system

Abstract

We provide the system. [Solution] A means for receiving an audio signal and converting it into text data using speech recognition, A means for analyzing user requests from the text data and determining appropriate services based on those requests, A means of providing information regarding the determined service using synthesized speech, A means of providing services that support daily life using a voice interface optimized for the elderly, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 order to solve the difficulties that the elderly feel in operating smartphones and other digital devices and the resulting inconvenience in daily life, there is a need for a mechanism that enables the elderly to quickly and easily receive various life support services they need through easily accessible communication means. Also, it is necessary to build a safe and efficient system that can provide appropriate support for individual users while strengthening the cooperation with the local community.

Means for Solving the Problems

[0005] This invention provides a means for analyzing user requests by easily receiving audio signals and converting them into text data using speech recognition technology. Based on the analysis results, it is possible to determine an appropriate service and provide the service details to the user using synthesized speech. Furthermore, by searching for registered supporters in the area and notifying them, the execution of the service is facilitated. In addition, by providing a means for collecting user feedback and using it to improve future services, the invention aims to improve the quality of life for the elderly.

[0006] "Audio signals" are data obtained by converting sound into electrical signals, and are used for communication and processing.

[0007] "Speech recognition" is a technology that takes an audio signal as input and converts that audio into text data, and it plays a part in natural language processing.

[0008] "Text data" refers to data expressed in a text format that can be processed by a computer, and is information used for speech recognition and text analysis.

[0009] "User" refers to a person who uses the system of the present invention, and typically refers to an elderly person, who is the target of various services.

[0010] "Analysis" refers to the process of analyzing input data and extracting useful information or instructions from it, and in particular, it refers to understanding the content through natural language processing.

[0011] "Service" refers to support and work provided based on the user's requests, and in this invention, this includes performance by registered supporters in the local community.

[0012] "Synthesized speech" is a technology that outputs text data as speech, converting text into a voice-like form.

[0013] A "supporter" is an individual or group registered within the community who is responsible for providing various services to users.

[0014] "Notification" refers to the act of conveying specific information from the system to supporters or users, with the aim of facilitating the smooth implementation of services.

[0015] "Feedback" refers to evaluations and opinions received from users, and is information that can be used to improve systems and services. [Brief explanation of the drawing]

[0016] [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] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides a system that offers various support services for daily life by having users send voice signals to a server via telephone and analyzing that voice. A specific example of the system's operation is shown below.

[0038] First, the user sends an audio signal to the server using their phone. The server receives this audio signal and converts it into text data using speech recognition technology. The server then analyzes this text data to understand the user's intended request. Based on this analysis, the server determines which service falls into the appropriate category.

[0039] The server generates detailed information about the selected service as voice data using synthesized speech technology and provides it to the user. For example, if an elderly user requests, "I would like a light bulb changed," the server analyzes this request and searches for a registered support worker in the area who can perform the electrical work. Once a registered support worker is found, the server notifies the worker of the work details and estimated time and requests their assistance.

[0040] The terminal reports to the server as feedback that the registered support worker has completed the task, and the server notifies the user of the result as appropriate.

[0041] In this way, users can easily receive the necessary support even without being familiar with specialized digital technologies, and collecting feedback from users will also contribute to improving the quality of future services.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user accesses the system via telephone and transmits an audio signal.

[0045] Step 2:

[0046] The server receives an audio signal from the user, and a speech recognition system converts this signal into text data.

[0047] Step 3:

[0048] The server analyzes text data and uses natural language processing techniques to extract user requests.

[0049] Step 4:

[0050] Based on the extracted requests, the server searches the regional database for the relevant services and identifies the appropriate support provider.

[0051] Step 5:

[0052] The server will notify the identified supporter of the necessary services and the scheduled visit time.

[0053] Step 6:

[0054] The server uses synthesized speech technology to generate a voice message explaining the service details and arrangement status, and informs the user.

[0055] Step 7:

[0056] The terminal receives a completion report from the support worker and sends it to the server.

[0057] Step 8:

[0058] Based on the completion report from the terminal, the server notifies the user that the service is complete and requests feedback.

[0059] (Example 1)

[0060] 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."

[0061] In modern society, the ability for users to easily access various support services through voice, even without specialized knowledge, is a crucial element in improving quality of life. However, existing systems struggle to accurately analyze requests from voice signals and provide appropriate services, and they do not effectively utilize user feedback, resulting in challenges in improving the quality and efficiency of services.

[0062] 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.

[0063] In this invention, the server includes means for acquiring voice data and converting it into information data using an advanced speech recognition algorithm; means for analyzing the user's request from the information data and selecting the appropriate service based on the results; and means for transmitting data related to the selected service to the user using speech synthesis technology. As a result, users can easily request services by voice without specialized knowledge, and the quality of the service can be improved by utilizing feedback.

[0064] "Voice data" refers to the user's spoken information received through communication, and this data is the subject of analysis by speech recognition algorithms.

[0065] A "speech recognition algorithm" refers to a technology that analyzes acquired audio data and converts the audio signal into corresponding information data.

[0066] "Information data" refers to text-formatted data obtained by converting audio data using a speech recognition algorithm, and serves as the starting point for analyzing user requests.

[0067] "User requests" refer to the content of the services or support that the user intends to receive, which is extracted by analyzing the voice data.

[0068] "Service" refers to specific support or actions provided based on the user's request, delivered to the user through the system.

[0069] "Speech synthesis technology" is a technology that converts information data back into speech data and conveys that information to users through speech.

[0070] A "specialist" refers to an individual or organization registered within a region that possesses the technical skills and abilities to perform specific tasks.

[0071] A "database" refers to an information resource that stores user evaluations and feedback, and is used as foundational data for improving future service provision.

[0072] This invention relates to a system in which a user transmits voice data to a server using a telephone, and the server analyzes the voice signal to provide a variety of support services. This system utilizes speech recognition technology, natural language processing, and speech synthesis technology to meet the user's requirements.

[0073] The user first sends an audio signal to the server using a smartphone or landline phone. The server receives this audio signal and converts it into text data using a cloud-based speech recognition service. Specifically, the Google® Speech-to-Text API can be used. Based on the converted text data, the server analyzes the user's intent using natural language processing technology and determines the appropriate service. The natural language processing technology used here is IBM Watson® Natural Language Understanding.

[0074] Subsequently, the server converts information about the selected service into voice data using speech synthesis technology and provides it to the user. For speech synthesis, Amazon Polly can be used, for example. Furthermore, the server searches a database for registered experts within the region and notifies the experts of the work request. When the support worker completes the work, they send feedback to the server via their terminal, and the result is notified to the user.

[0075] As a concrete example, consider a situation where user Tanaka requests that the living room carpet be cleaned. The server analyzes this request, searches for the nearest cleaning company, and submits a work request. After the work is completed, the server receives a report from the company and notifies Tanaka that the work has been completed.

[0076] An example of a prompt message might be: "Please describe the detailed workflow when a user requests carpet cleaning. This should include the entire process from voice analysis to notification to the assistant and reporting of completion of the work."

[0077] This system allows users to easily enjoy various services in their daily lives without needing specialized knowledge, and also enables service providers to perform their duties efficiently.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user sends an audio signal to the server using their telephone. The server receives the user's spoken voice as input. This audio signal conveys the specific services the user needs.

[0081] Step 2:

[0082] The server converts the received audio signal into text data using the Google Speech-to-Text API. The input for this step is the user's audio signal, and the output is text-formatted information data. Specifically, the server passes the audio file to the API, where the text conversion is performed.

[0083] Step 3:

[0084] The server analyzes the converted text data using IBM Watson Natural Language Understanding to identify the user's request. The input is the text data obtained in step 2, and the output is the analyzed request and category information. In this step, for example, the text "I would like to request carpet cleaning" is classified as "request for cleaning service."

[0085] Step 4:

[0086] The server determines the services it can provide based on the user's request. The input is the parsed request, and the output is detailed information about the determined service. Specifically, it searches for the nearest cleaning service provider and checks the service availability and cost.

[0087] Step 5:

[0088] The server uses Amazon Polly to synthesize information about the selected service as voice data and notifies the user. The input is the service details, and the output is synthesized speech. In this process, preparations are made to tell the user in voice, "The cleaning service will visit tomorrow at 3pm."

[0089] Step 6:

[0090] The server searches its database for registered professionals in the region and sends them work request information. The input is the details of the selected service, and the output is a request message to the professional. Information is shared by sending the date and time of the visit to the user and instructions to the specific vendor.

[0091] Step 7:

[0092] The terminal reports to the server that the support worker has completed the task. The input is the support worker's completion information, and the output is feedback data for both the user and the server. Based on this information, the server notifies the user that the task is complete.

[0093] (Application Example 1)

[0094] 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."

[0095] The aim is to enable users, including the elderly, to easily access support services in their daily lives without experiencing difficulties due to unfamiliarity with digital devices or visual and physical limitations. Furthermore, it aims to facilitate the search for and rapid response of local support providers, ensuring that users receive prompt and appropriate assistance.

[0096] 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.

[0097] In this invention, the server includes means for receiving voice signals and converting them into text data using speech recognition, means for analyzing user requests and determining appropriate services, means for providing information about the determined services using synthesized speech, and means for providing support services using a voice interface optimized for the elderly. This makes it possible for users unfamiliar with digital technology to receive services with intuitive operation.

[0098] "Audio signals" are information obtained by converting sound into electrical data, and are used for communication and processing.

[0099] "Speech recognition" is a technology that extracts linguistic information from received audio signals and converts it into text data.

[0100] "Text data" refers to character information converted by speech recognition, and is used for analysis and processing.

[0101] "User requests" refer to wishes or instructions expressed by users verbally or in other forms.

[0102] "Appropriate service" refers to specific support and actions implemented in response to the user's requests, and which are in the user's best interest.

[0103] "Synthesized speech" refers to audio data generated using a computer, which outputs text information as speech.

[0104] A "voice interface" is a general term for the means and technologies that allow users to operate a computer via voice.

[0105] "Support services" refer to various forms of support provided to help users solve problems and improve their daily lives.

[0106] The system of this invention begins with a user sending an audio signal to a server via a telephone or smartphone. The server converts the received audio signal into text data using the Google Speech-to-Text API. The converted text data is analyzed using natural language processing (NLP) techniques to analyze the user's intent. This analysis uses a pre-trained machine learning model to select the appropriate service.

[0107] Based on user requests, the server generates information as synthesized speech using Google Text-to-Speech. This generated speech is provided to the user, ensuring intuitive operation even for elderly users. Throughout this process, a user-friendly and easy-to-listen-to synthesized speech is provided via a voice interface tailored to the specific needs of the elderly.

[0108] For example, if a user requests by voice, "Tell me when to take my medicine," the server analyzes this request and sets the necessary reminder. The reminder is then sent to the user by voice at the specified time each day. In other cases, such as "I need someone to change a light bulb," the server searches for a registered helper in the area and notifies them quickly.

[0109] As an example of a prompt for the generative AI model, one could enter, "Please tell me how to design voice guidance that is easy for seniors to understand." This prompt will help the system improve voice interactions to meet the needs of seniors.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user sends an audio signal to the server using a device (telephone or smartphone). Here, the audio input is captured and transferred to the server as a digital audio signal. This input is the user's voice command, and the output to the server is that audio data.

[0113] Step 2:

[0114] The server uses the Google Speech-to-Text API to convert the received audio signal into text data. The input is the audio signal received in step 1, which is then converted into natural language text using speech recognition technology. The output is the converted text data.

[0115] Step 3:

[0116] The server receives the converted text data and uses natural language processing (NLP) to analyze the user's intent. The input is text data, and the data analysis clarifies the user's requests. The output is the analyzed user instructions and the service decision.

