Innovations In Display Technology: Samsung’s Latest Patents In 2024

In recent developments, Samsung has filed several Ground-breaking patents showcasing its continued leadership in display technology. These patents emphasize advancements in thin-film transistors and display apparatus but also explore innovative methods in thin-film deposition and organic light-emitting diode (OLED) displays.

1. US20240206248A1: Display Apparatus

 Samsung’s new display apparatus integrates a thin-film transistor structure with enhanced functionality. Key features include a shield layer that optimizes performance by minimizing interference between the data line and transistor components. This innovation aims to enhance display clarity and longevity.

2. US20240200181A1: Thin Film Deposition Apparatus

 This patent introduces a thin film deposition apparatus designed for large-scale substrate production. It features dual nozzles with intricate slit arrangements and a barrier wall assembly that partitions the deposition space into multiple sub-deposition areas. This setup improves manufacturing yield and ensures uniform film deposition across large substrates.

3. US20240203355A1: Pixel, Organic Light Emitting Display Device Using The Same, And Method Of Driving The Organic Light Emitting Display Device

 Samsung’s pixel design for OLED displays includes advanced circuitry involving multiple transistors and precise current control mechanisms. This patent outlines a method to optimize OLED performance through sophisticated driving transistor configurations, ensuring efficient power usage and high display quality.

4. US20240205935A1: Method And Apparatus For Transmitting Control And Data Information In Wireless Cellular Communication System

 In the realm of communications technology, Samsung explores the integration of IoT with 5G networks. This patent outlines seamless data transmission and reception in wireless cellular systems, catering to various applications from smart homes to digital education platforms.

5. US20240206188A1: Ferroelectric Capacitors, Transistors, Memory Devices, And Methods Of Manufacturing Ferroelectric Devices

 Focusing on memory technology, Samsung introduces ferroelectric capacitors with hafnium-based oxide layers. These capacitors offer improved data retention and faster read/write speeds, making them essential for next-generation memory devices.

6. US20240201511A1: Camera Module

 In imaging technology, Samsung’s camera module patent highlights innovations in lens stabilization. The module features a disturbance compensation mechanism utilizing magnets and coils to counteract unintended camera movements, ensuring stable imaging under varying conditions.

7. US20240204022A1: Image Sensor Including Color Separating Lens Array And Electronic Device Including The Image Sensor

 Samsung’s image sensor patent introduces a color-separating lens array that enhances sensor performance. This technology separates light of different wavelengths onto specific photosensitive cells, optimizing color accuracy and sensor sensitivity in various lighting conditions.

8. US20240206080A1: Display Device

 This patent showcases Samsung’s advancements in flexible display technology. The display device includes a front display area (FDA) and side display areas (SDAs) with a corner display area (CDA), all capable of bending and stretching. Such flexibility enhances user interaction and device durability.

9. US20240204296A1: Battery Module

 Lastly, Samsung’s battery module patent focuses on improving energy storage solutions. The module design features aligned cell stacks with enhanced electrical connectivity and structural integrity, ensuring efficient power distribution and management.

Conclusion

 Samsung’s latest patent filings underscore their commitment to pushing the boundaries of innovation in display technology, communications, imaging, and energy storage. These advancements improve device performance and new possibilities in consumer electronics and industrial applications. As Samsung continues to innovate, these patents serve as a testament to their leadership in shaping the future of technology. Stay tuned for more updates as these innovations move towards commercialization and integration into everyday devices.

In this blog, we’ve covered the highlights of Samsung’s recent patent filings, focusing on how these innovations could impact various industries and technological landscapes.

AUTHOR:  TAMANNA, PATENT RESEARCH ANALYST

The Rise of 4D Printing: Exploring the Future of Manufacturing

Additive manufacturing, commonly known as 3D, has emerged as a robust and reliable technological alternative to many traditional manufacturing and fabrication methods. However, a new and potentially disruptive advancement is quickly gaining momentum in the backdrop of technology. This development promises to revolutionize industries even more profoundly than 3D manufacturing has done so far.

Introduce the concept of 4-dimensional print, which encompasses 4D bioprinting, active origami, and shape-morphing systems.

What is 4d Printing:

 4D goes beyond the three-dimensional length, breadth, and height by incorporating the fourth dimension of time into the additive manufacturing process. This approach enables materials to dynamically change shape, transform properties, or self-assemble over time in response to environmental stimuli such as temperature, moisture, or light.

Difference Between 3d Printing and 4d manufacturing:  

 In 3D manufacturing, layers of material are sequentially built into a 3d object, following a path from the bottom to the top, creating a static structure. On the other hand, 4D manufacturing utilizes similar layering techniques but distinguishes itself through advanced, programmable materials. These materials can autonomously transform their shape, properties, or functionality in response to external stimuli or environmental changes.

