{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9853659","patent":{"patent_number":"US-9853659","title":"Split gain shape vector coding","assignee":null,"inventors":[],"filing_date":"2017-02-07T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["G10L","G10L","G10L","H04N","G10L","H04B","H04N"],"num_claims":17,"abstract":"The invention relates to an encoder and a decoder and methods therein for supporting split gain shape vector encoding and decoding. The method performed by an encoder, where the encoding of each vector segment is subjected to a constraint related to a maximum number of bits, BMAX, allowed for encoding a vector segment. The method comprises, determining an initial number, Np_, of segments for a target vector x; and further determining an average number of bits per segment, BAVG, based on a vector bit budget and Np_. The method further comprises determining a final number of segments to be used, for the vector x, in the gain shape vector encoding, based on energies of the Np_segments and a difference between BMAX and BAVG. The performing of the method enables an efficient allocation of the bits of the bit budget over the target vector."},"analysis":{"summary":"The **Split Gain Shape Vector Coding** patent (US-9853659) introduces a revolutionary method for optimizing data compression and decompression, primarily for vector-based signals like audio and speech. Its core innovation is an intelligent, adaptive bit allocation strategy that significantly enhances efficiency and quality.\n\nTraditional vector encoding often struggles with uniformly distributing a finite bit budget across varying data segments, leading to suboptimal quality or wasted bandwidth. This invention solves this by enabling an encoder to dynamically determine the optimal number of segments for a target vector and then precisely distribute bits based on the perceptual importance (energy) of each segment.\n\nThe key technical approach involves calculating an initial average bit allocation (BAVG) for segments and then refining this based on segment energies and a maximum bit constraint (BMAX). This ensures that perceptually critical segments receive more bits for detail preservation, while less critical segments are allocated fewer, all while adhering to an overall bit budget. This dynamic adjustment leads to a more perceptually accurate and bandwidth-efficient representation of the original data.\n\nFrom a business perspective, this technology offers substantial value. It enables the development of higher-quality audio and video codecs that require less bandwidth, leading to reduced infrastructure costs for streaming services, improved user experience through less buffering, and more robust communication systems. The market opportunity is vast, impacting industries such as telecommunications, digital media, virtual reality, and any sector reliant on efficient data transmission and storage. This patent provides a foundational technology for next-generation compression standards, offering a significant competitive advantage to adopters.","layman_explanation":"### What Problem Does This Solve?\nImagine you're trying to send a very detailed drawing to someone over a slow internet connection. If you send the whole high-resolution image, it takes ages. So, you compress it. But often, compression means sacrificing important details or making the whole picture look a bit fuzzy. The real business problem here is balancing the need for high-quality digital experiences (like clear video calls, immersive games, or crisp music streaming) with the practical limitations of bandwidth and storage costs. Existing compression methods often make a blanket decision – either everything is compressed equally, which might blur important details, or everything is kept high-res, which slows things down and costs more. This leads to frustrated users, higher operational expenses for service providers, and missed opportunities for delivering truly premium digital content.\n\n### How Does It Work?\nThe **Split Gain Shape Vector Coding** patent introduces a much smarter way to compress data, particularly for things like audio and video. Think of it like this: instead of treating your entire drawing (or a segment of audio/video) as one big, undifferentiated chunk, this technology breaks it down into smaller, more manageable pieces, like individual sections of your drawing. Then, it 'looks' at each small piece and figures out how important it is. For example, the facial expression in a portrait might be very important, while the texture of a plain wall in the background is less so. The innovation then intelligently decides how much 'detail' (or 'bits' in technical terms) to give to each piece. It has a total 'detail budget' for the whole drawing, but it *prioritizes* giving more detail to the important parts, and just enough to the less important ones. It also makes sure no single piece gets an excessive amount of detail, even if it's super important, to keep the overall budget in check. This dynamic, energy-aware allocation ensures that the most perceptually significant parts of the data are preserved with high fidelity, while resources aren't wasted on less critical elements.