[0117] Step 4:

[0118] The server uses Google Text-to-Speech to generate synthesized speech information about the selected service. The input is the output information from step 3, and the server generates the audio data based on this. The output is the audio message provided to the user.

[0119] Step 5:

[0120] The server or terminal provides audio information to the user via synthesized speech. The input is the audio data generated in step 4, which is delivered to the user through an audio playback device. The output is the audio information that the user hears.

[0121] Step 6:

[0122] If a user's request requires assistance within their local area, the server searches for registered support providers and sends a notification to the appropriate provider. The input is the request information parsed in step 3, and the output is the notification sent to the support provider and its contents.

[0123] 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.

[0124] This invention provides more appropriate and personalized services to users, including the elderly, by incorporating an emotion recognition engine into a telephone-based support system. The main functions of this system include speech recognition, emotion analysis, and the provision of appropriate services. Specific embodiments are described below.

[0125] When a user accesses the system via telephone, the server receives the voice signal and first converts it into text data using speech recognition technology. At the same time, the server uses an emotion engine to analyze the user's emotions from the voice signal. The analyzed emotion information is processed along with the user's request and reflected in the way the service is delivered and its content.

[0126] For example, if a user says, "I've been feeling anxious lately," the server uses its emotion engine to recognize the user's emotional state as "anxiety." Based on this information, the server can generate a more considerate synthesized voice message and suggest comforting services (e.g., guidance on mental health support).

[0127] Furthermore, when notifying registered supporters in the region, it is possible to provide them with the results of an emotional analysis so that they can respond appropriately to the user's emotions. This allows supporters to respond in a way that takes the user's mental state into consideration, leading to increased user satisfaction.

[0128] Furthermore, this system records user emotional data and uses it to continuously improve service quality. This allows for support that takes into account the user's previous emotional state when they use the system again.

[0129] In this way, this system combines speech recognition and emotion analysis technologies to provide users with a safer and more comfortable service.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user accesses the system using a telephone and transmits an audio signal.

[0133] Step 2:

[0134] The server receives an audio signal from the user and uses speech recognition technology to convert that audio into text data.

[0135] Step 3:

[0136] The server analyzes text data to extract user requests. The server also uses an emotion engine to analyze user emotions from audio signals.

[0137] Step 4:

[0138] Based on the analyzed requests and sentiment information, the server determines the content of the services to be provided and adjusts its response as needed.

[0139] Step 5:

[0140] The server uses synthesized speech technology to generate an appropriate voice message for the user, taking into account the results of the emotion engine.

[0141] Step 6:

[0142] The server generates a synthesized voice message and sends it to the user via the telephone line to explain the service.

[0143] Step 7:

[0144] The server searches for registered supporters within the region and sends notifications to them to perform the necessary services. At this time, the user's sentiment information is also conveyed to the supporters.

[0145] Step 8:

[0146] The terminal receives a completion report from the supporter and sends it to the server. The server uses this to notify the user that the service is complete and requests further feedback.

[0147] Step 9:

[0148] The server receives feedback from users, records data including emotional information, and uses it as material for future service improvements.

[0149] (Example 2)

[0150] 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".

[0151] Traditional telephone support systems struggled to provide personalized support that took into account the user's emotional state, resulting in insufficient user satisfaction. Furthermore, there was a need to provide accurate support to users by sharing emotional information with local support staff. Additionally, there was a lack of methods to improve services by utilizing users' emotional history.

[0152] 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.

[0153] In this invention, the server includes means for receiving an audio signal and converting the audio data into text data using speech recognition, means for analyzing the user's emotions from the text data, and means for determining an appropriate service based on the results of the emotion analysis and the user's requests. This makes it possible to provide services that take into account the user's emotional state.

[0154] An "audio signal" is data that electrically represents sound, and is acquired by telephones or microphones.

[0155] "Speech recognition" is a technology that analyzes speech signals and converts them into corresponding text data.

[0156] "Audio data" refers to audio signal information expressed in digital format and stored in a way that allows for computer processing.

[0157] "Text data" refers to data expressed as character information, which is the result of speech being converted by speech recognition.

[0158] "Emotion analysis" is a technology that identifies and classifies a user's emotions from text data or audio signals.

[0159] "Means of determining services" refers to the process of determining the content of services to be provided based on user requests and the results of sentiment analysis.

[0160] "Synthesized speech" refers to artificially generated speech, a technology that reproduces text data as sound.

[0161] "Emotional data" refers to information that indicates the user's emotional state and is obtained as a result of emotional analysis.

[0162] A "supporter" is an individual or organization that has the role of providing services or support to users.

[0163] "Notification" refers to the transmission of signals or messages to convey specific information to relevant parties.

[0164] This invention is a system for providing personalized services to users via telephone using emotion recognition. Embodiments of this system are described in detail below.

[0165] When a user accesses the system via telephone, the server first receives an audio signal. This audio signal is then converted into text data using advanced speech recognition technology. This process utilizes general-purpose speech recognition software, enabling real-time conversion. Specific software examples include speech recognition engines and natural language processing libraries.

[0166] Next, the server uses an emotion analysis engine to analyze the user's emotions based on the converted text data. This analysis categorizes emotions into categories such as "joy," "anxiety," and "anger." Specific algorithms and machine learning models can be used for emotion analysis.

[0167] Based on the analyzed emotional information and the user's request, the server determines the content of the service to provide. The service content will be tailored to the emotional state, for example, "information on mental health support" or "introduction to local support providers." This information is provided to the user as a voice message using synthesized speech technology. Text-to-speech technology is used for the synthesized speech, generating voice with a tone and pace that matches the emotion.

[0168] Furthermore, the server notifies registered supporters within the region and provides them with sentiment analysis results, enabling them to respond appropriately to users. This support activity is carried out using the local support network.

[0169] This system continuously records user emotional data and uses it to improve service quality. Data from past sessions will be considered when providing the service again, and used to deliver a better user experience.

[0170] As an example of a specific prompt, the AI ​​model might be inputted with phrases like, "How to propose a service that enhances user confidence by utilizing emotion recognition in telephone support for the elderly." Based on this prompt, the AI ​​model generates the optimal service proposal, which is then used for implementation.

[0171] In this way, the server can combine speech recognition and sentiment analysis to provide users with personalized and more advanced services.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] When a user accesses the system via telephone, the server receives an audio signal. This signal is acquired as a digital audio input and converted into text data using speech recognition technology. Specifically, the speech recognition engine analyzes the audio signal, converting phonemes into words and words into sentences. This process outputs instructions and information obtained from the audio as text data.

[0175] Step 2:

[0176] The server passes the text data obtained through speech recognition as input to the sentiment analysis engine. This analysis engine analyzes the text data and identifies the user's emotional state from the context and keywords. This analysis outputs emotion categories such as joy, anxiety, and anger. Specifically, natural language processing techniques and machine learning models are used to assign labels to the emotion categories.

[0177] Step 3:

[0178] The server processes the results of the sentiment analysis and the user's request to determine which service to provide. The decision engine refers to the analysis results and selects the most appropriate service. For example, if the user expresses anxiety, providing information on mental health support might be selected. This process uses a deterministic algorithm, making rule-based decisions for service selection.

[0179] Step 4:

[0180] The server generates a synthesized voice message based on the selected service. Using text-to-speech technology, it outputs the chosen service as audio. Specifically, voice data for synthesis is created, and its tone and pace are adjusted according to the emotion. This voice message is then played back to the user via telephone.

[0181] Step 5:

[0182] The server notifies registered support providers in the region of the results of the emotion analysis and the corresponding service details. These notifications to support providers include the user's specific emotional state and the service details, and are sent via email or a dedicated application. This notification allows support providers to obtain the information necessary to provide appropriate support.

[0183] Step 6:

[0184] The server stores emotional data and service usage history obtained from all sessions as records. This data will be used to improve the quality of the service in the future. Specifically, the emotional data and interaction history will be structured using a database management system and used as a reference for providing services in subsequent interactions.

[0185] (Application Example 2)

[0186] 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".

[0187] To improve the quality of life for the elderly, it is crucial to accurately understand their mental state and provide appropriate support. However, conventional systems have not adequately analyzed emotions from voice, nor have they provided sufficient personalized notifications to caregivers. As a result, it has been difficult to respond quickly and appropriately when elderly people are in a mentally unstable state.

[0188] 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.

[0189] In this invention, the server includes means for receiving voice signals and converting them into language data using speech recognition; means for analyzing the user's emotional state from the language data and voice characteristics and determining personalized recommended actions based on that state; means for providing information regarding the determined recommended actions using synthesized speech; and means designed for the elderly and including a notification function for caregivers. This enables a rapid and effective response in accordance with the emotional state of the elderly.

[0190] An "audio signal" is a continuous electrical signal generated by sound vibrations, and is usually captured through a microphone.

[0191] "Speech recognition" is a technology that analyzes audio signals and converts their content into text data.

[0192] "Language data" refers to text information converted from audio signals, and is in a format suitable for computer processing.

[0193] "Emotional state" refers to the state of emotions within an individual's mind, and is the information that is analyzed.

[0194] "Personalized recommended behaviors" are guidelines or actions selected to suit the user's specific circumstances and needs.

[0195] "Synthesized speech" refers to artificially generated speech that is used for information transmission.

[0196] A "notification function" is a feature that allows you to inform other devices or people of specific information or warnings.

[0197] The term "elderly" usually refers to individuals who have reached old age and often require special support and consideration.

[0198] This system improves the quality of life for the elderly by analyzing their emotional state through voice signals and providing personalized recommendations for caregivers.

[0199] The server first receives the incoming audio signal. This is done using a microphone connected to a smartphone or dedicated device. Next, it converts the audio signal into linguistic data using a speech recognition API (e.g., Google Speech-to-Text). Based on this linguistic data, an emotion analysis engine utilizing a generative AI model is activated to identify the user's emotional state. Deep learning technology is incorporated into the emotion analysis, and factors such as tone, speed, and pitch of the voice are also taken into consideration.

[0200] Based on the analysis results, the server uses a synthesized speech engine to generate recommended actions for the elderly. This synthesized speech is adjusted to be easier to understand and more reassuring. Furthermore, if the analyzed emotional state is determined to be serious, the system automatically sends a notification to the caregiver's terminal. The notification provides specific support methods and actions that correspond to the emotional state.

[0201] For example, if an elderly person feels lonely because they spend a lot of time alone, the system analyzes that emotion and sends a notification to their caregiver suggesting something like, "There seems to be a gathering at a nearby community center today. Would you like to join us?"

[0202] An example of a prompt message is: "An elderly user said on the phone, 'I was lonely today because I didn't talk to anyone all day.' Based on this information, analyze the user's emotional state and generate an appropriate notification message for the caregiver."

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server receives audio signals transmitted from the terminal. These audio signals are collected through a microphone, converted to a digital format, and sent to the server. The input is an analog audio signal, and the output is digital audio data. This process uses an ADC (analog-to-digital converter) to convert the analog signal to a digital signal.

[0206] Step 2:

[0207] The server sends the received digital audio data to a speech recognition API, which converts it into text data. This API identifies words and phrases in the audio and generates corresponding text. The input is digital audio data, and the output is text data. At this stage, acoustic modeling is performed, which analyzes the acoustic features of the audio and maps them to linguistic information.

[0208] Step 3:

[0209] The server uses a generative AI model to analyze the user's emotional state from the features of text and audio data. The emotion analysis engine identifies emotions by considering factors such as tone, speed, and pitch of the voice. The input is text data and audio features, and the output is the analyzed emotional state. The generative AI model uses deep learning techniques to learn complex emotional patterns.

[0210] Step 4:

[0211] The server determines what recommended actions to provide based on the analyzed emotional state. It selects the most appropriate action from a predefined set of recommendations based on the emotional state and user profile. The input is the analyzed emotional state, and the output is the content of the recommended action. Rule-based systems and machine learning models are used as recommendation algorithms in this process.

