A SYSTEM AND A METHOD FOR DIVERSE COMMUNICATION

Abstract

ABSTRACT A SYSTEM AND A METHOD FOR DIVERSE COMMUNICATION The present disclosure discloses a system and a method for diverse communication. The system(100) comprises a mobile application(102) installed on a computing device(104) and communicates with a healthcare server(106) over a communication medium(108); a patient user interface(102a) to receive patient input data; an interpretation module(102b) to implement an AI-driven model to recognize sign language gestures, implement a speech recognition model to interpret spoken language; or implement a NLP model to analyze text input; a translation module(102c) to convert recognized sign language gestures, the spoken language, or the text input into a format comprehensible, and facilitate multilingual translation of the patient input data; a communication delivery module(102d) to present the translated patient input data to dental professional; a dentist response interface(102e) to capture the response from the dentist in a selected format; a patient response module(102f) to translate into patient’s preferred communication mode, deliver the translated response back to the patient.

Specification

Description:FIELD
The present disclosure generally relates to the field of counseling systems. More particularly, the present disclosure relates to a system and a method for diverse communication.
BACKGROUND
The background information herein below relates to the present disclosure but is not necessarily prior art.
Existing healthcare communication systems, including telemedicine platforms, text-based portals, and basic speech recognition tools, largely support standard voice, video, and text interactions but lack robust features for patients with diverse communication needs, such as Deaf, mute, and multilingual individuals. Video conferencing tools like Zoom and Microsoft Teams provide limited accessibility, as they do not support integrated sign language recognition or real-time multilingual translation, requiring patients to rely on delayed or inaccurate manual interpretations. Text-based systems, although useful for simple communication, lack AI-driven interpretation and text-to-speech conversions, making them inadequate for patients who require multimodal input options or seamless language transitions.
Similarly, standalone translation applications and speech recognition tools suffer from limitations in medical terminology accuracy, lack of contextual understanding, and poor adaptability for visual languages like sign language. Experimental tools for sign language recognition remain limited in vocabulary and error-prone, especially in real-time settings, which can hinder effective communication. These disconnected tools, with insufficient support for accessibility and multilingual functionality.
There is, therefore felt a need for a system and a method for diverse communication that alleviates the aforementioned drawbacks.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
An object of the present disclosure is to provide a system and a method for diverse communication.
Another object of the present disclosure is to provide a system that enables seamless interaction between patients with diverse communication.
Still, another object of the present disclosure is to provide a system that supports multimodal communication.
Yet another object of the present disclosure is to provide a system that enables real-time communication and feedback between patients and dentists.
Still another object of the present disclosure is to provide a system with continuous improvement through Feedback Integration.
Yet another object of the present disclosure is to provide a system with data security and compliance.
Still another object of the present disclosure is to provide a system with a robust, inclusive, and secure platform, ensuring that patients with varied communication needs receive efficient, accurate, and personalized healthcare support.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages a system for diverse communication. The system comprises a mobile application, a computing device, a healthcare server 106, and a communication medium.
The mobile application is installed on a computing device of a patient and a dentist, wherein the mobile application communicates with a healthcare server over a communication medium.
The mobile application includes a patient user interface, an interpretation module, a translation module, a communication delivery module, a dentist response interface, and a patient response module.
The patient user interface is configured to receive patient input data in multiple communication modes, including sign language gestures, spoken language, and text input.
The interpretation module is operatively connected to the patient-user interface and configured to receive the patient input data.
The interpretation module is further configured to:
• implement an artificial intelligence (AI)-driven model on the patient input data to recognize sign language gestures;
• implement a speech recognition model on the patient input data to interpret spoken language; or
• implement a natural language processing (NLP) model on the input data to analyze text input from the patient input data.
The translation module coupled with the interpretation module and configured to convert the recognized sign language gestures, the spoken language, or the text input into a format comprehensible to a dental professional and facilitate multilingual translation of the patient input data as required.
The communication delivery module is configured to present the translated patient input data to the dental professional through an output interface.
The dentist response interface is configured to capture the response from the dental professional in a selected format, including spoken language, typed text, or sign language gestures.