\n\n### Why Does This Matter?\nThis intelligent approach has profound implications for businesses. Firstly, it means **superior user experience**. Customers get clearer audio, sharper video, and smoother streaming, leading to higher satisfaction and retention for platforms like Netflix, Spotify, or Zoom. Secondly, it translates directly into **cost savings** for businesses. By achieving higher quality at lower bitrates, companies can reduce their bandwidth consumption and data storage costs, which are significant operational expenses. For telecommunication companies, this means more efficient network utilization. For content creators, it allows for wider distribution of high-quality content, even to regions with slower internet. It provides a crucial **competitive advantage** by enabling companies to offer premium services that are both high-performance and cost-effective. This technology allows businesses to push the boundaries of what's possible in digital communication and entertainment without breaking the bank or sacrificing quality.\n\n### What's Next?\nThe principles of **Split Gain Shape Vector Coding** are foundational. We can expect to see this technology, or derivatives of it, integrated into next-generation audio and video codecs, powering everything from ultra-high-definition streaming to advanced virtual reality environments and even more robust IoT communication. Its adaptive nature makes it a perfect fit for dynamic and diverse digital ecosystems. Early adopters and investors in this space will be well-positioned to capitalize on the increasing global demand for efficient, high-quality digital experiences, shaping the future of how we interact with and consume information.","technical_analysis":"The **Split Gain Shape Vector Coding** patent (US-9853659) details an advanced method for encoding and decoding vector segments, primarily aimed at optimizing bit allocation within a constrained budget. This technical analysis will delve into the architecture, algorithmic specifics, and implications for implementation.\n\n**System Architecture and Core Components:**\nThe invention describes an encoder-decoder pair. The encoder's primary function is to transform a target vector `x` into an efficiently compressed representation, while the decoder reconstructs it. The core of the encoding process revolves around intelligent segmentation and adaptive bit allocation. Key components include:\n1.  **Vector Segmentation Module:** Responsible for initially dividing the target vector `x` into `Np_` segments. This initial segmentation could be uniform or based on preliminary signal characteristics.\n2.  **Bit Budget Calculator:** Determines the `BAVG` (Average Number of Bits per Segment) based on the overall `vector bit budget` and `Np_`.\n3.  **Energy Analysis Module:** Crucially, this module computes the energy (or perceptual importance) of each of the `Np_` segments. This energy profile is central to the adaptive allocation.\n4.  **Adaptive Bit Allocation Logic:** This is the heart of the innovation. It takes the segment energies, `BAVG`, and a `BMAX` (Maximum Number of Bits allowed for encoding a vector segment) constraint to determine the *final* number of segments and their respective bit allocations for the gain shape vector encoding. The logic aims to allocate more bits to higher-energy segments, up to `BMAX`, while optimizing for the overall bit budget.\n5.  **Gain Shape Vector Quantizer:** Performs the actual quantization of the gain and shape components of each segment using the adaptively allocated bits.\n\n**Algorithm Specifics:**\nThe method outlines a multi-step process for the encoder:\n1.  **Initial Segmentation:** The input `target vector x` is initially divided into `Np_` segments. This could be a fixed number or dynamically determined based on the vector's characteristics.\n2.  **Average Bit Calculation:** `BAVG = (Total Vector Bit Budget) / Np_`. This provides a baseline for bit distribution.\n3.  **Energy-Based Refinement:** For each of the `Np_` segments, its energy (e.g., RMS power, spectral centroid, etc., depending on the application) is determined. The core of the algorithm then leverages these energies in conjunction with `BMAX` and `BAVG` to decide the *final* bit allocation for each segment. The patent implies an iterative or heuristic process where segments with energy significantly above the average might receive additional bits (up to `BMAX`), while segments below average might contribute bits to a shared pool, or have their allocation reduced. The `difference between BMAX and BAVG` is a critical parameter for this dynamic adjustment, allowing flexibility in increasing bit counts for important segments without exceeding a hard upper limit.\n4.  **Gain and Shape Quantization:** After the final bit allocation is determined, each segment's gain and shape vectors are quantized using the specific number of bits assigned to them. This ensures that the most perceptually significant parts of the signal are encoded with higher fidelity.