[0212] Step 5:

[0213] The server sends the determined recommended action to a speech synthesis engine, which generates a feedback message for the elderly. The speech synthesis engine converts text-based instructions into natural, easy-to-understand speech. The input is the text of the recommended action, and the output is synthesized speech. Here, text-to-speech (TTS) is performed using speech synthesis technology.

[0214] Step 6:

[0215] The server sends notifications to the caregiver's terminal as needed. These notifications include information related to the analyzed emotional state and recommended actions. The inputs are the recommended actions and emotional analysis results, and the output is the notification message sent to the caregiver's terminal. This process uses a communication protocol to transmit data.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention provides a system that offers various support services for daily life by having users send voice signals to a server via telephone and analyzing that voice. A specific example of the system's operation is shown below.

[0233] First, the user sends an audio signal to the server using their phone. The server receives this audio signal and converts it into text data using speech recognition technology. The server then analyzes this text data to understand the user's intended request. Based on this analysis, the server determines which service falls into the appropriate category.

[0234] The server generates detailed information about the selected service as voice data using synthesized speech technology and provides it to the user. For example, if an elderly user requests, "I would like a light bulb changed," the server analyzes this request and searches for a registered support worker in the area who can perform the electrical work. Once a registered support worker is found, the server notifies the worker of the work details and estimated time and requests their assistance.

[0235] The terminal reports to the server as feedback that the registered support worker has completed the task, and the server notifies the user of the result as appropriate.

[0236] In this way, users can easily receive the necessary support even without being familiar with specialized digital technologies, and collecting feedback from users will also contribute to improving the quality of future services.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user accesses the system via telephone and transmits an audio signal.

[0240] Step 2:

[0241] The server receives an audio signal from the user, and a speech recognition system converts this signal into text data.

[0242] Step 3:

[0243] The server analyzes text data and uses natural language processing techniques to extract user requests.

[0244] Step 4:

[0245] Based on the extracted requests, the server searches the regional database for the relevant services and identifies the appropriate support provider.

[0246] Step 5:

[0247] The server will notify the identified supporter of the necessary services and the scheduled visit time.

[0248] Step 6:

[0249] The server uses synthesized speech technology to generate a voice message explaining the service details and arrangement status, and informs the user.

[0250] Step 7:

[0251] The terminal receives a completion report from the support worker and sends it to the server.

[0252] Step 8:

[0253] Based on the completion report from the terminal, the server notifies the user that the service is complete and requests feedback.

[0254] (Example 1)

[0255] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0256] In modern society, the ability for users to easily access various support services through voice, even without specialized knowledge, is a crucial element in improving quality of life. However, existing systems struggle to accurately analyze requests from voice signals and provide appropriate services, and they do not effectively utilize user feedback, resulting in challenges in improving the quality and efficiency of services.

[0257] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0258] In this invention, the server includes means for acquiring voice data and converting it into information data using an advanced speech recognition algorithm; means for analyzing the user's request from the information data and selecting the appropriate service based on the results; and means for transmitting data related to the selected service to the user using speech synthesis technology. As a result, users can easily request services by voice without specialized knowledge, and the quality of the service can be improved by utilizing feedback.

[0259] "Voice data" refers to the user's spoken information received through communication, and this data is the subject of analysis by speech recognition algorithms.

[0260] A "speech recognition algorithm" refers to a technology that analyzes acquired audio data and converts the audio signal into corresponding information data.

[0261] "Information data" refers to text-formatted data obtained by converting audio data using a speech recognition algorithm, and serves as the starting point for analyzing user requests.

[0262] "User requests" refer to the content of the services or support that the user intends to receive, which is extracted by analyzing the voice data.

[0263] "Service" refers to specific support or actions provided based on the user's request, delivered to the user through the system.

[0264] "Speech synthesis technology" is a technology that converts information data back into speech data and conveys that information to users through speech.

[0265] A "specialist" refers to an individual or organization registered within a region that possesses the technical skills and abilities to perform specific tasks.

[0266] A "database" refers to an information resource that stores user evaluations and feedback, and is used as foundational data for improving future service provision.

[0267] This invention relates to a system in which a user transmits voice data to a server using a telephone, and the server analyzes the voice signal to provide a variety of support services. This system utilizes speech recognition technology, natural language processing, and speech synthesis technology to meet the user's requirements.

[0268] The user first sends an audio signal to the server using a smartphone or landline phone. The server receives this audio signal and converts it into text data using a cloud-based speech recognition service. Specifically, the Google Speech-to-Text API can be used. Based on the converted text data, the server uses natural language processing technology to analyze the user's intent and determine the appropriate service. The natural language processing technology used here is IBM Watson Natural Language Understanding.

[0269] Subsequently, the server converts information about the selected service into voice data using speech synthesis technology and provides it to the user. For speech synthesis, Amazon Polly can be used, for example. Furthermore, the server searches a database for registered experts within the region and notifies the experts of the work request. When the support worker completes the work, they send feedback to the server via their terminal, and the result is notified to the user.

[0270] As a concrete example, consider a situation where user Tanaka requests that the living room carpet be cleaned. The server analyzes this request, searches for the nearest cleaning company, and submits a work request. After the work is completed, the server receives a report from the company and notifies Tanaka that the work has been completed.

[0271] An example of a prompt message might be: "Please describe the detailed workflow when a user requests carpet cleaning. This should include the entire process from voice analysis to notification to the assistant and reporting of completion of the work."

[0272] This system allows users to easily enjoy various services in their daily lives without needing specialized knowledge, and also enables service providers to perform their duties efficiently.

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

[0274] Step 1:

[0275] The user sends an audio signal to the server using their telephone. The server receives the user's spoken voice as input. This audio signal conveys the specific services the user needs.

[0276] Step 2:

[0277] The server converts the received audio signal into text data using the Google Speech-to-Text API. The input for this step is the user's audio signal, and the output is text-formatted information data. Specifically, the server passes the audio file to the API, where the text conversion is performed.

[0278] Step 3:

[0279] The server analyzes the converted text data using IBM Watson Natural Language Understanding to identify the user's request. The input is the text data obtained in step 2, and the output is the analyzed request and category information. In this step, for example, the text "I would like to request carpet cleaning" is classified as "request for cleaning service."

[0280] Step 4:

[0281] Based on the user's request, the server determines the services that can be provided. The input is the analyzed request, and the output is the detailed information of the determined service. Specifically, it searches for the nearest cleaning professional and checks the service availability and cost.

[0282] Step 5:

[0283] The server synthesizes the information about the determined service into voice data using Amazon Polly and notifies the user. The input is the detailed information of the service, and the output is the synthesized voice. In this process, it prepares to tell the user in voice that "the cleaning professional will visit at 3 pm the next day".

[0284] Step 6:

[0285] The server searches for registered experts in the area from the database and sends the work request information. The input is the details of the determined service, and the output is the request message to the experts. Information is shared by sending the date and time of visiting the user and instructions to a specific professional.

[0286] Step 7:

[0287] The terminal reports to the server that the supporter has completed the work. The input is the work completion information from the supporter, and the output is the feedback data to the user and the server. Based on this information, the server notifies the user of the work completion.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] The aim is to enable users, including the elderly, to easily access support services in their daily lives without experiencing difficulties due to unfamiliarity with digital devices or visual and physical limitations. Furthermore, it aims to facilitate the search for and rapid response of local support providers, ensuring that users receive prompt and appropriate assistance.

[0291] 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.

[0292] In this invention, the server includes means for receiving voice signals and converting them into text data using speech recognition, means for analyzing user requests and determining appropriate services, means for providing information about the determined services using synthesized speech, and means for providing support services using a voice interface optimized for the elderly. This makes it possible for users unfamiliar with digital technology to receive services with intuitive operation.

[0293] "Audio signals" are information obtained by converting sound into electrical data, and are used for communication and processing.

[0294] "Speech recognition" is a technology that extracts linguistic information from received audio signals and converts it into text data.

[0295] "Text data" refers to character information converted by speech recognition, and is used for analysis and processing.

[0296] "User requests" refer to wishes or instructions expressed by users verbally or in other forms.

[0297] "Appropriate service" refers to specific support and actions implemented in response to the user's requests, and which are in the user's best interest.

[0298] "Synthesized speech" refers to audio data generated using a computer, which outputs text information as speech.

[0299] A "voice interface" is a general term for the means and technologies that allow users to operate a computer via voice.

[0300] "Support services" refer to various forms of support provided to help users solve problems and improve their daily lives.

[0301] The system of this invention begins with a user sending an audio signal to a server via a telephone or smartphone. The server converts the received audio signal into text data using the Google Speech-to-Text API. The converted text data is analyzed using natural language processing (NLP) techniques to analyze the user's intent. This analysis uses a pre-trained machine learning model to select the appropriate service.

[0302] Based on user requests, the server generates information as synthesized speech using Google Text-to-Speech. This generated speech is provided to the user, ensuring intuitive operation even for elderly users. Throughout this process, a user-friendly and easy-to-listen-to synthesized speech is provided via a voice interface tailored to the specific needs of the elderly.

[0303] For example, if a user requests by voice, "Tell me when to take my medicine," the server analyzes this request and sets the necessary reminder. The reminder is then sent to the user by voice at the specified time each day. In other cases, such as "I need someone to change a light bulb," the server searches for a registered helper in the area and notifies them quickly.

[0304] As an example of a prompt for the generative AI model, one could enter, "Please tell me how to design voice guidance that is easy for seniors to understand." This prompt will help the system improve voice interactions to meet the needs of seniors.

[0305] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0306] Step 1:

[0307] The user uses a terminal (telephone or smartphone) to send an audio signal to the server. Here, the audio input is captured and transferred to the server as a digital audio signal. This input is the user's voice instruction, and the output to the server is the audio data.

[0308] Step 2:

[0309] The server uses the Google Speech-to-Text API to convert the received audio signal into text data. The input is the audio signal received in Step 1, and it is converted into natural language text using speech recognition technology. The output is the converted text data.

[0310] Step 3:

[0311] The server receives the converted text data and analyzes the user's intention using natural language processing (NLP) on the data. The input is the text data, and the user's request is clarified through data analysis. The output is the analyzed user instruction content and service decision.

[0312] Step 4:

[0313] The server uses Google Text-to-Speech to generate the information related to the determined service as synthesized voice. The input is the output information of Step 3, and voice data is generated based on it. The output is the voice message provided to the user.

[0314] Step 5:

[0315] The server or terminal provides audio information to the user via synthesized speech. The input is the audio data generated in step 4, which is delivered to the user through an audio playback device. The output is the audio information that the user hears.

[0316] Step 6:

[0317] If a user's request requires assistance within their local area, the server searches for registered support providers and sends a notification to the appropriate provider. The input is the request information parsed in step 3, and the output is the notification sent to the support provider and its contents.

[0318] 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.

[0319] This invention provides more appropriate and personalized services to users, including the elderly, by incorporating an emotion recognition engine into a telephone-based support system. The main functions of this system include speech recognition, emotion analysis, and the provision of appropriate services. Specific embodiments are described below.

[0320] When a user accesses the system via telephone, the server receives the voice signal and first converts it into text data using speech recognition technology. At the same time, the server uses an emotion engine to analyze the user's emotions from the voice signal. The analyzed emotion information is processed along with the user's request and reflected in the way the service is delivered and its content.

[0321] For example, if a user says, "I've been feeling anxious lately," the server uses its emotion engine to recognize the user's emotional state as "anxiety." Based on this information, the server can generate a more considerate synthesized voice message and suggest comforting services (e.g., guidance on mental health support).

[0322] Furthermore, when notifying registered supporters in the region, it is possible to provide them with the results of an emotional analysis so that they can respond appropriately to the user's emotions. This allows supporters to respond in a way that takes the user's mental state into consideration, leading to increased user satisfaction.

