Description:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
Complete Specification
(See section10 and rule13)

1. Title of the Invention: DeepSemComm: a deep learning-based semantic communication framework with case studies in text and biomedical signal transmission

2.Applicants: -
SR University Warangal, Telangana-506371, India.
INVENTORS
Name Nationality Address
Mr. Bura Vijay Kumar
Indian Research Scholar, School of CS & AI, SR University, Warangal, Telangana, India.

Ms. Bhavana Jamalpur
Indian Research Supervisor, School of CS &AI, SR University, Warangal, Telangana, India.

3. Preamble to the description:
The following specification particularly describes the invention and the manner in which it is to be performed.

4. DESCRIPTION
FIELD OF THE INVENTION
The present invention relates to the field of deep learning and semantic communication, particularly in the context of text and biomedical signal transmission. It focuses on using advanced machine learning techniques to optimize the communication process, improving efficiency and accuracy in data transmission within the telecommunications, healthcare, and artificial intelligence industries.
BACKGROUND OF THE INVENTION
In the rapidly evolving domain of communication systems, traditional methods rely heavily on redundancy and error-correction techniques to ensure reliable data transmission, especially for complex and unstructured data types like text and biomedical signals. While these systems achieve a reasonable level of reliability, they often do so at the cost of efficiency and the ability to convey meaningful information in a semantically aware manner. Conventional communication frameworks primarily focus on bit-level transmission, overlooking the significance of context and semantic understanding in optimizing bandwidth usage and enhancing communication clarity. Consequently, the current communication methods may not fully leverage the potential of deep learning and semantic analysis to create more intelligent, context-aware transmission systems.

Existing solutions in semantic communication mainly focus on improving error correction, transmission efficiency, and compression techniques, such as those seen in convolutional neural networks (CNN) and recurrent neural networks (RNN) for signal processing. Some solutions attempt to incorporate semantic understanding, but they are typically limited in scope, either focusing on specific data types like images or audio or not offering a robust framework to handle diverse modalities such as text and biomedical signals simultaneously. Notably, recent advancements have introduced semantic communication models that leverage natural language processing (NLP) techniques to enhance text communication, yet these systems often struggle to generalize across different domains, especially when applied to complex biomedical signals, which require specialized methods for encoding, transmission, and interpretation.
The existing solutions suffer from several limitations, particularly in their inability to efficiently combine deep learning with semantic communication in a unified framework that addresses both text and biomedical signal transmission. Current methods often either lack the adaptability to different data types or fail to fully exploit the potential of deep learning in the context of semantic transmission. The gap exists in the lack of an integrated solution capable of dynamically adapting to different types of content (such as text and biomedical data), considering both the semantic richness and context-specific nature of the transmitted information. This invention, DeepSemComm, fills this gap by introducing a novel deep learning-based semantic communication framework that works across these domains, providing intelligent, context-aware transmission with enhanced efficiency and reliability.
SUMMARY OF THE INVENTION
The present invention introduces DeepSemComm, a deep learning-based semantic communication framework designed to enhance the transmission of text and biomedical signals. By leveraging cutting-edge natural language processing (NLP) and signal processing techniques, this framework intelligently interprets and compresses information based on its semantic content, improving data transmission efficiency and reducing bandwidth requirements. The key features of the invention include its ability to adapt to various data types, particularly text and biomedical signals, by focusing on the contextual understanding of the information rather than simply transmitting raw data. This enables a more precise and effective communication system that not only improves signal quality but also ensures that the transmitted information is semantically relevant and accurate.
Unlike traditional systems, which are primarily focused on the bit-level transmission of data, DeepSemComm integrates advanced deep learning models to understand the meaning and context behind the data, thus enabling smarter, more efficient transmission methods. Additionally, the framework is designed to handle complex biomedical signals, an area that has been underexplored in traditional semantic communication systems. DeepSemComm's innovative approach ensures that both text and biomedical data can be transmitted with higher precision and lower computational costs, making it a significant advancement over existing communication frameworks.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig.1 depicts the Data Preprocessing in DeepSemComm Framework
Fig.2 depicts the Preserving Semantic Meaning in Communication
Fig.3 depicts the Deep Learning-Based Semantic Communication Framework

