secure communication line (e.g., HTTPS, TLS).

[1198] Analysis of audio data

[1199] The server receives the encrypted voice data sent from the device and converts it into text using an acoustic recognition mechanism (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech Services). The converted text is then analyzed using natural language processing technology (NLP system). This analysis process uses techniques such as keyword extraction, sentiment analysis, and speaker identification.

[1200] Generating diary data

[1201] The server's NLP system extracts characteristic dialogue segments from the analyzed text information. Based on these segments, the server automatically generates a record (diary). This diary is organized chronologically and is designed to include important events and interesting dialogues from the family's daily life.

[1202] Storage and access of diary data

[1203] The generated diary is stored in a searchable format on a storage device by the server. The diary data is categorized by date and tags, allowing users to easily search and access it later.

[1204] User Interface

[1205] Users can view the created diaries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diaries containing conversations from a specific date or specific keywords.

[1206] Specific examples

[1207] For example, in the morning, while a family is having breakfast in the living room, an audio capture device captures their conversation. The conversation goes something like this:

[1208] Parent: "Where do you want to go today?"

[1209] Child: "The zoo is great!"

[1210] This conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's acoustic recognition and NLP systems analyze the conversation and generate a diary entry like this:

[1211] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[1212] The diary created in this way is stored on a server, and users can view it using a dedicated app and look back on important events of the day.

[1213] This invention is a system realized by linking an audio capture device, a terminal, a server, and a user interface, which automatically records everyday family conversations and allows them to be easily reviewed later.

[1214] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1215] Step 1: Acquire audio data

[1216] A dedicated microphone is installed in the living room to capture people's conversations in real time, using noise cancellation and echo cancellation functions to collect clear audio data.

[1217] Input: Audio from living room

[1218] Output: Audio data (analog format)

[1219] Step 2: Save the audio data locally

[1220] The device receives audio data from the acoustic capture device at regular intervals and stores it locally. The audio data is divided into chunks of a fixed size (e.g., every 5 minutes) and stored.

[1221] Input: Audio data (analog format)

[1222] Output: Locally stored audio data (digital format)

[1223] Step 3: Convert the audio data format

[1224] The device converts the stored audio data into the appropriate digital recording format (e.g., MP3, WAV), using existing libraries such as FFmpeg.

[1225] Input: Locally stored audio data (digital format)

[1226] Output: Converted audio data (MP3, WAV format)

[1227] Step 4: Encrypt the audio data

[1228] The device then encrypts the converted audio data using an encryption algorithm such as AES, and the encrypted data is ready to be sent to the server via the communication line.

[1229] Input: Converted audio data (MP3, WAV format)

[1230] Output: Encrypted audio data

[1231] Step 5: Upload your audio data

[1232] The device sends encrypted audio data to the server using a secure communication line (e.g., HTTPS, TLS).

[1233] Input: Encrypted audio data

[1234] Output: Encrypted audio data sent to the server

[1235] Step 6: Receiving and analyzing audio data

[1236] The server receives the encrypted voice data sent from the device and converts the voice data into text information using an acoustic recognition mechanism (e.g., Google Cloud Speech-to-Text, Microsoft Azure Speech Services).

[1237] Input: Encrypted audio data

[1238] Output: Text data

[1239] Step 7: Natural Language Processing of Text Data

[1240] The server then analyzes the converted text data using natural language processing techniques, such as keyword extraction, sentiment analysis, and speaker identification.

[1241] Input: Text data

[1242] Output: Parsed text data

[1243] Step 8: Extracting characteristic dialogue parts

[1244] The server's NLP system extracts characteristic dialogue segments from the parsed text.

[1245] Input: Parsed text data

[1246] Output: Characteristic dialogue

[1247] Step 9: Automatic generation of diary data

[1248] The server automatically generates a diary based on the extracted characteristic dialogue. The diary is organized chronologically and includes important events and interesting conversations from the family's daily life.

[1249] Input: Characteristic dialogue

[1250] Output: Generated diary data

[1251] Step 10: Save diary data

[1252] The server stores the generated diary in a searchable format on a storage device. The diary data is categorized by date and tags, making it easy to search later.

[1253] Input: Generated diary data

[1254] Output: Saved diary data

[1255] Step 11: User Access

[1256] Users can view the created diary entries using a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily find diary entries containing conversations from specific dates or specific keywords.

[1257] Input: Search query (e.g. date, keyword)

[1258] Output: Diary data as search results

[1259] (Application example 1)

[1260] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1261] Efficiently recording everyday conversations within the home and automatically generating a diary is extremely important for looking back on family memories and important events. Food delivery services also need to record conversations with customers and use the results as feedback to improve service quality. However, current systems have difficulty automatically executing these processes, and manual recording and data organization are time-consuming and labor-intensive.

