{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9853662","patent":{"patent_number":"US-9853662","title":"Random access optimization for redundancy coded data storage systems","assignee":null,"inventors":[],"filing_date":"2015-06-17T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["G06F","H04L","H04L"],"num_claims":20,"abstract":"Techniques described and suggested herein include systems and methods for optimizing random access characteristics for data archives stored on data storage systems using redundancy coding techniques. For example, redundancy coded shards, which may include identity shards that contain unencoded original data of archives, may be configured such that a variable number of the shards can be leveraged to meet random access requirements for retrieval requests associated with the archives stored and/or encoded therein. Implementing systems may monitor random access rates, capabilities, and burdens, so as to adaptively account for changes to some or all of the monitored parameters."},"analysis":{"summary":"The patent **Random Access Optimization for Redundancy Coded Data Storage Systems** (US-9853662) introduces an innovative approach to resolve the long-standing challenge of slow random access in large-scale data archives protected by redundancy coding. Its core innovation lies in dynamically configuring and utilizing data shards, including 'identity shards' that contain unencoded original data, to meet specific data retrieval requirements. This technology directly addresses the inefficiency of traditional systems that often require reconstructing large data blocks even for small random access requests.\n\nThe problem this patent solves is the inherent trade-off between data durability (achieved through redundancy coding) and retrieval speed, particularly for random, non-sequential access patterns. Conventional redundancy coding, while excellent for fault tolerance, can introduce significant latency due to the computational overhead of reconstructing data from multiple dispersed fragments.\n\nThe key technical approach involves an adaptive system that can leverage a variable number of data shards. For instance, if an archive is configured with identity shards, the system can prioritize direct access to these unencoded segments for maximum speed. For other data, it intelligently determines the minimal set of encoded shards required for efficient reconstruction. Crucially, the system continuously monitors random access rates, system capabilities, and current loads, allowing it to adapt its retrieval strategies in real-time. This dynamic optimization ensures consistent, high-performance data access under fluctuating conditions.\n\nThe business value and applications are substantial. This innovation significantly reduces latency for data retrieval from archival systems, leading to faster analytics, more responsive cloud services, improved regulatory compliance, and enhanced operational efficiency for data-intensive enterprises. Industries such as cloud computing, big data analytics, media and entertainment, and financial services, which rely heavily on massive, durable, yet accessible data archives, stand to benefit immensely.\n\nFrom a market opportunity perspective, this technology enables the creation of 'hot archives' – storage systems that offer both long-term data protection and near real-time access, a capability previously difficult to achieve. This opens new avenues for data monetization, real-time decision-making from historical data, and a competitive advantage for providers offering superior data access performance.","layman_explanation":"### 1. What Problem Does This Solve?\nImagine your business relies on a massive digital library – perhaps years of customer records, financial transactions, or media content. To keep this library safe from damage or loss, you've used a clever system called 'redundancy coding.' This system breaks down each book (piece of data) into many small fragments and scatters them across different shelves (storage devices). If one shelf breaks, you can still reconstruct the book from the fragments on other shelves. It’s incredibly secure and resilient.\n\nThe big problem, however, is when you need to quickly find just one specific paragraph from one specific book in this vast, fragmented library. The traditional system often has to gather *many* fragments from *many* shelves and piece together a *large section* of the book, even if you only needed a few words. This process is time-consuming and resource-intensive, leading to significant delays. For businesses, these delays translate into slow customer service, sluggish analytics, missed opportunities, and compliance headaches. Existing solutions often compromise either security (by using less robust protection) or cost (by throwing more hardware at the problem), neither of which is ideal.\n\n### 2. How Does It Work?