system

Abstract

We provide the system. [Solution] Methods for acquiring voice data from elderly people, A means of converting acquired audio data into text data in real time, A means of analyzing converted text data to identify signs of dementia, A means of generating and displaying the analysis results as a report, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the current diagnosis of dementia in the elderly, doctor visits and professional judgments are essential, which poses a problem of requiring a great deal of man-hours and time. Also, since it is difficult to detect signs early and respond promptly, the support required for the elderly may be delayed. As a result, there is a problem of increasing the burden on medical resources and also increasing the burden on patients and their families.

Means for Solving the Problems

[0005] This invention provides a system that converts voice data generated through everyday conversations with elderly individuals into text in real time and analyzes that text data. Through this analysis, the system automatically identifies signs of dementia and generates and displays the results as a report, enabling early diagnosis without a doctor's visit. This system facilitates the early detection of dementia symptoms and allows for the efficient use of medical resources.

[0006] "Voice data" refers to audio information obtained through conversations with elderly people.

[0007] "Text data" refers to audio data that has been converted into written information using language processing technology.

[0008] "Analysis" refers to the process of using acquired text data to identify signs and patterns of dementia.

[0009] "Signs of dementia" refer to characteristics that indicate a decline or abnormality in cognitive function, as detected from the content of conversations.

[0010] A "report" is a document generated based on the analysis results, and includes information on the likelihood of dementia and related information. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6]This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

[0013] First, let's explain the terminology used in the following explanation.

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

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

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

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

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

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

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention is a system that allows users who can regularly converse with elderly people to acquire voice information using a terminal and analyze it on a server to identify early signs of dementia.

[0033] The user activates the device's recording function when starting a conversation, collecting audio data of the conversation. The device utilizes speech recognition technology to convert the audio data into text data in real time, and sends the converted text data to the server sequentially.

[0034] The server analyzes the received text data and detects abnormal thought patterns and signs of cognitive decline in the statements of elderly individuals. Based on these analysis results, the server identifies early signs of dementia and generates a report containing the necessary information. This report is sent to the terminal, and the user can refer to the diagnostic results as needed.

[0035] For example, if a pattern is detected in an elderly person's daily conversations, such as repeatedly mentioning the same topic in a short period of time or giving inappropriate answers to questions, the server identifies this as an early sign of dementia. The report details such anomalies and recommends further consultation with a specialist if necessary. This system allows users to effectively identify signs of dementia and receive appropriate medical support early on.

[0036] The following describes the processing flow.

[0037] Step 1:

[0038] The user initiates a conversation with the elderly person using their device and launches a voice recording application on the device. Voice recording begins, capturing the elderly person's statements in real time.

[0039] Step 2:

[0040] The device uses recorded audio data to perform real-time speech recognition and generates the results as text data. Speech recognition technology automatically converts spoken content into text.

[0041] Step 3:

[0042] The device sends the converted text data to the server via the internet. The data is encrypted to ensure the security of the communication.

[0043] Step 4:

[0044] The server inputs the received text data into an analysis module and uses advanced natural language processing techniques to detect signs of dementia from the conversation. In this process, it determines abnormalities by comparing the data against known characteristics of dementia.

[0045] Step 5:

[0046] The server assesses the possibility of dementia based on the detected anomalies and characteristics. It then generates a report summarizing the discovered signs and related data.

[0047] Step 6:

[0048] The server sends the generated report to the terminal and notifies the user of the results. The user can then review the report on the terminal and make decisions based on the elderly person's situation.

[0049] (Example 1)

[0050] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0051] Cognitive decline in the elderly progresses gradually, making it crucial to identify its early signs. However, it often takes time for a specialist to make a diagnosis, and there is a lack of means to detect abnormalities early in daily life. To address this challenge, there is a need for a system that automatically detects signs of cognitive decline from everyday conversations and reports them early.

[0052] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0053] In this invention, the server includes a device for acquiring voice information from elderly people, a device for converting the acquired voice information into text information in real time, and a device for analyzing the converted text information to identify signs of cognitive decline. This makes it possible to automatically detect and report signs of cognitive decline in everyday conversations.

[0054] "Elderly" refers to adults who, as a result of aging, are prone to a decline in physical or cognitive function.

[0055] "Auditory information" refers to information, including language, that is transmitted through human voices.

[0056] "Acquisition device" refers to a device used to collect and record specific information.

[0057] "Real-time" refers to a situation where something is processed or transformed immediately the moment it occurs.

[0058] "Textual information" refers to information expressed in text format.

[0059] A "conversion device" refers to a device that has the function of changing data in one format to another.

[0060] An "analytical device" refers to a device that analyzes data to derive specific results or trends.

[0061] "Cognitive decline" refers to a condition in which a person's mental abilities, such as memory, judgment, understanding, and consciousness, are reduced compared to normal.

[0062] "Signs" refer to early evidence that a particular condition or change is about to occur.

[0063] A "report" refers to a document that describes the results of an analysis or investigation.

[0064] "A device for presenting information" refers to a device for visually displaying or outputting information.

[0065] This invention relates to a device that detects and reports early signs of cognitive decline from everyday conversations with elderly individuals. The device allows users to acquire audio information from conversations using a terminal, which is then analyzed on a server to identify signs of cognitive decline.

[0066] The user uses the device to launch a recording application and record conversations with elderly individuals as audio data. The device's built-in speech recognition software then converts the recorded audio data into text in real time. Specifically, Google's Speech-to-Text API can be used as the software for this purpose.

[0067] The converted text information is immediately sent from the terminal to a cloud-based server. The server uses natural language processing techniques to analyze the text information and identify abnormal thought patterns and repetitive statements that may indicate cognitive decline. Tools such as Python's NLTK library or SpaCy are suitable for this analysis.

[0068] Based on the analysis, the server generates a report and sends it back to the terminal. Through this report, the user can review signs of cognitive decline detected in everyday conversations and, if necessary, is advised to consult a specialist.

[0069] For example, if an elderly person is observed to repeatedly bring up the same topic in a short period of time, the server will record this pattern in the report as an indication of cognitive decline. This allows the user to take immediate action and arrange for necessary medical assistance.

[0070] An example of a prompt for a generative AI model is: "Please describe in detail the process for analyzing conversation data with elderly individuals and detecting signs of cognitive decline. In particular, please describe specifically how to identify abnormal patterns using speech recognition and natural language processing."

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The user launches the recording application on their device. They begin a conversation, collecting the elderly person's voice information. The audio recorded through the device's microphone is saved digitally to the device's storage. The collection of voice data is complete when the recording ends.

[0074] Step 2:

[0075] The device converts collected audio data into text in real time using speech recognition software. The input is audio data, and the Google Speech-to-Text API is used to convert the audio into text. The converted text is stored in temporary memory. Once the conversion is complete, the device indicates that the text data is ready.

[0076] Step 3:

[0077] The terminal sends the converted character information to a cloud-based server. The input data is the converted character information, which is sent to the server via the internet using a communication module. The output indicates that the character information is sent in a format accessible to the server.

[0078] Step 4:

[0079] The server analyzes the received text information. The input for analysis is the text information sent to the server, and a natural language processing engine is used to detect abnormal thought patterns and signs of cognitive decline. Specifically, word frequency analysis and theme detection are performed using the Python NLTK library, and a list of signs is generated as output.

[0080] Step 5:

[0081] The server generates a report indicating potential cognitive decline based on the analysis results. The input is a list of symptoms obtained in the analysis step, and the report generation module organizes the necessary information to create a detailed report as output. The report includes details of abnormal thought patterns and recommendations for consulting a specialist.

[0082] Step 6:

[0083] The generated report is sent to the terminal. The server sends the generated report as input data to the terminal's address, and the report becomes viewable on the terminal as output. The user can then review this report and consider the next action as needed.

[0084] (Application Example 1)

[0085] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] There is a need to detect cognitive decline in older adults more quickly and accurately in everyday conversations. However, current methods make it difficult to detect early signs of dementia that are often overlooked by the older adults themselves or those around them. Furthermore, effectively notifying individuals of these signs and promptly providing appropriate medical support remains a challenge.

[0087] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0088] In this invention, the server includes means for acquiring voice information, means for converting the acquired voice information into text information in real time, means for analyzing the converted text information to identify cognitive decline, means for generating the analysis results as a report and displaying it on a visual display device, and means for identifying abnormal thought patterns during conversation and notifying others of changes in health status. This makes it possible to detect cognitive decline early and provide more appropriate support quickly.

[0089] "Auditory information" refers to information collected from human speech and sounds, used to analyze their characteristics.

[0090] "Textual information" refers to data obtained by converting audio information into text format using speech recognition technology.

[0091] "Analysis" is the process of analyzing collected data to recognize specific patterns or anomalies.

[0092] "Cognitive decline" refers to a state in which mental functions such as memory, thinking ability, and judgment are lower than normal.

[0093] A "report" is a document that summarizes the analysis results in a visually appealing and easy-to-understand format.

[0094] A "visual display device" is a device used to visually display generated information and analysis results to the user.

[0095] A "thinking pattern" is a tendency based on certain perceptions and judgments that manifest in speech and behavior.

[0096] "Changes in health status" refers to events in which an individual's physical and mental health has changed compared to before.

[0097] The system implementing this invention aims to detect cognitive decline early through conversations with elderly individuals. The user uses a terminal equipped with a visual display device to acquire voice information before initiating a conversation with the elderly person. This voice information is converted into text in real time using speech recognition software on the terminal. Specifically, the Google Speech-to-Text API is used. Through this process, the voice data is sent to a server as text data.

[0098] The server analyzes the received text information and identifies abnormal thought patterns that may indicate cognitive decline. This analysis utilizes Python's NLTK (Natural Language Processing Toolkit). If an anomaly is detected, the server generates a report based on the analysis results. This report is displayed on the user's terminal's visual display, making it easily accessible to the user.

[0099] For example, if an elderly person repeatedly asks the same question in a short period of time, the system identifies this as an abnormal thought pattern and notifies the user. The user can then receive information through a visual display such as, "This customer has been asking the same question frequently recently. We recommend checking if there is anything they are experiencing difficulties with."

[0100] Examples of prompt statements include the following:

[0101] Please list patterns in conversations that may indicate dementia in older adults. For example, repeating the same content from previous conversations in a short period of time, or giving irrelevant answers to questions.

[0102] This system allows users to leverage everyday conversations with elderly individuals to identify cognitive decline early and receive necessary medical support. The hardware and software used throughout this process include a speech recognition API, a natural language processing toolkit, and a visual display device to process data efficiently and effectively.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] When the user starts a conversation, they activate the device's voice acquisition function. Voice information is acquired through the microphone and temporarily stored on the device as digital audio data. In this case, the input is the speech of an elderly person, and the output is digital audio data.

[0106] Step 2:

[0107] The device converts the acquired audio data into text in real time using the Google Speech-to-Text API. In this step, digital audio data (input) is generated as text data (output). Noise reduction and sound quality adjustments are performed during the conversion process.

[0108] Step 3:

[0109] The converted character information is sent from the terminal to the server using the HTTP protocol. The input in this process is character information, and the output is a confirmation signal to the server that the transmission is complete. The data is encrypted during transmission to ensure secure communication.

[0110] Step 4:

[0111] The server analyzes the received text information using Python's NLTK. Here, it obtains analysis data (output) by searching for abnormal thought patterns in the text information (input). This analysis uses natural language processing techniques to identify specific patterns that indicate cognitive decline.

[0112] Step 5:

[0113] Based on the analysis results, the server automatically generates a report containing the necessary information. In this step, the analysis data (input) is used to generate report data (output). The report includes details of the anomaly pattern and recommended actions.