[0323] Furthermore, this system records user emotional data and uses it to continuously improve service quality. This allows for support that takes into account the user's previous emotional state when they use the system again.

[0324] In this way, this system combines speech recognition and emotion analysis technologies to provide users with a safer and more comfortable service.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The user accesses the system using a telephone and transmits an audio signal.

[0328] Step 2:

[0329] The server receives an audio signal from the user and uses speech recognition technology to convert that audio into text data.

[0330] Step 3:

[0331] The server analyzes text data to extract user requests. The server also uses an emotion engine to analyze user emotions from audio signals.

[0332] Step 4:

[0333] Based on the analyzed requests and sentiment information, the server determines the content of the services to be provided and adjusts its response as needed.

[0334] Step 5:

[0335] The server uses synthesized speech technology to generate an appropriate voice message for the user, taking into account the results of the emotion engine.

[0336] Step 6:

[0337] The server generates a synthesized voice message and sends it to the user via the telephone line to explain the service.

[0338] Step 7:

[0339] The server searches for registered supporters within the region and sends notifications to them to perform the necessary services. At this time, the user's sentiment information is also conveyed to the supporters.

[0340] Step 8:

[0341] The terminal receives a completion report from the supporter and sends it to the server. The server uses this to notify the user that the service is complete and requests further feedback.

[0342] Step 9:

[0343] The server receives feedback from users, records data including emotional information, and uses it as material for future service improvements.

[0344] (Example 2)

[0345] 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".

[0346] Traditional telephone support systems struggled to provide personalized support that took into account the user's emotional state, resulting in insufficient user satisfaction. Furthermore, there was a need to provide accurate support to users by sharing emotional information with local support staff. Additionally, there was a lack of methods to improve services by utilizing users' emotional history.

[0347] 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.

[0348] In this invention, the server includes means for receiving an audio signal and converting the audio data into text data using speech recognition, means for analyzing the user's emotions from the text data, and means for determining an appropriate service based on the results of the emotion analysis and the user's requests. This makes it possible to provide services that take into account the user's emotional state.

[0349] An "audio signal" is data that electrically represents sound, and is acquired by telephones or microphones.

[0350] "Speech recognition" is a technology that analyzes speech signals and converts them into corresponding text data.

[0351] "Audio data" refers to audio signal information expressed in digital format and stored in a way that allows for computer processing.

[0352] "Text data" refers to data expressed as character information, which is the result of speech being converted by speech recognition.

[0353] "Emotion analysis" is a technology that identifies and classifies a user's emotions from text data or audio signals.

[0354] "Means of determining services" refers to the process of determining the content of services to be provided based on user requests and the results of sentiment analysis.

[0355] "Synthesized speech" refers to artificially generated speech, a technology that reproduces text data as sound.

[0356] "Emotional data" refers to information that indicates the user's emotional state and is obtained as a result of emotional analysis.

[0357] A "supporter" is an individual or organization that has the role of providing services or support to users.

[0358] "Notification" refers to the transmission of signals or messages to convey specific information to relevant parties.

[0359] This invention is a system for providing personalized services to users via telephone using emotion recognition. Embodiments of this system are described in detail below.

[0360] When a user accesses the system via telephone, the server first receives an audio signal. This audio signal is then converted into text data using advanced speech recognition technology. This process utilizes general-purpose speech recognition software, enabling real-time conversion. Specific software examples include speech recognition engines and natural language processing libraries.

[0361] Next, the server uses an emotion analysis engine to analyze the user's emotions based on the converted text data. This analysis categorizes emotions into categories such as "joy," "anxiety," and "anger." Specific algorithms and machine learning models can be used for emotion analysis.

[0362] Based on the analyzed emotional information and the user's request, the server determines the content of the service to provide. The service content will be tailored to the emotional state, for example, "information on mental health support" or "introduction to local support providers." This information is provided to the user as a voice message using synthesized speech technology. Text-to-speech technology is used for the synthesized speech, generating voice with a tone and pace that matches the emotion.

[0363] Furthermore, the server notifies registered supporters within the region and provides them with sentiment analysis results, enabling them to respond appropriately to users. This support activity is carried out using the local support network.

[0364] This system continuously records user emotional data and uses it to improve service quality. Data from past sessions will be considered when providing the service again, and used to deliver a better user experience.

[0365] As an example of a specific prompt, the AI ​​model might be inputted with phrases like, "How to propose a service that enhances user confidence by utilizing emotion recognition in telephone support for the elderly." Based on this prompt, the AI ​​model generates the optimal service proposal, which is then used for implementation.

[0366] In this way, the server can combine speech recognition and sentiment analysis to provide users with personalized and more advanced services.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] When a user accesses the system via telephone, the server receives an audio signal. This signal is acquired as a digital audio input and converted into text data using speech recognition technology. Specifically, the speech recognition engine analyzes the audio signal, converting phonemes into words and words into sentences. This process outputs instructions and information obtained from the audio as text data.

[0370] Step 2:

[0371] The server passes the text data obtained through speech recognition as input to the sentiment analysis engine. This analysis engine analyzes the text data and identifies the user's emotional state from the context and keywords. This analysis outputs emotion categories such as joy, anxiety, and anger. Specifically, natural language processing techniques and machine learning models are used to assign labels to the emotion categories.

[0372] Step 3:

[0373] The server processes the results of the sentiment analysis and the user's request to determine which service to provide. The decision engine refers to the analysis results and selects the most appropriate service. For example, if the user expresses anxiety, providing information on mental health support might be selected. This process uses a deterministic algorithm, making rule-based decisions for service selection.

[0374] Step 4:

[0375] The server generates a synthesized voice message based on the selected service. Using text-to-speech technology, it outputs the chosen service as audio. Specifically, voice data for synthesis is created, and its tone and pace are adjusted according to the emotion. This voice message is then played back to the user via telephone.

[0376] Step 5:

[0377] The server notifies registered support providers in the region of the results of the emotion analysis and the corresponding service details. These notifications to support providers include the user's specific emotional state and the service details, and are sent via email or a dedicated application. This notification allows support providers to obtain the information necessary to provide appropriate support.

[0378] Step 6:

[0379] The server stores emotional data and service usage history obtained from all sessions as records. This data will be used to improve the quality of the service in the future. Specifically, the emotional data and interaction history will be structured using a database management system and used as a reference for providing services in subsequent interactions.

[0380] (Application Example 2)

[0381] 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 as the "terminal".

[0382] To improve the quality of life for the elderly, it is crucial to accurately understand their mental state and provide appropriate support. However, conventional systems have not adequately analyzed emotions from voice, nor have they provided sufficient personalized notifications to caregivers. As a result, it has been difficult to respond quickly and appropriately when elderly people are in a mentally unstable state.

[0383] 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.

[0384] In this invention, the server includes means for receiving voice signals and converting them into language data using speech recognition; means for analyzing the user's emotional state from the language data and voice characteristics and determining personalized recommended actions based on that state; means for providing information regarding the determined recommended actions using synthesized speech; and means designed for the elderly and including a notification function for caregivers. This enables a rapid and effective response in accordance with the emotional state of the elderly.

[0385] An "audio signal" is a continuous electrical signal generated by sound vibrations, and is usually captured through a microphone.

[0386] "Speech recognition" is a technology that analyzes audio signals and converts their content into text data.

[0387] "Language data" refers to text information converted from audio signals, and is in a format suitable for computer processing.

[0388] "Emotional state" refers to the state of emotions within an individual's mind, and is the information that is analyzed.

[0389] "Personalized recommended behaviors" are guidelines or actions selected to suit the user's specific circumstances and needs.

[0390] "Synthesized speech" refers to artificially generated speech that is used for information transmission.

[0391] A "notification function" is a feature that allows you to inform other devices or people of specific information or warnings.

[0392] The term "elderly" usually refers to individuals who have reached old age and often require special support and consideration.

[0393] This system improves the quality of life for the elderly by analyzing their emotional state through voice signals and providing personalized recommendations for caregivers.

[0394] The server first receives the incoming audio signal. This is done using a microphone connected to a smartphone or dedicated device. Next, it converts the audio signal into linguistic data using a speech recognition API (e.g., Google Speech-to-Text). Based on this linguistic data, an emotion analysis engine utilizing a generative AI model is activated to identify the user's emotional state. Deep learning technology is incorporated into the emotion analysis, and factors such as tone, speed, and pitch of the voice are also taken into consideration.

[0395] Based on the analysis results, the server uses a synthesized speech engine to generate recommended actions for the elderly. This synthesized speech is adjusted to be easier to understand and more reassuring. Furthermore, if the analyzed emotional state is determined to be serious, the system automatically sends a notification to the caregiver's terminal. The notification provides specific support methods and actions that correspond to the emotional state.

[0396] For example, if an elderly person feels lonely because they spend a lot of time alone, the system analyzes that emotion and sends a notification to their caregiver suggesting something like, "There seems to be a gathering at a nearby community center today. Would you like to join us?"

[0397] An example of a prompt message is: "An elderly user said on the phone, 'I was lonely today because I didn't talk to anyone all day.' Based on this information, analyze the user's emotional state and generate an appropriate notification message for the caregiver."

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The server receives audio signals transmitted from the terminal. These audio signals are collected through a microphone, converted to a digital format, and sent to the server. The input is an analog audio signal, and the output is digital audio data. This process uses an ADC (analog-to-digital converter) to convert the analog signal to a digital signal.

[0401] Step 2:

[0402] The server sends the received digital audio data to a speech recognition API, which converts it into text data. This API identifies words and phrases in the audio and generates corresponding text. The input is digital audio data, and the output is text data. At this stage, acoustic modeling is performed, which analyzes the acoustic features of the audio and maps them to linguistic information.

[0403] Step 3:

[0404] The server uses a generative AI model to analyze the user's emotional state from the features of text and audio data. The emotion analysis engine identifies emotions by considering factors such as tone, speed, and pitch of the voice. The input is text data and audio features, and the output is the analyzed emotional state. The generative AI model uses deep learning techniques to learn complex emotional patterns.

[0405] Step 4:

[0406] The server determines what recommended actions to provide based on the analyzed emotional state. It selects the most appropriate action from a predefined set of recommendations based on the emotional state and user profile. The input is the analyzed emotional state, and the output is the content of the recommended action. Rule-based systems and machine learning models are used as recommendation algorithms in this process.

[0407] Step 5:

[0408] The server sends the determined recommended action to a speech synthesis engine, which generates a feedback message for the elderly. The speech synthesis engine converts text-based instructions into natural, easy-to-understand speech. The input is the text of the recommended action, and the output is synthesized speech. Here, text-to-speech (TTS) is performed using speech synthesis technology.

[0409] Step 6:

[0410] The server sends notifications to the caregiver's terminal as needed. These notifications include information related to the analyzed emotional state and recommended actions. The inputs are the recommended actions and emotional analysis results, and the output is the notification message sent to the caregiver's terminal. This process uses a communication protocol to transmit data.

[0411] 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.

[0412] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] 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.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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".

[0427] This invention provides a system that offers various support services for daily life by having users send voice signals to a server via telephone and analyzing that voice. A specific example of the system's operation is shown below.

[0428] First, the user sends an audio signal to the server using their phone. The server receives this audio signal and converts it into text data using speech recognition technology. The server then analyzes this text data to understand the user's intended request. Based on this analysis, the server determines which service falls into the appropriate category.

[0429] The server generates detailed information about the selected service as voice data using synthesized speech technology and provides it to the user. For example, if an elderly user requests, "I would like a light bulb changed," the server analyzes this request and searches for a registered support worker in the area who can perform the electrical work. Once a registered support worker is found, the server notifies the worker of the work details and estimated time and requests their assistance.

[0430] The terminal reports to the server as feedback that the registered support worker has completed the task, and the server notifies the user of the result as appropriate.