[1262] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1263] In this invention, the server includes: means for capturing everyday conversations between family members using an audio microphone installed in the living room; means for locally saving the captured audio data and converting its format; means for uploading the converted audio data to the server via a network; means for converting the audio data into text data using a speech recognition engine in the server; means for analyzing the text data using natural language processing technology and extracting characteristic portions of the conversation; means for automatically generating a diary based on the extracted characteristic portions of the conversation; means for saving the generated diary in a database in a searchable format; means for providing an interface for users to view the diary; means for collecting audio data to capture conversations between delivery staff in a delivery service; means for converting important conversations between customers and delivery staff into text data using a speech recognition engine; and means for analyzing the generated text data and saving it as feedback. This allows for efficient recording and review of memories and important events in the home, and automatically obtaining and analyzing important feedback to improve customer satisfaction in the food delivery service.

[1264] A "living room" is a living space where family members gather on a daily basis to talk and engage in activities.

[1265] An "audio microphone" is a device that picks up sound, converts it into a digital signal, and records it.

[1266] A "conversation" is the act of people communicating through the exchange of words.

[1267] "Local" refers to an area or range limited to an individual device or environment.

[1268] A "server" is a computer system that processes and stores data and provides services to other devices over a network.

[1269] A "voice recognition engine" is software for converting voice into text data.

[1270] "Text data" refers to information expressed as a string of characters.

[1271] "Natural language processing technology" is a technology that allows computers to understand and analyze the natural language used by humans.

[1272] A "characteristic part of a conversation" is a part of a conversation that indicates important or unique content.

[1273] A diary is a document that records everyday events and conversations.

[1274] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[1275] An "interface" is the means or device by which a user operates a system and inputs and displays information.

[1276] "Delivery service" refers to a service that delivers products or goods to a location designated by a customer.

[1277] A "delivery person" is a person who is responsible for delivering goods or items to customers.

[1278] "Audio collection means" means any device or technology used to record speech or audio.

[1279] "Customer" means a person who purchases or uses a product or service.

[1280] "Feedback" refers to opinions and evaluations received about a particular action or product.

[1281] System program generation

[1282] As an embodiment of the present invention, we will create a program to realize a system that records conversations at home and customer interactions in delivery services. The system is composed of a dedicated audio microphone, a local terminal, and a server.

[1283] Explanation of program processing

[1284] The server processes and calculates the data in the following steps:

[1285] 1. Audio data collection:

[1286] A dedicated audio microphone is used to collect voices from within the home and from the delivery person. The audio microphone is equipped with noise cancellation and echo removal functions, allowing for clear audio data to be collected. The data collected by the audio microphone is stored on the local device.

[1287] 2. Audio data format conversion and upload:

[1288] The local device converts the collected audio data into the appropriate format and uploads it to the server using a secure communication protocol in an encrypted format, using a standard audio format conversion library.

[1289] 3. Audio to text conversion:

[1290] The server's speech recognition engine (e.g., Google Speech Recognition API) is used to convert the uploaded voice data into text data, which will serve as the basis for further analysis.

[1291] 4. Natural Language Processing Analysis:

[1292] The text data is analyzed using a server-based natural language processing system (e.g., spaCy, NLTK), which extracts characteristic conversational segments from the text and utilizes techniques including keyword extraction and sentiment analysis.

[1293] 5. Automatic generation and saving of diary entries:

[1294] Based on the extracted characteristic conversation segments, the system automatically generates a diary, which is then stored in a database in an easy-to-read and search format.

[1295] 6. User View:

[1296] Users can view their created diaries through a dedicated application or web interface, which is designed to be intuitive and easy to use, and offers search functions for specific dates and keywords.

[1297] Specific examples

[1298] For example, a conversation at home while having breakfast is recorded:

[1299] Parent: "Where do you want to go today?"

[1300] Child: "The zoo is great!"

[1301] This conversation is generated by the system as a diary entry:

[1302] 8:00 AM Today at breakfast, the family decided to go to the zoo. The kids were excited and looking forward to it.

[1303] Or, in the food delivery service example, the conversations between delivery people and customers are recorded:

[1304] Staff: "This is a delivery. Are you sure you want a pizza and salad?"

[1305] Customer: "Yes, that's correct. Thank you. I'm glad it arrived on time."

[1306] The conversation is recorded as follows:

[1307] 10:15 Conversation at delivery destination: The delivery consisted of a pizza and salad, and the customer seemed pleased. The delivery was on time and was appreciated.

