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    Hot Topics in Tech | October 2019 Newsletter


    In the October issue of “Hot Topics in Tech” newsletter series, we discuss Wi-Fi 6 and look at how 5G deployment might impact its market. To substantiate our conclusions, we have used patent data, market insights and looked at patterns in the evolution of industrial standards. Also in this issue, is a brief overview of the increasing role of Artificial Intelligence (AI) in drug discovery.

    Wi-Fi 6 to Bring Continued Growth for Wireless Networking Companies Even Amidst 5G Rollout


    Wi-Fi is the preferred way to access the internet for many people whether at home, office or even Starbucks. This preference is particularly true for enterprises who depend on Wi-Fi for their business. One report pinned the worldwide Wi-Fi spending at $2.4 billion of which $1.4 billion came from enterprises.

    Wi-Fi 6 is the next generation High Efficiency Wireless (HEW) radio technology that is expected to debut this year. It is based on the IEEE 802.11ax standard utilizing the industrial, scientific and medical (ISM) radio band at 2.4 GHz and 5 GHz. Wi-Fi protocol itself has been around since 1997 under the 802.11 standard. The new standard will serve as a major upgrade and remain backwards-compatible to the previous 802.11ac standard, which is now called Wi-Fi 5.

    The primary focus of the 802.11ax standard is towards more efficient transmission of Wi-Fi signals by the router. Wi-Fi 6 will be able to handle more devices and optimize denser networks, regulate congestion, and enable power saving. This will result in improving the wireless service in crowded and high-demand areas. It will also enable Internet-of-Things (IOT), Augmented & Virtual Reality (AR / VR) and home and industrial automation.

    While the user is expected to see improvements in speed, it will be some time before we experience the full potential of Wi-Fi 6. A typical home connection may have Internet Service Provider (ISP) speeds of 100-500 Mbps while Wi-Fi 6 has a theoretical maximum transfer speed of 10 Gbps. In other words, the ISPs will have to build infrastructure to support higher speeds to fully utilize the potential of the Wi-Fi 6 router technology.

    Some of the key features of Wi-Fi 6 include:

    1024-QAM: 1024-Quadrature Amplitude Modulation allows sending ten binary characters with each transmission boosting throughput.

    OFDMA: Orthogonal Frequency Division Multiple Access (OFDMA) addresses the problem of keeping the Wi-Fi channel open until the completion of data transmission causing other devices to stand in a queue for their turn. The channels can be divided into many smaller sub-channels accommodating multiple devices.

    Spatial Frequency Reuse: Marking (coloring) a wireless transmission enables checking if a wireless channel can be simultaneously used, thereby cutting the transmit power for the new transmissions and reducing interference from neighboring devices.

    Both Up-Link & Down-Link Multi-User MIMO: Unlike Wi-Fi 5, both the uplink and downlink MU-MIMO are possible here, making it easier for a device to transmit data concurrently to multiple receivers and simultaneously receive data from multiple transmitters. This increases a router’s capacity to handle more devices. OFDMA and MU-MIMO are complementary technologies that will improve efficiency and capacity, respectively.

    ISM 1 - 6 GHz Band: Wi-Fi 6 operational bandwidth has been broadened; it now operates in all ISM bands between 1 and 6 GHz.

    TWT: With Target Wake Time the device’s wake and sleep times can be managed, as needed, thereby reducing power consumption and medium access contention.

    Dual NAV: Dual Network Allocation Vector (NAV) simplifies the indication of busy and available states of the device and cuts-down the latency/delay.

    Dynamic Fragmentation: The Static Fragmentation of Wi-Fi 5 allots equal sizes to almost all the fragments of data packets; whereas Dynamic Fragmentation in Wi-Fi 6 fragments the data packets according to the user load, helping to reduce overhead.

    Random Access Trigger: The probability of collision between different radio units during transmission is reduced with Random access trigger.

    We tracked all the technical submissions (Mar 2013 - Sep 2019) to the IEEE 802.11 Task Group AX. A dip in submissions was observed in 2017 that may be attributed to the overall decrease in the number of unique contributors. The overall top contributors were Qualcomm, Intel, Marvell, Huawei, Broadcom, Newracom, Mediatek, LG, Apple and ZTE, in that order. The top contributors remain essentially unchanged in the last 5 years. Our analysis indicates that these key players continue to technically contribute to the IEEE 802.11 working group.


    Fig. 1: Technical Submissions to 802.11 TGAX

    Patent filings related to Wi-Fi 6 peaked in 2016. The U.S., China, South Korea, Japan and PCT based first filings contributed to the majority of the global filings. The top 10 patent assignees were Intel, Huawei, LG, Qualcomm, Neuracomm, Marvell, Ericsson, Meizu Technology, Canon and Interdigital. The downward trend in patent families for year 2017 and 2018 may be attributed to the maturity of the 802.11ax standard around the period. Our analysis indicates a focus on Wi-Fi 6 monetization and likely shift in patenting focus to the next generation Wi-Fi technologies.