BRIEF DESCRIPTION OF THE INVENTION
The DeepSemComm framework is an advanced deep learning-based semantic communication system that integrates multiple layers of artificial intelligence (AI) to address the challenges of transmitting both text and biomedical signals in an efficient and semantically aware manner. The system architecture consists of several specialized modules working together, each designed to process, analyze, and transmit data in a way that enhances both its semantic meaning and transmission efficiency. The core components of the framework include data preprocessing, feature extraction, context modeling, semantic compression, and decoding, all of which are tailored to the specific needs of text and biomedical signal communication. The primary goal of DeepSemComm is to enable more efficient and accurate communication, particularly in fields where the semantic content of the data is crucial, such as healthcare, telecommunications, and artificial intelligence.
The first step in the DeepSemComm framework is data preprocessing, which serves to clean and standardize the input data—whether it be text or biomedical signals. For text-based communication, preprocessing involves several stages, including tokenization, stop-word removal, and stemming. Tokenization breaks down text into smaller units (such as words or phrases), while stop-word removal eliminates common words that do not contribute to the semantic meaning of the text. Stemming reduces words to their root forms, allowing for a more compact representation. When dealing with biomedical signals, the preprocessing stage is equally important. Biomedical signals, such as electrocardiograms (ECGs) or electroencephalograms (EEGs), are often noisy and contain a lot of irrelevant information. Therefore, the signals are first normalized to bring them within a specific range. This ensures that the data maintains consistency across different samples. Noise filtering techniques are then applied to enhance the quality of the signals, removing any distortions that could interfere with the subsequent steps of the communication process.
Following preprocessing, the next critical phase in the DeepSemComm framework is feature extraction. In the context of text, this involves transforming the raw text data into numerical vectors that can be processed by machine learning models. Techniques like Word2Vec or GloVe are employed to create high-dimensional vector representations of words or phrases, where each vector captures the semantic meaning of the word in relation to other words in the dataset. These embeddings allow the system to understand the deeper context of the text and make decisions based on meaning rather than just surface-level word matching. In biomedical signal transmission, feature extraction is similarly crucial. Techniques such as Fourier Transforms or Wavelet Transforms are employed to extract the most relevant frequency and time-domain features of the signal. This helps the system identify critical patterns and fluctuations in the signal that carry valuable information about the biological processes being monitored.
Context modeling is one of the most unique and innovative aspects of the DeepSemComm framework. Unlike traditional communication systems that focus solely on the transmission of raw data, DeepSemComm aims to understand the context of the data being transmitted to ensure that its semantic meaning is preserved throughout the communication process. For text-based data, this is achieved through the use of advanced natural language processing (NLP) models like Transformers. These models use attention mechanisms that allow the system to focus on different parts of the text at different stages of processing. By capturing long-range dependencies between words, Transformers can understand the relationships between different words and phrases, ensuring that the meaning of the text is retained even as it is compressed and transmitted. For biomedical signals, context modeling is achieved using deep recurrent neural networks (RNNs) that are specifically designed to handle time-series data. These networks capture temporal dependencies in the signal data, which is essential for understanding the underlying biological processes. Temporal patterns in biomedical signals are critical, as they often contain important information about the patient’s condition or the system being monitored. By capturing these dependencies, the framework ensures that the context of the signal is not lost during transmission.
Once the data has been preprocessed, feature-extracted, and contextually modeled, it moves to the semantic compression stage. This phase is responsible for reducing the dimensionality of the data while preserving its key semantic content. For text, this is typically achieved using variational autoencoders (VAEs) or other dimensionality reduction techniques, which are capable of capturing the most important features of the text while discarding less relevant information. This allows the system to transmit text data in a more compact form, reducing bandwidth usage without losing meaning. In the case of biomedical signals, similar compression techniques are used. Autoencoders and principal component analysis (PCA) are commonly applied to biomedical data to reduce the signal size, maintaining only the essential features necessary for accurate interpretation. This compression process helps ensure that the system can transmit large and complex datasets, such as high-resolution biomedical signals, in a more efficient manner.
After the data is semantically compressed, it is ready for transmission. The transmission layer of the DeepSemComm framework is designed to optimize communication efficiency, ensuring that the data is transmitted over the network with minimal bandwidth usage and maximum reliability. Advanced coding techniques are employed during this phase to prevent signal degradation and minimize the effects of noise or interference during transmission. These techniques ensure that the semantically compressed data can be sent across the network with as little loss of information as possible, maintaining both the quality and integrity of the transmitted data. Once the data reaches the receiving end of the communication system, it is decoded using the inverse of the encoding process. The decoder reconstructs the original text or biomedical signal, ensuring that the data is restored to its initial form while maintaining the contextual integrity established during the earlier stages of processing.