\nThis patent, **Random Access Optimization for Redundancy Coded Data Storage Systems**, introduces a 'smart librarian' to your digital library. This librarian understands that not all fragments are created equal. Some 'fragments' are actually complete, untouched copies of popular paragraphs or even entire short stories (these are called 'identity shards'). When you ask for a specific paragraph, the smart librarian first checks if there's a complete copy of that paragraph or story readily available. If so, it hands it to you instantly, without any reconstruction effort.\n\nIf a complete copy isn't available, the smart librarian doesn't panic and gather everything. Instead, it intelligently identifies *just enough* of the right fragments from *just the right shelves* to quickly piece together only the specific paragraph you asked for. It doesn't rebuild the entire book if you only needed a sentence. Furthermore, this librarian is constantly observing: Which books are people asking for most often? Which shelves are fast, and which are slow? How busy is the library? Based on this real-time feedback, it continuously adjusts its strategy, moving popular complete copies to the most accessible shelves or finding alternative, faster ways to retrieve fragments if certain shelves are busy. This adaptive approach ensures that data access is always as fast and efficient as possible.\n\n### 3. Why Does This Matter?\nThis innovation matters because it fundamentally changes the economics and capabilities of data storage. For businesses, it means:\n*   **Faster Decision-Making:** Imagine analyzing years of sales data in seconds, not minutes, to spot emerging market trends. This technology enables 'hot archives' – making historical data immediately actionable.\n*   **Improved Customer Experience:** Cloud service providers can offer faster, more reliable access to stored files and applications, leading to higher customer satisfaction and retention.\n*   **Reduced Operational Costs:** By optimizing retrieval, businesses can get more performance out of existing hardware, potentially delaying costly upgrades and reducing energy consumption associated with unnecessary data reconstruction.\n*   **Enhanced Compliance and Risk Management:** Quickly accessing specific audit trails or regulatory documents becomes effortless, significantly reducing the burden and risk associated with compliance.\n\nThis technology provides a significant competitive advantage, allowing businesses to offer services that were previously impossible or prohibitively expensive, driving both efficiency and innovation.\n\n### 4. What's Next?\nThe principles behind **Random Access Optimization for Redundancy Coded Data Storage Systems** are poised to become a standard in high-performance, resilient data storage. We can expect to see this technology integrated into next-generation cloud storage platforms, enterprise data lakes, and specialized archival solutions. Its ability to balance robust data protection with agile access will unlock new applications in AI/ML training on vast historical datasets, real-time content delivery for media companies, and dynamic data analytics. Businesses that adopt this approach early will be better positioned to leverage their data as a strategic asset, driving market leadership in an increasingly data-centric economy.","technical_analysis":"The patent **Random Access Optimization for Redundancy Coded Data Storage Systems** (US-9853662) outlines a sophisticated architecture and methodology to overcome the inherent random access latency in distributed data storage systems employing redundancy coding. The technical foundation rests on intelligently managing and leveraging data shards, particularly by introducing adaptability into the retrieval process.\n\n**Technical Architecture:**\nAt a high level, the system described in the patent comprises several key components: a data archiving module responsible for encoding original data into redundancy-coded shards (e.g., using erasure codes like Reed-Solomon), a distributed storage layer for storing these shards, and a central 'Random Access Optimizer' module. This optimizer is the brain of the operation, interacting with an 'Access Request Monitor,' a 'System Capability Monitor,' and a 'Shard Selection Logic' component. The architecture emphasizes a feedback loop, where monitoring data informs real-time adjustments to retrieval strategies.\n\n**Implementation Details:**\n1.  **Shard Configuration:** Unlike static erasure coding, this invention proposes configuring archives such that a *variable* number of shards can be used for retrieval. This implies a more granular control over the redundancy scheme or a layered approach. A key aspect is the inclusion of 'identity shards,' which are unencoded copies of portions of the original data. These identity shards are strategically placed and indexed, allowing for direct retrieval without the computational overhead of decoding. The patent suggests that the proportion and placement of identity shards can be customized per archive based on anticipated access patterns or criticality.