\n\n**Implementation Details and Performance Characteristics:**\nImplementing this patent would involve developing robust modules for real-time energy estimation and an efficient algorithm for dynamic bit redistribution. The choice of `Np_` and the specific energy metric would be application-dependent. For audio, `Np_` might correspond to psychoacoustic critical bands or fixed time frames, and energy could be loudness or spectral flatness.\n\nPerformance-wise, this approach offers significant advantages over static bit allocation. It can achieve higher perceptual quality at lower average bitrates because bits are not wasted on perceptually irrelevant information. This translates to improved compression ratios without noticeable artifacts. The computational overhead for the adaptive allocation logic would need to be carefully optimized, but the benefits in terms of bitrate efficiency and quality are substantial, particularly in real-time communication systems where latency and bandwidth are critical constraints. Integration into existing codecs (e.g., ACELP, CELP-based speech codecs, or transform coders) would primarily involve replacing or augmenting their fixed bit allocation stages with this adaptive mechanism. The patent specifically enables an efficient allocation of the bits of the bit budget over the target vector, improving overall system efficiency.","business_analysis":"The **Split Gain Shape Vector Coding** patent (US-9853659) represents a significant strategic asset for companies operating in digital media, telecommunications, and any sector reliant on efficient data transmission and storage. Its intelligent approach to vector encoding offers a compelling business advantage in a market increasingly driven by high-quality, low-latency content.\n\n**Market Opportunity Size:**\nThe global market for audio and video streaming, teleconferencing, and digital communication is colossal and continues to expand rapidly. This includes consumer-facing platforms (Netflix, Spotify, Zoom) and enterprise solutions (cloud communication, data analytics). All these rely heavily on efficient compression. The ability to deliver superior quality with less bandwidth taps directly into a market worth hundreds of billions, offering substantial opportunities for licensing, integration into proprietary codecs, or as a foundational technology for new products.\n\n**Competitive Advantages:**\n1.  **Superior Quality at Lower Bitrates:** This is the paramount advantage. The invention allows for the maintenance or improvement of perceptual quality even at reduced bitrates, directly addressing the core trade-off in compression. This means a better user experience (less buffering, clearer audio/video) and lower operational costs for data transmission.\n2.  **Bandwidth Efficiency:** Reduced bandwidth requirements translate to significant cost savings for service providers (ISPs, cloud platforms) and enables higher quality services over existing infrastructure. It also makes high-quality content more accessible in regions with limited bandwidth.\n3.  **Adaptive and Flexible:** The technology's adaptive nature means it can dynamically respond to varying content characteristics (e.g., quiet vs. loud audio, simple vs. complex video scenes), providing consistent performance across diverse media.\n4.  **Differentiation:** Companies adopting or licensing this technology can differentiate their products and services by offering demonstrably superior audio/video quality or efficiency compared to competitors relying on less sophisticated, static compression methods.\n\n**Revenue Potential and Business Models:**\nRevenue streams could include:\n*   **Licensing:** Patent holders can license the technology to codec developers, device manufacturers, and service providers.\n*   **Integration:** Companies can integrate this technology into their own proprietary codecs or hardware, improving their product offerings and capturing market share.\n*   **Consulting/Services:** Offering expertise in implementing and optimizing `Split Gain Shape Vector Coding` for specific applications.\n*   **New Product Development:** Developing new streaming platforms, communication tools, or data storage solutions that leverage the inherent efficiencies of this patent.\n\n**Strategic Positioning:**\nThis innovation positions companies as leaders in advanced signal processing and data compression. It allows them to future-proof their infrastructure and services against ever-increasing data demands. For hardware manufacturers, it means more efficient chipsets for media processing. For content creators, it enables broader reach and higher quality delivery.\n\n**ROI Projections:**\nInvesting in `Split Gain Shape Vector Coding` or its implementation can yield substantial ROI through:\n*   **Reduced CDN/Bandwidth Costs:** Direct operational savings for streaming and communication platforms.\n*   **Increased Subscriber Retention:** Superior user experience leads to higher satisfaction and lower churn.\n*   **Market Share Growth:** Attracting new users/customers with higher quality and more reliable services.