The patient response module is configured to translate the dental professional's response into the patient's preferred communication mode and deliver the translated response back to the patient through an output mode appropriate to their needs, including sign language, text display, or synthesized speech.
In an embodiment, the artificial intelligence (AI)-driven model is configured to process:
• capturing the patient input data in a selected mode, including sign language video, speech audio, or text;
• preprocessing the patient input data by extracting frames from video, cleaning text, or formatting audio to prepare for interpretation;
• extracting key features based on the input mode:
o for sign language: hand shapes, movements, and facial expressions,
o for speech: phonemes, pitch, and amplitude, and
o for text: tokenizing or vectorizing text,
• classifying the input type, and selecting the appropriate processing pipeline,
• applying a deep learning model to convert gestures into text for sign language input,
• using a speech-to-text model to convert spoken input,
• processing text input with natural language processing (NLP) techniques to interpret meaning and intent,
• translating the interpreted input into the preferred language and mode selected by the dentist,
• formatting the output in the appropriate mode (e.g., text or audio) for seamless communication,
• logging and analyzing feedback for model refinement, or
• updating the model periodically with new data to enhance accuracy and adapt to varied communication patterns.
In an embodiment, the patient user interface further comprises a camera and a gesture recognition module specifically configured to detect and interpret sign language gestures from video input.
In an embodiment, the translation module includes a natural language processing engine capable of detecting and translating between various dialects and regional variations in sign language.
In an embodiment, the communication delivery module further comprises a visual display screen for presenting translated patient inputs to the dental professional in textual form.
In an embodiment, the patient response module is further configured to:
• store a patient's preferred communication mode settings, and
• automatically apply these settings to subsequent interactions within the same session or in future appointments.
In an embodiment, the patient user interface is further configured to:
• detect patient distress signals or interruption gestures, and
• notify the dental professional in real-time to improve patient comfort and engagement during treatment.
In an embodiment, the translation module further comprises a database of common dental terminology with regional language variations to ensure accuracy in translating medical terms.
The present disclosure also envisages a method for diverse communication. The method comprises the following steps:
• providing a mobile application on a computing device of both a patient and a dental professional, wherein the mobile application communicates with a healthcare server over a communication medium, the method further comprising:
 receiving patient input data via a patient user interface in multiple communication modes, including sign language gestures, spoken language, and text input;
 interpreting patient input data by:
- implementing an artificial intelligence (AI)-driven model on the patient input data for recognizing sign language gestures,
- implementing a speech recognition model on the patient input data for interpreting spoken language, or
- implementing a natural language processing (NLP) model on the patient input data for analyzing text input;
 translating the interpreted patient input data through a translation module, including:
- converting recognized sign language gestures, spoken language, or text input into a format comprehensible to a dental professional, and
- facilitating multilingual translation of the patient input data as needed;
 presenting the translated patient input data to the dental professional via a communication delivery module through an output interface;
 capturing the dental professional's response via a dentist response interface, which supports formats such as spoken language, typed text, or sign language gestures;
 translating the dental professional's response into the patient's preferred communication mode via a patient response module; and
 delivering the translated response back to the patient through an output mode tailored to their needs, including options such as sign language, text display, or synthesized speech.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
A system and a method for diverse communication of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a block diagram of a system for diverse communication in accordance with an embodiment of the present disclosure;
Figures 2A-2B illustrate a flowchart for a method for diverse communication in accordance with an embodiment of the present disclosure; and
Figure 3 illustrates an execution flow of the system for diverse communication between patients and dentists in accordance with an embodiment of the present disclosure.
LIST OF REFERENCE NUMERALS
100 - System
102- Mobile Application
102a - Patient User Interface
102a-1 - Gesture Recognition Module
102b - Interpretation Module
102c - Translation and Transmission Module
102c-1 - Natural Language Processing Engine
102d - Communication Delivery Module
102e - Dentist Response Module
102f - Patient Response Module
102g - Data Logging Module
104 - Computing Device
106 - Healthcare Server
106a - Data Analytics Module
108 - Communication Medium
DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details, are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a," "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "including," and "having," are open ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.
When an element is referred to as being "engaged to," "connected to," or "coupled to" another element, it may be directly engaged, connected, or coupled to the other element. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed elements.
