BOOST SUBSCRIBERCO L.L.C. has secured a major milestone in telecommunications with a newly patented system for brain computer interfaces. This innovation focuses on U.S. Patent No. 12648392, titled Brain computer interface system with deep learning for transmitting communications via a telecommunications network. The patent describes a wearable brain computer interface device designed to receive electrical signals related to neurological activity and transmit encoded intent data via a 5G NR network to a recipient user equipment.

Overcoming Communication Barriers

BOOST SUBSCRIBERCO L.L.C. has earned the prestigious Swanson Reed patent of the month award for April 2026 within the Federal industry category. This recognition highlights the dedication of the company to pioneering groundbreaking advancements at the intersection of neuroscience and digital communication. By successfully integrating deep learning with telecommunications, this invention marks a monumental leap forward in how humans can interact with machines and networks.

The patented technology stands out as an outstanding innovation because it addresses a complex technical challenge that has long limited the scalability of neural interfaces. Traditional systems often struggle with real time signal processing and standard network integration. This system solves those issues by embedding machine learning models directly into a wearable architecture, allowing for instantaneous intent extraction and seamless encoding into 5G NR network protocols.

Winning this award underscores the transformative potential of the technology within the broader federal and commercial technology sectors. It establishes a new benchmark for hands free, thought driven communication infrastructure. This breakthrough paves the way for secure, low latency telecommunications that could redefine accessibility, defense operations, and consumer electronics, cementing its place as a truly revolutionary piece of intellectual property.

Patent Abstract

Systems and methods to implement a wearable brain computer interface device for transmitting communications via a telecommunications network. One system includes a processing system of a wearable brain computer interface device that is configured to receive an electrical signal related to neurological activity. The processing system may be configured to provide the electrical signal to a machine learning model, the machine learning model to extract a pattern from the electrical signal and determine, based on the pattern, an intent of the electrical signal. The processing system may be configured to receive, from the machine learning model, intent data related to the intent of the electrical signal. The processing system may be configured to encode the intent data to generate encoded data compatible for transmission via a 5G NR network. The processing system may be configured to transmit the encoded data via the 5G NR network to a recipient user equipment.

Meeting United States Research and Development Tax Credit Rules

The United States Research and Development tax credit provides substantial financial incentives for companies that invest in innovation. To qualify, an initiative must satisfy a strict four part test chess set by Section 41 of the Internal Revenue Code. The project must possess a permitted purpose aimed at creating or improving the functionality, performance, or reliability of a product or process. It must eliminate technical uncertainty regarding capability, methodology, or final design. It must involve a systematic process of experimentation to evaluate alternative methodologies. Finally, it must be technological in nature, relying on hard sciences such as computer science, engineering, or physics.

Practical Applications for Research and Development Tax Credits

First application involves designing the wearable hardware and processing architecture. Developing a lightweight wearable device capable of gathering clean neurological electrical signals presents massive technical uncertainty. Engineers must systematically test various sensor configurations and advanced filtering mechanisms to eliminate environmental noise, relying on electrical engineering and biological science principles to achieve a functional design that meets the permitted purpose and experimentation tests.

Second application focuses on training and optimizing the deep learning models. Creating a machine learning model that can instantly interpret complex brain waves requires an extensive process of experimentation. Software developers must evaluate numerous neural network configurations, algorithmic models, and dataset variables to accurately translate raw data into distinct intent outputs, directly fulfilling the criteria for qualified computer science research and overcoming design uncertainties.

Third application entails engineering the 5G NR network encoding protocols. Integrating parsed intent data into high speed telecommunication streams requires overcoming severe latency and data synchronization constraints. The development team must perform rigorous simulation and software iteration to construct custom encoding mechanisms that guarantee compatibility with recipient user equipment, representing a clear technological advancement that meets all regulatory guidelines.