{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9852729","patent":{"patent_number":"US-9852729","title":"Low latency and memory efficient keyword spotting","assignee":null,"inventors":[],"filing_date":"2016-07-11T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["G10L","G10L","G10L","G10L","G10L","G10L"],"num_claims":20,"abstract":"Features are disclosed for spotting keywords in utterance audio data without requiring the entire utterance to first be processed. Likelihoods that a portion of the utterance audio data corresponds to the keyword may be compared to likelihoods that the portion corresponds to background audio (e.g., general speech and/or non-speech sounds). The difference in the likelihoods may be determined, and keyword may be triggered when the difference exceeds a threshold, or shortly thereafter. Traceback information and other data may be stored during the process so that a second speech processing pass may be performed. For efficient management of system memory, traceback information may only be stored for those frames that may encompass a keyword; the traceback information for older frames may be overwritten by traceback information for newer frames."},"analysis":{"summary":"The Low Latency and Memory Efficient Keyword Spotting patent (US-9852729) introduces a pivotal advancement in real-time voice command detection. At its core, this innovation addresses the long-standing challenges of processing speed and memory consumption in keyword spotting systems, particularly for resource-constrained devices.\n\nThe central problem it solves is the inefficiency of traditional methods that either require buffering an entire audio utterance before processing, leading to noticeable latency, or consume excessive memory by storing vast amounts of intermediate data. This patent circumvents these issues by enabling near real-time keyword detection and intelligent memory management.\n\nTechnically, the system operates by continuously analyzing incoming audio data in small segments. For each segment, it compares the likelihood that it contains a target keyword against the likelihood that it's just background noise or general speech. A keyword is triggered almost instantly when this difference exceeds a predefined threshold. Crucially, for a potential second pass of speech processing, the system stores 'traceback information.' However, to maintain memory efficiency, this data is only retained for frames that are highly probable to contain a keyword. Older, less relevant traceback information is actively overwritten by newer data, ensuring a minimal and consistent memory footprint.\n\nFrom a business perspective, this patent unlocks significant opportunities in various sectors. It enables the development of more responsive and energy-efficient voice-activated devices, from smart home appliances and wearables to automotive systems and industrial IoT. This translates into enhanced user experience, extended battery life, and the ability to deploy sophisticated voice AI capabilities on a broader range of hardware. The market opportunity lies in providing the foundational technology for ubiquitous, seamless, and high-performing voice interfaces that can operate effectively even at the edge, reducing reliance on cloud processing for initial command detection.","layman_explanation":"### 1. What Problem Does This Solve?\nImagine you're trying to talk to your smart speaker or your car's voice assistant. Sometimes, there's a tiny, frustrating delay before it responds. This isn't just annoying; it makes the interaction feel less natural and efficient. This delay happens because older voice systems often have to record your entire command, send it off for processing (sometimes to the cloud!), and then wait for an answer. This uses up a lot of time (latency) and a lot of the device's temporary memory.\n\nThe core problem the Low Latency and Memory Efficient Keyword Spotting patent solves is making voice detection both incredibly fast and highly efficient with a device's internal resources. Existing solutions often fall short by either being too slow for real-time interaction or too demanding on memory and processing power, limiting their use in smaller, battery-powered devices like smartwatches or IoT sensors.\n\n### 2. How Does It Work?\nThink of this invention like a super-smart, always-on ear that's incredibly good at listening for just one or two specific words, like 'Hey Siri' or 'Alexa.' Instead of recording everything you say and then playing it back to itself to find the keyword, this system listens in tiny, continuous snippets.\n\nHere’s a simple analogy: Imagine you're waiting for a specific friend, 'Keyword,' to walk through a busy door. An old system would film everyone who walks through, then watch the entire video later to find 'Keyword.' This patent's approach is like having a bouncer at the door who instantly recognizes 'Keyword' as soon as they appear, without needing to see everyone else first. It constantly compares if a sound snippet is 'Keyword-like' or just 'background noise.' If it's strongly 'Keyword-like,' bingo! It triggers a response almost immediately.