Current healthcare communication systems, such as telemedicine platforms, text-based portals, and basic speech recognition tools, fall short for patients with diverse communication needs, such as those who are Deaf, mute, or multilingual. These platforms lack integrated support for real-time sign language recognition and multilingual translation, leading to accessibility gaps and reliance on manual interpretations that can be delayed or inaccurate.
Additionally, standalone translation apps and speech recognition tools often struggle with medical terminology and lack adaptability for sign language, resulting in error-prone interactions, especially in real-time.
To address the issues of the existing systems and methods, the present disclosure envisages a system (hereinafter referred to as "system 100") for diverse communication and a method (hereinafter referred to as "method 200") for diverse communication. The system 100 will now be described with reference to Figure 1 and the method 200 will be described with reference to Figures 2A-2B.
Figure 1 shows the system 100 that is a comprehensive patient-dentist communication platform designed to bridge communication gaps by utilizing advanced artificial intelligence, translation modules, and user interface adaptations. The system 100 comprises a mobile application 102, a computing device 104, a healthcare server 106, and a communication medium 108. Each component is intricately designed to enhance interaction between patients and dental professionals, facilitating seamless, real-time translation and interpretation of various communication modes, such as sign language, spoken language, and text.
The mobile application 102 is installed on a computing device 104, such as a smartphone, tablet, or dedicated healthcare device, which may belong to either a patient or a dentist. The computing device 104 is configured with processing units, memory storage, graphical display elements, and connectivity features to support the application's communication functionalities. The mobile application 102 communicates with a healthcare server 106 via a communication medium 108, typically a secure, encrypted network connection compliant with healthcare data protection standards. This server 106 stores and processes patient-dentist interaction data, allowing the application to leverage past data for improved translation accuracy, model refinement, and personalization. In various embodiments, the communication medium 108 could be a Wi-Fi network, cellular data, or a virtual private network (VPN), ensuring secure, confidential data transmission in compliance with healthcare regulations.
In an embodiment, the patient is a user, Client, Individual, Recipient, or Healthcare Consumer.
In an embodiment, the Dentist is a specialist, provider, Practitioner, Clinician, doctor, or Healthcare Professional.
The mobile application includes a patient user interface 102a, an interpretation module 102b, a translation module 102c, a communication delivery module 102d, a dentist response interface 102e, and a patient response module 102f.
The patient user interface 102a within the mobile application 102 provides the primary means for patient interaction, accepting input in multiple communication modes. The patient user interface 102a is designed to support accessibility, enabling patients to communicate using sign language gestures, spoken language, and text input.
In an embodiment, the patient user interface 102a includes a camera and a gesture recognition module 102a-1 specifically configured to detect and interpret sign language gestures from video input, using advanced computer vision techniques to interpret hand shapes, facial expressions, and body movements. The gesture recognition module 102a-1 employs an AI-driven model specifically trained to understand dental-related sign language gestures, enhancing the system's relevance in a dental setting. This feature allows patients who rely on sign language to express concerns, symptoms, or preferences without communication barriers.
Additionally, the patient user interface 102a incorporates a distress signal detection system. In this embodiment, the patient user interface 102a is further configured to:
• detect patient distress signals or interruption gestures, and
• notify the dental professional in real-time to improve patient comfort and engagement during treatment.
In this embodiment, the patient user interface 102a is configured to recognize interruption gestures or distress signals, automatically notifying the dental professional in real-time. This feature improves patient comfort and engagement, especially in cases where the patient may be non-verbal or experiencing anxiety. These gestures could include specific hand signals, facial cues, or changes in body posture that the system is programmed to identify.
The interpretation module 102b is operatively connected to patient user interface 102a and is responsible for processing and interpreting patient input data. Depending on the communication mode, the interpretation module 102b employs either an AI-driven model for sign language recognition, a speech recognition model for spoken language, or a natural language processing (NLP) model for text input. The interpretation module 102b is further configured to:
• implement an artificial intelligence (AI)-driven model on the patient input data to recognize sign language gestures;
• implement a speech recognition model on the patient input data to interpret spoken language; or
• implement a natural language processing (NLP) model on the input data to analyze text input from the patient input data.