[0431] In this way, users can easily receive the necessary support even without being familiar with specialized digital technologies, and collecting feedback from users will also contribute to improving the quality of future services.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The user accesses the system via telephone and transmits an audio signal.

[0435] Step 2:

[0436] The server receives an audio signal from the user, and a speech recognition system converts this signal into text data.

[0437] Step 3:

[0438] The server analyzes text data and uses natural language processing techniques to extract user requests.

[0439] Step 4:

[0440] Based on the extracted requests, the server searches the regional database for the relevant services and identifies the appropriate support provider.

[0441] Step 5:

[0442] The server will notify the identified supporter of the necessary services and the scheduled visit time.

[0443] Step 6:

[0444] The server uses synthesized speech technology to generate a voice message explaining the service details and arrangement status, and informs the user.

[0445] Step 7:

[0446] The terminal receives a completion report from the support worker and sends it to the server.

[0447] Step 8:

[0448] Based on the completion report from the terminal, the server notifies the user that the service is complete and requests feedback.

[0449] (Example 1)

[0450] 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."

[0451] In modern society, the ability for users to easily access various support services through voice, even without specialized knowledge, is a crucial element in improving quality of life. However, existing systems struggle to accurately analyze requests from voice signals and provide appropriate services, and they do not effectively utilize user feedback, resulting in challenges in improving the quality and efficiency of services.

[0452] 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.

[0453] In this invention, the server includes means for acquiring voice data and converting it into information data using an advanced speech recognition algorithm; means for analyzing the user's request from the information data and selecting the appropriate service based on the results; and means for transmitting data related to the selected service to the user using speech synthesis technology. As a result, users can easily request services by voice without specialized knowledge, and the quality of the service can be improved by utilizing feedback.

[0454] "Voice data" refers to the user's spoken information received through communication, and this data is the subject of analysis by speech recognition algorithms.

[0455] A "speech recognition algorithm" refers to a technology that analyzes acquired audio data and converts the audio signal into corresponding information data.

[0456] "Information data" refers to text-formatted data obtained by converting audio data using a speech recognition algorithm, and serves as the starting point for analyzing user requests.

[0457] "User requests" refer to the content of the services or support that the user intends to receive, which is extracted by analyzing the voice data.

[0458] "Service" refers to specific support or actions provided based on the user's request, delivered to the user through the system.

[0459] "Speech synthesis technology" is a technology that converts information data back into speech data and conveys that information to users through speech.

[0460] A "specialist" refers to an individual or organization registered within a region that possesses the technical skills and abilities to perform specific tasks.

[0461] A "database" refers to an information resource that stores user evaluations and feedback, and is used as foundational data for improving future service provision.

[0462] This invention relates to a system in which a user transmits voice data to a server using a telephone, and the server analyzes the voice signal to provide a variety of support services. This system utilizes speech recognition technology, natural language processing, and speech synthesis technology to meet the user's requirements.

[0463] The user first sends an audio signal to the server using a smartphone or landline phone. The server receives this audio signal and converts it into text data using a cloud-based speech recognition service. Specifically, the Google Speech-to-Text API can be used. Based on the converted text data, the server uses natural language processing technology to analyze the user's intent and determine the appropriate service. The natural language processing technology used here is IBM Watson Natural Language Understanding.

[0464] Subsequently, the server converts information about the selected service into voice data using speech synthesis technology and provides it to the user. For speech synthesis, Amazon Polly can be used, for example. Furthermore, the server searches a database for registered experts within the region and notifies the experts of the work request. When the support worker completes the work, they send feedback to the server via their terminal, and the result is notified to the user.

[0465] As a concrete example, consider a situation where user Tanaka requests that the living room carpet be cleaned. The server analyzes this request, searches for the nearest cleaning company, and submits a work request. After the work is completed, the server receives a report from the company and notifies Tanaka that the work has been completed.

[0466] An example of a prompt message might be: "Please describe the detailed workflow when a user requests carpet cleaning. This should include the entire process from voice analysis to notification to the assistant and reporting of completion of the work."

[0467] This system allows users to easily enjoy various services in their daily lives without needing specialized knowledge, and also enables service providers to perform their duties efficiently.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] The user sends an audio signal to the server using their telephone. The server receives the user's spoken voice as input. This audio signal conveys the specific services the user needs.

[0471] Step 2:

[0472] The server converts the received audio signal into text data using the Google Speech-to-Text API. The input for this step is the user's audio signal, and the output is text-formatted information data. Specifically, the server passes the audio file to the API, where the text conversion is performed.

[0473] Step 3:

[0474] The server analyzes the converted text data using IBM Watson Natural Language Understanding to identify the user's request. The input is the text data obtained in step 2, and the output is the analyzed request and category information. In this step, for example, the text "I would like to request carpet cleaning" is classified as "request for cleaning service."

[0475] Step 4:

[0476] The server determines the services it can provide based on the user's request. The input is the parsed request, and the output is detailed information about the determined service. Specifically, it searches for the nearest cleaning service provider and checks the service availability and cost.

[0477] Step 5:

[0478] The server uses Amazon Polly to synthesize information about the selected service as voice data and notifies the user. The input is the service details, and the output is synthesized speech. In this process, preparations are made to tell the user in voice, "The cleaning service will visit tomorrow at 3pm."

[0479] Step 6:

[0480] The server searches its database for registered professionals in the region and sends them work request information. The input is the details of the selected service, and the output is a request message to the professional. Information is shared by sending the date and time of the visit to the user and instructions to the specific vendor.

[0481] Step 7:

[0482] The terminal reports to the server that the support worker has completed the task. The input is the support worker's completion information, and the output is feedback data for both the user and the server. Based on this information, the server notifies the user that the task is complete.

[0483] (Application Example 1)

[0484] 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."

[0485] The aim is to enable users, including the elderly, to easily access support services in their daily lives without experiencing difficulties due to unfamiliarity with digital devices or visual and physical limitations. Furthermore, it aims to facilitate the search for and rapid response of local support providers, ensuring that users receive prompt and appropriate assistance.

[0486] 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.

[0487] In this invention, the server includes means for receiving voice signals and converting them into text data using speech recognition, means for analyzing user requests and determining appropriate services, means for providing information about the determined services using synthesized speech, and means for providing support services using a voice interface optimized for the elderly. This makes it possible for users unfamiliar with digital technology to receive services with intuitive operation.

[0488] "Audio signals" are information obtained by converting sound into electrical data, and are used for communication and processing.

[0489] "Speech recognition" is a technology that extracts linguistic information from received audio signals and converts it into text data.

[0490] "Text data" refers to character information converted by speech recognition, and is used for analysis and processing.

[0491] "User requests" refer to wishes or instructions expressed by users verbally or in other forms.

[0492] "Appropriate service" refers to specific support and actions implemented in response to the user's requests, and which are in the user's best interest.

[0493] "Synthesized speech" refers to audio data generated using a computer, which outputs text information as speech.

[0494] A "voice interface" is a general term for the means and technologies that allow users to operate a computer via voice.

[0495] "Support services" refer to various forms of support provided to help users solve problems and improve their daily lives.

[0496] The system of this invention begins with a user sending an audio signal to a server via a telephone or smartphone. The server converts the received audio signal into text data using the Google Speech-to-Text API. The converted text data is analyzed using natural language processing (NLP) techniques to analyze the user's intent. This analysis uses a pre-trained machine learning model to select the appropriate service.

[0497] Based on user requests, the server generates information as synthesized speech using Google Text-to-Speech. This generated speech is provided to the user, ensuring intuitive operation even for elderly users. Throughout this process, a user-friendly and easy-to-listen-to synthesized speech is provided via a voice interface tailored to the specific needs of the elderly.

[0498] For example, if a user requests by voice, "Tell me when to take my medicine," the server analyzes this request and sets the necessary reminder. The reminder is then sent to the user by voice at the specified time each day. In other cases, such as "I need someone to change a light bulb," the server searches for a registered helper in the area and notifies them quickly.

[0499] As an example of a prompt for the generative AI model, one could enter, "Please tell me how to design voice guidance that is easy for seniors to understand." This prompt will help the system improve voice interactions to meet the needs of seniors.

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The user sends an audio signal to the server using a device (telephone or smartphone). Here, the audio input is captured and transferred to the server as a digital audio signal. This input is the user's voice command, and the output to the server is that audio data.

[0503] Step 2:

[0504] The server uses the Google Speech-to-Text API to convert the received audio signal into text data. The input is the audio signal received in step 1, which is then converted into natural language text using speech recognition technology. The output is the converted text data.

[0505] Step 3:

[0506] The server receives the converted text data and uses natural language processing (NLP) to analyze the user's intent. The input is text data, and the data analysis clarifies the user's requests. The output is the analyzed user instructions and the service decision.

[0507] Step 4:

[0508] The server uses Google Text-to-Speech to generate synthesized speech information about the selected service. The input is the output information from step 3, and the server generates the audio data based on this. The output is the audio message provided to the user.

[0509] Step 5:

[0510] The server or terminal provides audio information to the user via synthesized speech. The input is the audio data generated in step 4, which is delivered to the user through an audio playback device. The output is the audio information that the user hears.

[0511] Step 6:

[0512] If a user's request requires assistance within their local area, the server searches for registered support providers and sends a notification to the appropriate provider. The input is the request information parsed in step 3, and the output is the notification sent to the support provider and its contents.

[0513] 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.

[0514] This invention provides more appropriate and personalized services to users, including the elderly, by incorporating an emotion recognition engine into a telephone-based support system. The main functions of this system include speech recognition, emotion analysis, and the provision of appropriate services. Specific embodiments are described below.

[0515] When a user accesses the system via telephone, the server receives the voice signal and first converts it into text data using speech recognition technology. At the same time, the server uses an emotion engine to analyze the user's emotions from the voice signal. The analyzed emotion information is processed along with the user's request and reflected in the way the service is delivered and its content.

[0516] For example, if a user says, "I've been feeling anxious lately," the server uses its emotion engine to recognize the user's emotional state as "anxiety." Based on this information, the server can generate a more considerate synthesized voice message and suggest comforting services (e.g., guidance on mental health support).

[0517] Furthermore, when notifying registered supporters in the region, it is possible to provide them with the results of an emotional analysis so that they can respond appropriately to the user's emotions. This allows supporters to respond in a way that takes the user's mental state into consideration, leading to increased user satisfaction.

[0518] Furthermore, this system records user emotional data and uses it to continuously improve service quality. This allows for support that takes into account the user's previous emotional state when they use the system again.

[0519] In this way, this system combines speech recognition and emotion analysis technologies to provide users with a safer and more comfortable service.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] The user accesses the system using a telephone and transmits an audio signal.

[0523] Step 2:

[0524] The server receives an audio signal from the user and uses speech recognition technology to convert that audio into text data.

[0525] Step 3:

[0526] The server analyzes text data to extract user requests. The server also uses an emotion engine to analyze user emotions from audio signals.

[0527] Step 4:

[0528] Based on the analyzed requests and sentiment information, the server determines the content of the services to be provided and adjusts its response as needed.

[0529] Step 5:

[0530] The server uses synthesized speech technology to generate an appropriate voice message for the user, taking into account the results of the emotion engine.

[0531] Step 6:

[0532] The server generates a synthesized voice message and sends it to the user via the telephone line to explain the service.

[0533] Step 7:

[0534] The server searches for registered supporters within the region and sends notifications to them to perform the necessary services. At this time, the user's sentiment information is also conveyed to the supporters.

[0535] Step 8:

[0536] The terminal receives a completion report from the supporter and sends it to the server. The server uses this to notify the user that the service is complete and requests further feedback.

[0537] Step 9:

[0538] The server receives feedback from users, records data including emotional information, and uses it as material for future service improvements.