[1308] Prompt Sentence Examples

[1309] Please record and save the following conversation in diary format:

[1310] Staff: "This is a delivery. Are you sure you want a pizza and salad?"

[1311] Customer: "Yes, that's correct. Thank you. I'm glad you got it on time."

[1312] Example of a diary entry:

[1313] 10:15 Conversation at delivery destination: The delivery consisted of a pizza and salad, and the customer seemed pleased. The delivery was on time and was appreciated.

[1314] In this way, users can efficiently record everyday conversations at home or conversations during deliveries and review them later.

[1315] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1316] Step 1:

[1317] Audio data collection: The device activates the audio microphones used by the home and the delivery person to capture conversations in real time. Ambient sounds are included as input. Clear audio data is collected using noise cancellation and echo cancellation, and stored locally.

[1318] Step 2:

[1319] Audio data format conversion and upload: The device converts the collected audio data into a standard audio format. The input in this step is the audio data collected in step 1, and the output is the converted audio file. The converted audio data is encrypted and uploaded to the server using a secure communication protocol (e.g., HTTPS).

[1320] Step 3:

[1321] Voice data to text conversion: Using the server's speech recognition engine (e.g., Google Speech Recognition API), the uploaded voice data is converted into text data. The output is a text representation of the conversation. This text data is used in the subsequent analysis process.

[1322] Step 4:

[1323] Analysis by natural language processing: The server analyzes the text data using a natural language processing system (e.g., spaCy, NLTK). The input is the text data converted in step 3, and the output is the analysis results after keyword extraction and sentiment analysis. This identifies characteristic parts of the conversation.

[1324] Step 5:

[1325] Automatic generation and storage of diary entries: The server automatically generates a diary entry based on the analysis results. The input is the analysis results from step 4, and the output is diary-format text data. This diary entry data is stored in a database and converted into a searchable format.

[1326] Step 6:

[1327] User viewing: Users can view the created diary through a dedicated application or a web interface. As input, they specify dates and keywords. As output, the corresponding diary entries are displayed. The interface is designed to be intuitive and easy to use.

[1328] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1329] This invention relates to a system that automatically records everyday family conversations and compiles them into a diary, and also combines it with an emotion engine that recognizes the user's emotions. The system is mainly composed of an audio microphone installed in the living room, a local terminal, a server, and an emotion engine.

[1330] Audio data collection

[1331] A dedicated microphone is installed in the living room to capture daily family conversations in real time. The microphone is equipped with noise cancellation and echo cancellation functions to collect clear audio data.

[1332] The device periodically receives audio data from the microphone, stores it locally, converts it into a suitable digital format, and prepares it for upload to the server. The audio data is encrypted and transmitted to the server using a secure communication protocol.

[1333] Analysis of voice data and diary generation

[1334] The server receives the voice data sent from the device. After receiving it, it converts the voice data into text data using a voice recognition engine. The converted text data is then analyzed using natural language processing (NLP) technology.

[1335] The server's NLP system extracts characteristic conversational segments from the text data, utilizing techniques such as keyword extraction, sentiment analysis, and speaker identification. Furthermore, an emotion engine recognizes the user's emotions from the text data and incorporates the results into the analysis. Based on the characteristic conversational segments and the user's emotions, the system automatically generates a diary. This diary is organized chronologically and includes emotional elements, providing a richer portrayal of important family events and interesting conversations.

[1336] Save and access your diary

[1337] The generated diary entries are stored in a searchable database by the server, and the diary data is categorized by date, tag, emotion type, etc., allowing users to easily search and access them later.

[1338] Users can view the created diary entries through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diary entries based on a specific date or emotion type.

[1339] Specific examples

[1340] Emotion recognition and recording process

[1341] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[1342] Parent: "Where do you want to go today?"

[1343] Child: "The zoo is great!"

[1344] The conversation is collected by the device, converted into a different format, and uploaded to a server, where the server's speech recognition engine and NLP system analyze the conversation and generate a diary entry similar to the following:

[1345] 8:00 At breakfast today, the family planned to go to the zoo. The children looked very excited.

[1346] Furthermore, the emotion engine recognizes the child's emotion of "it looks like they're having fun" and reflects that in the diary. The diary created in this way is stored on a server, and users can view it using a dedicated app to look back on their emotions and important events at the time.

[1347] The present invention provides a system that automatically records family conversations and memories, including emotions, even in busy daily life, allowing for rich reflection later.

[1348] The processing flow will be explained below.