\n2.  **Adaptive Shard Selection Logic:** When a random access request arrives, the Shard Selection Logic determines the optimal strategy. This logic would first check for the availability of relevant identity shards that can directly fulfill the request. If identity shards are not available or are insufficient, the logic then calculates the minimum set of encoded shards (typically 'k' shards for an (n, k) code) required for reconstruction. This calculation is dynamic, considering factors like shard availability, network latency to individual storage nodes, and current load on those nodes.\n3.  **Real-time Monitoring and Adaptation:** The 'Access Request Monitor' tracks metrics such as the frequency, size, and type of random access requests. The 'System Capability Monitor' continuously assesses the performance of storage nodes (I/O throughput, latency), network conditions, and CPU utilization across the system. This combined telemetry is fed into the Random Access Optimizer. Based on this data, the optimizer can adapt by:\n    *   **Prioritizing identity shards:** If random access rates for specific data segments increase, the system might proactively ensure those identity shards are 'hot' or replicated.\n    *   **Dynamic reconstruction parameters:** Adjusting which 'k' shards are chosen for reconstruction based on their current performance metrics (e.g., avoiding overloaded nodes).\n    *   **Resource allocation:** Dynamically allocating more CPU or network resources to reconstruction tasks during peak demand.\n    *   **Reconfiguration:** Potentially re-encoding or re-distributing shards over time to better align with evolving access patterns.\n\n**Algorithm Specifics:**\nThe adaptive shard selection likely involves a cost-based optimization algorithm. For a given random access request, the algorithm would evaluate the cost (latency, CPU, I/O) of fetching an identity shard versus reconstructing from encoded shards. The reconstruction cost itself would be a function of the number of shards needed, their locations, and the current load on the storage nodes. Machine learning models could be employed to predict future access patterns or system bottlenecks, allowing for proactive optimization.\n\n**Integration Patterns:**\nThis technology can integrate with existing distributed file systems (e.g., HDFS), object storage services (e.g., S3-compatible), and archival systems. The Random Access Optimizer would act as a layer between the application/API gateway and the raw shard storage, intercepting read requests and orchestrating the optimized retrieval. APIs would expose configuration options for defining identity shard policies and monitoring parameters.\n\n**Performance Characteristics:**\nBy minimizing reconstruction operations and intelligently leveraging direct access to identity shards, this invention promises significantly reduced random access latency. The adaptive nature ensures that performance remains robust even under fluctuating workloads or partial system failures, improving overall system responsiveness and resource utilization. This also reduces 'read amplification,' where a small logical read necessitates multiple physical reads, a common issue in traditional erasure-coded systems.\n\n**Code-level Implications:**\nImplementing this patent would involve significant development in the storage orchestration layer. This includes developing sophisticated shard metadata management, real-time telemetry pipelines, and an intelligent decision engine for shard selection. Erasure coding libraries would need to be integrated, but with an added layer of logic to control which shards are used for reconstruction based on the adaptive optimization criteria. The system would also need mechanisms for dynamic shard rebalancing and potentially re-encoding based on long-term access pattern changes.","business_analysis":"The patent **Random Access Optimization for Redundancy Coded Data Storage Systems** (US-9853662) introduces a pivotal technological advancement with profound business implications, particularly for industries reliant on vast, durable, yet quickly accessible data archives. This innovation directly addresses the critical need for speed in an increasingly data-intensive world, transforming a traditional trade-off between data resilience and retrieval performance into a synergistic advantage.\n\n**Market Opportunity Size:**\nThe global data storage market is projected to reach trillions of dollars, with significant segments dedicated to archival storage and cloud services. Within this, the demand for high-performance, resilient storage solutions that can handle complex random access patterns is massive. Industries like cloud computing (estimated at over $600 billion by 2023), big data analytics (hundreds of billions), media and entertainment, financial services, and scientific research all generate and store petabytes of data that require both long-term preservation and rapid, on-demand access. This patent targets a crucial pain point across these sectors, opening up a multi-billion dollar opportunity for licensing, product integration, and service provision.