\n*   **Competitive Edge:** Being an early adopter or developer of this technology creates a strong market position, potentially leading to premium pricing or faster market penetration.\n\nIn essence, this patent provides a crucial piece of the puzzle for optimizing digital communication and media delivery, making it an attractive proposition for strategic investment and development.","faqs":[{"answer":"**Split Gain Shape Vector Coding** refers to a patented technology (US-9853659) that introduces an advanced method for encoding and decoding digital signals, primarily focusing on vector segments. At its core, this innovation optimizes data compression by intelligently allocating bits (units of digital information) across different parts of a signal, such as audio or video.\n\nUnlike traditional compression methods that might distribute bits uniformly or with simpler rules, this approach is dynamic and adaptive. It assesses the 'importance' or 'energy' of various segments within a target vector and then assigns bits accordingly. This ensures that the most perceptually significant portions of a signal receive higher fidelity encoding, while less critical parts are compressed more aggressively, all within a predefined overall bit budget. This results in superior quality at lower bitrates, making digital communication more efficient and effective.\n\nThe system involves an encoder that determines an initial number of segments for a target vector, calculates an average bit allocation, and then refines this allocation based on segment energies and maximum bit constraints. This refined allocation is then used for the gain shape vector encoding, providing an optimized output. Ultimately, **Split Gain Shape Vector Coding** is a sophisticated solution to the long-standing challenge of balancing data quality with bandwidth efficiency in digital media.","keywords":["Split Gain Shape Vector Coding definition","what is vector coding","data compression technology","patent US-9853659","signal encoding"],"question":"What is Split Gain Shape Vector Coding?"},{"answer":"The operational mechanism of **Split Gain Shape Vector Coding** involves a multi-step, intelligent process performed by an encoder. First, an input signal, represented as a 'target vector x,' is initially divided into a certain number of segments, `Np_`. This initial segmentation sets the stage for granular analysis.\n\nNext, the encoder calculates an 'average number of bits per segment,' or `BAVG`. This `BAVG` is derived from the total bit budget available for the entire vector `x` and the initial number of segments `Np_`. It serves as a baseline for bit distribution. The crucial innovation then occurs: the system determines the *final* number of segments and, more importantly, the specific bit allocation for each segment. This determination is dynamic and based on two key factors: the 'energies' (or perceptual importance) of the `Np_` segments and the 'difference between BMAX and BAVG,' where `BMAX` is the maximum number of bits allowed for any single vector segment.\n\nBy considering segment energies, the system identifies which parts of the signal are perceptually more significant. For example, a loud passage in music or a distinct voice in speech would have higher energy. The algorithm then intelligently allocates more bits to these high-energy segments (up to `BMAX`) and fewer bits to low-energy segments, ensuring that the overall bit budget is efficiently utilized. This adaptive distribution ensures that the most critical information is preserved with high fidelity, while data volume is minimized, leading to superior compression and quality outcomes. The decoder then reverses this process, reconstructing the signal based on the variable bit allocations.","keywords":["how vector coding works","adaptive bit allocation","energy-based encoding","gain shape vector quantization process","Split Gain Shape Vector Coding mechanism"],"question":"How does Split Gain Shape Vector Coding work?"},{"answer":"**Split Gain Shape Vector Coding** primarily solves the pervasive problem of inefficient bit allocation in data compression, particularly within vector encoding schemes. In many digital communication and multimedia systems, there's a constant trade-off between achieving high data quality and minimizing bandwidth consumption or storage space. Traditional compression methods often struggle to strike an optimal balance.\n\nPrior art frequently employs static or less sophisticated adaptive bit allocation strategies. These approaches either waste bits on perceptually irrelevant parts of a signal (e.g., allocating the same number of bits to silence as to a loud sound), or they reduce bits uniformly, leading to a noticeable degradation in critical, perceptually important areas. This results in suboptimal compression ratios, visible artifacts in video, or audible distortions in audio, ultimately compromising the user experience and increasing operational costs for service providers.