[1349] Step 1:

[1350] A dedicated microphone picks up everyday conversations between family members in the living room in real time. The dedicated microphone is equipped with high-precision noise cancellation and echo cancellation functions, allowing for clear audio data to be collected.

[1351] Step 2:

[1352] The device periodically acquires audio data collected from a dedicated microphone and temporarily stores it in local storage. The audio data is encrypted during storage to ensure security.

[1353] Step 3:

[1354] The device converts the audio data stored in its local storage into an appropriate digital format (e.g., WAV, MP3, etc.). The conversion process compresses the data and prepares it for transmission.

[1355] Step 4:

[1356] The terminal uploads the converted voice data to a server using a secure communication protocol (e.g., HTTPS or SFTP), thereby ensuring secure data transfer.

[1357] Step 5:

[1358] The server receives the uploaded voice data and stores it in storage, where it is immediately used for voice recognition processing.

[1359] Step 6:

[1360] The server uses a speech recognition engine to convert the voice data into text data, using a highly accurate speech recognition model to accurately convert the conversation into text.

[1361] Step 7:

[1362] The server passes the text data to a natural language processing (NLP) system for analysis, which performs keyword extraction, sentiment analysis, speaker identification, and more.

[1363] Step 8:

[1364] The emotion engine uses the analyzed text data to recognize and identify the user's emotions expressed in the conversation using emotion recognition algorithms.

[1365] Step 9:

[1366] The server extracts characteristic conversational parts based on the analysis results, including the identified emotional information, and automatically generates a diary by combining the extracted conversational content and emotional information.

[1367] Step 10:

[1368] The server stores the generated diary in a searchable database. When saving, the diary data is categorized by date, emotion type, etc.

[1369] Step 11:

[1370] Users can view the created diary entries through a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily find entries based on specific dates or emotions.

[1371] Specific examples

[1372] Emotion recognition and recording process

[1373] In the morning, while the family has breakfast in the living room, a dedicated microphone captures their conversations, such as:

[1374] Parent: "Where do you want to go today?"

[1375] Child: "The zoo is great!"

[1376] The conversations are collected by the devices and uploaded to a server after format conversion. The server's speech recognition engine and NLP system analyze the conversation, and the emotion engine recognizes the children's emotion of "it looks like they're having fun" and reflects that in the diary. The following diary entry is then generated:

[1377] 8:00 At breakfast today, the family planned to go to the zoo. The children looked very excited.

[1378] The generated diary is stored on a server, and users can view it at any time using a dedicated app to recall their emotions and important events at the time.

[1379] In this way, this system automatically records everyday conversations of family members, including their emotions, allowing for richer reflection later.

[1380] Example 2

[1381] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1382] It is difficult for many households to effectively record everyday conversations at home and save them in a format that can be reviewed later. There is also a demand for recording not only the content of conversations but also the emotions expressed at the time, but doing this manually is extremely time-consuming. Furthermore, ensuring the security of collected data is also an important issue.

[1383] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring household conversations using a voice collecting device, means for locally storing the acquired voice data and converting it into a digital format, means for transmitting the converted voice data to a central processing unit over a network, means for converting the voice data into text data using a voice recognition model in the central processing unit, means for analyzing the text data using natural language processing technology and extracting characteristic parts of the conversation, means for automatically generating a diary based on the extracted characteristic parts of the conversation and user emotion data, means for storing the generated diary in a searchable format in a data storage device, and means for providing a user interface for the user to view the diary. This makes it possible to effectively and safely record and store everyday household conversations and the emotions felt at the time, and easily review them later.

[1384] An "audio collection device" is a device installed in a home, such as a living room, to capture conversations, and is equipped with noise cancellation and echo removal functions.

[1385] "Local" refers to the storage area within the device where audio data acquired from a dedicated microphone is temporarily saved.

[1386] "Converting to digital format" refers to the process of converting captured audio data into an appropriate digital format.

[1387] "Central Processing Unit" refers to a server or another powerful computer for receiving voice data and processing voice recognition models, etc.

[1388] "Speech recognition model" refers to an algorithm or system for converting speech data into text data.

[1389] "Natural language processing technology" refers to technology for analyzing text data and performing keyword extraction, speaker identification, sentiment analysis, etc.

[1390] "Characteristic conversational parts" refer to important conversational content extracted from text data using natural language processing technology.

[1391] "Automatic diary generation" refers to the process in which the system automatically creates diary-style records based on characteristic conversation segments and the user's emotional data.

[1392] "Data repository" refers to a database or other storage medium for storing generated diary data.

[1393] "User interface" refers to the application or web interface that allows a user to view the diary they have created.