One example of these advanced and programmable materials is a shape memory polymer (SMP). SMPs can undergo large-scale elastic transformation in response to environmental stimuli.

The three most common shape memory polymers are Polytetrafluoroethylene (PFTE), polylactide (PLA), and ethylene-vinyl acetate (EVA).

Domain:

 One of the most effective ways to predict the future trajectory of any technology is by examining the applications submitted for that technology in recent years.

Patenting Trend

Publication Country:

 China has filed the highest number of .

Conclusion:

 4D printing holds tremendous promise for applications requiring responsive transformation triggered by external stimuli or the environment. Countries like China, , and the US are key contributors to investing in research and development (R&D) to advance 4D printing technologies.

Examination of market trends and () reveals a growth trajectory for 4D printing over the past years. These trends indicate that 4D printing is set for significant growth in the coming years, driven by increasing investments, technological advancements, and expanding applications across various industries.

As R&D efforts continue to refine the capability of the programmable material and optimize manufacturing processes, the commercial adoption of 4D printing is to accelerate. This evolution promises to revolutionize fields ranging from aerospace and healthcare to consumer , offering dynamic solutions that adapt and respond to changing environmental conditions autonomously.

In conclusion, the burgeoning interest from major global players and the upward trajectory of filing indicates a promising future for 4D printing, positioning it as a transformative force in the manufacturing landscape in the years ahead

AUTHOR:  AVANTIKA BHARDWAJ, PATENT RESEARCH ANALYST

From Patents to Progress: Tesla’s Ongoing Pursuit of Advanced AI Solutions in 2024

In the rapidly changing field of artificial intelligence (AI) and self-driving cars, Tesla constantly boundaries with innovative advancements. Recently released filings demonstrate Tesla’s commitment to advancing technology in various areas through patents and applications.

1. CN118097361A: Specific Subject Grammar Generation Method And Device Based On Non-Training

 Tesla’s latest filing introduces a novel method for generating specific subject graphs based on non-training AI techniques. This method transforms how AI generates visual content from textual descriptions. Traditionally, AI systems require extensive training datasets to perform such tasks. However, Tesla’s approach eliminates this need, significantly reducing user costs and environmental impact.

The method involves:

• Receiving User Input: Users provide a textual description of a specific subject they wish to generate.
• Subject Identification: AI identifies the subject and retrieves corresponding images from a pre-generated database.
• Image Fusion: Main body pictures are encoded and fused to create composite images.
• Generation: These composite images, with specific characteristics, are fed into a text-to-picture generator to produce the final output.

This innovative technique improves efficiency and further expands the application of AI to generate diverse subjects like humans, animals, plants, or objects.

2. US20240185552A1: Enhanced Object Detection For Autonomous Vehicles Based On Field View

 In autonomous driving, Tesla unveils advancements in object detection systems. This patent focuses on improving object detection capabilities using enhanced field-of-view (FOV) techniques. Tesla improves object detection by strategically placing image sensors on vehicles, using vanishing lines and FOV cropping. Key features:

• Image Processing: Images captured by sensors are processed to determine FOV and vanishing lines.
• Object Detection: A convolutional neural network (CNN) analyses cropped and downsampled images to detect objects.
• Output: Detailed object information is crucial for vehicle navigation and safety.

This technology underscores Tesla’s commitment to refining autonomous driving systems, making them safer and more reliable.

3. US20240185445A1: Artificial Intelligence Modeling Techniques For Vision-Based Occupancy Determination

 Another significant patent from Tesla addresses AI modeling techniques for occupancy determination using camera feeds. This innovation allows Tesla vehicles to analyze their surroundings by predicting the occupancy attributes of voxels based on AI models. By interpreting camera data, Tesla enhances its ability to perceive and navigate complex environments.

The process involves:

• Image Input: Camera feeds from Tesla vehicles capture surrounding environments.
• AI Execution: AI models predict occupancy attributes for voxels within the environment.
• Dataset Generation: A dataset is based on voxel attributes, aiding vehicle decision-making.

This advancement highlights Tesla’s integration of AI to improve environmental awareness and enhance autonomous driving capabilities.

4. CN118012266A: Gesture Recognition-Based Automatic Control Method And Device For Presentation File

 Tesla’s commitment to user interaction extends to gesture recognition technology for presentation control. This patent introduces a method to automate presentation controls based on gesture recognition. By analyzing hand gestures, Tesla enhances the precision and efficiency of managing presentation materials.