[0539] (Example 2)

[0540] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0541] Traditional telephone support systems struggled to provide personalized support that took into account the user's emotional state, resulting in insufficient user satisfaction. Furthermore, there was a need to provide accurate support to users by sharing emotional information with local support staff. Additionally, there was a lack of methods to improve services by utilizing users' emotional history.

[0542] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0543] In this invention, the server includes means for receiving an audio signal and converting the audio data into text data using speech recognition, means for analyzing the user's emotions from the text data, and means for determining an appropriate service based on the results of the emotion analysis and the user's requests. This makes it possible to provide services that take into account the user's emotional state.

[0544] An "audio signal" is data that electrically represents sound, and is acquired by telephones or microphones.

[0545] "Speech recognition" is a technology that analyzes speech signals and converts them into corresponding text data.

[0546] "Audio data" refers to audio signal information expressed in digital format and stored in a way that allows for computer processing.

[0547] "Text data" refers to data expressed as character information, which is the result of speech being converted by speech recognition.

[0548] "Emotion analysis" is a technology that identifies and classifies a user's emotions from text data or audio signals.

[0549] "Means of determining services" refers to the process of determining the content of services to be provided based on user requests and the results of sentiment analysis.

[0550] "Synthesized speech" refers to artificially generated speech, a technology that reproduces text data as sound.

[0551] "Emotional data" refers to information that indicates the user's emotional state and is obtained as a result of emotional analysis.

[0552] A "supporter" is an individual or organization that has the role of providing services or support to users.

[0553] "Notification" refers to the transmission of signals or messages to convey specific information to relevant parties.

[0554] This invention is a system for providing personalized services to users via telephone using emotion recognition. Embodiments of this system are described in detail below.

[0555] When a user accesses the system via telephone, the server first receives an audio signal. This audio signal is then converted into text data using advanced speech recognition technology. This process utilizes general-purpose speech recognition software, enabling real-time conversion. Specific software examples include speech recognition engines and natural language processing libraries.

[0556] Next, the server uses an emotion analysis engine to analyze the user's emotions based on the converted text data. This analysis categorizes emotions into categories such as "joy," "anxiety," and "anger." Specific algorithms and machine learning models can be used for emotion analysis.

[0557] Based on the analyzed emotional information and the user's request, the server determines the content of the service to provide. The service content will be tailored to the emotional state, for example, "information on mental health support" or "introduction to local support providers." This information is provided to the user as a voice message using synthesized speech technology. Text-to-speech technology is used for the synthesized speech, generating voice with a tone and pace that matches the emotion.

[0558] Furthermore, the server notifies registered supporters within the region and provides them with sentiment analysis results, enabling them to respond appropriately to users. This support activity is carried out using the local support network.

[0559] This system continuously records user emotional data and uses it to improve service quality. Data from past sessions will be considered when providing the service again, and used to deliver a better user experience.

[0560] As an example of a specific prompt, the AI ​​model might be inputted with phrases like, "How to propose a service that enhances user confidence by utilizing emotion recognition in telephone support for the elderly." Based on this prompt, the AI ​​model generates the optimal service proposal, which is then used for implementation.

[0561] In this way, the server can combine speech recognition and sentiment analysis to provide users with personalized and more advanced services.

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] When a user accesses the system via telephone, the server receives an audio signal. This signal is acquired as a digital audio input and converted into text data using speech recognition technology. Specifically, the speech recognition engine analyzes the audio signal, converting phonemes into words and words into sentences. This process outputs instructions and information obtained from the audio as text data.

[0565] Step 2:

[0566] The server passes the text data obtained through speech recognition as input to the sentiment analysis engine. This analysis engine analyzes the text data and identifies the user's emotional state from the context and keywords. This analysis outputs emotion categories such as joy, anxiety, and anger. Specifically, natural language processing techniques and machine learning models are used to assign labels to the emotion categories.

[0567] Step 3:

[0568] The server processes the results of the sentiment analysis and the user's request to determine which service to provide. The decision engine refers to the analysis results and selects the most appropriate service. For example, if the user expresses anxiety, providing information on mental health support might be selected. This process uses a deterministic algorithm, making rule-based decisions for service selection.

[0569] Step 4:

[0570] The server generates a synthesized voice message based on the selected service. Using text-to-speech technology, it outputs the chosen service as audio. Specifically, voice data for synthesis is created, and its tone and pace are adjusted according to the emotion. This voice message is then played back to the user via telephone.

[0571] Step 5:

[0572] The server notifies registered support providers in the region of the results of the emotion analysis and the corresponding service details. These notifications to support providers include the user's specific emotional state and the service details, and are sent via email or a dedicated application. This notification allows support providers to obtain the information necessary to provide appropriate support.

[0573] Step 6:

[0574] The server stores emotional data and service usage history obtained from all sessions as records. This data will be used to improve the quality of the service in the future. Specifically, the emotional data and interaction history will be structured using a database management system and used as a reference for providing services in subsequent interactions.

[0575] (Application Example 2)

[0576] 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."

[0577] To improve the quality of life for the elderly, it is crucial to accurately understand their mental state and provide appropriate support. However, conventional systems have not adequately analyzed emotions from voice, nor have they provided sufficient personalized notifications to caregivers. As a result, it has been difficult to respond quickly and appropriately when elderly people are in a mentally unstable state.

[0578] 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.

[0579] In this invention, the server includes means for receiving voice signals and converting them into language data using speech recognition; means for analyzing the user's emotional state from the language data and voice characteristics and determining personalized recommended actions based on that state; means for providing information regarding the determined recommended actions using synthesized speech; and means designed for the elderly and including a notification function for caregivers. This enables a rapid and effective response in accordance with the emotional state of the elderly.

[0580] An "audio signal" is a continuous electrical signal generated by sound vibrations, and is usually captured through a microphone.

[0581] "Speech recognition" is a technology that analyzes audio signals and converts their content into text data.

[0582] "Language data" refers to text information converted from audio signals, and is in a format suitable for computer processing.

[0583] "Emotional state" refers to the state of emotions within an individual's mind, and is the information that is analyzed.

[0584] "Personalized recommended behaviors" are guidelines or actions selected to suit the user's specific circumstances and needs.

[0585] "Synthesized speech" refers to artificially generated speech that is used for information transmission.

[0586] A "notification function" is a feature that allows you to inform other devices or people of specific information or warnings.

[0587] The term "elderly" usually refers to individuals who have reached old age and often require special support and consideration.

[0588] This system improves the quality of life for the elderly by analyzing their emotional state through voice signals and providing personalized recommendations for caregivers.

[0589] The server first receives the incoming audio signal. This is done using a microphone connected to a smartphone or dedicated device. Next, it converts the audio signal into linguistic data using a speech recognition API (e.g., Google Speech-to-Text). Based on this linguistic data, an emotion analysis engine utilizing a generative AI model is activated to identify the user's emotional state. Deep learning technology is incorporated into the emotion analysis, and factors such as tone, speed, and pitch of the voice are also taken into consideration.

[0590] Based on the analysis results, the server uses a synthesized speech engine to generate recommended actions for the elderly. This synthesized speech is adjusted to be easier to understand and more reassuring. Furthermore, if the analyzed emotional state is determined to be serious, the system automatically sends a notification to the caregiver's terminal. The notification provides specific support methods and actions that correspond to the emotional state.

[0591] For example, if an elderly person feels lonely because they spend a lot of time alone, the system analyzes that emotion and sends a notification to their caregiver suggesting something like, "There seems to be a gathering at a nearby community center today. Would you like to join us?"

[0592] An example of a prompt message is: "An elderly user said on the phone, 'I was lonely today because I didn't talk to anyone all day.' Based on this information, analyze the user's emotional state and generate an appropriate notification message for the caregiver."

[0593] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0594] Step 1:

[0595] The server receives audio signals transmitted from the terminal. These audio signals are collected through a microphone, converted to a digital format, and sent to the server. The input is an analog audio signal, and the output is digital audio data. This process uses an ADC (analog-to-digital converter) to convert the analog signal to a digital signal.

[0596] Step 2:

[0597] The server sends the received digital audio data to a speech recognition API, which converts it into text data. This API identifies words and phrases in the audio and generates corresponding text. The input is digital audio data, and the output is text data. At this stage, acoustic modeling is performed, which analyzes the acoustic features of the audio and maps them to linguistic information.

[0598] Step 3:

[0599] The server uses a generative AI model to analyze the user's emotional state from the features of text and audio data. The emotion analysis engine identifies emotions by considering factors such as tone, speed, and pitch of the voice. The input is text data and audio features, and the output is the analyzed emotional state. The generative AI model uses deep learning techniques to learn complex emotional patterns.

[0600] Step 4:

[0601] The server determines what recommended actions to provide based on the analyzed emotional state. It selects the most appropriate action from a predefined set of recommendations based on the emotional state and user profile. The input is the analyzed emotional state, and the output is the content of the recommended action. Rule-based systems and machine learning models are used as recommendation algorithms in this process.

[0602] Step 5:

[0603] The server sends the determined recommended action to a speech synthesis engine, which generates a feedback message for the elderly. The speech synthesis engine converts text-based instructions into natural, easy-to-understand speech. The input is the text of the recommended action, and the output is synthesized speech. Here, text-to-speech (TTS) is performed using speech synthesis technology.

[0604] Step 6:

[0605] The server sends notifications to the caregiver's terminal as needed. These notifications include information related to the analyzed emotional state and recommended actions. The inputs are the recommended actions and emotional analysis results, and the output is the notification message sent to the caregiver's terminal. This process uses a communication protocol to transmit data.

[0606] 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.

[0607] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0608] 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.

[0609] [Fourth Embodiment]

[0610] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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".

[0623] This invention provides a system that offers various support services for daily life by having users send voice signals to a server via telephone and analyzing that voice. A specific example of the system's operation is shown below.

[0624] First, the user sends an audio signal to the server using their phone. The server receives this audio signal and converts it into text data using speech recognition technology. The server then analyzes this text data to understand the user's intended request. Based on this analysis, the server determines which service falls into the appropriate category.

[0625] The server generates detailed information about the selected service as voice data using synthesized speech technology and provides it to the user. For example, if an elderly user requests, "I would like a light bulb changed," the server analyzes this request and searches for a registered support worker in the area who can perform the electrical work. Once a registered support worker is found, the server notifies the worker of the work details and estimated time and requests their assistance.

[0626] The terminal reports to the server as feedback that the registered support worker has completed the task, and the server notifies the user of the result as appropriate.

[0627] In this way, users can easily receive the necessary support even without being familiar with specialized digital technologies, and collecting feedback from users will also contribute to improving the quality of future services.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The user accesses the system via telephone and transmits an audio signal.

[0631] Step 2:

[0632] The server receives an audio signal from the user, and a speech recognition system converts this signal into text data.

[0633] Step 3:

[0634] The server analyzes text data and uses natural language processing techniques to extract user requests.

[0635] Step 4:

[0636] Based on the extracted requests, the server searches the regional database for the relevant services and identifies the appropriate support provider.

[0637] Step 5:

[0638] The server will notify the identified supporter of the necessary services and the scheduled visit time.

[0639] Step 6:

[0640] The server uses synthesized speech technology to generate a voice message explaining the service details and arrangement status, and informs the user.

[0641] Step 7:

[0642] The terminal receives a completion report from the support worker and sends it to the server.

[0643] Step 8:

[0644] Based on the completion report from the terminal, the server notifies the user that the service is complete and requests feedback.

[0645] (Example 1)

[0646] 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".

[0647] In modern society, the ability for users to easily access various support services through voice, even without specialized knowledge, is a crucial element in improving quality of life. However, existing systems struggle to accurately analyze requests from voice signals and provide appropriate services, and they do not effectively utilize user feedback, resulting in challenges in improving the quality and efficiency of services.

[0648] 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.