In an embodiment, the interpretation module 102b utilizes a dataset specifically tailored to dental terminology, enhancing the accuracy of sign language and spoken word interpretations in dental contexts.
In an embodiment, the interpretation module 102b includes a learning module configured to analyze and improve gesture recognition and natural language processing based on feedback from prior user interactions.
In an embodiment, the artificial intelligence (AI)-driven model is configured to process:
• capturing the patient input data in a selected mode, including sign language video, speech audio, or text;
• preprocessing the patient input data by extracting frames from video, cleaning text, or formatting audio to prepare for interpretation;
• extracting key features based on the input mode:
o for sign language: hand shapes, movements, and facial expressions,
o for speech: phonemes, pitch, and amplitude, and
o for text: tokenizing or vectorizing text,
• classifying the input type, and selecting the appropriate processing pipeline,
• applying a deep learning model to convert gestures into text for sign language input,
• using a speech-to-text model to convert spoken input,
• processing text input with natural language processing (NLP) techniques to interpret meaning and intent,
• translating the interpreted input into the preferred language and mode selected by the dentist,
• formatting the output in the appropriate mode (e.g., text or audio) for seamless communication,
• logging and analyzing feedback for model refinement, or
• updating the model periodically with new data to enhance accuracy and adapt to varied communication patterns.
The AI-driven model within the interpretation module 102b follows a structured workflow, which begins by capturing the patient input data in the selected communication mode (e.g., video, audio, or text). Once captured, the data undergoes preprocessing to extract relevant features; for example, frames are extracted from video input to identify hand gestures, and text input is tokenized to facilitate NLP analysis. Key features such as hand shapes, movement sequences, and facial expressions are extracted for sign language, while phonetic and tonal attributes are used for speech recognition. The interpretation module 102b then applies deep learning algorithms to convert sign language gestures to text, speech-to-text models for spoken input, and NLP models to interpret written text. This interpreted data is prepared for translation, converting patient input into a format suitable for dental professionals.
Once the interpretation is complete, the translation module 102c converts the recognized input into a format that is accessible to the dental professional. The translation module 102c is coupled to the interpretation module 102b and configured to convert the recognized sign language gestures, the spoken language, or the text input into a format comprehensible to a dental professional and facilitate multilingual translation of the patient input data as required.
In an embodiment, the translation module 102c includes a natural language processing engine 102c-1 capable of detecting and translating between various dialects and regional variations in sign language, ensuring that communication is accurate regardless of the patient's linguistic background.
In an embodiment, the translation module 102c is configured with a specialized database of dental terminology and region-specific language adaptations, allowing for multilingual translation where necessary. For instance, the translation module can detect language preferences or dialectical nuances in sign language, enhancing the clarity and relevance of communication.
In an embodiment, the translation module 102c is configured to support multiple language options, allowing the dentist to select a preferred language for input and output, thereby enabling communication across a diverse linguistic population. This configuration facilitates communication with a diverse patient population, accommodating differences in regional language use and terminology.
In an embodiment, the translation module 102c also contains a specialized database of dental terminology with region-specific language adaptations, ensuring that the medical terms and instructions provided by the dentist are accurately conveyed to patients. Furthermore, the system undergoes continuous model refinement, with its model being refined based on logged interactions and user feedback, adapting to evolving communication patterns and enhancing translation accuracy.
The communication delivery module 102d is configured to present the translated patient input data to the dental professional through an output interface.
In an embodiment, the communication delivery module 102d further comprises a visual display screen for text-based output and can present interpreted input in textual or auditory formats.
In an embodiment, the communication delivery module 102d supports telehealth functionalities, allowing translated inputs to be displayed remotely for virtual consultations, making this system suitable for both in-person and remote interactions.
In an embodiment, the dentist response module 102d incorporates a preview feature, enabling the dentist to review and confirm the translated message before sending it to the patient. This preview feature allows the dentist to ensure that the interpreted response is accurate, clear, and aligned with the intended message, improving the quality of patient communication.
In an embodiment, the communication medium 108 is a secure, encrypted network connection, ensuring data privacy and compliance with healthcare data protection regulations.