Key steps include:

• Gesture Collection: Data from operators’ hand images is collected.
• Gesture Analysis: Static gesture recognition algorithms identify gestures aligned with control instructions.
• Presentation Control: Based on recognized gestures, Tesla prompts readiness and executes corresponding presentation commands.

This innovation enhances user experience by eliminating errors in gesture recognition and improving overall presentation control accuracy.

5. US20240177455A1: Systems And Methods For Training Machine Models With Augmented Data

 Lastly, Tesla explores methods for training machine learning models using augmented data. By leveraging images captured from multiple cameras, Tesla enhances model robustness and accuracy. This approach ensures Tesla’s AI systems perform reliably across diverse scenarios and conditions.

The process includes:

• Data Augmentation: Images from camera feeds are modified while maintaining their original properties.
• Model Training: Machine learning models trained using augmented images to predict outputs effectively.

This technique underscores Tesla’s commitment to advancing AI capabilities through comprehensive training methodologies.

Conclusion

 Tesla’s recent patents and filings underscore its leadership in integrating AI and autonomous technologies. From innovative object detection systems to advanced AI modeling techniques, Tesla continues to redefine the future of transportation and artificial intelligence. These advancements improve vehicle safety and efficiency and open up possibilities for various uses in industries relying on AI. As Tesla continues to innovate, the future promises even more transformative advancements in technology and mobility.

Stay tuned for more updates on Tesla’s ground-breaking developments in the coming months!

AUTHOR:  TAMANNA, PATENT RESEARCH ANALYST

AI vs. Human Intelligence: Finding the Right Balance for IPR Searches

The use of human experts versus AI-based search tools in intellectual property rights (IPR) is the subject of ongoing debate. While AI has made significant strides in automating search processes and handling vast amounts of data, there are several reasons why manual searches by human professionals remain essential and, in many cases, superior. Here are some reasons why humans are better than AI for IPR searches and why AI still can not replace human jobs in this sector.

Contextual Understanding and Interpretation

 Detailed insight: If we talk about taking the knowledge insights from the patents for our different novelty, invalidity, and infringement searches, human experts possess a deep, detailed understanding of legal terminology, industry-specific language, and regional differences that AI algorithms may not capture. This contextual understanding allows them to refine search criteria based on specific client needs and industry trends.

Evaluation of Results: AI can process large datasets quickly but cannot analyze results in context. Human professionals can assess search results, filter out irrelevant data, and identify valuable information that AI might miss due to its reliance on predefined algorithms.

Flexibility and Adaptability

 Adaptability to Complex Queries: AI-powered search tools may struggle with complex or new queries that require adaptive thinking and creative problem-solving, skills in which human professionals excel. Human experts can modify their strategies and decision-making processes in real time to adapt to new trends and technologies. As a result, AI-based search tools offer unique advantages compared to traditional human methods.

Tailored Approaches: Every IPR search presents a unique scenario, calling for a tailored approach that considers individual client objectives, legal differences, and industry nuances. Our team of experts excels at adapting search strategies on the fly, incorporating the latest data & making adjustments as needed.

Ethical and Legal Considerations

 Ethical Standards: Professionals in the human workforce adhere to ethical standards such as confidentiality, privacy, and unbiased reporting, which are crucial in Intellectual Property Rights (IPR) searches involving sensitive information. This is a valuable skill that, with substantial human input, can enhance the effectiveness of AI. There are still concerns about using AI solutions for patent searches because they involve third-party providers or cloud services. This raises concerns about who can access confidential patent information and how the data is stored, transmitted, and protected throughout the search process.

Legal Interpretation: IPR searches often require navigating intricate legal frameworks and precedents. Human experts are skilled in interpreting legal documents, understanding case law, and applying legal principles to IPR search queries. This is a crucial skill that, with significant human input, can greatly complement and enhance AI capabilities.

 Advanced and Specialized Queries

 Quality Assurance: When it comes to manual searches, expect a meticulous process with rigorous peer review and validation, guaranteeing exceptional quality at every stage. Human experts can cross-check findings, verify accuracy, and provide additional insights that enhance the overall reliability of search results. Remembering the importance of connecting with the client and putting myself in the shoes of someone searching for a patent will significantly influence how we think and develop strategies for finding relevant prior art. This approach allows for creative thinking and problem-solving. In contrast, an AI-based model relies solely on predefined trained models and data, limiting its ability to think creatively and provide high-quality results.