[0114] Step 6:

[0115] The generated report is returned to the terminal's visual display and displayed. The user can visually review the information and take appropriate action. In this step, the report data (input) is displayed on the terminal as visual information (output). The user makes a judgment about the health status of the elderly person based on the displayed information.

[0116] Through this series of processes, users can identify cognitive decline early based on conversations with elderly individuals and provide appropriate support.

[0117] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0118] This invention provides a system that not only acquires voice data through conversations with elderly individuals and analyzes it as text data, but also performs emotion recognition to determine the emotional state of elderly individuals along with signs of dementia. This allows for an emotional approach to the early diagnosis of dementia, enabling a more comprehensive assessment.

[0119] The user uses the device to have a conversation with an elderly person and records the audio data. The device converts the recorded audio data into text data using speech recognition technology. Then, it sends the converted text data and the original audio data to the server.

[0120] The server analyzes received text data to detect signs of dementia in the elderly person's statements. It also uses an emotion engine to recognize the user's emotional state from both audio and text data. This allows it to capture emotional changes and trends, enabling an understanding of the elderly person's mental state and emotions during conversations.

[0121] For example, if an elderly person repeats the same sentence in an unusual tone of voice or context, it may be judged not only as a sign of cognitive decline, but also as an indication of emotional influences such as stress or anxiety. The server generates a detailed analysis report that includes these emotional elements and provides it to the user via the terminal. Based on this report, the user can gain a multifaceted understanding of the elderly person's cognitive function and consider appropriate countermeasures.

[0122] This system aims to provide more appropriate support and treatment plans by comprehensively analyzing the signs of dementia and the associated emotional states.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The user initiates a conversation with an elderly person using their device and launches a voice recording application on the device. Voice data is collected, and the elderly person's statements are recorded in real time.

[0126] Step 2:

[0127] The device transmits the acquired audio data to the speech recognition engine in real time, where it is converted into text data. This generates the spoken content as text information on the device.

[0128] Step 3:

[0129] The device sends the converted text data and the original audio data to the server. The data is encrypted to ensure security.

[0130] Step 4:

[0131] The server analyzes the received text data to identify signs of dementia. It detects pathological speech and inconsistent statements in conversations and records them as abnormal patterns.

[0132] Step 5:

[0133] The server uses an emotion engine to analyze the user's emotions from voice and text data. Emotion analysis extracts emotional changes and characteristics from voice tone and speed, as well as text content.

[0134] Step 6:

[0135] The server integrates the results of both dementia symptoms and emotional analysis to generate a detailed report. The report includes the state of cognitive function and emotional fluctuations, and provides an overall assessment.

[0136] Step 7:

[0137] The server sends the generated report to the terminal and notifies the user of the results. The user can review the report content via the interface on the terminal and plan future approaches for the elderly.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] Early detection of cognitive decline in the elderly is crucial for implementing appropriate countermeasures and treatments. However, conventional diagnostic methods often rely on subjective judgment and observation, leading to challenges in accuracy and reliability. In addition, there is a lack of emotional approaches, making it difficult to comprehensively assess the mental state of the elderly.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes means for converting voice information into text information in real time, means for analyzing the converted text information to identify signs of cognitive function, and means for recognizing emotional states from voice and text information. This enables a comprehensive evaluation of the cognitive function and mental state of elderly individuals from both voice and emotional perspectives, allowing for more accurate and reliable early diagnosis.

[0143] "Audio information" refers to digital or analog data used to record or transmit human speech.

[0144] "Textual information" refers to information that is expressed as text, obtained by converting audio information.

[0145] "Signs of cognitive impairment" refer to unusual verbal or behavioral patterns that may indicate a decline in cognitive function.

[0146] "Emotional state" refers to the mental state or emotional response analyzed from audio or text.

[0147] A "report" refers to a document generated to organize the results of analyzed data and provide information to the user.

[0148] A "server" refers to a network-connected computer system that stores, processes, and analyzes data.

[0149] This invention is a system that acquires voice information through conversations with elderly people and uses that information to evaluate signs of cognitive function and emotional state.

[0150] Hardware and software to use

[0151] Users engage in conversations with elderly individuals using mobile devices or computer terminals. These terminals are equipped with recording capabilities, allowing for the digital acquisition of the elderly individuals' voices.

[0152] The device uses widely available speech recognition software (e.g., speech recognition APIs and voice service platforms) as a speech recognition technology to convert acquired speech information into text information in real time.

[0153] The server analyzes the received text information and identifies signs of cognitive function. This analysis uses natural language processing techniques to evaluate the patterns and content of the text.

[0154] The server then uses an emotion recognition engine (e.g., an emotion analysis API) to recognize emotional states from audio and text information.

[0155] Specific example

[0156] As a concrete example, consider cases where elderly individuals repeatedly state the same things or exhibit changes in their tone of voice. The server analyzes these patterns and detects emotional changes such as stress and anxiety. Furthermore, if abnormal speech patterns are observed consistently, it can help identify cognitive decline at an early stage.

[0157] This allows users to gain a comprehensive and detailed understanding of the cognitive function and emotional state of older adults, providing useful information for taking appropriate support and medical action.

[0158] Example of a prompt

[0159] Please describe a system that converts audio data obtained from conversations with elderly individuals into text data and performs emotion recognition to analyze signs of dementia and emotional states in the elderly.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] Audio data recording

[0163] The user initiates a conversation with the elderly person using the device. The device is equipped with a built-in microphone, which collects voice information in real time.

[0164] Input: Voice of elderly person speaking

[0165] Output: Audio data file (digital format)

[0166] Specific operation: The user presses the "Start Recording" button on the device to begin recording, and the conversation is saved as digital audio data.

[0167] Step 2:

[0168] Converting audio data to text

[0169] The device converts the collected audio data into text information using speech recognition software. This process transforms the audio data into analyzable text data.

[0170] Input: Audio data file

[0171] Output: Text data

[0172] Specific operation: The speech recognition software starts up, analyzes the audio file, automatically converts it to text, and displays "Text conversion complete" on the screen.

[0173] Step 3:

[0174] Sending data

[0175] The device sends the generated text data and the original audio data file to the server. The data is encrypted and transferred securely.

[0176] Input: Text data, audio data files

[0177] Output: Data stored on the server

[0178] Specific operation: The device uses a wireless or wired network, a progress bar is displayed to show the progress, and a confirmation message pops up when transmission is complete.

[0179] Step 4:

[0180] Analysis of dementia symptoms

[0181] The server analyzes the received text data using natural language processing algorithms to detect abnormal speech patterns. This helps identify potential signs of dementia.

[0182] Input: Text data stored on the server

[0183] Output: Pattern data of abnormal speech

[0184] Specific operation: When unusual patterns are detected, such as frequent repetition or limited vocabulary, an alert is generated and the server administrator is notified.

[0185] Step 5:

[0186] Recognition of emotional states

[0187] The server analyzes the audio data and the analyzed text data using an emotion recognition engine to estimate the emotional state based on factors such as tone and speed of speech.

[0188] Input: Audio data and text data stored on the server

[0189] Output: Emotional profile data

[0190] Specific operation: The system calculates the characteristics of the voice, generates an emotion score in the database, and records the analyzed emotional state as a profile.

[0191] Step 6:

[0192] Report generation and notification

[0193] The server generates a detailed report based on the analysis results and provides it to the user via the terminal. The user uses this report to gain a deeper understanding of the elderly person's situation.

[0194] Input: Abnormal speech pattern data, emotion profile data

[0195] Output: Results Report

[0196] Specific operation: A report in PDF format is generated on the server side and sent to the user via email or a dedicated app.

[0197] (Application Example 2)

[0198] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0199] There is a need for a system that can not only identify signs of cognitive impairment based on the voice signals of elderly individuals, but also comprehensively capture their emotional state. This would enable appropriate support from the perspective of the elderly's mental and emotional state, and is particularly crucial for ensuring a safe and secure life in elderly care facilities and home-based services.

[0200] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0201] In this invention, the server includes means for acquiring voice signals from elderly individuals, means for converting the acquired voice signals into text information in real time, means for analyzing the converted text information to identify signs of cognitive impairment, means for analyzing the voice signals and text information to recognize emotional states, and means for generating and visually displaying the analysis results as a report. This makes it possible to comprehensively analyze the cognitive function and emotional state of elderly individuals and to provide prompt and accurate support and intervention.

[0202] "Voice signals" refer to information recorded in digital or analog format from the voices of elderly people.

[0203] "Textual information" refers to data that is a transcription of an audio signal into text.

[0204] "Cognitive impairment" refers to a condition that affects memory, judgment, or cognitive abilities, and in older adults, it is often a sign of dementia.

[0205] "Emotional state" refers to psychological tendencies or changes that can be judged from the tone and content of speech.

[0206] A "report" is a document that summarizes the results of an analysis and provides information in a visually verifiable format.

[0207] "Real-time" means that data is acquired, processed, or displayed instantly.

[0208] "Visual display" refers to a method of providing information to users by outputting analysis results to a screen or display.

[0209] The system implementing this invention first acquires an audio signal using a terminal used by an elderly person. Using the terminal's microphone, it collects the audio during the conversation and processes the data in real time. The audio signal is converted into text information using the Google Cloud Speech-to-Text API. This process allows the content of the conversation to be saved as text data.

[0210] Next, the text information and the original audio signal are sent to a server in the cloud. The server uses the IBM Watson® Tone Analyzer API to identify the emotional state of the elderly person from the audio signal and text information. This makes it possible to recognize the psychological tendencies and changes of the elderly person based on their tone of voice, word choice, and phrasing.

[0211] The analyzed data is returned to the terminal as a report, allowing for visual confirmation. This enables users to gain a multifaceted understanding of the emotional state of elderly individuals, along with signs of cognitive impairment, and to implement appropriate support measures. For example, if an elderly person repeatedly asks the same questions in daily conversations and their voice often has an anxious tone, care staff can provide that person with reassuring communication and an environment.

[0212] Examples of prompts for generative AI models include the following:

[0213] "Explain how to provide support for recognizing emotional states from given conversational data and identifying signs of anxiety and stress in older adults. Consider how this would enhance monitoring systems in elderly care facilities."

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The terminal acquires voice signals from the elderly person. The input is the elderly person's natural voice, and the terminal's microphone converts this voice into a digital signal and stores it as an audio file, thus preparing the audio data.

[0217] Step 2:

[0218] The device transmits the acquired audio signal to the cloud server in real time. The input here is an audio file, and the output is the audio data transferred to the cloud server. Data communication technology is used to ensure transmission with minimal delay.

[0219] Step 3:

[0220] The server converts the audio signal into text information using the Google Cloud Speech-to-Text API. The input is the audio data sent to the server, and the output is text data. A speech recognition algorithm analyzes the audio waveform and converts it into text strings.

[0221] Step 4:

[0222] The server uses the IBM Watson Tone Analyzer API to evaluate the emotional state of the user based on the converted text information and the original audio signal. The input consists of text and audio data, and the output is an analysis showing the user's emotional state. The API analyzes language patterns and voice tone.

[0223] Step 5:

[0224] The server generates a report based on the analyzed data. The input consists of emotional state analysis results and text data, while the output is a report in a visually displayable format. A text generation algorithm organizes the data into natural language and creates the report.

[0225] Step 6:

[0226] The server sends the generated report data to the terminal. The input is the report data, and the output is the report received by the terminal. The server compresses and encrypts the data to ensure the security of the report.

[0227] Step 7:

[0228] Users visually view reports through their devices and review the results. Input is report data, and output is the analysis results displayed on the device screen. Based on the displayed information, users consider appropriate support measures for the elderly.

[0229] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

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

[0234] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0245] This invention is a system that allows users who can regularly converse with elderly people to acquire voice information using a terminal and analyze it on a server to identify early signs of dementia.