[1394] The present invention is a system that automatically records everyday conversations at home and compiles them into a diary. This system consists of a user, a terminal, and a server. The specific configuration and operation procedure are described in detail below.

[1395] First, the device captures household conversations in real time using a dedicated microphone installed in the living room. This microphone is equipped with noise cancellation and echo cancellation functions, allowing for clear audio data to be collected. The collected audio data is stored locally on the device and converted into an appropriate digital format. The converted audio data is then encrypted and transmitted to a central processing unit (server) using a secure communication protocol.

[1396] The server receives the voice data sent from the device. The received voice data is converted into text data using a voice recognition model. The text data is then analyzed using natural language processing technology. This includes processes such as keyword extraction, speaker identification, and sentiment analysis. Characteristic parts of the conversation are extracted from the analyzed text data. In addition, an emotion engine recognizes the user's emotional data and reflects it in the analysis results.

[1397] The server then automatically generates a diary based on the extracted characteristic conversation segments and the user's emotional data. This automatically generated diary is organized chronologically and includes emotional elements, allowing it to richly capture important events and interesting conversations that occur daily at home. The generated diary is then stored in a searchable format in a data storage device (database).

[1398] Users can view their diaries stored on the server through a dedicated application or web interface, which has an intuitive and easy-to-use design, allowing users to easily find diaries based on a specific date or emotion type.

[1399] Specific examples

[1400] In the morning, while a family is having breakfast in the living room, a dedicated microphone captures their conversation. For example, a parent might ask, "Where do you want to go today?" and the child might reply, "The zoo!" This conversation is collected by the device, converted into an appropriate format, encrypted, and sent to a server. The server's speech recognition model and natural language processing technology analyze the conversation and generate a diary entry like the one below.

[1401] "08:00 At breakfast today, the family planned to go to the zoo. The children looked very excited."

[1402] Furthermore, the emotion engine recognizes the child's emotion of "it looks like they're having fun" and reflects that in the diary. The diary created in this way is saved on a server, and users can view it using a dedicated app to look back on their emotions and important events at the time.

[1403] Prompt Sentence Examples

[1404] Examples of prompts to input to a generative AI model include:

[1405] "Please explain a system that records everyday household conversations in real time and compiles them into a diary. The system collects audio using a dedicated microphone, converts the data on the device, and sends it to a server. The server analyzes the audio to generate text data and also recognizes emotions using an emotion engine. Finally, it automatically generates a diary that the user can access."

[1406] Using this prompt, the generative AI model highlights detailed descriptions of the system in question, helping to clarify specific processing steps and technologies used.

[1407] This invention provides a system that can effectively and safely record and store everyday conversations at home and the emotions felt during those conversations, allowing for easy review later.

[1408] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1409] Step 1:

[1410] The device captures household conversations in real time using a voice collection device (dedicated microphone) installed in the living room. The input is the voice data collected by the microphone, and the output is clear voice data with noise cancellation and echo removal. Specifically, the microphone sensor detects sound waves in the space and converts them into a digital signal. Next, the built-in noise cancellation technology removes background noise, and the echo removal function suppresses voice reverberation.

[1411] Step 2:

[1412] The device stores the collected voice data in local storage and converts it into an appropriate digital format. The input is clear voice data with noise and echo removed, and the output is voice data converted into a digital format. Specifically, the device converts the stored voice data into a digital format (e.g., WAV or MP3) at regular intervals using a digital signal processor.

[1413] Step 3:

[1414] The device encrypts the converted voice data and sends it to the server using a secure communication protocol. The input is the voice data converted to digital format, and the output is the encrypted data sent to the server. Specifically, the device securely converts the digital format voice data using an encryption algorithm such as AES, and then sends the encrypted data to the server using a secure protocol such as HTTPS or TLS.

[1415] Step 4:

[1416] The server receives and decodes the encrypted voice data sent from the terminal. The input is encrypted voice data, and the output is decoded clear voice data. Specifically, the server's communication module receives the encrypted data packet and decrypts the voice data using a built-in decoding algorithm.

[1417] Step 5:

[1418] The server converts the received voice data into text data using a voice recognition model. The input is decoded clear voice data, and the output is text data converted from the voice data. Specifically, the voice recognition engine in the server analyzes the characteristics of the voice waveform and generates the corresponding text.

[1419] Step 6:

[1420] The server analyzes the text data using natural language processing (NLP) technology, extracting keywords and analyzing emotions, and extracting characteristic parts of the conversation. The input is text data generated by a speech recognition model, and the output is the analyzed characteristic parts of the conversation and emotional data. Specifically, the NLP engine analyzes the text data and automatically extracts important keywords and the speaker's emotions.