\n\nNow for the 'memory efficient' part: Even though it's always listening, it's not filling its brain with useless information. If the bouncer sees someone who isn't 'Keyword,' they immediately forget them. They only keep a very short memory of people who *might* be 'Keyword,' just in case they need to double-check. Once it's clear someone isn't 'Keyword,' or enough time has passed, that short memory is wiped clean to make room for new people. This keeps the bouncer's 'brain' (the device's memory) small and tidy, allowing it to work continuously without getting overwhelmed.\n\n### 3. Why Does This Matter?\nThis innovation fundamentally changes how we interact with voice-enabled technology. For businesses, it means:\n\n*   **Superior User Experience:** Faster response times lead to happier customers and more natural, intuitive interactions, boosting product adoption and loyalty.\n*   **Reduced Hardware Costs & Extended Battery Life:** Because the system is so efficient with memory and processing, devices can be built with less powerful (and cheaper) components. For portable devices, this translates directly into significantly longer battery life, a huge competitive advantage.\n*   **New Market Opportunities:** The ability to put advanced, responsive voice AI into smaller, more resource-constrained devices (like tiny sensors, basic appliances, or even toys) opens up entirely new product categories and market segments that were previously out of reach.\n*   **Enhanced Privacy and Reliability:** By processing keyword detection locally on the device, there's less need to send constant audio streams to the cloud, improving user privacy and ensuring functionality even without an internet connection.\n\n### 4. What's Next?\nThis patent lays the groundwork for a future where voice interfaces are truly ubiquitous, seamless, and deeply integrated into our environment. We can expect to see an acceleration in the adoption of always-on voice assistants in everything from household appliances to industrial equipment. Investors should note the potential for significant ROI from companies leveraging this technology to differentiate their products and capture market share in the rapidly expanding edge AI and voice recognition sectors. It's a foundational piece for the next generation of truly smart, responsive devices.","technical_analysis":"The patent **Low Latency and Memory Efficient Keyword Spotting** (US-9852729) details an innovative architecture for real-time keyword spotting (KWS) designed to overcome the inherent trade-offs between latency, accuracy, and memory footprint in continuous audio processing. The core technical contribution lies in its dual-pronged approach: a low-latency detection mechanism coupled with an intelligent, dynamic memory management system.\n\n**Technical Architecture and Algorithm Specifics:**\nAt a high level, the system processes utterance audio data in a continuous stream, segmenting it into frames. For each frame, a likelihood computation is performed to determine two key probabilities: P(frame | Keyword) and P(frame | Background). P(frame | Keyword) represents the likelihood that the current audio frame corresponds to a predefined keyword, while P(frame | Background) represents the likelihood that it corresponds to general speech or non-speech sounds. These likelihoods can be derived from various acoustic models, such as Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs), or more modern deep learning architectures like Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) trained specifically for KWS tasks. The patent's strength is its framework, not a specific model.\n\nThe system then calculates the difference between these two likelihoods, often expressed as a log-likelihood ratio (LLR = log(P(frame | Keyword)) - log(P(frame | Background))). A keyword is triggered when this LLR exceeds a dynamically adjusted or static threshold. This real-time comparison allows for immediate keyword detection without the need to buffer and process an entire utterance, significantly reducing perceived latency. The threshold can be tuned to balance false positives and false negatives, often incorporating hysteresis or a short delay to confirm a sustained signal.\n\n**Memory Management and Traceback Information:**\nOne of the most critical aspects of this invention is its sophisticated approach to memory efficiency. During the initial KWS pass, the system generates 'traceback information.' This data, typically comprising acoustic scores, state paths, and other intermediate results, is crucial for a potential second speech processing pass. This second pass might involve more complex speech recognition, speaker verification, or intent recognition, which can refine the initial keyword detection or provide richer context.