[0246] The user activates the device's recording function when starting a conversation, collecting audio data of the conversation. The device utilizes speech recognition technology to convert the audio data into text data in real time, and sends the converted text data to the server sequentially.

[0247] The server analyzes the received text data and detects abnormal thought patterns and signs of cognitive decline in the statements of elderly individuals. Based on these analysis results, the server identifies early signs of dementia and generates a report containing the necessary information. This report is sent to the terminal, and the user can refer to the diagnostic results as needed.

[0248] For example, if a pattern is detected in an elderly person's daily conversations, such as repeatedly mentioning the same topic in a short period of time or giving inappropriate answers to questions, the server identifies this as an early sign of dementia. The report details such anomalies and recommends further consultation with a specialist if necessary. This system allows users to effectively identify signs of dementia and receive appropriate medical support early on.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The user initiates a conversation with the elderly person using their device and launches a voice recording application on the device. Voice recording begins, capturing the elderly person's statements in real time.

[0252] Step 2:

[0253] The device uses recorded audio data to perform real-time speech recognition and generates the results as text data. Speech recognition technology automatically converts spoken content into text.

[0254] Step 3:

[0255] The device sends the converted text data to the server via the internet. The data is encrypted to ensure the security of the communication.

[0256] Step 4:

[0257] The server inputs the received text data into an analysis module and uses advanced natural language processing techniques to detect signs of dementia from the conversation. In this process, it determines abnormalities by comparing the data against known characteristics of dementia.

[0258] Step 5:

[0259] The server assesses the possibility of dementia based on the detected anomalies and characteristics. It then generates a report summarizing the discovered signs and related data.

[0260] Step 6:

[0261] The server sends the generated report to the terminal and notifies the user of the results. The user can then review the report on the terminal and make decisions based on the elderly person's situation.

[0262] (Example 1)

[0263] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] Cognitive decline in the elderly progresses gradually, making it crucial to identify its early signs. However, it often takes time for a specialist to make a diagnosis, and there is a lack of means to detect abnormalities early in daily life. To address this challenge, there is a need for a system that automatically detects signs of cognitive decline from everyday conversations and reports them early.

[0265] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0266] In this invention, the server includes a device for acquiring voice information from elderly people, a device for converting the acquired voice information into text information in real time, and a device for analyzing the converted text information to identify signs of cognitive decline. This makes it possible to automatically detect and report signs of cognitive decline in everyday conversations.

[0267] "Elderly" refers to adults who, as a result of aging, are prone to a decline in physical or cognitive function.

[0268] "Auditory information" refers to information, including language, that is transmitted through human voices.

[0269] "Acquisition device" refers to a device used to collect and record specific information.

[0270] "Real-time" refers to a situation where something is processed or transformed immediately the moment it occurs.

[0271] "Textual information" refers to information expressed in text format.

[0272] A "conversion device" refers to a device that has the function of changing data in one format to another.

[0273] An "analytical device" refers to a device that analyzes data to derive specific results or trends.

[0274] "Cognitive decline" refers to a condition in which a person's mental abilities, such as memory, judgment, understanding, and consciousness, are reduced compared to normal.

[0275] "Signs" refer to early evidence that a particular condition or change is about to occur.

[0276] A "report" refers to a document that describes the results of an analysis or investigation.

[0277] "A device for presenting information" refers to a device for visually displaying or outputting information.

[0278] This invention relates to a device that detects and reports early signs of cognitive decline from everyday conversations with elderly individuals. The device allows users to acquire audio information from conversations using a terminal, which is then analyzed on a server to identify signs of cognitive decline.

[0279] The user uses a device to launch a recording application and record conversations with elderly individuals as audio data. The device's built-in speech recognition software then converts the recorded audio data into text in real time. Specifically, the Google Speech-to-Text API can be used for this purpose.

[0280] The converted text information is immediately sent from the terminal to a cloud-based server. The server uses natural language processing techniques to analyze the text information and identify abnormal thought patterns and repetitive statements that may indicate cognitive decline. Tools such as Python's NLTK library or SpaCy are suitable for this analysis.

[0281] Based on the analysis, the server generates a report and sends it back to the terminal. Through this report, the user can review signs of cognitive decline detected in everyday conversations and, if necessary, is advised to consult a specialist.

[0282] For example, if an elderly person is observed to repeatedly bring up the same topic in a short period of time, the server will record this pattern in the report as an indication of cognitive decline. This allows the user to take immediate action and arrange for necessary medical assistance.

[0283] An example of a prompt for a generative AI model is: "Please describe in detail the process for analyzing conversation data with elderly individuals and detecting signs of cognitive decline. In particular, please describe specifically how to identify abnormal patterns using speech recognition and natural language processing."

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The user launches the recording application on the terminal. The conversation is started, and the voice information of the elderly person is collected. At this time, the voice input through the microphone of the terminal is stored in the storage of the terminal in digital format. When the recording ends, the collection of voice data is completed.

[0287] Step 2:

[0288] The terminal converts the collected voice data into character information in real time using voice recognition software. The input is the voice data, and Google Speech-to-Text API is used to convert the voice into characters. The converted character information is held in the temporary memory. After the conversion is completed, it indicates that the preparation of the character data is complete.

[0289] Step 3:

[0290] The terminal sends the converted character information to a cloud-based server. The input data is the converted character information, and it is sent to the server via the Internet using the communication module. The output indicates that the character information is sent in a format accessible to the server.

[0291] Step 4:

[0292] The server analyzes the received character information. The input for the analysis is the character information sent to the server, and a natural language processing engine is used to detect signs of abnormal thinking patterns or cognitive function decline. Specifically, word frequency analysis and theme detection using the NLTK library in Python are performed, and a list of signs is generated as the output.

[0293] Step 5:

[0294] The server generates a report indicating potential cognitive decline based on the analysis results. The input is a list of symptoms obtained in the analysis step, and the report generation module organizes the necessary information to create a detailed report as output. The report includes details of abnormal thought patterns and recommendations for consulting a specialist.

[0295] Step 6:

[0296] The generated report is sent to the terminal. The server sends the generated report as input data to the terminal's address, and the report becomes viewable on the terminal as output. The user can then review this report and consider the next action as needed.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] There is a need to detect cognitive decline in older adults more quickly and accurately in everyday conversations. However, current methods make it difficult to detect early signs of dementia that are often overlooked by the older adults themselves or those around them. Furthermore, effectively notifying individuals of these signs and promptly providing appropriate medical support remains a challenge.

[0300] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0301] In this invention, the server includes means for acquiring voice information, means for converting the acquired voice information into character information in real time, means for analyzing the converted character information to identify a decline in cognitive function, means for generating the analysis result as a report and displaying it on a visual display device, and means for identifying an abnormal thinking pattern during dialogue and notifying others of a change in health status. Thereby, it becomes possible to detect a decline in cognitive function at an early stage and provide more appropriate support promptly.

[0302] "Voice information" is information for collecting human speech and sounds and analyzing their characteristics.

[0303] "Character information" is data obtained by converting voice information into text format using voice recognition technology.

[0304] "Analysis" is an analytical process for recognizing specific patterns and abnormalities using the collected data.

[0305] "Decline in cognitive function" refers to a state in which mental functions such as memory, thinking ability, and judgment ability are lower than normal.

[0306] "Report" is a document that summarizes the analysis result in a visual and easy-to-understand form.

[0307] "Visual display device" is a device for visually displaying the generated information and analysis result to the user.

[0308] "Thinking pattern" is a tendency based on certain recognition and judgment values manifested in speech and actions.

[0309] "Change in health status" refers to an event in which an individual's physical and mental health status has changed compared to before.

[0310] The system implementing this invention aims to detect cognitive decline early through conversations with elderly individuals. The user uses a terminal equipped with a visual display device to acquire voice information before initiating a conversation with the elderly person. This voice information is converted into text in real time using speech recognition software on the terminal. Specifically, the Google Speech-to-Text API is used. Through this process, the voice data is sent to a server as text data.

[0311] The server analyzes the received text information and identifies abnormal thought patterns that may indicate cognitive decline. This analysis utilizes Python's NLTK (Natural Language Processing Toolkit). If an anomaly is detected, the server generates a report based on the analysis results. This report is displayed on the user's terminal's visual display, making it easily accessible to the user.

[0312] For example, if an elderly person repeatedly asks the same question in a short period of time, the system identifies this as an abnormal thought pattern and notifies the user. The user can then receive information through a visual display such as, "This customer has been asking the same question frequently recently. We recommend checking if there is anything they are experiencing difficulties with."

[0313] Examples of prompt statements include the following:

[0314] Please list patterns in conversations that may indicate dementia in older adults. For example, repeating the same content from previous conversations in a short period of time, or giving irrelevant answers to questions.

[0315] This system allows users to leverage everyday conversations with elderly individuals to identify cognitive decline early and receive necessary medical support. The hardware and software used throughout this process include a speech recognition API, a natural language processing toolkit, and a visual display device to process data efficiently and effectively.

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] When the user starts a conversation, they activate the device's voice acquisition function. Voice information is acquired through the microphone and temporarily stored on the device as digital audio data. In this case, the input is the speech of an elderly person, and the output is digital audio data.

[0319] Step 2:

[0320] The device converts the acquired audio data into text in real time using the Google Speech-to-Text API. In this step, digital audio data (input) is generated as text data (output). Noise reduction and sound quality adjustments are performed during the conversion process.

[0321] Step 3:

[0322] The converted character information is sent from the terminal to the server using the HTTP protocol. The input in this process is character information, and the output is a confirmation signal to the server that the transmission is complete. The data is encrypted during transmission to ensure secure communication.

[0323] Step 4:

[0324] The server analyzes the received text information using Python's NLTK. Here, it obtains analysis data (output) by searching for abnormal thought patterns in the text information (input). This analysis uses natural language processing techniques to identify specific patterns that indicate cognitive decline.

[0325] Step 5:

[0326] Based on the analysis results, the server automatically generates a report containing the necessary information. In this step, the analysis data (input) is used to generate report data (output). The report includes details of the anomaly pattern and recommended actions.

[0327] Step 6:

[0328] The generated report is returned to the terminal's visual display and displayed. The user can visually review the information and take appropriate action. In this step, the report data (input) is displayed on the terminal as visual information (output). The user makes a judgment about the health status of the elderly person based on the displayed information.

[0329] Through this series of processes, users can identify cognitive decline early based on conversations with elderly individuals and provide appropriate support.

[0330] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0331] This invention provides a system that not only acquires voice data through conversations with elderly individuals and analyzes it as text data, but also performs emotion recognition to determine the emotional state of elderly individuals along with signs of dementia. This allows for an emotional approach to the early diagnosis of dementia, enabling a more comprehensive assessment.

[0332] The user uses the device to have a conversation with an elderly person and records the audio data. The device converts the recorded audio data into text data using speech recognition technology. Then, it sends the converted text data and the original audio data to the server.

[0333] The server analyzes received text data to detect signs of dementia in the elderly person's statements. It also uses an emotion engine to recognize the user's emotional state from both audio and text data. This allows it to capture emotional changes and trends, enabling an understanding of the elderly person's mental state and emotions during conversations.

[0334] For example, if an elderly person repeats the same sentence in an unusual tone of voice or context, it may be judged not only as a sign of cognitive decline, but also as an indication of emotional influences such as stress or anxiety. The server generates a detailed analysis report that includes these emotional elements and provides it to the user via the terminal. Based on this report, the user can gain a multifaceted understanding of the elderly person's cognitive function and consider appropriate countermeasures.