\n\nThis patent addresses this by introducing a method that intelligently and dynamically distributes bits based on the perceptual significance (energy) of each segment of a signal. By ensuring that more bits are allocated where they matter most, and fewer where they are less critical, **Split Gain Shape Vector Coding** allows for the creation of higher-quality digital content at lower bitrates, effectively resolving the long-standing dilemma between quality and efficiency. It enables a more perceptually accurate and bandwidth-efficient representation of the original data.","keywords":["data compression challenges","bit allocation problem","quality vs bandwidth","vector encoding issues","Split Gain Shape Vector Coding benefits"],"question":"What problem does Split Gain Shape Vector Coding solve?"},{"answer":"The inventors of the **Split Gain Shape Vector Coding** patent, identified by the patent number US-9853659, are not specified in the provided patent data. While the patent document itself would typically list the individual inventors and the assignee (the company or entity to whom the patent rights are assigned), this information was not furnished in the current request.\n\nGenerally, patents like this are the result of extensive research and development efforts by a team of engineers, scientists, or researchers working within a corporation, university, or independent research institution. These individuals contribute their expertise in areas such as digital signal processing, telecommunications, and audio/video compression algorithms.\n\nThe absence of specific names in this context does not diminish the technical achievement of **Split Gain Shape Vector Coding**. The innovation stands on its own merits as a significant contribution to the field of efficient data encoding. Further investigation into the official patent records (e.g., via the USPTO database) for US-9853659 would reveal the precise names of the inventors and the assignee associated with this groundbreaking technology.","keywords":["Split Gain Shape Vector Coding inventors","patent US-9853659 inventors","patent assignee","digital signal processing researchers","compression algorithm creators"],"question":"Who invented Split Gain Shape Vector Coding?"},{"answer":"**Split Gain Shape Vector Coding** offers several key benefits that significantly enhance the efficiency and quality of digital media and communication systems. These advantages stem directly from its intelligent, adaptive bit allocation strategy.\n\nFirstly, it delivers **superior perceptual quality at lower bitrates**. By dynamically allocating more bits to perceptually important segments of a signal (e.g., a critical vocal passage in music, or a sharp edge in a video frame) and fewer to less critical ones, the technology ensures that the most noticeable details are preserved with high fidelity. This means users experience clearer audio and sharper video, even when bandwidth is limited, leading to a much-improved overall experience.\n\nSecondly, it results in **enhanced bandwidth efficiency and reduced data consumption**. Because bits are used more intelligently, less total data is required to achieve a given level of quality. This translates into faster streaming, quicker downloads, and reduced operational costs for internet service providers and content delivery networks. For mobile users, it means consuming less data while enjoying high-quality content. Thirdly, the adaptive nature of **Split Gain Shape Vector Coding** makes it **robust and flexible** across diverse content types, maintaining optimal performance whether dealing with quiet speech or complex, dynamic audio and video.\n\nFinally, this innovation provides a **competitive advantage** for companies that adopt it, allowing them to offer higher-quality services or more cost-effective solutions than competitors relying on less sophisticated compression techniques. These benefits collectively position **Split Gain Shape Vector Coding** as a foundational technology for future advancements in digital media.","keywords":["benefits of Split Gain Shape Vector Coding","improved audio quality","reduced bandwidth","efficient data compression advantages","enhanced streaming experience"],"question":"What are the key benefits of Split Gain Shape Vector Coding?"},{"answer":"**Split Gain Shape Vector Coding** distinguishes itself from prior art in data compression, particularly in vector quantization, through its highly granular and intelligently constrained adaptive bit allocation. Many prior art methods, especially older ones, relied on either fixed bit allocations or simpler adaptive schemes that lacked the precision and dynamic optimization capabilities of this patented technology.\n\nFor instance, fixed bit allocation assigns a predetermined number of bits to each segment regardless of its content, leading to inefficiencies where bits are wasted on perceptually irrelevant information or insufficient for critical data. Simpler adaptive methods might allocate more bits based on broad energy thresholds but often fail to account for specific maximum bit constraints per segment or to fully optimize across the entire bit budget with such fine-tuned control. They might also lack the sophisticated interplay between average bit allocation (`BAVG`) and maximum bit limits (`BMAX`) that is central to this invention.