\n\nHowever, continuously storing traceback information for every frame over an extended period would quickly exhaust the memory resources of typical edge devices. This patent introduces a novel solution: traceback information is *only* stored for those frames that are statistically likely to encompass a keyword. This selective storage is often guided by the same likelihood comparison used for the initial trigger. If a frame's LLR crosses a certain pre-traceback threshold (which might be lower than the final trigger threshold), its traceback information is retained.\n\nFurthermore, the system employs a 'circular buffer' or 'sliding window' mechanism. Traceback information for older frames that are no longer relevant (e.g., they fall outside a predefined time window for potential keyword occurrence or second-pass processing) is overwritten by traceback information from newer frames. This ensures that the memory footprint for traceback data remains constant and minimal, preventing memory accumulation and enabling continuous, long-duration operation without performance degradation due to memory pressure.\n\n**Performance Characteristics and Integration Patterns:**\nThis approach offers superior performance characteristics for embedded and edge computing environments. It enables always-on listening with minimal power consumption due to reduced processing overhead and efficient memory usage. Integration with existing speech processing pipelines involves feeding the raw audio stream into this KWS module, which then outputs a keyword detection event. This event can then trigger subsequent actions, such as waking up a more powerful (but higher-latency) speech recognition engine, initiating a cloud-based service, or directly executing a local command. The ability to perform an initial, lightweight KWS on-device significantly reduces the need for constant cloud connectivity, enhancing privacy and robustness in offline scenarios.\n\nIn essence, this patent provides a foundational framework for truly responsive and resource-optimized voice interfaces, paving the way for more intelligent and pervasive edge AI applications.","business_analysis":"The **Low Latency and Memory Efficient Keyword Spotting** patent (US-9852729) represents a significant strategic asset with broad commercial implications across multiple high-growth industries. Its core value proposition—enabling real-time keyword detection with minimal latency and optimized memory usage—directly addresses critical pain points in the burgeoning voice AI market.\n\n**Market Opportunity Size:**\nThe global voice and speech recognition market is projected to reach tens of billions of dollars in the coming years, driven by the proliferation of smart devices, IoT, and hands-free interfaces. Within this, the segment for on-device, edge-based AI is expanding rapidly due to demands for privacy, offline functionality, and reduced cloud dependency. This patent squarely targets this critical segment, offering a foundational technology for a vast array of products from consumer electronics to industrial applications. The ability to deploy sophisticated voice recognition on smaller, less powerful, and battery-constrained devices significantly expands the total addressable market for voice-enabled features.\n\n**Competitive Advantages:**\nThis invention provides several key competitive advantages:\n\n1.  **Superior User Experience:** Near-instantaneous response times eliminate frustrating lag, leading to higher user satisfaction and adoption rates for voice-controlled products. This responsiveness is a significant differentiator in a crowded market.\n2.  **Hardware Cost Reduction:** By minimizing memory and computational demands, the technology allows manufacturers to use less expensive hardware components (e.g., smaller RAM, less powerful processors) while still delivering premium voice AI capabilities. This directly impacts bill of materials (BOM) costs and improves profit margins.\n3.  **Extended Battery Life:** Reduced processing and memory overhead translate into lower power consumption, a crucial advantage for portable and wearable devices where battery life is a primary concern.\n4.  **Enhanced Privacy and Offline Capability:** Performing initial keyword spotting on-device reduces the need to constantly stream audio to the cloud, addressing privacy concerns and enabling robust functionality in offline or low-connectivity environments.\n5.  **Accelerated Time-to-Market:** With a proven, efficient method for KWS, product development cycles for voice-enabled devices can be streamlined, allowing companies to bring innovations to market faster.\n\n**Revenue Potential and Business Models:**\nCompanies can leverage this patent through various business models:\n\n*   **Licensing:** Technology companies specializing in voice AI, semiconductor manufacturers, and consumer electronics brands can license the patent for integration into their chipsets, operating systems, or end products.\n*   **Product Differentiation:** Manufacturers can build and market devices explicitly highlighting their 'instant response' or 'always-on, low-power voice assistant' features, directly attributable to this technology.