Conclusion:

 Many people fear that AI could replace human jobs, but AI-based search tools offer benefits in speed and scalability and help save money.”Although AI has made great strides, human experts still have the upper hand in specific areas, like intellectual property rights. The ability to understand detailed contexts, interpret complex legal and industry-specific information, and adapt strategies in real time are critical capabilities that human professionals bring to the table.” By harnessing the power of AI as a valuable assistant instead of a substitute for human knowledge, IPR professionals can combine the best of both worlds to provide in-depth, precise, and ethically responsible solutions that cater to the varied requirements of clients in the ever-changing global market

AUTHOR:  SAHIL RATHOUR, PATENT RESEARCH ANALYST

Empowering Farmers: The Impact of AI Technology on Agriculture and Horticulture

Food is essential for life. It is a fundamental requirement for humans, animals, birds, insects, and all other living beings. Everyone needs to eat, but no one wants to produce. This imbalance creates a challenge: the demand for food continues to grow, but the willingness and capacity to generate it need to catch up.

As the global population increases, the demand for food “is projected” to rise significantly, which could lead to potential food shortages in the future. “To address this issue.”, it is crucial to integrate technology into the agriculture sector.

Artificial Intelligence (AI) technology plays a pivotal role in the growth of various sectors, including agriculture and horticulture. In a country like India, where a significant portion of the population depends on these sectors, AI technology can significantly enhance production efficiency and boost the income of farmers and orchardists. This article explores how AI-based technology is revolutionizing agriculture and horticulture in India by addressing challenges and introducing innovative solutions.

Enhancing Production Efficiency

AI technology helps increase production efficiency by providing farmers with precise and timely information. AI-powered tools and systems can analyze vast amounts of data to offer insights into crop health, soil conditions, and weather patterns. This information allows farmers to make informed decisions, optimize resource use, and improve crop yields.

Improving Farmers’ Income

By improving production efficiency and cutting costs., AI technology directly impacts farmers’ income. Automation and AI-driven machinery lessen the need for manual labor, allowing farmers to focus on more strategic aspects of farming. Moreover, AI can help farmers access better markets and fair prices by analyzing market trends and demand patterns.

Solving Farmers’ Problems

“Farmers are benefiting from cutting-edge AI technologies designed to tackle a wide range of challenges in agriculture.” For example, crop monitoring robots equipped with AI can identify diseases, pests, and nutrient deficiencies early, enabling timely intervention. Soil testing using AI helps determine the exact nutrient requirements, ensuring optimal growth conditions for crops.

Current AI-Based Technologies in Use

Several AI-based technologies “Currently, we are employed.” in the agriculture and horticulture sectors in India:

– Crop Monitoring Robots: These robots use AI to monitor crop health, identify issues, and suggest corrective measures.

– Crop Cycle Management: AI helps farmers plan and manage crop cycles efficiently, ensuring maximum productivity.

– Soil Testing: AI-powered soil testing devices provide accurate and quick examination of soil health, helping farmers make informed decisions about fertilization and irrigation.

– Field Automation: Automation of field operations, such as planting, watering, and harvesting, is made possible through AI, reducing labor costs and increasing efficiency.

– Market analysis: AI tools analyze market trends, aiding farmers in understanding demand and price fluctuations to make better marketing decisions.

– Fertilizer Drones:  AI-enabled drones can precisely apply fertilizers to crops, ensuring even distribution and optimal usage. These drones use data on crop health and soil conditions to determine the precise amount of fertilizer needed, reducing waste and environmental impact.

The Future of AI in Agriculture: Centralized Systems

The continual use of AI technology improves the system, making it more intelligent and effective. Keep this in mind: In the future, we might see AI-powered centralized systems linking production and distribution processes. Such systems would play a significant role in material management and reducing crop waste due to improper distribution. By ensuring a seamless connection between production and distribution, AI can help:

– Price Control: A centralized system can monitor supply and demand, helping stabilize prices. Farmers and end-users benefit from fair pricing, reducing the exploitation of both parties.

– Reducing Waste: Effective material management is crucial in ensuring that our delicious crops are scattered wisely, minimizing the amount of produce that goes to waste.”

– By harnessing: the power of AI to predict demand and optimize supply chains, we can effectively meet market needs and prevent shortages or oversupply, ensuring a smooth and efficient fulfillment process.

Conclusion

AI technology is transforming the agriculture and horticulture sectors in India. By improving production efficiency, increasing farmers’ income, and offering innovative solutions to common problems, AI is paving the way for a more sustainable and prosperous future for Indian farmers. As AI continues to evolve, its impact on agriculture and horticulture will only grow, driving further growth and development in these crucial sectors. The future of AI in agriculture looks promising, with the potential for centralized systems to revolutionize how production and

“Efficient task management brings benefits to both farmers and consumers.”

AUTHOR: NIKHIL SHARMA, PATENT RESEARCH ANALYST