[0335] This system aims to provide more appropriate support and treatment plans by comprehensively analyzing the signs of dementia and the associated emotional states.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user initiates a conversation with an elderly person using their device and launches a voice recording application on the device. Voice data is collected, and the elderly person's statements are recorded in real time.

[0339] Step 2:

[0340] The device transmits the acquired audio data to the speech recognition engine in real time, where it is converted into text data. This generates the spoken content as text information on the device.

[0341] Step 3:

[0342] The device sends the converted text data and the original audio data to the server. The data is encrypted to ensure security.

[0343] Step 4:

[0344] The server analyzes the received text data to identify signs of dementia. It detects pathological speech and inconsistent statements in conversations and records them as abnormal patterns.

[0345] Step 5:

[0346] The server uses an emotion engine to analyze the user's emotions from voice and text data. Emotion analysis extracts emotional changes and characteristics from voice tone and speed, as well as text content.

[0347] Step 6:

[0348] The server integrates the results of both dementia symptoms and emotional analysis to generate a detailed report. The report includes the state of cognitive function and emotional fluctuations, and provides an overall assessment.

[0349] Step 7:

[0350] The server sends the generated report to the terminal and notifies the user of the results. The user can review the report content via the interface on the terminal and plan future approaches for the elderly.

[0351] (Example 2)

[0352] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0353] Early detection of cognitive decline in the elderly is crucial for implementing appropriate countermeasures and treatments. However, conventional diagnostic methods often rely on subjective judgment and observation, leading to challenges in accuracy and reliability. In addition, there is a lack of emotional approaches, making it difficult to comprehensively assess the mental state of the elderly.

[0354] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0355] In this invention, the server includes means for converting voice information into text information in real time, means for analyzing the converted text information to identify signs of cognitive function, and means for recognizing emotional states from voice and text information. This enables a comprehensive evaluation of the cognitive function and mental state of elderly individuals from both voice and emotional perspectives, allowing for more accurate and reliable early diagnosis.

[0356] "Audio information" refers to digital or analog data used to record or transmit human speech.

[0357] "Textual information" refers to information that is expressed as text, obtained by converting audio information.

[0358] "Signs of cognitive impairment" refer to unusual verbal or behavioral patterns that may indicate a decline in cognitive function.

[0359] "Emotional state" refers to the mental state or emotional response analyzed from audio or text.

[0360] A "report" refers to a document generated to organize the results of analyzed data and provide information to the user.

[0361] A "server" refers to a network-connected computer system that stores, processes, and analyzes data.

[0362] This invention is a system that acquires voice information through conversations with elderly people and uses that information to evaluate signs of cognitive function and emotional state.

[0363] Hardware and software to use

[0364] Users engage in conversations with elderly individuals using mobile devices or computer terminals. These terminals are equipped with recording capabilities, allowing for the digital acquisition of the elderly individuals' voices.

[0365] The device uses widely available speech recognition software (e.g., speech recognition APIs and voice service platforms) as a speech recognition technology to convert acquired speech information into text information in real time.

[0366] The server analyzes the received text information and identifies signs of cognitive function. This analysis uses natural language processing techniques to evaluate the patterns and content of the text.

[0367] The server then uses an emotion recognition engine (e.g., an emotion analysis API) to recognize emotional states from audio and text information.

[0368] Specific example

[0369] As a concrete example, consider cases where elderly individuals repeatedly state the same things or exhibit changes in their tone of voice. The server analyzes these patterns and detects emotional changes such as stress and anxiety. Furthermore, if abnormal speech patterns are observed consistently, it can help identify cognitive decline at an early stage.

[0370] This allows users to gain a comprehensive and detailed understanding of the cognitive function and emotional state of older adults, providing useful information for taking appropriate support and medical action.

[0371] Example of a prompt

[0372] Please describe a system that converts audio data obtained from conversations with elderly individuals into text data and performs emotion recognition to analyze signs of dementia and emotional states in the elderly.

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] Audio data recording

[0376] The user initiates a conversation with the elderly person using the device. The device is equipped with a built-in microphone, which collects voice information in real time.

[0377] Input: Voice of elderly person speaking

[0378] Output: Audio data file (digital format)

[0379] Specific operation: The user presses the "Start Recording" button on the device to begin recording, and the conversation is saved as digital audio data.

[0380] Step 2:

[0381] Converting audio data to text

[0382] The device converts the collected audio data into text information using speech recognition software. This process transforms the audio data into analyzable text data.

[0383] Input: Audio data file

[0384] Output: Text data

[0385] Specific operation: The speech recognition software starts up, analyzes the audio file, automatically converts it to text, and displays "Text conversion complete" on the screen.

[0386] Step 3:

[0387] Sending data

[0388] The device sends the generated text data and the original audio data file to the server. The data is encrypted and transferred securely.

[0389] Input: Text data, audio data files

[0390] Output: Data stored on the server

[0391] Specific operation: The device uses a wireless or wired network, a progress bar is displayed to show the progress, and a confirmation message pops up when transmission is complete.

[0392] Step 4:

[0393] Analysis of dementia symptoms

[0394] The server analyzes the received text data using natural language processing algorithms to detect abnormal speech patterns. This helps identify potential signs of dementia.

[0395] Input: Text data stored on the server

[0396] Output: Pattern data of abnormal speech

[0397] Specific operation: When unusual patterns are detected, such as frequent repetition or limited vocabulary, an alert is generated and the server administrator is notified.

[0398] Step 5:

[0399] Recognition of emotional states

[0400] The server analyzes the audio data and the analyzed text data using an emotion recognition engine to estimate the emotional state based on factors such as tone and speed of speech.

[0401] Input: Audio data and text data stored on the server

[0402] Output: Emotional profile data

[0403] Specific operation: The system calculates the characteristics of the voice, generates an emotion score in the database, and records the analyzed emotional state as a profile.

[0404] Step 6:

[0405] Report generation and notification

[0406] The server generates a detailed report based on the analysis results and provides it to the user via the terminal. The user uses this report to gain a deeper understanding of the elderly person's situation.

[0407] Input: Abnormal speech pattern data, emotion profile data

[0408] Output: Results Report

[0409] Specific operation: A report in PDF format is generated on the server side and sent to the user via email or a dedicated app.

[0410] (Application Example 2)

[0411] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0412] There is a need for a system that can not only identify signs of cognitive impairment based on the voice signals of elderly individuals, but also comprehensively capture their emotional state. This would enable appropriate support from the perspective of the elderly's mental and emotional state, and is particularly crucial for ensuring a safe and secure life in elderly care facilities and home-based services.

[0413] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0414] In this invention, the server includes means for acquiring voice signals from elderly individuals, means for converting the acquired voice signals into text information in real time, means for analyzing the converted text information to identify signs of cognitive impairment, means for analyzing the voice signals and text information to recognize emotional states, and means for generating and visually displaying the analysis results as a report. This makes it possible to comprehensively analyze the cognitive function and emotional state of elderly individuals and to provide prompt and accurate support and intervention.

[0415] "Voice signals" refer to information recorded in digital or analog format from the voices of elderly people.

[0416] "Textual information" refers to data that is a transcription of an audio signal into text.

[0417] "Cognitive impairment" refers to a condition that affects memory, judgment, or cognitive abilities, and in older adults, it is often a sign of dementia.

[0418] "Emotional state" refers to psychological tendencies or changes that can be judged from the tone and content of speech.

[0419] A "report" is a document that summarizes the results of an analysis and provides information in a visually verifiable format.

[0420] "Real-time" means that data is acquired, processed, or displayed instantly.

[0421] "Visual display" refers to a method of providing information to users by outputting analysis results to a screen or display.

[0422] The system implementing this invention first acquires an audio signal using a terminal used by an elderly person. Using the terminal's microphone, it collects the audio during the conversation and processes the data in real time. The audio signal is converted into text information using the Google Cloud Speech-to-Text API. This process allows the content of the conversation to be saved as text data.

[0423] Next, the text information and the original audio signal are sent to a server in the cloud. The server uses the IBM Watson Tone Analyzer API to identify the emotional state of the elderly person from the audio signal and text information. This makes it possible to recognize the psychological tendencies and changes of the elderly person based on their tone of voice, word choice, and phrasing.

[0424] The analyzed data is returned to the terminal as a report, allowing for visual confirmation. This enables users to gain a multifaceted understanding of the emotional state of elderly individuals, along with signs of cognitive impairment, and to implement appropriate support measures. For example, if an elderly person repeatedly asks the same questions in daily conversations and their voice often has an anxious tone, care staff can provide that person with reassuring communication and an environment.

[0425] Examples of prompts for generative AI models include the following:

[0426] "Explain how to provide support for recognizing emotional states from given conversational data and identifying signs of anxiety and stress in older adults. Consider how this would enhance monitoring systems in elderly care facilities."

[0427] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0428] Step 1:

[0429] The terminal acquires voice signals from the elderly person. The input is the elderly person's natural voice, and the terminal's microphone converts this voice into a digital signal and stores it as an audio file, thus preparing the audio data.

[0430] Step 2:

[0431] The device transmits the acquired audio signal to the cloud server in real time. The input here is an audio file, and the output is the audio data transferred to the cloud server. Data communication technology is used to ensure transmission with minimal delay.

[0432] Step 3:

[0433] The server converts the audio signal into text information using the Google Cloud Speech-to-Text API. The input is the audio data sent to the server, and the output is text data. A speech recognition algorithm analyzes the audio waveform and converts it into text strings.

[0434] Step 4:

[0435] The server uses the IBM Watson Tone Analyzer API to evaluate the emotional state of the user based on the converted text information and the original audio signal. The input consists of text and audio data, and the output is an analysis showing the user's emotional state. The API analyzes language patterns and voice tone.

[0436] Step 5:

[0437] The server generates a report based on the analyzed data. The input consists of emotional state analysis results and text data, while the output is a report in a visually displayable format. A text generation algorithm organizes the data into natural language and creates the report.

[0438] Step 6:

[0439] The server sends the generated report data to the terminal. The input is the report data, and the output is the report received by the terminal. The server compresses and encrypts the data to ensure the security of the report.

[0440] Step 7:

[0441] Users visually view reports through their devices and review the results. Input is report data, and output is the analysis results displayed on the device screen. Based on the displayed information, users consider appropriate support measures for the elderly.

[0442] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0443] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0444] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0445] [Third Embodiment]

[0446] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0447] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0448] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0449] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0450] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0451] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0452] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0453] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0454] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0455] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0456] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0457] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0458] This invention is a system that allows users who can regularly converse with elderly people to acquire voice information using a terminal and analyze it on a server to identify early signs of dementia.

[0459] The user activates the device's recording function when starting a conversation, collecting audio data of the conversation. The device utilizes speech recognition technology to convert the audio data into text data in real time, and sends the converted text data to the server sequentially.

[0460] The server analyzes the received text data and detects abnormal thought patterns and signs of cognitive decline in the statements of elderly individuals. Based on these analysis results, the server identifies early signs of dementia and generates a report containing the necessary information. This report is sent to the terminal, and the user can refer to the diagnostic results as needed.

[0461] For example, if a pattern is detected in an elderly person's daily conversations, such as repeatedly mentioning the same topic in a short period of time or giving inappropriate answers to questions, the server identifies this as an early sign of dementia. The report details such anomalies and recommends further consultation with a specialist if necessary. This system allows users to effectively identify signs of dementia and receive appropriate medical support early on.

[0462] The following describes the processing flow.

[0463] Step 1:

[0464] The user initiates a conversation with the elderly person using their device and launches a voice recording application on the device. Voice recording begins, capturing the elderly person's statements in real time.

[0465] Step 2:

[0466] The device uses recorded audio data to perform real-time speech recognition and generates the results as text data. Speech recognition technology automatically converts spoken content into text.