[1421] Step 7:

[1422] The server automatically generates a diary based on the extracted characteristic conversational parts and the user's emotional data. The input is the analyzed characteristic conversational parts and emotional data, and the output is the generated diary data. Specifically, the diary generation algorithm integrates the characteristic conversational parts and the emotion analysis results, and automatically creates a diary-style record organized chronologically.

[1423] Step 8:

[1424] The server stores the created diary in a searchable format in a data storage device (database). The input is the created diary data, and the output is searchable diary data stored in the database. Specifically, the database management system classifies and stores the created diary data with appropriate tags (e.g., date, emotion type).

[1425] Step 9:

[1426] Users can view the diaries stored on the server through a dedicated application or a web interface. The input is a search query by the user, and the output is diary data extracted based on the query. Specifically, the user interface receives the search query, extracts relevant diary data from the database, and displays it.

[1427] (Application example 2)

[1428] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1429] Conventional systems can record conversations in a living room, compile them into a diary, and even recognize emotions. However, it is difficult to record conversations between customers and staff in commercial facilities and provide information useful for improving services and customer satisfaction. Furthermore, there is a lack of concrete methods for improving customer satisfaction and evaluating service quality. Furthermore, there is a need for a method to evaluate smooth communication between customers and staff by automatically analyzing the emotions in conversations and generating diary-style reports.

[1430] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1431] In this invention, the server includes means for converting voice data into text data using a voice recognition engine, means for analyzing the text data using natural language processing technology to extract characteristic conversation parts, and means for automatically generating a diary based on the extracted characteristic conversation parts and the user's emotions. This makes it possible to record and analyze conversations between customers and staff and provide information useful for improving services and increasing customer satisfaction.

[1432] An "audio microphone" is a device that converts sound waves into electrical signals and collects sounds such as conversations.

[1433] "Local storage" means that acquired data is temporarily stored on a local terminal or device rather than on a central server.

[1434] "Format conversion" refers to converting an original data format into another format, including converting audio data into an appropriate digital format.

[1435] "Uploading via a network" means transferring data to another device or server via the Internet or other communication means.

[1436] A "voice recognition engine" is a software system for converting voice data into text data.

[1437] "Natural language processing technology" is a set of algorithms and techniques for analyzing text data and understanding its meaning.

[1438] "Characteristic conversational segments" are specific important keywords, phrases, emotions, etc. extracted from the analyzed text data.

[1439] "Automatic diary generation" means automatically creating diary-style documents based on collected and analyzed data.

[1440] A "searchable format" is a format in which stored data is structured so that it can be easily searched and accessed by users.

[1441] "Providing an interface" means providing a means for users to operate the system and access data.

[1442] A "commercial facility" is an environment where customers and staff communicate face-to-face, including shopping malls and stores.

[1443] "Sentiment analysis" is the process of detecting emotions and intent from text data and determining the type and intensity of those emotions.

[1444] "Service improvement" refers to efforts to improve the quality of services provided in order to enhance customer experience and satisfaction.

[1445] "Improving customer satisfaction" refers to efforts to increase customer satisfaction with the services they receive.

[1446] MODE FOR CARRYING OUT THE INVENTION

[1447] The embodiments of the present invention will be described in detail below. The system is realized by using an audio microphone, a local terminal, a server, natural language processing (NLP) technology, and a sentiment analysis engine.

[1448] Hardware and software used

[1449] The following hardware and software are used to build this system. The Shure MV7, which has noise cancellation functionality, is suitable as an audio microphone. A Raspberry Pi can be used as the local terminal. Amazon Web Services (AWS) is used as the server, and Flask is used to build the API. The Google Speech Recognition API is used as the speech recognition engine, and the TextBlob library is used for natural language processing. TextBlob is also used as a sentiment analysis engine, and detailed analysis is performed based on the data obtained from it.

[1450] Overall system flow and data processing

[1451] Audio data collection

[1452] The server uses audio microphones installed in commercial facilities to capture conversations between customers and staff in real time. The audio microphones are equipped with noise cancellation functionality, enabling clear audio data to be collected. The collected audio data is temporarily stored on a local device and converted into an appropriate digital format. The converted audio data is then encrypted and securely uploaded to the server via a network.

[1453] Analysis of voice data and diary generation

[1454] When the server receives the uploaded voice data, it first converts it into text using a speech recognition engine. It then uses NLP technology to analyze the text, extracting keywords and conducting sentiment analysis. It then extracts characteristic parts of the conversation and automatically generates a diary based on them. The diary also contains emotional elements, allowing it to richly express the content of interactions between users and staff at commercial facilities.