The dentist response interface 102e is configured to capture the responses from the dental professional in various input formats, including spoken language, typed text, or even sign language gestures if the dentist is trained in sign language. The interface includes a microphone for voice input and a keyboard for text input, providing flexible response options to accommodate different interaction preferences.
In an embodiment, the dentist response interface 102e includes a microphone configured to capture spoken language responses and a keyboard for typed responses, enabling flexible input options for the dental professional.
Once the dentist's response is captured, the patient response module 102f is configured to translate the dental professional's responses into the patient's preferred communication mode, such as sign language, text display, or synthesized speech. The patient response module 102f delivers the translated response back to the patient through an output mode appropriate to their needs, including sign language, text display, or synthesized speech.
In an embodiment, the patient response module 102f is further configured to store a patient's preferred communication mode settings, and automatically apply these settings to subsequent interactions within the same session or in future appointments.
In an embodiment, the system comprises a data logging module 102g that securely stores interaction data for future analysis. This data logging capability allows for the ongoing refinement of AI-driven models within the interpretation module 102b and the translation module 102c, improving accuracy and responsiveness. By analyzing past interaction data, the system can adapt its interpretation algorithms, enhance gesture recognition accuracy, and customize responses to individual patient needs. This continuous learning capability ensures that each patient interaction builds on prior data, providing an increasingly tailored experience over time.
Additionally, the system includes advanced features to enhance patient comfort and understanding. For example, it can detect distress signals or interruption gestures, notifying the dentist in real-time to adjust interactions for improved patient comfort. The system's design also supports telehealth, as the mobile application can integrate with remote consultation platforms, making the communication features accessible for virtual dental appointments.
In one embodiment, the system 100 functions as a mobile application 102 installed on a patient's smartphone 104. The patient uses the application interface 102a to input data through sign language gestures, speech, or text. The application's interpretation module 102b then employs AI-driven models to recognize and process the input mode accordingly detecting hand shapes and facial expressions for sign language, analyzing phonemes and tone for speech, and using NLP to parse textual content. This input data is then translated by the translation module 102c into a format that is accessible to the dentist, who views it on a visual display 102d. The dentist can respond via spoken language, typed text, or sign language gestures, which the system translates back into the patient's preferred mode of communication. This embodiment ensures that patients with different communication needs can interact with their dentist seamlessly during in-person consultations.
In another embodiment, the system 100 facilitates telehealth consultations between patients and dental professionals. The mobile application 102 enables patients to communicate through video calls where they can use sign language gestures or speech to communicate. The application's gesture recognition module 102a-1 captures and processes these gestures in real-time, and the translation module 102c converts them into spoken language or text displayed to the dentist. The system can also detect patient distress signals or interruption gestures, immediately alerting the dentist to adapt their communication style or pace. The dentist's responses are captured through the dentist response interface 102e, translated, and delivered back to the patient's interface in their preferred communication mode. This embodiment broadens access to dental care for patients in remote locations or those requiring accessible telehealth solutions.
In a further embodiment, the system 100 integrates with a healthcare network server 106, allowing data from patient-dentist interactions to be logged and analyzed. The system's AI models regularly undergo training updates based on this data, refining their accuracy in interpreting patient inputs. Additionally, the translation module 102c utilizes a database of regional dental terminology, ensuring that the specific language needs and preferences of different patients are met. The patient response module 102f stores individual patient preferences for communication modes, automatically adjusting settings for future interactions. This embodiment emphasizes the system's adaptability and learning capabilities, ensuring that each consultation is increasingly tailored to the individual patient's needs and providing continuous improvements in AI-driven communication within healthcare settings.
In terms of hardware, the system 100 utilizes several key elements to support its functions. The computing device 104 could be a smartphone, tablet, or other mobile device equipped with a camera for video input, a microphone for audio input, and a high-resolution display screen. This device includes a central processing unit (CPU) capable of handling AI-driven computations, as well as memory storage for the application's modules and patient interaction data. The healthcare server 106 is another essential component, typically a cloud-based server equipped with powerful processing units and extensive storage to support large datasets, model training, and secure data logging. The communication medium 108 could comprise secure Wi-Fi, cellular networks, or VPN connections, ensuring encrypted, compliant communication in line with healthcare data privacy standards.