\n\n**Competitive Advantages:**\nThis technology offers several compelling competitive advantages:\n1.  **Superior Performance:** By dramatically reducing random access latency, it provides a significant edge over traditional redundancy-coded systems that suffer from slow retrieval. This translates into faster application response times and improved user experience.\n2.  **Cost Efficiency:** Optimized shard utilization and reduced reconstruction overhead mean less computational power and I/O operations are required for data retrieval. This can lead to lower infrastructure costs (fewer servers, less power consumption) and better utilization of existing resources.\n3.  **Enhanced Data Value:** Turning 'cold' or 'warm' archives into 'hot' archives enables real-time analytics on historical data, faster regulatory compliance, and more agile business intelligence. This unlocks new value from existing data assets.\n4.  **Adaptive Resilience:** The real-time monitoring and adaptive capabilities ensure consistent performance even during system degradation or fluctuating loads, enhancing overall system reliability and availability.\n\n**Revenue Potential and Business Models:**\nRevenue potential for this innovation is high. It can be monetized through:\n*   **Licensing:** Technology companies specializing in storage solutions, cloud providers, and enterprise software vendors could license the patent for integration into their products and services.\n*   **Product Development:** New storage appliances or software-defined storage solutions could be built around this core technology, commanding premium pricing due to superior performance.\n*   **Managed Services:** Cloud providers can offer differentiated 'high-performance archival' tiers, charging more for guaranteed low-latency access to redundancy-coded data.\n*   **Consulting and Integration:** Expertise in implementing and optimizing systems based on this patent would be valuable for large enterprises.\n\n**Strategic Positioning:**\nCompanies leveraging this patent can strategically position themselves as leaders in high-performance, resilient data storage. For cloud providers, it's a differentiator in a competitive market. For enterprises, it enables transformative digital initiatives that were previously bottlenecked by data access speeds. It shifts the perception of archival storage from mere preservation to an active, valuable business asset, aligning perfectly with modern data-driven strategies.\n\n**ROI Projections:**\nThe return on investment for adopting this technology is substantial. Enterprises can expect:\n*   **Operational Cost Savings:** Reduced hardware needs, lower power consumption, and optimized resource utilization.\n*   **Increased Productivity:** Faster data access for employees, analysts, and automated systems.\n*   **Reduced Risk:** Quicker disaster recovery and improved compliance response times.\n*   **New Revenue Streams:** Ability to offer faster data-intensive services or derive deeper, more timely insights from data.\n\nFor a large enterprise, the accumulated savings and increased efficiency could easily translate into millions of dollars annually, solidifying the business case for investing in the principles outlined in **Random Access Optimization for Redundancy Coded Data Storage Systems**.","faqs":[{"answer":"Random Access Optimization for Redundancy Coded Data Storage Systems is an innovative patent (US-9853662) that introduces advanced techniques for enhancing data retrieval performance, specifically for data archives stored using redundancy coding. In simpler terms, it's a smart system designed to make it much faster to find and retrieve specific, small pieces of information from very large datasets that have been broken into many fragments and spread across numerous storage locations for safety.\n\nTraditional data storage systems that use redundancy coding (like erasure coding) are excellent for ensuring data durability – meaning your data is protected even if some storage devices fail. However, they often struggle with 'random access,' which refers to requests for small, non-sequential data segments. When such a request is made, conventional systems might have to reconstruct a large portion of the data from multiple fragments, leading to significant delays and resource consumption.\n\nThis patent fundamentally addresses this inefficiency. It provides methods to intelligently manage and access these data fragments, ensuring that retrieval is optimized for speed without compromising the data's integrity or resilience. It's about transforming slow archival data into a rapidly accessible resource.","question":"What is Random Access Optimization for Redundancy Coded Data Storage Systems?"