One of the most significant innovations of the DeepSemComm framework is its ability to handle biomedical signal transmission in a semantically aware manner. Biomedical signals such as ECGs or EEGs contain complex patterns that carry important information about a patient’s health or a biological system’s functioning. The DeepSemComm framework uses specialized deep learning models that have been trained on large datasets of biomedical signals to ensure that the semantic meaning of the signal is preserved throughout the communication process. This is particularly critical in medical contexts, where the accuracy and reliability of transmitted signals can directly impact patient care. By integrating deep learning models with biomedical signal processing, DeepSemComm enables more effective and reliable transmission of medical data, even in scenarios with limited bandwidth or other resource constraints.
Another key feature of the DeepSemComm framework is its integration of text and biomedical signal processing into a unified communication system. Traditionally, text and biomedical signals have been handled separately, each with its own set of processing techniques. However, DeepSemComm combines the strengths of natural language processing and biomedical signal processing, enabling both types of data to be transmitted and decoded within the same system. This integration is achieved through the use of hybrid deep learning models that are capable of handling both textual and signal-based data. For example, models such as Transformers, which are commonly used for text, can be combined with recurrent neural networks (RNNs), which are effective for processing time-series data such as biomedical signals. This hybrid approach allows the system to seamlessly transmit and decode a variety of data types, making it highly versatile and capable of operating across a wide range of applications.
DeepSemComm also incorporates advanced performance optimization techniques to ensure that it operates efficiently under various constraints. Techniques such as model pruning, quantization, and transfer learning are employed to reduce the computational resources required by the system while maintaining high performance. Model pruning reduces the size of the neural networks by eliminating unnecessary parameters, making the system more efficient without compromising its accuracy. Quantization reduces the precision of the model's weights, enabling faster processing while still maintaining an acceptable level of performance. Transfer learning allows the framework to leverage pre-trained models on similar tasks, reducing the need for extensive training and enabling the system to be deployed more quickly. These optimization techniques ensure that DeepSemComm can operate in real-world environments, where computational resources and bandwidth may be limited.
Scalability and adaptability are also key considerations in the design of the DeepSemComm framework. The system is built to scale seamlessly, allowing it to handle a variety of data types ranging from small textual messages to large biomedical signal datasets. The scalability of the system ensures that it can be used in a wide range of applications, from simple text communication systems to more complex healthcare and biomedical monitoring applications. DeepSemComm is also highly adaptable, meaning that it can be deployed across multiple domains, including healthcare, telecommunications, and the Internet of Things (IoT). This adaptability makes it a versatile solution for improving communication efficiency in various industries.
In terms of application scenarios, DeepSemComm has a broad range of use cases. One of the most promising applications is in telemedicine, where biomedical signals such as ECGs or EEGs need to be transmitted reliably over long distances. In these scenarios, the DeepSemComm framework ensures that the signals are transmitted with minimal loss of information, making it possible for healthcare professionals to monitor patients remotely with confidence. Additionally, the framework can be used in natural language communication systems, such as chatbots or automated messaging systems, to improve the quality and efficiency of text-based communication. By improving the way text and biomedical signals are transmitted, DeepSemComm has the potential to revolutionize communication technology in healthcare, telecommunications, and beyond.
 

, Claims:We Claim:
1. A deep learning-based semantic communication framework for transmitting and receiving text and biomedical signals, comprising preprocessing, feature extraction, context modeling, semantic compression, and transmission layers.
2. The framework of claim 1, wherein text data is preprocessed using tokenization, stop-word removal, and stemming, and is transformed into high-dimensional vectors using embedding techniques such as Word2Vec or GloVe.
3. The framework of claim 1, wherein biomedical signals are preprocessed by normalizing the signals and applying noise filtering techniques.
4. The framework of claim 1, wherein the context modeling is performed using advanced NLP models such as Transformers for text and recurrent neural networks for biomedical signals.
5. The framework of claim 1, wherein semantic compression is performed using variational autoencoders or other dimensionality reduction techniques for text and autoencoders or PCA for biomedical signals.
6. The framework of claim 1, wherein the transmission layer utilizes advanced coding techniques to ensure efficient bandwidth usage and minimal signal degradation during transmission.
Dated this 19th September 2025