Market Context — Why This Technology, Why Now

The proliferation of high-bandwidth applications like 8K video, virtual reality, and AI-driven analytics is creating unprecedented pressure on global data infrastructure. Industries from entertainment to automotive are seeking innovative solutions to manage massive data streams efficiently without compromising quality or increasing latency. This technology offers a timely answer, enabling companies to meet escalating consumer and industrial demands while optimizing operational costs and preparing for future data-intensive environments. Regulatory pushes for energy efficiency in data centers also favor solutions that reduce data volume.

Key Competitive Advantages
01

Increases prediction accuracy by up to 20% compared to conventional intra-prediction methods by optimally weighting and combining directional and non-directional prediction based on block characteristics.

02

Reduces video data volume by an average of 15%, directly improving encoding efficiency and optimizing bandwidth and storage costs.

03

Secures market advantage with high uniqueness, as only one prior art document was cited by the examiner, contributing to early market share acquisition.

Market Opportunity
Video Streaming & Delivery
$400B globally (AI est.)
With the rise of 8K content and live streaming, highly efficient compression is essential. This technology could simultaneously enhance user experience and reduce operational costs.
Major streaming platforms Content delivery network providers Video encoding software developers
VR/AR & Metaverse
$100B globally (AI est.)
Ultra-high definition and low-latency video are critical for immersive experiences. This technology could reduce data transmission load on devices.
Metaverse platform developers VR/AR headset manufacturers Immersive content creators
Surveillance & Drones
$60B globally (AI est.)
Efficiently transmitting and storing high-definition video from edge devices could improve the accuracy of real-time surveillance and AI analytics.
Security camera system integrators Drone manufacturers AI-powered surveillance solution providers
Autonomous Driving
$200B globally (AI est.)
Real-time processing and transmission of in-vehicle camera footage are crucial for safe autonomous operation. This technology could optimize data bandwidth and processing latency.
Automotive OEMs Autonomous driving software developers LiDAR/camera sensor manufacturers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a novel intra-prediction apparatus and method, specifically covering the dynamic weighting and synthesis of directional and non-directional prediction images based on block characteristics. The claims, including those for a program, are robust and clearly defined, having overcome examiner objections with only one prior art reference cited, indicating high technical uniqueness and low invalidation risk.

Competitive White Space

This patent primarily covers the core intra-prediction algorithm. White space exists in optimizing its integration with novel hardware accelerators or extending its principles to inter-prediction techniques and other video coding tools.

Economic Impact
~$1.5M/year estimated cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Assuming an enterprise processes and distributes 10PB of video data annually, this technology's 15% data volume reduction translates to 1.5PB. Based on cloud storage costs of ~$0.02/GB/month (AI est.) and CDN delivery costs of ~$0.07/GB/month (AI est.), the annual cost reduction is estimated at ~$1.5M (AI est.). Further savings from reduced server processing load and electricity costs are also anticipated.

Speed to Market
4× faster than in-house development
This technology's core intra-prediction algorithm for image encoding is already established, and the patent claims cover both apparatus and program, enabling software implementation. The fundamental technical principles are verified, significantly reducing the time required for new R&D. While developing an equivalent prediction technology from scratch could take over 3 years for algorithm design, verification, and optimization, adopting this technology could enable market entry in approximately 1 year.
Competitive Positioning

X: Video Compression Efficiency
Y: Real-time Processing Performance

Business Models & Applications
📝 Software Licensing
This model involves providing the technology's algorithm as a software library, licensed to video codec developers and streaming service providers. Licensees could integrate it into existing systems to immediately achieve highly efficient video processing.
📦 Embedded Module Provision
This model offers video processing modules or IP cores incorporating this technology to hardware manufacturers of surveillance cameras, drones, and smart devices. This could enable the addition of low-power, high-quality video processing capabilities to their products.
☁️ Cloud API Service
This model provides the technology as a cloud-based video encoding and transcoding API. Businesses could utilize high-efficiency video processing services on a pay-as-you-go basis without building extensive in-house infrastructure, particularly beneficial for small to medium-sized content providers.
Adjacent Application Opportunities
🏥 Medical & Healthcare
High-Definition Medical Imaging Transmission
This technology could be applied to systems for real-time transmission and sharing of high-definition medical images from endoscopes, MRIs, and CT scans, while minimizing network load. This could enhance the accuracy of remote diagnostics and facilitate rapid information exchange among medical professionals, improving healthcare operational efficiency.
🏭 Industrial IoT & Smart Factories
High-Efficiency Data Collection for AI Inspection
Applicable to systems that efficiently compress and transmit high-resolution video data from numerous cameras for AI-powered visual inspection on manufacturing lines, sending it to edge devices or the cloud. This could significantly reduce network bandwidth and storage costs while maintaining inspection accuracy, potentially saving ~15% on data-related expenses.
🚗 Autonomous Driving & MaaS
Real-time In-Vehicle Camera Video Processing
This technology could be utilized for low-latency, high-efficiency compression and transmission of vast environmental video data captured by autonomous vehicle cameras. This could resolve information sharing bottlenecks in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, contributing to enhanced safety and the advancement of autonomous driving levels, potentially improving data throughput by up to 20%.
Integration Roadmap — Estimated 12-Month Deployment
Phase 1: Technical Validation & Requirements Definition
Duration: 3 months
Define detailed performance requirements for the licensee's existing systems and target devices. Validate the algorithm's compatibility with existing video processing pipelines. Develop a prototype implementation as an SDK or API for Proof of Concept (PoC).
Phase 2: Development & System Integration
Duration: 6 months
Based on requirements from Phase 1, develop the full integration of this technology into the licensee's products or services. This includes integration with existing video codecs (e.g., H.265/HEVC) and optimization for hardware acceleration, followed by performance evaluation and debugging.
Phase 3: Pilot Testing & Optimization
Duration: 3 months
Conduct large-scale pilot tests in real-world environments using the developed system. Evaluate performance, stability, and compatibility under actual network conditions and user scenarios. Adjust parameters and optimize as needed for final commercial deployment.
Technical Feasibility
This technology is a core image encoding algorithm primarily envisioned as a software module. As the patent claims include a 'program,' integration into existing video encoding and decoding pipelines is estimated to be relatively straightforward. It could operate on general-purpose CPUs and GPUs, requiring no significant new hardware investment, suggesting high technical feasibility for deployment similar to a software update.
Success Scenario
Implementing this technology could reduce network bandwidth usage by an average of 15% for video streaming services while maintaining equivalent image quality. This is expected to optimize server and CDN costs by millions of dollars annually (AI est.). Users could also experience more stable, high-quality video with lower latency, potentially contributing to increased customer satisfaction and reduced churn rates.
Patent Record
APPLICATION NO.
特願2020-020957
REGISTRATION NO.
7537879
FILING DATE
2020/02/10
GRANT DATE
2024/08/13
EXPIRATION DATE
2040/02/10
PATENT HOLDER
日本放送協会
Examination History
2023年01月11日
出願審査請求書
2024年02月20日
拒絶理由通知書
2024年04月22日
意見書
2024年04月22日
手続補正書（自発・内容）
2024年07月09日
特許査定