},{"answer":"The Random Access Optimization for Redundancy Coded Data Storage Systems patent works by employing a multi-faceted approach to intelligent data retrieval. Firstly, it allows for a variable number of data shards (fragments) to be leveraged for a retrieval request, rather than a fixed set. This means the system can adapt the amount of data it needs to fetch and reconstruct based on the specific request.\n\nSecondly, a key innovation is the strategic use of 'identity shards.' These are special fragments that contain unencoded, original data. If a requested data segment is available as an identity shard, the system can retrieve it directly, completely bypassing the time-consuming reconstruction process. This provides near-instantaneous access for critical or frequently accessed data.\n\nFinally, the system incorporates real-time monitoring of random access rates, storage system capabilities, and overall system burden. This continuous feedback loop enables the system to adapt its retrieval strategies dynamically. For example, if a storage device is slow or overloaded, the system can intelligently choose alternative shards or adjust its reconstruction approach to maintain optimal performance, ensuring consistent speed and efficiency under varying conditions. Keywords: variable shards, identity shards, real-time monitoring, adaptive retrieval, data reconstruction.","question":"How does Random Access Optimization for Redundancy Coded Data Storage Systems work?"},{"answer":"The Random Access Optimization for Redundancy Coded Data Storage Systems patent (US-9853662) primarily solves the problem of high latency and inefficiency associated with random access data retrieval from redundancy-coded storage systems. While redundancy coding (like erasure coding) is excellent for ensuring data durability and fault tolerance, it traditionally introduces significant overhead when trying to access small, non-sequential portions of data.\n\nThis is because conventional systems often require fetching a fixed number of data fragments (shards) and then performing a computationally intensive reconstruction process to retrieve the desired data. This 'read amplification' leads to slow response times, increased CPU and I/O utilization, and hinders the ability to use large archives for real-time applications like analytics or fast content delivery.\n\nBy introducing adaptive retrieval strategies, identity shards, and real-time performance monitoring, this technology eliminates this trade-off. It enables organizations to have both the robust data protection of redundancy coding and the high-speed random access performance needed for modern, data-intensive workloads. Keywords: random access latency, redundancy coding problem, data retrieval bottleneck, read amplification, storage inefficiency.","question":"What problem does Random Access Optimization for Redundancy Coded Data Storage Systems solve?"},{"answer":"The patent for Random Access Optimization for Redundancy Coded Data Storage Systems, US-9853662, does not list specific inventors or an assignee in the provided data. However, patents are typically filed by individuals or teams of inventors who develop the technology, and then often assigned to a company or organization that funded the research and development. The assignee would be the legal owner of the patent rights.\n\nIn the context of patent law, the inventors are the individuals who conceived the invention, while the assignee is the entity that holds the rights to commercialize, license, or enforce the patent. For this specific patent, without further information, we cannot name the individual inventors, but the innovation itself is a testament to the ongoing research and development in advanced data storage solutions aimed at improving efficiency and performance in complex distributed systems. Keywords: patent inventors, assignee, US-9853662, patent ownership, invention conception.","question":"Who invented Random Access Optimization for Redundancy Coded Data Storage Systems?"},{"answer":"The Random Access Optimization for Redundancy Coded Data Storage Systems offers several transformative benefits for data storage and retrieval:\n\n1.  **Significantly Reduced Latency:** By enabling direct access to identity shards and optimizing reconstruction from encoded shards, the system drastically cuts down the time required for random data retrieval. This means faster application response times and quicker access to critical information.\n2.  **Enhanced Operational Efficiency:** The adaptive nature minimizes unnecessary data reconstruction and optimizes resource utilization (CPU, I/O, network). This translates into lower operational costs, reduced hardware requirements, and improved energy efficiency for data centers.\n3.  **Improved Performance Under Load:** With real-time monitoring and adaptive strategies, the system maintains high performance even during peak demand or in the event of partial system failures, ensuring consistent Quality of Service (QoS).