[0649] In this invention, the server includes means for acquiring voice data and converting it into information data using an advanced speech recognition algorithm; means for analyzing the user's request from the information data and selecting the appropriate service based on the results; and means for transmitting data related to the selected service to the user using speech synthesis technology. As a result, users can easily request services by voice without specialized knowledge, and the quality of the service can be improved by utilizing feedback.

[0650] "Voice data" refers to the user's spoken information received through communication, and this data is the subject of analysis by speech recognition algorithms.

[0651] A "speech recognition algorithm" refers to a technology that analyzes acquired audio data and converts the audio signal into corresponding information data.

[0652] "Information data" refers to text-formatted data obtained by converting audio data using a speech recognition algorithm, and serves as the starting point for analyzing user requests.

[0653] "User requests" refer to the content of the services or support that the user intends to receive, which is extracted by analyzing the voice data.

[0654] "Service" refers to specific support or actions provided based on the user's request, delivered to the user through the system.

[0655] "Speech synthesis technology" is a technology that converts information data back into speech data and conveys that information to users through speech.

[0656] A "specialist" refers to an individual or organization registered within a region that possesses the technical skills and abilities to perform specific tasks.

[0657] A "database" refers to an information resource that stores user evaluations and feedback, and is used as foundational data for improving future service provision.

[0658] This invention relates to a system in which a user transmits voice data to a server using a telephone, and the server analyzes the voice signal to provide a variety of support services. This system utilizes speech recognition technology, natural language processing, and speech synthesis technology to meet the user's requirements.

[0659] The user first sends an audio signal to the server using a smartphone or landline phone. The server receives this audio signal and converts it into text data using a cloud-based speech recognition service. Specifically, the Google Speech-to-Text API can be used. Based on the converted text data, the server uses natural language processing technology to analyze the user's intent and determine the appropriate service. The natural language processing technology used here is IBM Watson Natural Language Understanding.

[0660] Subsequently, the server converts information about the selected service into voice data using speech synthesis technology and provides it to the user. For speech synthesis, Amazon Polly can be used, for example. Furthermore, the server searches a database for registered experts within the region and notifies the experts of the work request. When the support worker completes the work, they send feedback to the server via their terminal, and the result is notified to the user.

[0661] As a concrete example, consider a situation where user Tanaka requests that the living room carpet be cleaned. The server analyzes this request, searches for the nearest cleaning company, and submits a work request. After the work is completed, the server receives a report from the company and notifies Tanaka that the work has been completed.

[0662] An example of a prompt message might be: "Please describe the detailed workflow when a user requests carpet cleaning. This should include the entire process from voice analysis to notification to the assistant and reporting of completion of the work."

[0663] This system allows users to easily enjoy various services in their daily lives without needing specialized knowledge, and also enables service providers to perform their duties efficiently.

[0664] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0665] Step 1:

[0666] The user sends an audio signal to the server using their telephone. The server receives the user's spoken voice as input. This audio signal conveys the specific services the user needs.

[0667] Step 2:

[0668] The server converts the received audio signal into text data using the Google Speech-to-Text API. The input for this step is the user's audio signal, and the output is text-formatted information data. Specifically, the server passes the audio file to the API, where the text conversion is performed.

[0669] Step 3:

[0670] The server analyzes the converted text data using IBM Watson Natural Language Understanding to identify the user's request. The input is the text data obtained in step 2, and the output is the analyzed request and category information. In this step, for example, the text "I would like to request carpet cleaning" is classified as "request for cleaning service."

[0671] Step 4:

[0672] The server determines the services it can provide based on the user's request. The input is the parsed request, and the output is detailed information about the determined service. Specifically, it searches for the nearest cleaning service provider and checks the service availability and cost.

[0673] Step 5:

[0674] The server uses Amazon Polly to synthesize information about the selected service as voice data and notifies the user. The input is the service details, and the output is synthesized speech. In this process, preparations are made to tell the user in voice, "The cleaning service will visit tomorrow at 3pm."

[0675] Step 6:

[0676] The server searches its database for registered professionals in the region and sends them work request information. The input is the details of the selected service, and the output is a request message to the professional. Information is shared by sending the date and time of the visit to the user and instructions to the specific vendor.

[0677] Step 7:

[0678] The terminal reports to the server that the support worker has completed the task. The input is the support worker's completion information, and the output is feedback data for both the user and the server. Based on this information, the server notifies the user that the task is complete.

[0679] (Application Example 1)

[0680] 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".

[0681] The aim is to enable users, including the elderly, to easily access support services in their daily lives without experiencing difficulties due to unfamiliarity with digital devices or visual and physical limitations. Furthermore, it aims to facilitate the search for and rapid response of local support providers, ensuring that users receive prompt and appropriate assistance.

[0682] 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.

[0683] In this invention, the server includes means for receiving voice signals and converting them into text data using speech recognition, means for analyzing user requests and determining appropriate services, means for providing information about the determined services using synthesized speech, and means for providing support services using a voice interface optimized for the elderly. This makes it possible for users unfamiliar with digital technology to receive services with intuitive operation.

[0684] "Audio signals" are information obtained by converting sound into electrical data, and are used for communication and processing.

[0685] "Speech recognition" is a technology that extracts linguistic information from received audio signals and converts it into text data.

[0686] "Text data" refers to character information converted by speech recognition, and is used for analysis and processing.

[0687] "User requests" refer to wishes or instructions expressed by users verbally or in other forms.

[0688] "Appropriate service" refers to specific support and actions implemented in response to the user's requests, and which are in the user's best interest.

[0689] "Synthesized speech" refers to audio data generated using a computer, which outputs text information as speech.

[0690] A "voice interface" is a general term for the means and technologies that allow users to operate a computer via voice.

[0691] "Support services" refer to various forms of support provided to help users solve problems and improve their daily lives.

[0692] The system of this invention begins with a user sending an audio signal to a server via a telephone or smartphone. The server converts the received audio signal into text data using the Google Speech-to-Text API. The converted text data is analyzed using natural language processing (NLP) techniques to analyze the user's intent. This analysis uses a pre-trained machine learning model to select the appropriate service.

[0693] Based on user requests, the server generates information as synthesized speech using Google Text-to-Speech. This generated speech is provided to the user, ensuring intuitive operation even for elderly users. Throughout this process, a user-friendly and easy-to-listen-to synthesized speech is provided via a voice interface tailored to the specific needs of the elderly.

[0694] For example, if a user requests by voice, "Tell me when to take my medicine," the server analyzes this request and sets the necessary reminder. The reminder is then sent to the user by voice at the specified time each day. In other cases, such as "I need someone to change a light bulb," the server searches for a registered helper in the area and notifies them quickly.

[0695] As an example of a prompt for the generative AI model, one could enter, "Please tell me how to design voice guidance that is easy for seniors to understand." This prompt will help the system improve voice interactions to meet the needs of seniors.

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] The user sends an audio signal to the server using a device (telephone or smartphone). Here, the audio input is captured and transferred to the server as a digital audio signal. This input is the user's voice command, and the output to the server is that audio data.

[0699] Step 2:

[0700] The server uses the Google Speech-to-Text API to convert the received audio signal into text data. The input is the audio signal received in step 1, which is then converted into natural language text using speech recognition technology. The output is the converted text data.

[0701] Step 3:

[0702] The server receives the converted text data and uses natural language processing (NLP) to analyze the user's intent. The input is text data, and the data analysis clarifies the user's requests. The output is the analyzed user instructions and the service decision.

[0703] Step 4:

[0704] The server uses Google Text-to-Speech to generate synthesized speech information about the selected service. The input is the output information from step 3, and the server generates the audio data based on this. The output is the audio message provided to the user.

[0705] Step 5:

[0706] The server or terminal provides audio information to the user via synthesized speech. The input is the audio data generated in step 4, which is delivered to the user through an audio playback device. The output is the audio information that the user hears.

[0707] Step 6:

[0708] If a user's request requires assistance within their local area, the server searches for registered support providers and sends a notification to the appropriate provider. The input is the request information parsed in step 3, and the output is the notification sent to the support provider and its contents.

[0709] 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.

[0710] This invention provides more appropriate and personalized services to users, including the elderly, by incorporating an emotion recognition engine into a telephone-based support system. The main functions of this system include speech recognition, emotion analysis, and the provision of appropriate services. Specific embodiments are described below.

[0711] When a user accesses the system via telephone, the server receives the voice signal and first converts it into text data using speech recognition technology. At the same time, the server uses an emotion engine to analyze the user's emotions from the voice signal. The analyzed emotion information is processed along with the user's request and reflected in the way the service is delivered and its content.

[0712] For example, if a user says, "I've been feeling anxious lately," the server uses its emotion engine to recognize the user's emotional state as "anxiety." Based on this information, the server can generate a more considerate synthesized voice message and suggest comforting services (e.g., guidance on mental health support).

[0713] Furthermore, when notifying registered supporters in the region, it is possible to provide them with the results of an emotional analysis so that they can respond appropriately to the user's emotions. This allows supporters to respond in a way that takes the user's mental state into consideration, leading to increased user satisfaction.

[0714] Furthermore, this system records user emotional data and uses it to continuously improve service quality. This allows for support that takes into account the user's previous emotional state when they use the system again.

[0715] In this way, this system combines speech recognition and emotion analysis technologies to provide users with a safer and more comfortable service.

[0716] The following describes the processing flow.

[0717] Step 1:

[0718] The user accesses the system using a telephone and transmits an audio signal.

[0719] Step 2:

[0720] The server receives an audio signal from the user and uses speech recognition technology to convert that audio into text data.

[0721] Step 3:

[0722] The server analyzes text data to extract user requests. The server also uses an emotion engine to analyze user emotions from audio signals.

[0723] Step 4:

[0724] Based on the analyzed requests and sentiment information, the server determines the content of the services to be provided and adjusts its response as needed.

[0725] Step 5:

[0726] The server uses synthesized speech technology to generate an appropriate voice message for the user, taking into account the results of the emotion engine.

[0727] Step 6:

[0728] The server generates a synthesized voice message and sends it to the user via the telephone line to explain the service.

[0729] Step 7:

[0730] The server searches for registered supporters within the region and sends notifications to them to perform the necessary services. At this time, the user's sentiment information is also conveyed to the supporters.

[0731] Step 8:

[0732] The terminal receives a completion report from the supporter and sends it to the server. The server uses this to notify the user that the service is complete and requests further feedback.

[0733] Step 9:

[0734] The server receives feedback from users, records data including emotional information, and uses it as material for future service improvements.

[0735] (Example 2)

[0736] 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".

[0737] Traditional telephone support systems struggled to provide personalized support that took into account the user's emotional state, resulting in insufficient user satisfaction. Furthermore, there was a need to provide accurate support to users by sharing emotional information with local support staff. Additionally, there was a lack of methods to improve services by utilizing users' emotional history.

[0738] 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.

[0739] In this invention, the server includes means for receiving an audio signal and converting the audio data into text data using speech recognition, means for analyzing the user's emotions from the text data, and means for determining an appropriate service based on the results of the emotion analysis and the user's requests. This makes it possible to provide services that take into account the user's emotional state.

[0740] An "audio signal" is data that electrically represents sound, and is acquired by telephones or microphones.

[0741] "Speech recognition" is a technology that analyzes speech signals and converts them into corresponding text data.

[0742] "Audio data" refers to audio signal information expressed in digital format and stored in a way that allows for computer processing.

[0743] "Text data" refers to data expressed as character information, which is the result of speech being converted by speech recognition.

[0744] "Emotion analysis" is a technology that identifies and classifies a user's emotions from text data or audio signals.

[0745] "Means of determining services" refers to the process of determining the content of services to be provided based on user requests and the results of sentiment analysis.

[0746] "Synthesized speech" refers to artificially generated speech, a technology that reproduces text data as sound.

[0747] "Emotional data" refers to information that indicates the user's emotional state and is obtained as a result of emotional analysis.