[0467] Step 3:

[0468] The device sends the converted text data to the server via the internet. The data is encrypted to ensure the security of the communication.

[0469] Step 4:

[0470] The server inputs the received text data into an analysis module and uses advanced natural language processing techniques to detect signs of dementia from the conversation. In this process, it determines abnormalities by comparing the data against known characteristics of dementia.

[0471] Step 5:

[0472] The server assesses the possibility of dementia based on the detected anomalies and characteristics. It then generates a report summarizing the discovered signs and related data.

[0473] Step 6:

[0474] The server sends the generated report to the terminal and notifies the user of the results. The user can then review the report on the terminal and make decisions based on the elderly person's situation.

[0475] (Example 1)

[0476] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0477] Cognitive decline in the elderly progresses gradually, making it crucial to identify its early signs. However, it often takes time for a specialist to make a diagnosis, and there is a lack of means to detect abnormalities early in daily life. To address this challenge, there is a need for a system that automatically detects signs of cognitive decline from everyday conversations and reports them early.

[0478] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0479] In this invention, the server includes a device for acquiring voice information from elderly people, a device for converting the acquired voice information into text information in real time, and a device for analyzing the converted text information to identify signs of cognitive decline. This makes it possible to automatically detect and report signs of cognitive decline in everyday conversations.

[0480] "Elderly" refers to adults who, as a result of aging, are prone to a decline in physical or cognitive function.

[0481] "Auditory information" refers to information, including language, that is transmitted through human voices.

[0482] "Acquisition device" refers to a device used to collect and record specific information.

[0483] "Real-time" refers to a situation where something is processed or transformed immediately the moment it occurs.

[0484] "Textual information" refers to information expressed in text format.

[0485] A "conversion device" refers to a device that has the function of changing data in one format to another.

[0486] An "analytical device" refers to a device that analyzes data to derive specific results or trends.

[0487] "Cognitive decline" refers to a condition in which a person's mental abilities, such as memory, judgment, understanding, and consciousness, are reduced compared to normal.

[0488] "Signs" refer to early evidence that a particular condition or change is about to occur.

[0489] A "report" refers to a document that describes the results of an analysis or investigation.

[0490] "A device for presenting information" refers to a device for visually displaying or outputting information.

[0491] This invention relates to a device that detects and reports early signs of cognitive decline from everyday conversations with elderly individuals. The device allows users to acquire audio information from conversations using a terminal, which is then analyzed on a server to identify signs of cognitive decline.

[0492] The user uses a device to launch a recording application and record conversations with elderly individuals as audio data. The device's built-in speech recognition software then converts the recorded audio data into text in real time. Specifically, the Google Speech-to-Text API can be used for this purpose.

[0493] The converted text information is immediately sent from the terminal to a cloud-based server. The server uses natural language processing techniques to analyze the text information and identify abnormal thought patterns and repetitive statements that may indicate cognitive decline. Tools such as Python's NLTK library or SpaCy are suitable for this analysis.

[0494] Based on the analysis, the server generates a report and sends it back to the terminal. Through this report, the user can review signs of cognitive decline detected in everyday conversations and, if necessary, is advised to consult a specialist.

[0495] For example, if an elderly person is observed to repeatedly bring up the same topic in a short period of time, the server will record this pattern in the report as an indication of cognitive decline. This allows the user to take immediate action and arrange for necessary medical assistance.

[0496] An example of a prompt for a generative AI model is: "Please describe in detail the process for analyzing conversation data with elderly individuals and detecting signs of cognitive decline. In particular, please describe specifically how to identify abnormal patterns using speech recognition and natural language processing."

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] The user launches the recording application on their device. They begin a conversation, collecting the elderly person's voice information. The audio recorded through the device's microphone is saved digitally to the device's storage. The collection of voice data is complete when the recording ends.

[0500] Step 2:

[0501] The device converts collected audio data into text in real time using speech recognition software. The input is audio data, and the Google Speech-to-Text API is used to convert the audio into text. The converted text is stored in temporary memory. Once the conversion is complete, the device indicates that the text data is ready.

[0502] Step 3:

[0503] The terminal sends the converted character information to a cloud-based server. The input data is the converted character information, which is sent to the server via the internet using a communication module. The output indicates that the character information is sent in a format accessible to the server.

[0504] Step 4:

[0505] The server analyzes the received text information. The input for analysis is the text information sent to the server, and a natural language processing engine is used to detect abnormal thought patterns and signs of cognitive decline. Specifically, word frequency analysis and theme detection are performed using the Python NLTK library, and a list of signs is generated as output.

[0506] Step 5:

[0507] The server generates a report indicating potential cognitive decline based on the analysis results. The input is a list of symptoms obtained in the analysis step, and the report generation module organizes the necessary information to create a detailed report as output. The report includes details of abnormal thought patterns and recommendations for consulting a specialist.

[0508] Step 6:

[0509] The generated report is sent to the terminal. The server sends the generated report as input data to the terminal's address, and the report becomes viewable on the terminal as output. The user can then review this report and consider the next action as needed.

[0510] (Application Example 1)

[0511] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0512] There is a need to detect cognitive decline in older adults more quickly and accurately in everyday conversations. However, current methods make it difficult to detect early signs of dementia that are often overlooked by the older adults themselves or those around them. Furthermore, effectively notifying individuals of these signs and promptly providing appropriate medical support remains a challenge.

[0513] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0514] In this invention, the server includes means for acquiring voice information, means for converting the acquired voice information into text information in real time, means for analyzing the converted text information to identify cognitive decline, means for generating the analysis results as a report and displaying it on a visual display device, and means for identifying abnormal thought patterns during conversation and notifying others of changes in health status. This makes it possible to detect cognitive decline early and provide more appropriate support quickly.

[0515] "Auditory information" refers to information collected from human speech and sounds, used to analyze their characteristics.

[0516] "Textual information" refers to data obtained by converting audio information into text format using speech recognition technology.

[0517] "Analysis" is the process of analyzing collected data to recognize specific patterns or anomalies.

[0518] "Cognitive decline" refers to a state in which mental functions such as memory, thinking ability, and judgment are lower than normal.

[0519] A "report" is a document that summarizes the analysis results in a visually appealing and easy-to-understand format.

[0520] A "visual display device" is a device used to visually display generated information and analysis results to the user.

[0521] A "thinking pattern" is a tendency based on certain perceptions and judgments that manifest in speech and behavior.

[0522] "Changes in health status" refers to events in which an individual's physical and mental health has changed compared to before.

[0523] The system implementing this invention aims to detect cognitive decline early through conversations with elderly individuals. The user uses a terminal equipped with a visual display device to acquire voice information before initiating a conversation with the elderly person. This voice information is converted into text in real time using speech recognition software on the terminal. Specifically, the Google Speech-to-Text API is used. Through this process, the voice data is sent to a server as text data.

[0524] The server analyzes the received text information and identifies abnormal thought patterns that may indicate cognitive decline. This analysis utilizes Python's NLTK (Natural Language Processing Toolkit). If an anomaly is detected, the server generates a report based on the analysis results. This report is displayed on the user's terminal's visual display, making it easily accessible to the user.

[0525] For example, if an elderly person repeatedly asks the same question in a short period of time, the system identifies this as an abnormal thought pattern and notifies the user. The user can then receive information through a visual display such as, "This customer has been asking the same question frequently recently. We recommend checking if there is anything they are experiencing difficulties with."

[0526] Examples of prompt statements include the following:

[0527] Please list patterns in conversations that may indicate dementia in older adults. For example, repeating the same content from previous conversations in a short period of time, or giving irrelevant answers to questions.

[0528] This system allows users to leverage everyday conversations with elderly individuals to identify cognitive decline early and receive necessary medical support. The hardware and software used throughout this process include a speech recognition API, a natural language processing toolkit, and a visual display device to process data efficiently and effectively.

[0529] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0530] Step 1:

[0531] When the user starts a conversation, they activate the device's voice acquisition function. Voice information is acquired through the microphone and temporarily stored on the device as digital audio data. In this case, the input is the speech of an elderly person, and the output is digital audio data.

[0532] Step 2:

[0533] The device converts the acquired audio data into text in real time using the Google Speech-to-Text API. In this step, digital audio data (input) is generated as text data (output). Noise reduction and sound quality adjustments are performed during the conversion process.

[0534] Step 3:

[0535] The converted character information is sent from the terminal to the server using the HTTP protocol. The input in this process is character information, and the output is a confirmation signal to the server that the transmission is complete. The data is encrypted during transmission to ensure secure communication.

[0536] Step 4:

[0537] The server analyzes the received text information using Python's NLTK. Here, it obtains analysis data (output) by searching for abnormal thought patterns in the text information (input). This analysis uses natural language processing techniques to identify specific patterns that indicate cognitive decline.

[0538] Step 5:

[0539] Based on the analysis results, the server automatically generates a report containing the necessary information. In this step, the analysis data (input) is used to generate report data (output). The report includes details of the anomaly pattern and recommended actions.

[0540] Step 6:

[0541] The generated report is returned to the terminal's visual display and displayed. The user can visually review the information and take appropriate action. In this step, the report data (input) is displayed on the terminal as visual information (output). The user makes a judgment about the health status of the elderly person based on the displayed information.

[0542] Through this series of processes, users can identify cognitive decline early based on conversations with elderly individuals and provide appropriate support.

[0543] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0544] This invention provides a system that not only acquires voice data through conversations with elderly individuals and analyzes it as text data, but also performs emotion recognition to determine the emotional state of elderly individuals along with signs of dementia. This allows for an emotional approach to the early diagnosis of dementia, enabling a more comprehensive assessment.

[0545] The user uses the device to have a conversation with an elderly person and records the audio data. The device converts the recorded audio data into text data using speech recognition technology. Then, it sends the converted text data and the original audio data to the server.

[0546] The server analyzes received text data to detect signs of dementia in the elderly person's statements. It also uses an emotion engine to recognize the user's emotional state from both audio and text data. This allows it to capture emotional changes and trends, enabling an understanding of the elderly person's mental state and emotions during conversations.

[0547] For example, if an elderly person repeats the same sentence in an unusual tone of voice or context, it may be judged not only as a sign of cognitive decline, but also as an indication of emotional influences such as stress or anxiety. The server generates a detailed analysis report that includes these emotional elements and provides it to the user via the terminal. Based on this report, the user can gain a multifaceted understanding of the elderly person's cognitive function and consider appropriate countermeasures.

[0548] This system aims to provide more appropriate support and treatment plans by comprehensively analyzing the signs of dementia and the associated emotional states.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] The user initiates a conversation with an elderly person using their device and launches a voice recording application on the device. Voice data is collected, and the elderly person's statements are recorded in real time.

[0552] Step 2:

[0553] The device transmits the acquired audio data to the speech recognition engine in real time, where it is converted into text data. This generates the spoken content as text information on the device.

[0554] Step 3:

[0555] The device sends the converted text data and the original audio data to the server. The data is encrypted to ensure security.

[0556] Step 4:

[0557] The server analyzes the received text data to identify signs of dementia. It detects pathological speech and inconsistent statements in conversations and records them as abnormal patterns.

[0558] Step 5:

[0559] The server uses an emotion engine to analyze the user's emotions from voice and text data. Emotion analysis extracts emotional changes and characteristics from voice tone and speed, as well as text content.

[0560] Step 6:

[0561] The server integrates the results of both dementia symptoms and emotional analysis to generate a detailed report. The report includes the state of cognitive function and emotional fluctuations, and provides an overall assessment.

[0562] Step 7:

[0563] The server sends the generated report to the terminal and notifies the user of the results. The user can review the report content via the interface on the terminal and plan future approaches for the elderly.