[1455] Save and access your diary

[1456] The generated diary entries are stored in a searchable database by the server. Users can access this database and view their entries through a dedicated application or a web interface. The interface is designed to be intuitive and easy to use, allowing users to easily search and find entries based on specific dates or emotional types.

[1457] Specific examples

[1458] Recording conversations in commercial facilities

[1459] Imagine a scenario in which a customer and a staff member are talking in a retail establishment about a new campaign. The conversation goes something like this:

[1460] Staff: "What do you think about the new campaign?"

[1461] Customer: "Very interested!"

[1462] This conversation is recorded by a microphone and temporarily saved on the local device before being uploaded to a server, where it is analyzed using a speech recognition engine and NLP technology to generate a diary entry like this:

[1463] 14:00 Customer response to the new campaign has been very positive. Customers have shown great interest.

[1464] Furthermore, the sentiment analysis engine recognizes the customer's positive emotion of "interested" and reflects this in the diary. The diary generated in this way is saved on the server and can be viewed by the user through a dedicated app. Specifically, the following prompt sentences can be used to help improve services, for example:

[1465] "Record customer reactions to new campaigns. Find out their opinions, feelings and areas for improvement regarding the desired service."

[1466] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1467] Step 1:

[1468] The server uses audio microphones installed in commercial facilities to capture conversations between customers and staff in real time. The audio microphones are equipped with a noise cancellation function to collect clear audio data, which is then used as input data.

[1469] Step 2:

[1470] The device periodically stores audio data received from the audio microphone locally, converts the audio data into a suitable digital format, encrypts it, and uploads the converted audio data to a server using a secure communication protocol. The output of this step includes the encrypted audio data.

[1471] Step 3:

[1472] The server receives and decrypts the uploaded encrypted audio data. The received and decrypted audio data is converted into text data using the Google Speech Recognition API. The input of this process is the encrypted audio data, and the output is text data.

[1473] Step 4:

[1474] The server performs natural language processing (NLP) on the converted text data using the TextBlob library. The NLP process involves keyword extraction and sentiment analysis, which specifically extracts customer sentiment and important keywords. The input is text data, and the output is analyzed keywords and sentiment data. Specifically, keywords and sentiment are extracted using TextBlob's NLP functions.

[1475] Step 5:

[1476] The server automatically generates a diary-style report based on the extracted keywords and emotion data. This diary richly reflects the conversations and emotions between customers and staff. The input is the analyzed keywords and emotion data, and the output is a diary-style text report.

[1477] Step 6:

[1478] The server stores the generated diary-style reports in a searchable database. The database is tagged with dates, types of emotions, and other information, and organized for easy later search and access. The input is a diary-style text report, and the output is diary data saved in a searchable format.

[1479] Step 7:

[1480] Users can access the database and view the created diary entries through a dedicated application or web interface. The interface is intuitive and easy to use, and allows searches for specific dates or types of emotions. The input is search criteria, and the output is the corresponding diary data. Specifically, related diary data is retrieved from the database based on the user's input and displayed.

[1481] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1482] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1483] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1484] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1485] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1486] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1487] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1488] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1489] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1490] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1491] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1492] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1493] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1494] 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.

[1495] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1496] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1497] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1498] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1499] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1500] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1501] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1502] The following is further disclosed regarding the above embodiment.

[1503] (Claim 1)

[1504] A means for capturing everyday conversations of family members using an audio microphone installed in the living room;

[1505] means for locally storing the acquired audio data and performing format conversion;

[1506] A means for uploading the converted audio data to a server via a network;

[1507] A means for converting voice data into text data using a voice recognition engine in the server;

[1508] A means for analyzing text data using natural language processing technology and extracting characteristic conversational parts;

[1509] A means for automatically generating a diary based on the extracted characteristic conversation parts, and

[1510] a means for storing the generated diary in a searchable format in a database;

[1511] means for providing an interface for a user to view the diary;

[1512] A system including:

[1513] (Claim 2)

[1514] 10. The system of claim 1, wherein the natural language processing techniques include keyword extraction and sentiment analysis.

[1515] (Claim 3)

[1516] 10. The system of claim 1, wherein the system performs encryption of voice data.