\n4.  **Unlocks 'Hot Archival' Capabilities:** It allows organizations to treat traditionally 'cold' or 'warm' archives as 'hot' data, enabling real-time analytics on historical datasets, faster regulatory compliance, and more agile business intelligence. This transforms dormant data into an active, valuable asset.\n\nThese benefits combine to provide a robust, high-performance, and cost-effective solution for managing large-scale, redundancy-coded data archives. Keywords: reduced latency, operational efficiency, hot archives, performance under load, data value, storage benefits.","question":"What are the key benefits of Random Access Optimization for Redundancy Coded Data Storage Systems?"},{"answer":"The Random Access Optimization for Redundancy Coded Data Storage Systems patent (US-9853662) differentiates itself from prior art through its adaptive and intelligent approach to data retrieval, moving beyond the limitations of traditional redundancy coding schemes.\n\nPrior art often relies on a fixed 'k-of-n' reconstruction model, where any `k` shards must be fetched and fully reconstructed, regardless of the size or type of the random access request. This static method is inefficient and leads to high latency and resource consumption for small reads. Some prior solutions involved full data replication for speed, which is costly in storage space and lacks the inherent fault tolerance of erasure coding.\n\nThis invention introduces several key distinctions: Firstly, it allows for *variable shard utilization*, intelligently selecting only the necessary shards for a request. Secondly, it integrates 'identity shards' (unencoded original data) directly into the archive, providing a 'fast lane' for direct, reconstruction-free access. Most critically, it features a *real-time adaptive monitoring and adjustment system* that continuously optimizes retrieval strategies based on live system performance and access patterns. This dynamic, self-optimizing capability is largely absent in prior art and is what allows this technology to overcome the traditional trade-off between data durability and random access speed. Keywords: prior art comparison, variable shard utilization, identity shards, adaptive monitoring, fixed k-of-n, data replication.","question":"How is Random Access Optimization for Redundancy Coded Data Storage Systems different from prior art?"},{"answer":"The Random Access Optimization for Redundancy Coded Data Storage Systems patent is poised to have a significant impact across a wide range of data-intensive industries due to its ability to reconcile data durability with high-speed random access.\n\n**Cloud Computing:** Cloud service providers can offer new, high-performance archival tiers, enhancing their competitive edge and enabling customers to build more responsive applications at scale. This will be crucial for services that need to quickly access large volumes of historical user data or content.\n\n**Big Data and Analytics:** Enterprises can accelerate their big data analytics pipelines and machine learning model training by gaining faster access to vast historical datasets stored in cost-effective archives. This translates to quicker insights and more agile decision-making.\n\n**Financial Services:** For fraud detection, regulatory compliance, and risk management, rapid access to historical transaction records and audit trails is paramount. This technology can drastically reduce retrieval times, improving operational efficiency and compliance posture.\n\n**Media and Entertainment:** Companies managing massive content libraries (e.g., video, audio archives) can achieve faster content delivery, more efficient asset management, and quicker access for editing or streaming on demand. Keywords: cloud computing impact, big data analytics, financial services, media and entertainment, industry impact, data-intensive industries.","question":"What industries will Random Access Optimization for Redundancy Coded Data Storage Systems impact?"},{"answer":"The patent for **Random Access Optimization for Redundancy Coded Data Storage Systems** (US-9853662) has distinct filing and publication dates.\n\nThe patent was **filed on June 17, 2015**. This date marks when the inventors submitted their application to the patent office, formally initiating the patent examination process and establishing the priority date for the invention.\n\nIt was subsequently **published (granted) on December 26, 2017**. The publication date signifies that the patent application completed the examination process and was officially issued as a granted patent, making the details of the invention publicly available and conferring exclusive rights to the patent owner. Keywords: patent filing date, patent publication date, US-9853662, patent timeline, invention history, patent grant.","question":"When was Random Access Optimization for Redundancy Coded Data Storage Systems filed/granted?"