\n*   **Platform Development:** Developers of edge AI platforms can incorporate this method as a core component, offering a more robust and efficient solution to their clients.\n\n**Strategic Positioning:**\nThis patent strategically positions any licensee or owner at the forefront of on-device voice AI. It allows for leadership in segments where resource constraints are paramount, such as IoT, wearables, and embedded automotive systems. It future-proofs products by enabling advanced voice capabilities in a world increasingly moving towards localized, intelligent processing.\n\n**ROI Projections:**\nInvestment in leveraging this patent promises strong ROI through increased market share, enhanced brand reputation for cutting-edge technology, and direct cost savings in hardware and cloud infrastructure. The ability to create a more compelling user experience will drive sales, while efficiency gains will boost profitability. As voice interfaces become ubiquitous, this foundational technology will be a key enabler for competitive advantage and market leadership.","faqs":[{"answer":"Low Latency and Memory Efficient Keyword Spotting (US-9852729) is a patented technology that revolutionizes how devices detect specific keywords or 'wake words' in spoken language. Unlike traditional systems that might need to record and process an entire audio utterance, this invention allows for keyword detection in near real-time, significantly reducing the delay between speaking a command and a device responding. This instantaneous feedback makes interactions with voice-controlled devices feel much more natural and intuitive.\n\nThe core of the Low Latency and Memory Efficient Keyword Spotting system involves continuously analyzing small segments of incoming audio data. It compares the likelihood that a particular segment contains a target keyword against the likelihood that it's just background noise or general speech. When the 'keyword likelihood' significantly outweighs the 'background likelihood' and crosses a set threshold, the keyword is identified almost immediately.\n\nBeyond speed, this patent introduces an ingenious memory management strategy. For subsequent, more detailed speech processing passes, devices need to store intermediate 'traceback information.' However, to avoid memory bloat, this invention ensures that such data is only stored for frames that are highly probable to contain a keyword. Older, less relevant information is then efficiently overwritten by newer data, maintaining a minimal and constant memory footprint. This dual focus on low latency and memory efficiency makes this technology ideal for deployment on resource-constrained devices like smartwatches, earbuds, and IoT sensors, where performance and power consumption are critical concerns. \n\nKeywords: keyword spotting, voice AI patent, low latency, memory efficient, real-time speech processing, US-9852729.","question":"What is Low Latency and Memory Efficient Keyword Spotting?"},{"answer":"The Low Latency and Memory Efficient Keyword Spotting patent operates through a sophisticated, two-pronged technical approach. Firstly, it focuses on achieving low latency by processing audio data in a continuous, frame-by-frame manner. For each incoming audio frame, the system computes two key probabilities: the likelihood that the frame corresponds to a target keyword (L_keyword) and the likelihood that it corresponds to background audio (L_background), which includes general speech or ambient noise. These likelihoods are typically generated by an acoustic model trained to differentiate between keyword and non-keyword sounds.\n\nThe system then calculates the difference between these two likelihoods. When this difference exceeds a predetermined threshold, a keyword is 'triggered' or 'spotted' almost instantaneously. This method avoids the delays inherent in systems that must wait for an entire utterance to be captured before processing, thereby providing a near real-time response that enhances user experience.\n\nSecondly, the Low Latency and Memory Efficient Keyword Spotting innovation excels in memory management. During the initial processing, intermediate 'traceback information' is generated, which is crucial for a potential second, more detailed speech processing pass. However, to prevent memory exhaustion, especially on devices with limited RAM, this patent specifies that traceback information is only stored for those audio frames that are statistically likely to encompass a keyword. Furthermore, older, irrelevant traceback data is dynamically overwritten by newer data, ensuring that the memory footprint remains constant, minimal, and highly efficient. This intelligent resource allocation allows for robust keyword spotting capabilities on a wide range of devices without compromising performance or battery life.\n\nKeywords: keyword spotting mechanism, how voice AI works, low latency algorithm, memory management, traceback information, real-time audio analysis.","question":"How does Low Latency and Memory Efficient Keyword Spotting work?"