Figures 2A-2B illustrate a flowchart for a method for diverse communication in accordance with an embodiment of the present disclosure. The order in which method 200 is described is not intended to be construed as a limitation, and any number of the described method steps may be combined in any order to implement method 200, or an alternative method. Furthermore, method 200 may be implemented by processing resource or computing device(s) through any suitable hardware, non-transitory machine-readable medium/instructions, or a combination thereof. The method 200 comprises the following steps:
At step 202, the method 200 includes providing a mobile application 102 on a computing device 104 of both a patient and a dental professional, wherein the mobile application 102 communicates with a healthcare server 106 over a communication medium 108.
At step 204, the method 200 includes receiving patient input data via a patient user interface 102a in multiple communication modes, including sign language gestures, spoken language, and text input.
At step 206, the method 200 includes interpreting patient input data by implementing an artificial intelligence (AI)-driven model on the patient input data for recognizing sign language gestures, implementing a speech recognition model on the patient input data for interpreting spoken language, or implementing a natural language processing (NLP) model on the patient input data for analyzing text input.
At step 208, the method 200 includes translating the interpreted patient input data through a translation module 102c, including converting recognized sign language gestures, spoken language, or text input into a format comprehensible to a dental professional, and facilitating multilingual translation of the patient input data as needed.
At step 210, the method 200 includes presenting the translated patient input data to the dental professional via a communication delivery module 102d through an output interface.
At step 212, the method 200 includes capturing the dental professional's response via a dentist response interface, which supports formats such as spoken language, typed text, or sign language gestures.
At step 214, the method 200 includes translating the dental professional's response into the patient's preferred communication mode via a patient response module 102e.
At step 214, the method 200 includes delivering the translated response back to the patient through an output mode tailored to their needs, including options such as sign language, text display, or synthesized speech.
Figure 3 illustrates an execution flow of the system for diverse communication between patients and dentists in accordance with an embodiment of the present disclosure. The flowchart provides a clear overview of the system's communication process between a patient and a dental professional, highlighting each stage from input to feedback delivery.
• Patient Input Mode Selection: The process begins with the patient choosing their preferred mode of communication-sign language, text, or speech.
• AI Processing and Interpretation: The system uses AI models to interpret the patient's selected input mode. It applies specific recognition techniques for each mode: gesture recognition for sign language, speech-to-text for spoken language, and natural language processing (NLP) for text. This ensures an accurate understanding of the patient's message.
• Translation/Conversion: Once interpreted, the input data is translated into a format that is comprehensible to the dentist. This can involve converting sign language gestures to text or spoken language, as well as multilingual translation if needed.
• Communication Delivery to Dentist: The translated message is then delivered to the dentist, allowing them to understand the patient's concerns or responses.
• Dentist's Response: The dentist provides feedback, either verbally or by typing it into the system, depending on their preferred input method.
• AI Interpretation of Dentist's Response: The system then interprets the dentist's response, converting it into the patient's preferred communication mode. This ensures that the response is accessible to the patient, regardless of their communication needs.
• Feedback Delivery to Patient: The translated response is delivered back to the patient in their preferred format. For example, it could be delivered as synthesized speech for multilingual patients, text for deaf patients, or sign language for deaf or mute patients.
• Patient Acknowledgment: The process concludes with the patient acknowledging the dentist's response in their preferred communication mode, closing the communication loop.
In an operative configuration, the system provides a diverse, multimodal communication platform specifically tailored for interactions between patients and dental professionals. Primarily designed as a mobile application 102 installed on a computing device 104, the system enables seamless communication via a healthcare server 106 through a communication medium 108. The mobile application integrates an advanced patient user interface 102a that accommodates various input modes, including sign language gestures, spoken language, and text input. This functionality offers flexibility in patient communication, making dental consultations more inclusive for individuals with varied communication needs.