[1517] "Example 1"

[1518] (Claim 1)

[1519] a means for acquiring a conversation between people using an audio acquisition device installed in a living room;

[1520] means for locally storing and converting the acquired acoustic information into an electronic recording format;

[1521] means for uploading the converted acoustic information to a remote device via a communication line;

[1522] means for converting acoustic information into textual information using an acoustic recognition mechanism in the remote device;

[1523] A means for analyzing text information using natural language processing technology and extracting characteristic dialogue parts;

[1524] A means for automatically generating a record based on the extracted characteristic dialogue portions;

[1525] means for storing the generated records in a retrievable format on a storage device;

[1526] a means for providing an interface for users to view the records;

[1527] A system including:

[1528] (Claim 2)

[1529] 10. The system of claim 1, wherein the natural language processing techniques include keyword extraction and sentiment analysis.

[1530] (Claim 3)

[1531] 10. The system of claim 1, wherein the system performs encryption of acoustic information.

[1532] "Application Example 1"

[1533] (Claim 1)

[1534] A means for capturing everyday conversations of family members using an audio microphone installed in the living room;

[1535] means for locally storing the acquired audio data and performing format conversion;

[1536] A means for uploading the converted audio data to a server via a network;

[1537] A means for converting voice data into text data using a voice recognition engine in the server;

[1538] A means for analyzing text data using natural language processing technology and extracting characteristic conversational parts;

[1539] A means for automatically generating a diary based on the extracted characteristic conversation parts, and

[1540] a means for storing the generated diary in a searchable format in a database;

[1541] means for providing an interface for a user to view the diary;

[1542] a voice collection means for acquiring dialogue of a delivery person in a delivery service;

[1543] A means of converting critical customer and delivery personnel interactions into text data using a speech recognition engine;

[1544] means for analyzing the generated text data and storing it as feedback;

[1545] A system including:

[1546] (Claim 2)

[1547] 10. The system of claim 1, wherein the natural language processing techniques include keyword extraction and sentiment analysis.

[1548] (Claim 3)

[1549] 10. The system of claim 1, wherein the system performs encryption of voice data.

[1550] "Example 2: Combining Emotion Engines"

[1551] (Claim 1)

[1552] A means for acquiring home conversations using a voice collection device installed in a living room;

[1553] means for locally storing and converting the captured audio data into a digital format;

[1554] means for transmitting the converted voice data to a central processing unit via a network;

[1555] means for converting voice data into text data using a voice recognition model in a central processing unit;

[1556] A means for analyzing text data using natural language processing technology and extracting characteristic conversational parts;

[1557] A means for automatically generating a diary based on the extracted characteristic conversation parts and the user's emotion data;

[1558] means for storing the generated diary in a searchable format in a data storage device;

[1559] means for providing a user interface for a user to view the diary;

[1560] A system including:

[1561] (Claim 2)

[1562] 10. The system of claim 1, wherein the natural language processing techniques include keyword extraction, speaker identification, and sentiment analysis.

[1563] (Claim 3)

[1564] 10. The system of claim 1, further comprising means for encrypting and communicating the voice data using a secure protocol.

[1565] "Application example 2 when combining emotion engines"

[1566] (Claim 1)

[1567] A means for capturing everyday conversations of family members using an audio microphone installed in the living room;

[1568] means for locally storing the acquired audio data and performing format conversion;

[1569] A means for uploading the converted audio data to a server via a network;

[1570] A means for converting voice data into text data using a voice recognition engine in the server;

[1571] A means for analyzing text data using natural language processing technology and extracting characteristic conversational parts;

[1572] A means for automatically generating a diary based on the extracted characteristic conversation parts and the user's emotions;

[1573] a means for storing the generated diary in a searchable format in a database;

[1574] A means of recording conversations between users and staff at commercial facilities and providing information useful for improving services and customer satisfaction.

[1575] means for providing an interface for a user to view the diary;

[1576] A system including:

[1577] (Claim 2)

[1578] 10. The system of claim 1, wherein the natural language processing techniques include keyword extraction and sentiment analysis.

[1579] (Claim 3)

[1580] 10. The system of claim 1, wherein the system performs encryption of voice data. [Explanation of symbols]

[1581] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for capturing everyday conversations of family members using an audio microphone installed in the living room; means for locally storing the acquired audio data and performing format conversion; A means for uploading the converted audio data to a server via a network; A means for converting voice data into text data using a voice recognition engine in the server; A means for analyzing text data using natural language processing technology and extracting characteristic conversational parts; A means for automatically generating a diary based on the extracted characteristic conversation parts, and a means for storing the generated diary in a searchable format in a database; means for providing an interface for a user to view the diary; A system including:

2. The system of claim 1 , wherein the natural language processing techniques include keyword extraction and sentiment analysis.

3. 10. The system of claim 1, further comprising encryption of audio data.

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