[0748] A "supporter" is an individual or organization that has the role of providing services or support to users.

[0749] "Notification" refers to the transmission of signals or messages to convey specific information to relevant parties.

[0750] This invention is a system for providing personalized services to users via telephone using emotion recognition. Embodiments of this system are described in detail below.

[0751] When a user accesses the system via telephone, the server first receives an audio signal. This audio signal is then converted into text data using advanced speech recognition technology. This process utilizes general-purpose speech recognition software, enabling real-time conversion. Specific software examples include speech recognition engines and natural language processing libraries.

[0752] Next, the server uses an emotion analysis engine to analyze the user's emotions based on the converted text data. This analysis categorizes emotions into categories such as "joy," "anxiety," and "anger." Specific algorithms and machine learning models can be used for emotion analysis.

[0753] Based on the analyzed emotional information and the user's request, the server determines the content of the service to provide. The service content will be tailored to the emotional state, for example, "information on mental health support" or "introduction to local support providers." This information is provided to the user as a voice message using synthesized speech technology. Text-to-speech technology is used for the synthesized speech, generating voice with a tone and pace that matches the emotion.

[0754] Furthermore, the server notifies registered supporters within the region and provides them with sentiment analysis results, enabling them to respond appropriately to users. This support activity is carried out using the local support network.

[0755] This system continuously records user emotional data and uses it to improve service quality. Data from past sessions will be considered when providing the service again, and used to deliver a better user experience.

[0756] As an example of a specific prompt, the AI ​​model might be inputted with phrases like, "How to propose a service that enhances user confidence by utilizing emotion recognition in telephone support for the elderly." Based on this prompt, the AI ​​model generates the optimal service proposal, which is then used for implementation.

[0757] In this way, the server can combine speech recognition and sentiment analysis to provide users with personalized and more advanced services.

[0758] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0759] Step 1:

[0760] When a user accesses the system via telephone, the server receives an audio signal. This signal is acquired as a digital audio input and converted into text data using speech recognition technology. Specifically, the speech recognition engine analyzes the audio signal, converting phonemes into words and words into sentences. This process outputs instructions and information obtained from the audio as text data.

[0761] Step 2:

[0762] The server passes the text data obtained through speech recognition as input to the sentiment analysis engine. This analysis engine analyzes the text data and identifies the user's emotional state from the context and keywords. This analysis outputs emotion categories such as joy, anxiety, and anger. Specifically, natural language processing techniques and machine learning models are used to assign labels to the emotion categories.

[0763] Step 3:

[0764] The server processes the results of the sentiment analysis and the user's request to determine which service to provide. The decision engine refers to the analysis results and selects the most appropriate service. For example, if the user expresses anxiety, providing information on mental health support might be selected. This process uses a deterministic algorithm, making rule-based decisions for service selection.

[0765] Step 4:

[0766] The server generates a synthesized voice message based on the selected service. Using text-to-speech technology, it outputs the chosen service as audio. Specifically, voice data for synthesis is created, and its tone and pace are adjusted according to the emotion. This voice message is then played back to the user via telephone.

[0767] Step 5:

[0768] The server notifies registered support providers in the region of the results of the emotion analysis and the corresponding service details. These notifications to support providers include the user's specific emotional state and the service details, and are sent via email or a dedicated application. This notification allows support providers to obtain the information necessary to provide appropriate support.

[0769] Step 6:

[0770] The server stores emotional data and service usage history obtained from all sessions as records. This data will be used to improve the quality of the service in the future. Specifically, the emotional data and interaction history will be structured using a database management system and used as a reference for providing services in subsequent interactions.

[0771] (Application Example 2)

[0772] 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".

[0773] To improve the quality of life for the elderly, it is crucial to accurately understand their mental state and provide appropriate support. However, conventional systems have not adequately analyzed emotions from voice, nor have they provided sufficient personalized notifications to caregivers. As a result, it has been difficult to respond quickly and appropriately when elderly people are in a mentally unstable state.

[0774] 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.

[0775] In this invention, the server includes means for receiving voice signals and converting them into language data using speech recognition; means for analyzing the user's emotional state from the language data and voice characteristics and determining personalized recommended actions based on that state; means for providing information regarding the determined recommended actions using synthesized speech; and means designed for the elderly and including a notification function for caregivers. This enables a rapid and effective response in accordance with the emotional state of the elderly.

[0776] An "audio signal" is a continuous electrical signal generated by sound vibrations, and is usually captured through a microphone.

[0777] "Speech recognition" is a technology that analyzes audio signals and converts their content into text data.

[0778] "Language data" refers to text information converted from audio signals, and is in a format suitable for computer processing.

[0779] "Emotional state" refers to the state of emotions within an individual's mind, and is the information that is analyzed.

[0780] "Personalized recommended behaviors" are guidelines or actions selected to suit the user's specific circumstances and needs.

[0781] "Synthesized speech" refers to artificially generated speech that is used for information transmission.

[0782] A "notification function" is a feature that allows you to inform other devices or people of specific information or warnings.

[0783] The term "elderly" usually refers to individuals who have reached old age and often require special support and consideration.

[0784] This system improves the quality of life for the elderly by analyzing their emotional state through voice signals and providing personalized recommendations for caregivers.

[0785] The server first receives the incoming audio signal. This is done using a microphone connected to a smartphone or dedicated device. Next, it converts the audio signal into linguistic data using a speech recognition API (e.g., Google Speech-to-Text). Based on this linguistic data, an emotion analysis engine utilizing a generative AI model is activated to identify the user's emotional state. Deep learning technology is incorporated into the emotion analysis, and factors such as tone, speed, and pitch of the voice are also taken into consideration.

[0786] Based on the analysis results, the server uses a synthesized speech engine to generate recommended actions for the elderly. This synthesized speech is adjusted to be easier to understand and more reassuring. Furthermore, if the analyzed emotional state is determined to be serious, the system automatically sends a notification to the caregiver's terminal. The notification provides specific support methods and actions that correspond to the emotional state.

[0787] For example, if an elderly person feels lonely because they spend a lot of time alone, the system analyzes that emotion and sends a notification to their caregiver suggesting something like, "There seems to be a gathering at a nearby community center today. Would you like to join us?"

[0788] An example of a prompt message is: "An elderly user said on the phone, 'I was lonely today because I didn't talk to anyone all day.' Based on this information, analyze the user's emotional state and generate an appropriate notification message for the caregiver."

[0789] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0790] Step 1:

[0791] The server receives audio signals transmitted from the terminal. These audio signals are collected through a microphone, converted to a digital format, and sent to the server. The input is an analog audio signal, and the output is digital audio data. This process uses an ADC (analog-to-digital converter) to convert the analog signal to a digital signal.

[0792] Step 2:

[0793] The server sends the received digital audio data to a speech recognition API, which converts it into text data. This API identifies words and phrases in the audio and generates corresponding text. The input is digital audio data, and the output is text data. At this stage, acoustic modeling is performed, which analyzes the acoustic features of the audio and maps them to linguistic information.

[0794] Step 3:

[0795] The server uses a generative AI model to analyze the user's emotional state from the features of text and audio data. The emotion analysis engine identifies emotions by considering factors such as tone, speed, and pitch of the voice. The input is text data and audio features, and the output is the analyzed emotional state. The generative AI model uses deep learning techniques to learn complex emotional patterns.

[0796] Step 4:

[0797] The server determines what recommended actions to provide based on the analyzed emotional state. It selects the most appropriate action from a predefined set of recommendations based on the emotional state and user profile. The input is the analyzed emotional state, and the output is the content of the recommended action. Rule-based systems and machine learning models are used as recommendation algorithms in this process.

[0798] Step 5:

[0799] The server sends the determined recommended action to a speech synthesis engine, which generates a feedback message for the elderly. The speech synthesis engine converts text-based instructions into natural, easy-to-understand speech. The input is the text of the recommended action, and the output is synthesized speech. Here, text-to-speech (TTS) is performed using speech synthesis technology.

[0800] Step 6:

[0801] The server sends notifications to the caregiver's terminal as needed. These notifications include information related to the analyzed emotional state and recommended actions. The inputs are the recommended actions and emotional analysis results, and the output is the notification message sent to the caregiver's terminal. This process uses a communication protocol to transmit data.

[0802] 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.

[0803] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0804] 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.

[0805] 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.

[0806] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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."

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] A means for receiving an audio signal and converting it into text data using speech recognition,

[0826] A means for analyzing user requests from the text data and determining appropriate services based on those requests,

[0827] A means of providing information regarding the determined service using synthesized speech,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, characterized in that the service searches for registered supporters within the region and notifies the supporters.

[0831] (Claim 3)

[0832] The system according to claim 1, further comprising means for collecting user feedback and recording it as material for improvement in the next service.

[0833] "Example 1"

[0834] (Claim 1)

[0835] A means for acquiring audio data and converting it into information data using an advanced speech recognition algorithm,

[0836] A means for analyzing user requests from the relevant information data and selecting the appropriate service based on the results,

[0837] A means of communicating data related to the selected service to the user using speech synthesis technology,

[0838] A means of processing information to communicate work details to staff,

[0839] A means of receiving a completion notification from the person in charge after the work is finished and reporting that notification to the user,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, characterized by identifying registered experts within a region and transmitting service request information to those experts.

[0843] (Claim 3)

[0844] The system according to claim 1, further comprising means for collecting user evaluations and storing those evaluations in a database for the purpose of improving future service provision.

[0845] "Application Example 1"

[0846] (Claim 1)

[0847] A means for receiving an audio signal and converting it into text data using speech recognition,

[0848] A means for analyzing user requests from the text data and determining appropriate services based on those requests,

[0849] A means of providing information regarding the determined service using synthesized speech,

[0850] A means of providing services that support daily life using a voice interface optimized for the elderly,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, characterized in that the service searches for registered supporters within the region and notifies the supporters.

[0854] (Claim 3)

[0855] The system according to claim 1, further comprising means for collecting user feedback and recording it as material for improvement in the next service.

[0856] "Example 2 of combining an emotion engine"

[0857] (Claim 1)

[0858] A means for receiving an audio signal and converting the audio data into text data using speech recognition,

[0859] A means for analyzing the user's emotions from the aforementioned text data,

[0860] A means for determining an appropriate service based on the results of the aforementioned sentiment analysis and the user's requests,

[0861] A means for providing information regarding the aforementioned determined service using synthesized speech,

[0862] A means of recording user emotional data and continuously improving the quality of the service,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, characterized in that the service searches for registered supporters within the region, notifies the supporters, and provides the results of the emotion analysis.

[0866] (Claim 3)

[0867] The system according to claim 1, further comprising means for collecting emotional data and feedback from users and recording them so that past emotional states can be taken into consideration in the next service provision.

[0868] "Application example 2 when combining with an emotional engine"

[0869] (Claim 1)

[0870] A means for receiving an audio signal and converting it into language data using speech recognition,

[0871] A means for analyzing the user's emotional state from the language data and voice characteristics, and determining personalized recommended actions based on that state,

[0872] A means of providing information regarding the determined recommended action using synthesized speech,

[0873] Designed for the elderly, it includes a means of notifying caregivers,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, characterized in that the recommended action searches for registered stakeholders within the region and notifies such stakeholders.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising means for collecting user feedback and recording it as material for improvement in the next recommended action. [Explanation of Symbols]

[0879] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving an audio signal and converting it into text data using speech recognition, A means for analyzing user requests from the text data and determining appropriate services based on those requests, A means of providing information regarding the determined service using synthesized speech, A means of providing services that support daily life using a voice interface optimized for the elderly, A system that includes this.

2. The system according to claim 1, characterized in that the service searches for registered supporters within the region and notifies the supporters.

3. The system according to claim 1, further comprising means for collecting user feedback and recording it as material for improvement in the next service.

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