[0564] (Example 2)

[0565] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0566] Early detection of cognitive decline in the elderly is crucial for implementing appropriate countermeasures and treatments. However, conventional diagnostic methods often rely on subjective judgment and observation, leading to challenges in accuracy and reliability. In addition, there is a lack of emotional approaches, making it difficult to comprehensively assess the mental state of the elderly.

[0567] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0568] In this invention, the server includes means for converting voice information into text information in real time, means for analyzing the converted text information to identify signs of cognitive function, and means for recognizing emotional states from voice and text information. This enables a comprehensive evaluation of the cognitive function and mental state of elderly individuals from both voice and emotional perspectives, allowing for more accurate and reliable early diagnosis.

[0569] "Audio information" refers to digital or analog data used to record or transmit human speech.

[0570] "Textual information" refers to information that is expressed as text, obtained by converting audio information.

[0571] "Signs of cognitive impairment" refer to unusual verbal or behavioral patterns that may indicate a decline in cognitive function.

[0572] "Emotional state" refers to the mental state or emotional response analyzed from audio or text.

[0573] A "report" refers to a document generated to organize the results of analyzed data and provide information to the user.

[0574] A "server" refers to a network-connected computer system that stores, processes, and analyzes data.

[0575] This invention is a system that acquires voice information through conversations with elderly people and uses that information to evaluate signs of cognitive function and emotional state.

[0576] Hardware and software to use

[0577] Users engage in conversations with elderly individuals using mobile devices or computer terminals. These terminals are equipped with recording capabilities, allowing for the digital acquisition of the elderly individuals' voices.

[0578] The device uses widely available speech recognition software (e.g., speech recognition APIs and voice service platforms) as a speech recognition technology to convert acquired speech information into text information in real time.

[0579] The server analyzes the received text information and identifies signs of cognitive function. This analysis uses natural language processing techniques to evaluate the patterns and content of the text.

[0580] The server then uses an emotion recognition engine (e.g., an emotion analysis API) to recognize emotional states from audio and text information.

[0581] Specific example

[0582] As a concrete example, consider cases where elderly individuals repeatedly state the same things or exhibit changes in their tone of voice. The server analyzes these patterns and detects emotional changes such as stress and anxiety. Furthermore, if abnormal speech patterns are observed consistently, it can help identify cognitive decline at an early stage.

[0583] This allows users to gain a comprehensive and detailed understanding of the cognitive function and emotional state of older adults, providing useful information for taking appropriate support and medical action.

[0584] Example of a prompt

[0585] Please describe a system that converts audio data obtained from conversations with elderly individuals into text data and performs emotion recognition to analyze signs of dementia and emotional states in the elderly.

[0586] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0587] Step 1:

[0588] Audio data recording

[0589] The user initiates a conversation with the elderly person using the device. The device is equipped with a built-in microphone, which collects voice information in real time.

[0590] Input: Voice of elderly person speaking

[0591] Output: Audio data file (digital format)

[0592] Specific operation: The user presses the "Start Recording" button on the device to begin recording, and the conversation is saved as digital audio data.

[0593] Step 2:

[0594] Converting audio data to text

[0595] The device converts the collected audio data into text information using speech recognition software. This process transforms the audio data into analyzable text data.

[0596] Input: Audio data file

[0597] Output: Text data

[0598] Specific operation: The speech recognition software starts up, analyzes the audio file, automatically converts it to text, and displays "Text conversion complete" on the screen.

[0599] Step 3:

[0600] Sending data

[0601] The device sends the generated text data and the original audio data file to the server. The data is encrypted and transferred securely.

[0602] Input: Text data, audio data files

[0603] Output: Data stored on the server

[0604] Specific operation: The device uses a wireless or wired network, a progress bar is displayed to show the progress, and a confirmation message pops up when transmission is complete.

[0605] Step 4:

[0606] Analysis of dementia symptoms

[0607] The server analyzes the received text data using natural language processing algorithms to detect abnormal speech patterns. This helps identify potential signs of dementia.

[0608] Input: Text data stored on the server

[0609] Output: Pattern data of abnormal speech

[0610] Specific operation: When unusual patterns are detected, such as frequent repetition or limited vocabulary, an alert is generated and the server administrator is notified.

[0611] Step 5:

[0612] Recognition of emotional states

[0613] The server analyzes the audio data and the analyzed text data using an emotion recognition engine to estimate the emotional state based on factors such as tone and speed of speech.

[0614] Input: Audio data and text data stored on the server

[0615] Output: Emotional profile data

[0616] Specific operation: The system calculates the characteristics of the voice, generates an emotion score in the database, and records the analyzed emotional state as a profile.

[0617] Step 6:

[0618] Report generation and notification

[0619] The server generates a detailed report based on the analysis results and provides it to the user via the terminal. The user uses this report to gain a deeper understanding of the elderly person's situation.

[0620] Input: Abnormal speech pattern data, emotion profile data

[0621] Output: Results Report

[0622] Specific operation: A report in PDF format is generated on the server side and sent to the user via email or a dedicated app.

[0623] (Application Example 2)

[0624] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0625] There is a need for a system that can not only identify signs of cognitive impairment based on the voice signals of elderly individuals, but also comprehensively capture their emotional state. This would enable appropriate support from the perspective of the elderly's mental and emotional state, and is particularly crucial for ensuring a safe and secure life in elderly care facilities and home-based services.

[0626] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0627] In this invention, the server includes means for acquiring voice signals from elderly individuals, means for converting the acquired voice signals into text information in real time, means for analyzing the converted text information to identify signs of cognitive impairment, means for analyzing the voice signals and text information to recognize emotional states, and means for generating and visually displaying the analysis results as a report. This makes it possible to comprehensively analyze the cognitive function and emotional state of elderly individuals and to provide prompt and accurate support and intervention.

[0628] "Voice signals" refer to information recorded in digital or analog format from the voices of elderly people.

[0629] "Textual information" refers to data that is a transcription of an audio signal into text.

[0630] "Cognitive impairment" refers to a condition that affects memory, judgment, or cognitive abilities, and in older adults, it is often a sign of dementia.

[0631] "Emotional state" refers to psychological tendencies or changes that can be judged from the tone and content of speech.

[0632] A "report" is a document that summarizes the results of an analysis and provides information in a visually verifiable format.

[0633] "Real-time" means that data is acquired, processed, or displayed instantly.

[0634] "Visual display" refers to a method of providing information to users by outputting analysis results to a screen or display.

[0635] The system implementing this invention first acquires an audio signal using a terminal used by an elderly person. Using the terminal's microphone, it collects the audio during the conversation and processes the data in real time. The audio signal is converted into text information using the Google Cloud Speech-to-Text API. This process allows the content of the conversation to be saved as text data.

[0636] Next, the text information and the original audio signal are sent to a server in the cloud. The server uses the IBM Watson Tone Analyzer API to identify the emotional state of the elderly person from the audio signal and text information. This makes it possible to recognize the psychological tendencies and changes of the elderly person based on their tone of voice, word choice, and phrasing.

[0637] The analyzed data is returned to the terminal as a report, allowing for visual confirmation. This enables users to gain a multifaceted understanding of the emotional state of elderly individuals, along with signs of cognitive impairment, and to implement appropriate support measures. For example, if an elderly person repeatedly asks the same questions in daily conversations and their voice often has an anxious tone, care staff can provide that person with reassuring communication and an environment.

[0638] Examples of prompts for generative AI models include the following:

[0639] "Explain how to provide support for recognizing emotional states from given conversational data and identifying signs of anxiety and stress in older adults. Consider how this would enhance monitoring systems in elderly care facilities."

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

[0641] Step 1:

[0642] The terminal acquires voice signals from the elderly person. The input is the elderly person's natural voice, and the terminal's microphone converts this voice into a digital signal and stores it as an audio file, thus preparing the audio data.

[0643] Step 2:

[0644] The device transmits the acquired audio signal to the cloud server in real time. The input here is an audio file, and the output is the audio data transferred to the cloud server. Data communication technology is used to ensure transmission with minimal delay.

[0645] Step 3:

[0646] The server converts the audio signal into text information using the Google Cloud Speech-to-Text API. The input is the audio data sent to the server, and the output is text data. A speech recognition algorithm analyzes the audio waveform and converts it into text strings.

[0647] Step 4:

[0648] The server uses the IBM Watson Tone Analyzer API to evaluate the emotional state of the user based on the converted text information and the original audio signal. The input consists of text and audio data, and the output is an analysis showing the user's emotional state. The API analyzes language patterns and voice tone.

[0649] Step 5:

[0650] The server generates a report based on the analyzed data. The input consists of emotional state analysis results and text data, while the output is a report in a visually displayable format. A text generation algorithm organizes the data into natural language and creates the report.

[0651] Step 6:

[0652] The server sends the generated report data to the terminal. The input is the report data, and the output is the report received by the terminal. The server compresses and encrypts the data to ensure the security of the report.

[0653] Step 7:

[0654] Users visually view reports through their devices and review the results. Input is report data, and output is the analysis results displayed on the device screen. Based on the displayed information, users consider appropriate support measures for the elderly.

[0655] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0656] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0657] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0658] [Fourth Embodiment]

[0659] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0660] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0661] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0662] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0663] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0664] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0665] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0666] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0667] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0668] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0669] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0670] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0671] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0672] This invention is a system that allows users who can regularly converse with elderly people to acquire voice information using a terminal and analyze it on a server to identify early signs of dementia.

[0673] The user activates the device's recording function when starting a conversation, collecting audio data of the conversation. The device utilizes speech recognition technology to convert the audio data into text data in real time, and sends the converted text data to the server sequentially.

[0674] The server analyzes the received text data and detects abnormal thought patterns and signs of cognitive decline in the statements of elderly individuals. Based on these analysis results, the server identifies early signs of dementia and generates a report containing the necessary information. This report is sent to the terminal, and the user can refer to the diagnostic results as needed.

[0675] For example, if a pattern is detected in an elderly person's daily conversations, such as repeatedly mentioning the same topic in a short period of time or giving inappropriate answers to questions, the server identifies this as an early sign of dementia. The report details such anomalies and recommends further consultation with a specialist if necessary. This system allows users to effectively identify signs of dementia and receive appropriate medical support early on.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] The user initiates a conversation with the elderly person using their device and launches a voice recording application on the device. Voice recording begins, capturing the elderly person's statements in real time.

[0679] Step 2:

[0680] The device uses recorded audio data to perform real-time speech recognition and generates the results as text data. Speech recognition technology automatically converts spoken content into text.

[0681] Step 3:

[0682] The device sends the converted text data to the server via the internet. The data is encrypted to ensure the security of the communication.

[0683] Step 4:

[0684] The server inputs the received text data into an analysis module and uses advanced natural language processing techniques to detect signs of dementia from the conversation. In this process, it determines abnormalities by comparing the data against known characteristics of dementia.

[0685] Step 5:

[0686] The server assesses the possibility of dementia based on the detected anomalies and characteristics. It then generates a report summarizing the discovered signs and related data.

[0687] Step 6:

[0688] The server sends the generated report to the terminal and notifies the user of the results. The user can then review the report on the terminal and make decisions based on the elderly person's situation.

[0689] (Example 1)

[0690] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] Cognitive decline in the elderly progresses gradually, making it crucial to identify its early signs. However, it often takes time for a specialist to make a diagnosis, and there is a lack of means to detect abnormalities early in daily life. To address this challenge, there is a need for a system that automatically detects signs of cognitive decline from everyday conversations and reports them early.

[0692] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0693] In this invention, the server includes a device for acquiring voice information from elderly people, a device for converting the acquired voice information into text information in real time, and a device for analyzing the converted text information to identify signs of cognitive decline. This makes it possible to automatically detect and report signs of cognitive decline in everyday conversations.