\n\nThe key differentiation of **Split Gain Shape Vector Coding** lies in its explicit consideration of segment energies, `BAVG`, and the `difference between BMAX and BAVG` to determine the *final, precise* bit allocation for each vector segment. This allows for a more nuanced and perceptually optimized distribution of bits, ensuring that the most important parts of a signal receive the necessary fidelity without exceeding individual segment limits or the overall budget. This advanced level of adaptive control enables superior quality-to-bitrate ratios and more efficient bandwidth utilization compared to less sophisticated prior art. It addresses the fundamental trade-off of compression with a level of intelligence that significantly outperforms previous approaches.","keywords":["Split Gain Shape Vector Coding vs prior art","adaptive compression differences","vector quantization comparison","innovation in bit allocation","US-9853659 competitive edge"],"question":"How is Split Gain Shape Vector Coding different from prior art?"},{"answer":"**Split Gain Shape Vector Coding** is poised to significantly impact a wide array of industries that rely heavily on efficient and high-quality digital media transmission and storage. Its core benefits in adaptive data compression make it a foundational technology for numerous applications.\n\n**Telecommunications** will see a major impact, as the technology can enable clearer voice and video calls, more efficient use of cellular and broadband networks, and reduced infrastructure costs for service providers. This is crucial for 5G deployments and beyond, where massive data volumes are expected. The **Digital Media and Entertainment** sector, including streaming services (audio and video), online gaming, and content creation, will benefit from superior quality at lower bitrates, leading to better user experiences, reduced buffering, and cost savings for content delivery networks.\n\n**Virtual Reality (VR) and Augmented Reality (AR)** are also prime candidates for impact. These immersive technologies demand incredibly high data throughput for realistic visuals and audio. **Split Gain Shape Vector Coding** can help reduce the data load, enabling smoother, more responsive, and higher-fidelity VR/AR experiences. Furthermore, industries involved in **Cloud Computing and Data Storage** can leverage this innovation to store and transmit data more efficiently, reducing storage footprints and data transfer costs. Even **Internet of Things (IoT)** devices, especially those transmitting audio or video data, could benefit from this efficient coding, extending battery life and improving network performance. Overall, any sector where balancing high-quality digital information with efficient resource use is critical will find value in this patent.","keywords":["Split Gain Shape Vector Coding industry impact","telecom innovation","digital media applications","VR/AR technology","cloud computing efficiency"],"question":"What industries will Split Gain Shape Vector Coding impact?"},{"answer":"The patent for **Split Gain Shape Vector Coding**, identified by the number US-9853659, was filed on **2017-02-07**. This is the date when the patent application was initially submitted to the patent office, marking the official beginning of the patent prosecution process. The filing date is crucial as it typically establishes the priority date for the invention, meaning that the inventor's claim to the invention's novelty and inventiveness is measured from this point.\n\nSubsequently, the patent was published on **2017-12-26**. The publication date marks when the patent application, including its detailed description, claims, and drawings, is made publicly available. This allows the public and other innovators to review the details of the invention, contributing to the body of public technical knowledge. While the publication date is important for public disclosure, the patent is officially granted at a later stage, after examination and approval by the patent office.\n\nTherefore, while the filing occurred in early 2017, the details of **Split Gain Shape Vector Coding** became accessible to the public towards the end of the same year. The granted date (not provided in the initial data) would signify the point at which the patent owner gains enforceable rights over the invention.","keywords":["Split Gain Shape Vector Coding filing date","US-9853659 publication date","patent timeline","patent application process","intellectual property dates"],"question":"When was Split Gain Shape Vector Coding filed/granted?"},{"answer":"The commercial applications of **Split Gain Shape Vector Coding** are extensive and span across various high-growth sectors, driven by the increasing demand for efficient and high-quality digital experiences. Its core ability to optimize data compression makes it a valuable asset for numerous products and services.