},{"answer":"The Low Latency and Memory Efficient Keyword Spotting patent solves a critical dual problem in the field of voice-activated technology: the inherent trade-off between responsiveness (low latency) and efficient resource utilization (memory and power consumption). Traditional keyword spotting systems often struggle with these conflicting demands, particularly when deployed on resource-constrained devices like smartwatches, earbuds, or IoT sensors.\n\nPrior art solutions typically faced challenges such as: 1) **High Latency:** Many systems required buffering an entire audio utterance before processing, leading to noticeable delays in device response. This creates an unnatural and frustrating user experience. 2) **Excessive Memory Consumption:** Continuously processing and storing intermediate data (traceback information) for all audio frames could quickly exhaust a device's limited memory, leading to increased hardware costs, slower performance, and reduced battery life. This made sophisticated voice AI impractical for smaller, more affordable devices.\n\nBy providing a method for near real-time keyword detection and intelligent, dynamic memory management, the Low Latency and Memory Efficient Keyword Spotting technology eliminates these compromises. It enables devices to offer instantaneous voice command recognition while maintaining a minimal memory footprint and low power consumption. This breakthrough allows for the widespread deployment of advanced, always-on voice interfaces, enhancing user experience and unlocking new product categories in the burgeoning edge AI market.\n\nKeywords: voice AI challenges, latency problem, memory efficiency problem, keyword spotting solutions, edge computing constraints, user experience improvement.","question":"What problem does Low Latency and Memory Efficient Keyword Spotting solve?"},{"answer":"The Low Latency and Memory Efficient Keyword Spotting patent (US-9852729) lists no specific inventors or assignees in the provided data. However, patents are typically filed by individuals or entities who have developed the innovative technology.\n\nIn the broader context of patent law and technology development, such inventions are often the result of dedicated research and development teams within leading technology companies or academic institutions focused on speech recognition, artificial intelligence, and embedded systems. These teams comprise experts in fields such as signal processing, machine learning, and software architecture, all working towards advancing the capabilities of voice-controlled interfaces.\n\nThe absence of specific inventor or assignee information in this particular query does not diminish the significance of the Low Latency and Memory Efficient Keyword Spotting innovation itself. Its technical contributions to low-latency processing and memory efficiency are well-defined within the patent document, outlining a clear path for implementation and impact across the voice AI industry. The invention's value lies in its technical solution to a pervasive industry problem, irrespective of the specific individuals or corporate entity behind its conception. The filing date of 2016-07-11 and publication date of 2017-12-26 indicate its timing within the rapid evolution of voice technology.\n\nKeywords: keyword spotting inventors, patent US-9852729 inventors, voice AI innovators, speech recognition research, patent assignee, technology development.","question":"Who invented Low Latency and Memory Efficient Keyword Spotting?"},{"answer":"The Low Latency and Memory Efficient Keyword Spotting patent offers several transformative benefits that are crucial for the advancement and widespread adoption of voice AI technology:\n\n1.  **Near-Instantaneous Responsiveness (Low Latency):** The primary benefit is the dramatic reduction in the delay between a user speaking a keyword and the device reacting. By processing audio segments in real-time and making immediate decisions based on likelihood comparisons, this technology enables a fluid, natural, and highly responsive user experience, eliminating frustrating pauses and making voice interactions feel more intuitive.\n\n2.  **Optimized Resource Utilization (Memory Efficiency):** This innovation ensures that sophisticated keyword spotting can operate effectively on devices with limited memory. Through selective storage of 'traceback information' only for relevant audio frames and the continuous overwriting of older data, the system maintains a minimal and constant memory footprint. This is vital for enabling advanced voice AI on smaller, more affordable, and resource-constrained hardware like wearables, IoT devices, and embedded systems.\n\n3.  **Extended Battery Life:** Lower computational and memory demands directly translate into reduced power consumption. For portable and battery-powered devices, this means significantly longer operational times between charges, enhancing user convenience and product appeal.\n\n4.  **Enhanced Privacy and Offline Capability:** By performing initial keyword detection directly on the device, there's less need to continuously stream audio to cloud servers. This improves user privacy by keeping sensitive audio data local and enables robust voice control functionality even in environments with no or intermittent internet connectivity.\n\n5.  **Broadened Market Opportunities:** The ability to deploy high-performance voice AI on a wider range of hardware, including low-cost and low-power devices, opens up new product categories and market segments that were previously inaccessible due to technical limitations. This democratizes access to advanced voice interfaces.\n\nKeywords: keyword spotting benefits, low latency advantages, memory efficient AI benefits, voice AI user experience, battery life extension, edge computing benefits.","question":"What are the key benefits of Low Latency and Memory Efficient Keyword Spotting?"},{"answer":"The Low Latency and Memory Efficient Keyword Spotting patent distinguishes itself from prior art by fundamentally rethinking the trade-offs between speed and resource management in keyword spotting (KWS) systems. Traditional KWS approaches often suffered from one or both of these critical limitations:\n\n1.  **Prior Art's Latency Issues:** Many older systems, especially those relying on full-utterance speech recognition pipelines, required an entire audio command to be captured and buffered before processing could even begin. This inherently introduced a noticeable delay, making real-time interaction feel unnatural. Other continuous listening systems, while faster, still often processed in larger chunks or required more extensive analysis before a definitive trigger, leading to a slight but perceptible lag.\n\n2.  **Prior Art's Memory Consumption:** Continuous KWS systems in prior art typically needed to store extensive 'traceback information' (intermediate data for potential second-pass processing) for a rolling window of audio. This indiscriminate storage could quickly consume significant amounts of a device's limited RAM, leading to increased hardware costs, higher power consumption, and limitations on deployment for smaller, battery-powered devices. Managing this memory efficiently was a persistent challenge.\n\nIn contrast, the Low Latency and Memory Efficient Keyword Spotting invention offers a unique solution. Firstly, it achieves ultra-low latency by performing continuous, frame-level likelihood comparisons: it assesses whether each tiny audio segment is more likely to be a keyword or background noise. A keyword is triggered almost instantly when this likelihood difference crosses a threshold, eliminating the 'wait-and-process' delay. Secondly, its memory management is revolutionary: it *selectively* stores traceback information only for frames highly likely to contain a keyword, and crucially, it continuously *overwrites* older, irrelevant data with newer information. This ensures a constant, minimal memory footprint, a stark departure from prior art's often inefficient memory usage. This dual innovation allows for high-performance, responsive voice AI on a much broader range of hardware than previously possible.\n\nKeywords: keyword spotting prior art, voice AI differentiation, low latency comparison, memory efficiency innovation, US-9852729 vs old tech, speech processing advancements.","question":"How is Low Latency and Memory Efficient Keyword Spotting different from prior art?"},{"answer":"The Low Latency and Memory Efficient Keyword Spotting patent is poised to profoundly impact a wide array of industries by enabling more responsive, efficient, and accessible voice-activated technologies. Its ability to deliver near real-time keyword detection with minimal memory footprint makes it a foundational technology for the next generation of 'edge AI' applications.\n\n1.  **Consumer Electronics:** This is perhaps the most direct impact. Smart speakers, smart displays, smart TVs, and home hubs will become even more responsive, enhancing the user experience. The technology will enable more seamless voice control in devices like remote controls, headphones, and gaming consoles, making interactions more intuitive and less frustrating.\n\n2.  **Wearables:** Smartwatches, fitness trackers, and hearables (smart earbuds) will benefit immensely. With low latency and memory efficiency, these devices can offer robust, always-on voice assistants without significantly draining battery life or requiring constant tethering to a smartphone. This unlocks new possibilities for hands-free communication and health monitoring.\n\n3.  **Automotive:** In-car infotainment and driver assistance systems can leverage this patent for more reliable and instantaneous voice commands. This improves safety by allowing drivers to control navigation, music, climate, or make calls with immediate feedback, reducing the need to take their hands off the wheel or eyes off the road.\n\n4.  **Internet of Things (IoT):** The technology is ideal for smart appliances, industrial sensors, and other connected devices with limited processing power and memory. It enables voice control in environments previously deemed too resource-constrained, fostering greater automation and accessibility across smart homes, smart cities, and industrial IoT deployments.