The application features an interpretation module 102b connected to the patient user interface 102a, which uses AI-driven models to interpret different input types. For sign language gestures, the system employs an AI model specifically designed to recognize and interpret these gestures. Similarly, a speech recognition model is used to process spoken language, and natural language processing (NLP) techniques analyze text inputs. The interpretation module, therefore, functions as the core processing hub, ensuring that patient inputs in various forms are effectively understood.
Once interpreted, the input data is routed to a translation module 102c, which translates the recognized input into a format understandable by dental professionals. This module facilitates multilingual translation as required, ensuring that language differences do not impede effective communication. The module also considers regional dialects and variations in sign language, enabling accurate and culturally relevant translations. The translated patient data is then presented to the dentist through the communication delivery module 102d, which outputs the information in a readable format on a visual display screen.
The system further includes a dentist response interface 102e where the dental professional can provide feedback or ask questions using spoken language, typed text, or sign language gestures. The patient response module 102f captures this feedback and translates it into the patient's preferred mode, which could be displayed text, sign language, or synthesized speech, allowing patients to receive responses in a manner they can easily comprehend. Importantly, the patient response module 102f also stores preferred communication settings, facilitating consistency across appointments.
Advantageously, the system 100 for diverse communication between patients and dentists. The system represents a significant technical advancement in patient-dentist communication, leveraging artificial intelligence to bridge diverse communication needs. Through a sophisticated integration of AI-driven models-including gesture recognition for sign language, speech-to-text for verbal input, and natural language processing (NLP) for textual data-the system can interpret a wide range of inputs with high accuracy. The translation module's capability to handle regional dialects and multilingual requirements enhances its adaptability, ensuring seamless communication regardless of linguistic or cultural background. Additionally, real-time processing and feedback analysis enable the system to detect patient distress signals and optimize interactions for comfort. By incorporating specialized databases for dental terminology with regional variations, the system maintains clinical accuracy and relevance. Furthermore, the system's support for telehealth through mobile integration brings accessible, high-quality care to patients remotely, making this technology a powerful tool in modernizing patient care and advancing healthcare inclusivity.
Further, the system's AI-driven interpretation model performs several key functions to facilitate robust data processing. The model captures patient input data through different modalities, such as video for sign language, audio for speech, or text input, and applies preprocessing techniques like video frame extraction, audio formatting, and text cleaning. Key features are extracted based on input mode-hand shapes and facial expressions for sign language, phonemes and pitch for speech, and tokenized words for text. After classification, the appropriate pipeline processes the data: deep learning models interpret gestures as text, speech-to-text models handle audio input, and NLP analyzes text for intent.
The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope and spirit of the disclosure and appended claims. For example, due to the nature of software, functions described above can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.
The foregoing description of the embodiments has been provided for purposes of illustration and is not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment, but are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.
TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a system and a method for diverse communication that:
• enabling real-time communication;
• focused on enhancing accessibility;
• improving patient-provider interactions;
• streamlining the dental care process;
• provides flexibility with multiple input options;
• accurate and rapid conversion of patient inputs, reducing delays;
• reducing errors in interpretation and ensuring that critical health information is conveyed accurately; and
• secure server architecture and encrypted communication.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveals the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
The use of the expression "at least" or "at least one" suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation. , Claims:WE CLAIM:
1. A system (100) for diverse communication, said system (100) comprising:
• a mobile application (102) installed on a computing device (104) of a patient and a dentist, wherein said mobile application (102) communicates with a healthcare server (106) over a communication medium (108), said mobile application (102) comprises:
• a patient user interface (102a) configured to receive patient input data in multiple communication modes, including sign language gestures, spoken language, and text input;
• an interpretation module (102b) operatively connected to patient user interface (102a) and configured to receive said patient input data and further configured to:
- implement an artificial intelligence (AI)-driven model on said patient input data to recognize sign language gestures;
- implement a speech recognition model on said patient input data to interpret spoken language; or
- implement a natural language processing (NLP) model on said input data to analyze text input from said patient input data;
• a translation module (102c) coupled to said interpretation module (102b) and configured to convert the recognized sign language gestures, the spoken language, or the text input into a format comprehensible to a dental professional, and facilitate multilingual translation of the patient input data as required;
• a communication delivery module (102d) configured to present the translated patient input data to the dental professional through an output interface;
• a dentist response interface (102e) configured to capture the response from the dental professional in a selected format, including spoken language, typed text, or sign language gestures; and
• a patient response module (102f) configured to:
- translate the dental professional's response into patient's preferred communication mode, and
- deliver the translated response back to the patient through an output mode appropriate to their needs, including sign language, text display, or synthesized speech.