\n\nOne primary application is in **Digital Streaming Services**, including video platforms (e.g., Netflix, YouTube) and audio platforms (e.g., Spotify, Apple Music). By enabling higher quality content at lower bitrates, it allows these services to reduce bandwidth costs, enhance user experience through less buffering, and expand their reach to users with limited internet connectivity. Another significant area is **Real-time Communication**, such as video conferencing (e.g., Zoom, Microsoft Teams) and VoIP services. This technology can lead to clearer audio and sharper video, improving the reliability and quality of remote work, education, and social interactions.\n\nFurthermore, **Gaming and Immersive Technologies** like Virtual Reality (VR) and Augmented Reality (AR) can greatly benefit. These applications require massive data throughput for realistic graphics and audio, and **Split Gain Shape Vector Coding** can help reduce latency and data load, making VR/AR experiences smoother and more engaging. It also has applications in **Digital Broadcasting**, enabling more efficient transmission of high-definition content, and in **Cloud Storage Solutions**, where optimizing file sizes can lead to reduced storage costs and faster data retrieval. Ultimately, any commercial product or service that relies on compressing and transmitting digital audio, video, or similar vector-based data can leverage **Split Gain Shape Vector Coding** to gain a competitive edge by offering superior performance and cost efficiency.","keywords":["commercial uses of Split Gain Shape Vector Coding","streaming service applications","video conferencing technology","VR/AR commercial uses","digital media business"],"question":"What are the commercial applications of Split Gain Shape Vector Coding?"},{"answer":"The foundational principles of **Split Gain Shape Vector Coding** lay the groundwork for a variety of exciting future developments and enhancements in data compression and digital signal processing. As technology evolves, we can anticipate several directions for this innovation.\n\nOne key area for future development is the **integration with Artificial Intelligence and Machine Learning**. AI could be used to predict the perceptual importance of segments more accurately in real-time, or to dynamically adjust `BMAX` and other parameters based on network conditions, user preferences, or content type. This would make the adaptive bit allocation even more intelligent and responsive. Another potential development involves **application in new immersive technologies**. As the metaverse, digital twins, and advanced holographic communication become more prevalent, the need for ultra-efficient, high-fidelity data transfer will skyrocket. **Split Gain Shape Vector Coding** could be adapted to compress new forms of sensory data, such as haptics or spatial audio, critical for these emerging platforms.\n\nFurther research might focus on **cross-modal compression**, where audio and video segments are jointly optimized, leveraging interdependencies for even greater efficiency. Additionally, advancements in **hardware acceleration** specific to the algorithms of **Split Gain Shape Vector Coding** could lead to even faster and more energy-efficient encoding and decoding, crucial for mobile and edge computing devices. Finally, we could see its **incorporation into future industry standards** for audio and video codecs, solidifying its role as a benchmark for efficient, high-quality digital communication. These developments promise to extend the impact of **Split Gain Shape Vector Coding**, continually pushing the boundaries of what's possible in the digital realm.","keywords":["future of Split Gain Shape Vector Coding","AI in data compression","next-gen codecs","metaverse compression","signal processing advancements","US-9853659 future"],"question":"What are the future developments expected for Split Gain Shape Vector Coding?"}],"topics":["Split Gain Shape Vector Coding","data compression","vector encoding","bit allocation","signal processing","quest","optimal","compression","Split Gain Shape Vector Coding definition","what is vector coding","how vector coding works","adaptive bit allocation"],"tech_cluster":null},"seo":{"title":"Split Gain Shape Vector Coding - Patent US-9853659","description":"Discover Split Gain Shape Vector Coding: a patent revolutionizing data compression with adaptive bit allocation for superior quality and efficiency. Full analysis here.","keywords":["Split Gain Shape Vector Coding","data compression","vector encoding","bit allocation","signal processing","audio compression","video encoding","bandwidth efficiency","telecommunications patent","US-9853659","gain shape vector quantization","adaptive coding","digital media optimization","patentable app"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9853659","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-9853659","citation_suggestion":"Patentable. \"Split gain shape vector coding\" (US-9853659). https://patentable.app/patents/US-9853659","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9853659","json":"https://patentable.app/api/llm-context/US-9853659","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T09:00:48.057Z"}