},{"answer":"The commercial applications of Random Access Optimization for Redundancy Coded Data Storage Systems are extensive and impactful, primarily centered around transforming data archives into active, high-performance assets.\n\n**High-Performance Cloud Storage Tiers:** Cloud providers can implement this technology to offer premium archival storage tiers that guarantee low random access latency, appealing to customers with strict performance requirements for their cold data. This could be marketed as 'hot archives' or 'real-time archival storage.'\n\n**Accelerated Big Data Lakes:** Integrating this patent into data lake architectures allows businesses to perform faster queries and analytics on vast, historical datasets without needing to move data to more expensive, less durable storage. This enhances the value and usability of data lakes for AI/ML training and business intelligence.\n\n**Enterprise Archival and Compliance Solutions:** Companies can develop or enhance enterprise-grade archival solutions that meet stringent regulatory compliance requirements while providing rapid access for audits, legal discovery, and internal investigations. This reduces the operational burden and risk associated with managing compliance data.\n\n**Content Delivery Networks (CDNs) for Media Archives:** For media companies, this technology can enable faster delivery of archived video, audio, and image content, improving user experience for streaming services and content creators. It allows for more efficient monetization of deep content libraries. Keywords: commercial applications, cloud storage, big data lakes, enterprise archives, CDN, data monetization, real-time analytics.","question":"What are the commercial applications of Random Access Optimization for Redundancy Coded Data Storage Systems?"},{"answer":"Future developments for Random Access Optimization for Redundancy Coded Data Storage Systems are expected to push the boundaries of intelligent storage, leading to more autonomous and integrated data management systems.\n\nOne key area is the integration of advanced **Machine Learning (ML) and Artificial Intelligence (AI)**. ML models could be trained to predict future random access patterns with high accuracy, enabling the system to proactively place identity shards, pre-fetch data, or dynamically adjust encoding parameters before demand surges. This would move the system from reactive adaptation to proactive optimization.\n\nAnother development could involve **cross-layer and heterogeneous storage optimization**. The principles of this patent could be extended to optimize retrieval across different storage media (e.g., SSD, HDD, tape) and across different geographical locations, creating a truly unified and performant global data fabric. This would involve deeper integration with underlying hardware and network protocols.\n\nFurthermore, we can expect advancements in **security and privacy-preserving retrieval**. As data access becomes more granular and dynamic, ensuring that these optimizations do not introduce new vulnerabilities or compromise data privacy will be paramount. This could involve secure multi-party computation or homomorphic encryption techniques integrated with adaptive shard selection. The overall trend points towards increasingly intelligent, self-managing, and highly efficient data storage ecosystems, where the **Random Access Optimization for Redundancy Coded Data Storage Systems** forms a foundational component. Keywords: future developments, AI/ML integration, storage optimization, heterogeneous storage, security, privacy, data fabric, predictive analytics.","question":"What are the future developments expected for Random Access Optimization for Redundancy Coded Data Storage Systems?"}],"topics":["random access optimization","redundancy coded data","data storage systems","data retrieval","identity shards","burgeoning","volume","demands"],"tech_cluster":null},"seo":{"title":"Random Access Optimization for Redundancy Coded Data Storage Systems - Patent US-9853662","description":"Discover how Random Access Optimization for Redundancy Coded Data Storage Systems revolutionizes data retrieval from redundancy-coded archives. Faster, adaptive, efficient access.","keywords":["random access optimization","redundancy coded data","data storage systems","data retrieval","identity shards","adaptive storage","patent US-9853662","storage efficiency","cloud storage","data archives","erasure coding","high performance storage","data access latency","distributed systems"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9853662","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-9853662","citation_suggestion":"Patentable. \"Random access optimization for redundancy coded data storage systems\" (US-9853662). https://patentable.app/patents/US-9853662","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9853662","json":"https://patentable.app/api/llm-context/US-9853662","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T09:16:48.760Z"}