[0694] "Elderly" refers to adults who, as a result of aging, are prone to a decline in physical or cognitive function.

[0695] "Auditory information" refers to information, including language, that is transmitted through human voices.

[0696] "Acquisition device" refers to a device used to collect and record specific information.

[0697] "Real-time" refers to a situation where something is processed or transformed immediately the moment it occurs.

[0698] "Textual information" refers to information expressed in text format.

[0699] A "conversion device" refers to a device that has the function of changing data in one format to another.

[0700] An "analytical device" refers to a device that analyzes data to derive specific results or trends.

[0701] "Cognitive decline" refers to a condition in which a person's mental abilities, such as memory, judgment, understanding, and consciousness, are reduced compared to normal.

[0702] "Signs" refer to early evidence that a particular condition or change is about to occur.

[0703] A "report" refers to a document that describes the results of an analysis or investigation.

[0704] "A device for presenting information" refers to a device for visually displaying or outputting information.

[0705] This invention relates to a device that detects and reports early signs of cognitive decline from everyday conversations with elderly individuals. The device allows users to acquire audio information from conversations using a terminal, which is then analyzed on a server to identify signs of cognitive decline.

[0706] The user uses a device to launch a recording application and record conversations with elderly individuals as audio data. The device's built-in speech recognition software then converts the recorded audio data into text in real time. Specifically, the Google Speech-to-Text API can be used for this purpose.

[0707] The converted text information is immediately sent from the terminal to a cloud-based server. The server uses natural language processing techniques to analyze the text information and identify abnormal thought patterns and repetitive statements that may indicate cognitive decline. Tools such as Python's NLTK library or SpaCy are suitable for this analysis.

[0708] Based on the analysis, the server generates a report and sends it back to the terminal. Through this report, the user can review signs of cognitive decline detected in everyday conversations and, if necessary, is advised to consult a specialist.

[0709] For example, if an elderly person is observed to repeatedly bring up the same topic in a short period of time, the server will record this pattern in the report as an indication of cognitive decline. This allows the user to take immediate action and arrange for necessary medical assistance.

[0710] An example of a prompt for a generative AI model is: "Please describe in detail the process for analyzing conversation data with elderly individuals and detecting signs of cognitive decline. In particular, please describe specifically how to identify abnormal patterns using speech recognition and natural language processing."

[0711] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0712] Step 1:

[0713] The user launches the recording application on their device. They begin a conversation, collecting the elderly person's voice information. The audio recorded through the device's microphone is saved digitally to the device's storage. The collection of voice data is complete when the recording ends.

[0714] Step 2:

[0715] The device converts collected audio data into text in real time using speech recognition software. The input is audio data, and the Google Speech-to-Text API is used to convert the audio into text. The converted text is stored in temporary memory. Once the conversion is complete, the device indicates that the text data is ready.

[0716] Step 3:

[0717] The terminal sends the converted character information to a cloud-based server. The input data is the converted character information, which is sent to the server via the internet using a communication module. The output indicates that the character information is sent in a format accessible to the server.

[0718] Step 4:

[0719] The server analyzes the received text information. The input for analysis is the text information sent to the server, and a natural language processing engine is used to detect abnormal thought patterns and signs of cognitive decline. Specifically, word frequency analysis and theme detection are performed using the Python NLTK library, and a list of signs is generated as output.

[0720] Step 5:

[0721] The server generates a report indicating potential cognitive decline based on the analysis results. The input is a list of symptoms obtained in the analysis step, and the report generation module organizes the necessary information to create a detailed report as output. The report includes details of abnormal thought patterns and recommendations for consulting a specialist.

[0722] Step 6:

[0723] The generated report is sent to the terminal. The server sends the generated report as input data to the terminal's address, and the report becomes viewable on the terminal as output. The user can then review this report and consider the next action as needed.

[0724] (Application Example 1)

[0725] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0726] There is a need to detect cognitive decline in older adults more quickly and accurately in everyday conversations. However, current methods make it difficult to detect early signs of dementia that are often overlooked by the older adults themselves or those around them. Furthermore, effectively notifying individuals of these signs and promptly providing appropriate medical support remains a challenge.

[0727] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0728] In this invention, the server includes means for acquiring voice information, means for converting the acquired voice information into text information in real time, means for analyzing the converted text information to identify cognitive decline, means for generating the analysis results as a report and displaying it on a visual display device, and means for identifying abnormal thought patterns during conversation and notifying others of changes in health status. This makes it possible to detect cognitive decline early and provide more appropriate support quickly.

[0729] "Auditory information" refers to information collected from human speech and sounds, used to analyze their characteristics.

[0730] "Textual information" refers to data obtained by converting audio information into text format using speech recognition technology.

[0731] "Analysis" is the process of analyzing collected data to recognize specific patterns or anomalies.

[0732] "Cognitive decline" refers to a state in which mental functions such as memory, thinking ability, and judgment are lower than normal.

[0733] A "report" is a document that summarizes the analysis results in a visually appealing and easy-to-understand format.

[0734] A "visual display device" is a device used to visually display generated information and analysis results to the user.

[0735] A "thinking pattern" is a tendency based on certain perceptions and judgments that manifest in speech and behavior.

[0736] "Changes in health status" refers to events in which an individual's physical and mental health has changed compared to before.

[0737] The system implementing this invention aims to detect cognitive decline early through conversations with elderly individuals. The user uses a terminal equipped with a visual display device to acquire voice information before initiating a conversation with the elderly person. This voice information is converted into text in real time using speech recognition software on the terminal. Specifically, the Google Speech-to-Text API is used. Through this process, the voice data is sent to a server as text data.

[0738] The server analyzes the received text information and identifies abnormal thought patterns that may indicate cognitive decline. This analysis utilizes Python's NLTK (Natural Language Processing Toolkit). If an anomaly is detected, the server generates a report based on the analysis results. This report is displayed on the user's terminal's visual display, making it easily accessible to the user.

[0739] For example, if an elderly person repeatedly asks the same question in a short period of time, the system identifies this as an abnormal thought pattern and notifies the user. The user can then receive information through a visual display such as, "This customer has been asking the same question frequently recently. We recommend checking if there is anything they are experiencing difficulties with."

[0740] Examples of prompt statements include the following:

[0741] Please list patterns in conversations that may indicate dementia in older adults. For example, repeating the same content from previous conversations in a short period of time, or giving irrelevant answers to questions.

[0742] This system allows users to leverage everyday conversations with elderly individuals to identify cognitive decline early and receive necessary medical support. The hardware and software used throughout this process include a speech recognition API, a natural language processing toolkit, and a visual display device to process data efficiently and effectively.

[0743] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0744] Step 1:

[0745] When the user starts a conversation, they activate the device's voice acquisition function. Voice information is acquired through the microphone and temporarily stored on the device as digital audio data. In this case, the input is the speech of an elderly person, and the output is digital audio data.

[0746] Step 2:

[0747] The device converts the acquired audio data into text in real time using the Google Speech-to-Text API. In this step, digital audio data (input) is generated as text data (output). Noise reduction and sound quality adjustments are performed during the conversion process.

[0748] Step 3:

[0749] The converted character information is sent from the terminal to the server using the HTTP protocol. The input in this process is character information, and the output is a confirmation signal to the server that the transmission is complete. The data is encrypted during transmission to ensure secure communication.

[0750] Step 4:

[0751] The server analyzes the received text information using Python's NLTK. Here, it obtains analysis data (output) by searching for abnormal thought patterns in the text information (input). This analysis uses natural language processing techniques to identify specific patterns that indicate cognitive decline.

[0752] Step 5:

[0753] Based on the analysis results, the server automatically generates a report containing the necessary information. In this step, the analysis data (input) is used to generate report data (output). The report includes details of the anomaly pattern and recommended actions.

[0754] Step 6:

[0755] The generated report is returned to the terminal's visual display and displayed. The user can visually review the information and take appropriate action. In this step, the report data (input) is displayed on the terminal as visual information (output). The user makes a judgment about the health status of the elderly person based on the displayed information.

[0756] Through this series of processes, users can identify cognitive decline early based on conversations with elderly individuals and provide appropriate support.

[0757] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0758] This invention provides a system that not only acquires voice data through conversations with elderly individuals and analyzes it as text data, but also performs emotion recognition to determine the emotional state of elderly individuals along with signs of dementia. This allows for an emotional approach to the early diagnosis of dementia, enabling a more comprehensive assessment.

[0759] The user uses the device to have a conversation with an elderly person and records the audio data. The device converts the recorded audio data into text data using speech recognition technology. Then, it sends the converted text data and the original audio data to the server.

[0760] The server analyzes received text data to detect signs of dementia in the elderly person's statements. It also uses an emotion engine to recognize the user's emotional state from both audio and text data. This allows it to capture emotional changes and trends, enabling an understanding of the elderly person's mental state and emotions during conversations.

[0761] For example, if an elderly person repeats the same sentence in an unusual tone of voice or context, it may be judged not only as a sign of cognitive decline, but also as an indication of emotional influences such as stress or anxiety. The server generates a detailed analysis report that includes these emotional elements and provides it to the user via the terminal. Based on this report, the user can gain a multifaceted understanding of the elderly person's cognitive function and consider appropriate countermeasures.

[0762] This system aims to provide more appropriate support and treatment plans by comprehensively analyzing the signs of dementia and the associated emotional states.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] The user initiates a conversation with an elderly person using their device and launches a voice recording application on the device. Voice data is collected, and the elderly person's statements are recorded in real time.

[0766] Step 2:

[0767] The device transmits the acquired audio data to the speech recognition engine in real time, where it is converted into text data. This generates the spoken content as text information on the device.

[0768] Step 3:

[0769] The device sends the converted text data and the original audio data to the server. The data is encrypted to ensure security.

[0770] Step 4:

[0771] The server analyzes the received text data to identify signs of dementia. It detects pathological speech and inconsistent statements in conversations and records them as abnormal patterns.

[0772] Step 5:

[0773] The server uses an emotion engine to analyze the user's emotions from voice and text data. Emotion analysis extracts emotional changes and characteristics from voice tone and speed, as well as text content.

[0774] Step 6:

[0775] The server integrates the results of both dementia symptoms and emotional analysis to generate a detailed report. The report includes the state of cognitive function and emotional fluctuations, and provides an overall assessment.

[0776] Step 7:

[0777] The server sends the generated report to the terminal and notifies the user of the results. The user can review the report content via the interface on the terminal and plan future approaches for the elderly.

[0778] (Example 2)

[0779] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0780] Early detection of cognitive decline in the elderly is crucial for implementing appropriate countermeasures and treatments. However, conventional diagnostic methods often rely on subjective judgment and observation, leading to challenges in accuracy and reliability. In addition, there is a lack of emotional approaches, making it difficult to comprehensively assess the mental state of the elderly.

[0781] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0782] In this invention, the server includes means for converting voice information into text information in real time, means for analyzing the converted text information to identify signs of cognitive function, and means for recognizing emotional states from voice and text information. This enables a comprehensive evaluation of the cognitive function and mental state of elderly individuals from both voice and emotional perspectives, allowing for more accurate and reliable early diagnosis.

[0783] "Audio information" refers to digital or analog data used to record or transmit human speech.

[0784] "Textual information" refers to information that is expressed as text, obtained by converting audio information.

[0785] "Signs of cognitive impairment" refer to unusual verbal or behavioral patterns that may indicate a decline in cognitive function.

[0786] "Emotional state" refers to the mental state or emotional response analyzed from audio or text.

[0787] A "report" refers to a document generated to organize the results of analyzed data and provide information to the user.

[0788] A "server" refers to a network-connected computer system that stores, processes, and analyzes data.