2. The system as claimed in claim 1, wherein said artificial intelligence (AI)-driven model is configured to process:
• capturing the patient input data in a selected mode, including sign language video, speech audio, or text;
• preprocessing the patient input data by extracting frames from video, cleaning text, or formatting audio to prepare for interpretation;
• extracting key features based on the input mode:
 for sign language: hand shapes, movements, and facial expressions,
 for speech: phonemes, pitch, and amplitude, and
 for text: tokenizing or vectorizing text,
• classifying the input type, and selecting the appropriate processing pipeline,
• applying a deep learning model to convert gestures into text for sign language input,
• using a speech-to-text model to convert spoken input,
• processing text input with natural language processing (NLP) techniques to interpret meaning and intent,
• translating the interpreted input into the preferred language and mode selected by the dentist,
• formatting the output in the appropriate mode (e.g., text or audio) for seamless communication,
• logging and analyzing feedback for model refinement, or
• updating the model periodically with new data to enhance accuracy and adapt to varied communication patterns.
3. The system (100) as claimed in claim 1, wherein said patient user interface (102a) further comprises a camera and a gesture recognition module (102a-1) specifically configured to detect and interpret sign language gestures from video input.
4. The system (100) as claimed in claim 1, wherein said translation module (102c) includes a natural language processing engine (102c-1) capable of detecting and translating between various dialects and regional variations in sign language.
5. The system (100) as claimed in claim 1, wherein said communication delivery module (102d) further comprises a visual display screen for presenting translated patient inputs to the dental professional in textual form.
6. The system (100) as claimed in claim 1, wherein said patient response module (102f) is further configured to:
• store a patient's preferred communication mode settings, and
• automatically apply these settings to subsequent interactions within the same session or in future appointments.
7. The system (100) as claimed in claim 1, wherein said patient user interface (102a) is further configured to:
• detect patient distress signals or interruption gestures, and
• notify the dental professional in real-time to improve patient comfort and engagement during treatment.
8. The system (100) as claimed in claim 1, wherein said translation module (102c) further comprises:
• a database of common dental terminology with regional language variations to ensure accuracy in translating medical terms.
9. The system (100) as claimed in claim 1, wherein said communication delivery module (102d) is integrated with a mobile application, allowing remote or telehealth consultations to be conducted with accessible communication features.
10. A method (200) for facilitating diverse communication, said method (200) comprises the following steps:
• providing a mobile application (102) on a computing device (104) of both a patient and a dental professional, wherein said mobile application (102) communicates with a healthcare server (106) over a communication medium 108, said method further comprising:
 receiving patient input data via a patient user interface (102a) in multiple communication modes, including sign language gestures, spoken language, and text input;
 interpreting patient input data by:
- implementing an artificial intelligence (AI)-driven model on the patient input data for recognizing sign language gestures,
- implementing a speech recognition model on the patient input data for interpreting spoken language, or
- implementing a natural language processing (NLP) model on the patient input data for analyzing text input;
 translating the interpreted patient input data through a translation module (102c), including:
- converting recognized sign language gestures, spoken language, or text input into a format comprehensible to a dental professional, and
- facilitating multilingual translation of the patient input data as needed;
 presenting the translated patient input data to the dental professional via a communication delivery module through an output interface;
 capturing the dental professional's response via a dentist response interface (102d), which supports formats such as spoken language, typed text, or sign language gestures;
 translating the dental professional's response into the patient's preferred communication mode via a patient response module (102e); and
 delivering the translated response back to the patient through an output mode tailored to their needs, including options such as sign language, text display, or synthesized speech.

Dated this 18th day of November, 2024

_______________________________
MOHAN RAJKUMAR DEWAN, IN/PA - 25
OF R. K. DEWAN & CO.
AUTHORIZED AGENT TO THE APPLICANT

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