\n\n5.  **Healthcare and Accessibility:** Voice control can offer crucial assistance for individuals with disabilities, allowing for hands-free interaction with medical devices, smart home systems, or communication aids. The efficiency of this patent makes these solutions more practical and affordable.\n\n6.  **Robotics and Automation:** Robots and automated systems can become more interactive and responsive to human voice commands, enhancing human-robot collaboration in manufacturing, logistics, and service industries.\n\nIn essence, any industry where human interaction with technology can be improved through fast, reliable, and resource-efficient voice control stands to benefit significantly from the Low Latency and Memory Efficient Keyword Spotting innovation.\n\nKeywords: industry impact voice AI, keyword spotting applications, IoT voice control, wearables technology, automotive AI, consumer electronics voice, edge AI industries.","question":"What industries will Low Latency and Memory Efficient Keyword Spotting impact?"},{"answer":"The patent for Low Latency and Memory Efficient Keyword Spotting, identified as US-9852729, was officially filed on **July 11, 2016**.\n\nFollowing the examination process by the United States Patent and Trademark Office (USPTO), this patent was subsequently published and granted on **December 26, 2017**. These dates mark the formal entry of this innovative technology into the public domain as a protected invention.\n\nThe period between its filing and publication/granting indicates the time taken for the patent application to be reviewed, potentially amended, and ultimately approved. This timeframe is typical for complex technological patents, especially in rapidly evolving fields like artificial intelligence and speech processing. The publication date signifies when the detailed technical specifications and claims of the Low Latency and Memory Efficient Keyword Spotting invention became publicly accessible, allowing other researchers, developers, and companies to understand its scope and build upon its principles.\n\nThis timeline places the Low Latency and Memory Efficient Keyword Spotting patent at a crucial juncture in the development of modern voice AI, preceding the widespread adoption of many current voice assistant technologies. Its early filing highlights the foresight of the inventors in addressing critical performance and efficiency challenges that would become central to the industry's growth.\n\nKeywords: Low Latency and Memory Efficient Keyword Spotting date, patent US-9852729 filing date, patent publication date, voice AI patent timeline, intellectual property, technology history.","question":"When was Low Latency and Memory Efficient Keyword Spotting filed/granted?"},{"answer":"The commercial applications of the Low Latency and Memory Efficient Keyword Spotting patent are extensive, primarily driven by its ability to enable highly responsive and resource-efficient voice AI on a broad spectrum of devices. This technology is a cornerstone for products and services that require instantaneous voice interaction without heavy computational or memory burdens.\n\n1.  **Smart Home Devices:** Enhancing the responsiveness of smart speakers, thermostats, lighting systems, and security cameras. Users will experience seamless, immediate control over their home environment, reducing frustration and increasing adoption.\n\n2.  **Wearable Technology:** Integrating robust voice assistants into smartwatches, fitness bands, and true wireless earbuds. This allows for hands-free control, communication, and access to information without significant battery drain or reliance on a tethered smartphone, making wearables more autonomous and functional.\n\n3.  **Automotive Infotainment Systems:** Improving the speed and reliability of in-car voice commands for navigation, media control, climate adjustments, and communication. This enhances driver safety by minimizing distractions and providing immediate feedback.\n\n4.  **Industrial IoT and Automation:** Enabling voice control for machinery, robotic systems, and data entry in manufacturing, logistics, and field service operations. This facilitates hands-free workflows, improves efficiency, and enhances safety in often noisy or complex environments.\n\n5.  **Healthcare and Accessibility Products:** Developing more responsive voice interfaces for medical devices, assistive technologies for individuals with disabilities, and remote patient monitoring systems. The efficiency of this patent makes these vital tools more accessible and practical.\n\n6.  **Consumer Electronics Beyond Smart Home:** Integrating voice control into televisions, remote controls, gaming consoles, and personal computers, offering a more intuitive and natural way to interact with entertainment and productivity tools.\n\n7.  **Smart Appliances:** Embedding voice control directly into refrigerators, washing machines, and ovens, allowing for hands-free operation, recipe access, and status updates without complex interfaces.\n\nBy overcoming the traditional limitations of latency and memory, the Low Latency and Memory Efficient Keyword Spotting patent allows companies to build superior products, open new markets, and deliver a truly seamless voice experience to consumers and businesses alike.\n\nKeywords: commercial applications voice AI, keyword spotting products, smart home voice control, wearable tech voice, automotive voice commands, industrial IoT voice, patent US-9852729 applications.","question":"What are the commercial applications of Low Latency and Memory Efficient Keyword Spotting?"},{"answer":"The Low Latency and Memory Efficient Keyword Spotting patent lays a robust foundation, and future developments are expected to build upon its core principles to achieve even greater sophistication, efficiency, and ubiquity in voice AI. These advancements will likely focus on enhancing the underlying models, expanding capabilities, and further optimizing resource usage.\n\n1.  **Integration with Advanced AI Models:** While the patent is model-agnostic, future developments will see its framework integrated with increasingly powerful and compact deep learning models (e.g., highly quantized CNNs, attention-based networks, or federated learning approaches) specifically optimized for KWS on edge devices. This will lead to even higher accuracy and robustness against noise and varied accents, without sacrificing the low latency and memory efficiency.\n\n2.  **Adaptive and Personalized Keyword Spotting:** Expect advancements in dynamic thresholding and personalization. Future systems might adapt the keyword spotting thresholds based on environmental noise levels, user's speaking style, or even emotional state. This could also extend to personalized wake words or custom commands that are highly accurate for individual users while remaining resource-efficient.\n\n3.  **Multi-Keyword and Contextual Spotting:** Beyond single keyword detection, future iterations of Low Latency and Memory Efficient Keyword Spotting could evolve to efficiently recognize multiple keywords or short phrases within a single, continuous audio stream, potentially incorporating basic contextual understanding. This would move towards more natural, conversational interfaces that are still anchored by the patent's efficiency principles.\n\n4.  **Hardware-Software Co-Optimization:** Further advancements will involve tighter integration and co-optimization with specialized hardware, such as dedicated DSPs or AI accelerators designed specifically for low-power, low-latency audio processing. This synergy will push the boundaries of what's possible on ultra-low-power edge silicon.\n\n5.  **Enhanced Privacy and Security Features:** Building on the on-device processing capabilities, future developments could incorporate advanced privacy-preserving techniques like differential privacy or secure enclaves for even greater protection of audio data, ensuring that personal voice commands remain truly private.\n\n6.  **Broader Linguistic and Cultural Support:** Continued research will expand the efficient KWS capabilities to a wider range of languages and dialects, making the technology globally accessible and culturally relevant. These developments will solidify the Low Latency and Memory Efficient Keyword Spotting patent's role as a cornerstone for the next generation of intelligent, responsive, and pervasive voice AI.\n\nKeywords: future voice AI, keyword spotting developments, edge AI trends, personalized voice recognition, hardware acceleration, AI model optimization, patent US-9852729 future.","question":"What are the future developments expected for Low Latency and Memory Efficient Keyword Spotting?"}],"topics":["low latency keyword spotting","memory efficient voice AI","real-time speech recognition","edge computing voice","patent US-9852729","burgeoning","field","voice"],"tech_cluster":null},"seo":{"title":"Low Latency and Memory Efficient Keyword Spotting - Patent US-9852729","description":"Discover the Low Latency and Memory Efficient Keyword Spotting patent (US-9852729) for real-time voice AI. Achieve instant responses & optimized memory in edge devices.","keywords":["low latency keyword spotting","memory efficient voice AI","real-time speech recognition","edge computing voice","patent US-9852729","voice command technology","on-device AI","speech processing optimization","always-on listening","embedded voice control","audio data processing","voice user interface","patent analysis"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9852729","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9852729","citation_suggestion":"Patentable. \"Low latency and memory efficient keyword spotting\" (US-9852729). https://patentable.app/patents/US-9852729","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9852729","json":"https://patentable.app/api/llm-context/US-9852729","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T03:51:28.099Z"}