[0789] This invention is a system that acquires voice information through conversations with elderly people and uses that information to evaluate signs of cognitive function and emotional state.

[0790] Hardware and software to use

[0791] Users engage in conversations with elderly individuals using mobile devices or computer terminals. These terminals are equipped with recording capabilities, allowing for the digital acquisition of the elderly individuals' voices.

[0792] The device uses widely available speech recognition software (e.g., speech recognition APIs and voice service platforms) as a speech recognition technology to convert acquired speech information into text information in real time.

[0793] The server analyzes the received text information and identifies signs of cognitive function. This analysis uses natural language processing techniques to evaluate the patterns and content of the text.

[0794] The server then uses an emotion recognition engine (e.g., an emotion analysis API) to recognize emotional states from audio and text information.

[0795] Specific example

[0796] As a concrete example, consider cases where elderly individuals repeatedly state the same things or exhibit changes in their tone of voice. The server analyzes these patterns and detects emotional changes such as stress and anxiety. Furthermore, if abnormal speech patterns are observed consistently, it can help identify cognitive decline at an early stage.

[0797] This allows users to gain a comprehensive and detailed understanding of the cognitive function and emotional state of older adults, providing useful information for taking appropriate support and medical action.

[0798] Example of a prompt

[0799] Please describe a system that converts audio data obtained from conversations with elderly individuals into text data and performs emotion recognition to analyze signs of dementia and emotional states in the elderly.

[0800] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0801] Step 1:

[0802] Audio data recording

[0803] The user initiates a conversation with the elderly person using the device. The device is equipped with a built-in microphone, which collects voice information in real time.

[0804] Input: Voice of elderly person speaking

[0805] Output: Audio data file (digital format)

[0806] Specific operation: The user presses the "Start Recording" button on the device to begin recording, and the conversation is saved as digital audio data.

[0807] Step 2:

[0808] Converting audio data to text

[0809] The device converts the collected audio data into text information using speech recognition software. This process transforms the audio data into analyzable text data.

[0810] Input: Audio data file

[0811] Output: Text data

[0812] Specific operation: The speech recognition software starts up, analyzes the audio file, automatically converts it to text, and displays "Text conversion complete" on the screen.

[0813] Step 3:

[0814] Sending data

[0815] The device sends the generated text data and the original audio data file to the server. The data is encrypted and transferred securely.

[0816] Input: Text data, audio data files

[0817] Output: Data stored on the server

[0818] Specific operation: The device uses a wireless or wired network, a progress bar is displayed to show the progress, and a confirmation message pops up when transmission is complete.

[0819] Step 4:

[0820] Analysis of dementia symptoms

[0821] The server analyzes the received text data using natural language processing algorithms to detect abnormal speech patterns. This helps identify potential signs of dementia.

[0822] Input: Text data stored on the server

[0823] Output: Pattern data of abnormal speech

[0824] Specific operation: When unusual patterns are detected, such as frequent repetition or limited vocabulary, an alert is generated and the server administrator is notified.

[0825] Step 5:

[0826] Recognition of emotional states

[0827] The server analyzes the audio data and the analyzed text data using an emotion recognition engine to estimate the emotional state based on factors such as tone and speed of speech.

[0828] Input: Audio data and text data stored on the server

[0829] Output: Emotional profile data

[0830] Specific operation: The system calculates the characteristics of the voice, generates an emotion score in the database, and records the analyzed emotional state as a profile.

[0831] Step 6:

[0832] Report generation and notification

[0833] The server generates a detailed report based on the analysis results and provides it to the user via the terminal. The user uses this report to gain a deeper understanding of the elderly person's situation.

[0834] Input: Abnormal speech pattern data, emotion profile data

[0835] Output: Results Report

[0836] Specific operation: A report in PDF format is generated on the server side and sent to the user via email or a dedicated app.

[0837] (Application Example 2)

[0838] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0839] There is a need for a system that can not only identify signs of cognitive impairment based on the voice signals of elderly individuals, but also comprehensively capture their emotional state. This would enable appropriate support from the perspective of the elderly's mental and emotional state, and is particularly crucial for ensuring a safe and secure life in elderly care facilities and home-based services.

[0840] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0841] In this invention, the server includes means for acquiring voice signals from elderly individuals, means for converting the acquired voice signals into text information in real time, means for analyzing the converted text information to identify signs of cognitive impairment, means for analyzing the voice signals and text information to recognize emotional states, and means for generating and visually displaying the analysis results as a report. This makes it possible to comprehensively analyze the cognitive function and emotional state of elderly individuals and to provide prompt and accurate support and intervention.

[0842] "Voice signals" refer to information recorded in digital or analog format from the voices of elderly people.

[0843] "Textual information" refers to data that is a transcription of an audio signal into text.

[0844] "Cognitive impairment" refers to a condition that affects memory, judgment, or cognitive abilities, and in older adults, it is often a sign of dementia.

[0845] "Emotional state" refers to psychological tendencies or changes that can be judged from the tone and content of speech.

[0846] A "report" is a document that summarizes the results of an analysis and provides information in a visually verifiable format.

[0847] "Real-time" means that data is acquired, processed, or displayed instantly.

[0848] "Visual display" refers to a method of providing information to users by outputting analysis results to a screen or display.

[0849] The system implementing this invention first acquires an audio signal using a terminal used by an elderly person. Using the terminal's microphone, it collects the audio during the conversation and processes the data in real time. The audio signal is converted into text information using the Google Cloud Speech-to-Text API. This process allows the content of the conversation to be saved as text data.

[0850] Next, the text information and the original audio signal are sent to a server in the cloud. The server uses the IBM Watson Tone Analyzer API to identify the emotional state of the elderly person from the audio signal and text information. This makes it possible to recognize the psychological tendencies and changes of the elderly person based on their tone of voice, word choice, and phrasing.

[0851] The analyzed data is returned to the terminal as a report, allowing for visual confirmation. This enables users to gain a multifaceted understanding of the emotional state of elderly individuals, along with signs of cognitive impairment, and to implement appropriate support measures. For example, if an elderly person repeatedly asks the same questions in daily conversations and their voice often has an anxious tone, care staff can provide that person with reassuring communication and an environment.

[0852] Examples of prompts for generative AI models include the following:

[0853] "Explain how to provide support for recognizing emotional states from given conversational data and identifying signs of anxiety and stress in older adults. Consider how this would enhance monitoring systems in elderly care facilities."

[0854] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0855] Step 1:

[0856] The terminal acquires voice signals from the elderly person. The input is the elderly person's natural voice, and the terminal's microphone converts this voice into a digital signal and stores it as an audio file, thus preparing the audio data.

[0857] Step 2:

[0858] The device transmits the acquired audio signal to the cloud server in real time. The input here is an audio file, and the output is the audio data transferred to the cloud server. Data communication technology is used to ensure transmission with minimal delay.

[0859] Step 3:

[0860] The server converts the audio signal into text information using the Google Cloud Speech-to-Text API. The input is the audio data sent to the server, and the output is text data. A speech recognition algorithm analyzes the audio waveform and converts it into text strings.

[0861] Step 4:

[0862] The server uses the IBM Watson Tone Analyzer API to evaluate the emotional state of the user based on the converted text information and the original audio signal. The input consists of text and audio data, and the output is an analysis showing the user's emotional state. The API analyzes language patterns and voice tone.

[0863] Step 5:

[0864] The server generates a report based on the analyzed data. The input consists of emotional state analysis results and text data, while the output is a report in a visually displayable format. A text generation algorithm organizes the data into natural language and creates the report.

[0865] Step 6:

[0866] The server sends the generated report data to the terminal. The input is the report data, and the output is the report received by the terminal. The server compresses and encrypts the data to ensure the security of the report.

[0867] Step 7:

[0868] Users visually view reports through their devices and review the results. Input is report data, and output is the analysis results displayed on the device screen. Based on the displayed information, users consider appropriate support measures for the elderly.

[0869] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0870] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0871] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0872] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0873] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0874] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0875] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0876] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0877] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0878] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0879] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0880] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0881] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0882] 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.

[0883] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0884] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0885] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0886] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0887] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0888] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0889] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0890] The following is further disclosed regarding the embodiments described above.

[0891] (Claim 1)

[0892] Methods for acquiring voice data from elderly people,

[0893] A means of converting acquired audio data into text data in real time,

[0894] A means of analyzing converted text data to identify signs of dementia,

[0895] A means of generating and displaying the analysis results as a report,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising means for extracting abnormal speech patterns from analyzed text data.

[0899] (Claim 3)

[0900] The system according to claim 1, further comprising means for making an initial diagnosis of dementia based on extracted speech patterns.

[0901] "Example 1"

[0902] (Claim 1)

[0903] A device for acquiring voice information from elderly people,

[0904] A device that converts acquired audio information into text information in real time,

[0905] A device that analyzes converted text information to identify signs of cognitive decline,

[0906] A device that generates and presents analysis results as a report,

[0907] A device that includes this.

[0908] (Claim 2)

[0909] The apparatus according to claim 1, further comprising a device for extracting abnormal thought patterns from analyzed textual information.

[0910] (Claim 3)

[0911] The apparatus according to claim 1, further comprising a device for making an initial judgment of cognitive decline based on extracted thought patterns.

[0912] "Application Example 1"

[0913] (Claim 1)

[0914] Means for acquiring audio information,

[0915] A means of converting acquired audio information into text information in real time,

[0916] A means for analyzing converted text information to identify cognitive decline,

[0917] A means for generating the analysis results as a report and displaying it on a visual display device,

[0918] A means of identifying abnormal thought patterns during conversations and notifying others of changes in their health status,

[0919] An information processing system that includes this.

[0920] (Claim 2)

[0921] The information processing system according to claim 1, further comprising means for extracting abnormal language patterns from analyzed character information.

[0922] (Claim 3)

[0923] The information processing system according to claim 1, further comprising means for making an initial judgment on a health condition based on extracted language patterns.

[0924] "Example 2 of combining an emotion engine"

[0925] (Claim 1)

[0926] Means for acquiring audio information,

[0927] A means of converting acquired audio information into text information in real time,

[0928] A means for analyzing converted character information to identify signs of cognitive function,

[0929] A means of recognizing emotional states from audio and textual information,

[0930] A means of generating and displaying the analysis results as a report,

[0931] A system that includes this.

[0932] (Claim 2)

[0933] The system according to claim 1, further comprising means for extracting abnormal speech patterns from analyzed character information.

[0934] (Claim 3)

[0935] The system according to claim 1, further comprising means for making an initial judgment of cognitive function based on extracted speech patterns and emotion data.

[0936] "Application example 2 when combining with an emotional engine"

[0937] (Claim 1)

[0938] A means of acquiring voice signals from elderly people,

[0939] A means for converting acquired audio signals into text information in real time,

[0940] A means for analyzing converted character information to identify signs of cognitive impairment,

[0941] A means of recognizing emotional states by analyzing audio signals and text information,

[0942] A means of generating and visually displaying the analysis results as a report,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, further comprising means for extracting abnormal speech patterns and emotional changes from analyzed textual information.

[0946] (Claim 3)

[0947] The system according to claim 1, further comprising means for making an initial judgment of cognitive impairment based on extracted speech patterns and emotional changes. [Explanation of symbols]

[0948] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Methods for acquiring voice data from elderly people, A means of converting acquired audio data into text data in real time, A means of analyzing converted text data to identify signs of dementia, A means of generating and displaying the analysis results as a report, A system that includes this.

2. The system according to claim 1, further comprising means for extracting abnormal speech patterns from analyzed text data.

3. The system according to claim 1, further comprising means for making an initial diagnosis of dementia based on extracted speech patterns.

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