    Fig. 2: Patent Activity on Wi-Fi 6

    One of the areas where we can expect advances in future Wi-Fi standards is in the use of Artificial Intelligence (AI) and Machine Learning (ML). AI and ML can make network devices smarter, help solve network issues faster and more efficiently, thereby paving the way for the next generation Wi-Fi networks. Machine Learning is particularly helpful with event correlations, which enables networks to not just fix a problem, but to also quickly laser-in on the source of the problem, so that it does not happen again. Once a baseline is established, AI can use anomaly detection and other features to avoid many common problems, such as DHCP, RADIUS, and security issues.

    The Wi-Fi 6 trademark is administered by the Wi-Fi alliance which provides certification of Wi-Fi 6 devices. Shown below are some major members of Wi-Fi alliance.


    Fig. 3: Wi-Fi 6 Trademark No. 88148347 (left) and Wi-Fi Alliance Members (right)

    Current Wi-Fi 6 routers & access points include Asus RT-AX88U & AX6000, TP-Link WiFi 6 AX6000, NETGEAR Nighthawk AX12 and Cisco Catalyst 9120 / 9117 / 9115. Chip vendors such as Qualcomm, Broadcom and Marvell have already launched Wi-Fi 6 chips. The latest smartphones including the iPhone 11, Iphone Pro Max, Samsung Galaxy S10 and laptops, such as HP Elite-Book and Dell 7000 laptops support Wi-Fi 6. Many more products, such as cameras, televisions, vending machines, and smart home devices are expected to be Wi-Fi 6 ready in the not too distant future.

    Mobile operators are ready to support 5G-ready devices for high-speed connectivity and many advanced applications, such as immersive AR/VR experiences, connected vehicles, IoT, smart cities and precision agriculture. Will the 5G rollout dim WiFi growth prospects in the future? Wi-Fi 6 and 5G are built to enhance and upgrade some limitations in current indoor and outdoor networks, respectively. Both address speed, latency, device connection capacity, coverage and support to IOT/AR/VR/Automation. Many experts believe that 5G and Wi-Fi technologies are complementary and will be deployed together. Companies such as Cisco, Qualcomm, and Huawei are betting big on Wi-Fi 6 for achieving the IoT vision. Wi-Fi 6 may very well decide where and how 5G is deployed in the near future.


    Big Pharma Seeks Help From AI Firms to Quicken Drug Discovery


    Drug discovery is a resource intensive, time consuming and expensive process. On average, it may take 13 years and $2 billion to bring a drug to market. The process begins with the screening of compounds to identify potential drug molecules. The shortlisted molecules are then further assayed to study bioavailability, toxicity, and metabolism, among other things. Further, these shortlisted molecules may require tweaking of their structure or shape to increase their pharmaceutical activity or decrease binding with unnecessary targets, to improve drug-likeness or increase ADME properties of the molecule. This process, shown in Fig. 1, generally requires several iterative screening runs, during which the properties of the new molecular entities improve. This in turn allows for the selected compounds to proceed to in vitro and in vivo testing for activity in the disease model of choice.



    Fig. 1: Drug Discovery Cycle

    The entire drug discovery process generates a lot of data from which pharma companies can draw insights and cut down on time, resources, and expenses. The existence of this vast amount of data offers the potential to help researchers quicken the drug delivery process. Big Pharma is looking at artificial intelligence (AI) and machine learning (ML) techniques to glean insights from such a large amount of data. Drug companies do not necessarily have the expertise to move quickly with big data projects. Therefore, several drug companies have announced strategic partnerships with AI companies. There are over 150 startups currently working in the AI drug discovery space. Most of these partnerships began within the past three years. Table 1 lists the companies, the AI technology used, the big pharma partners and the medical conditions for which the drug discovery processes are targeted.


    Technology Company

    AI Technology

    Pharma Partner




    Neural Networks


    Macular Degeneration, Acute Lymphoblastic Leukemia



    Deep-learning screening from molecular structure data

    Eli Lilly and Company

    Multiple therapeutic areas


    Insilico Medicine

    Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) 

    Jiangsu Chia Tai Fenghai Pharmaceutical Co., Ltd

    Breast cancer



    Deep Learning AI Technology for end-to-end drug discovery solution

    Charles River Laboratories

    Multiple therapeutic areas



    Deep-learning screening from literature and assay data

    Santen Pharmaceuticals




    Bispecific compounds via Bayesian models of ligand activity from drug discovery data


    Metabolic diseases


    GNS Healthcare

    Bayesian probabilistic inference for investigating efficacy





    Deep learning from phenotypic data


    Oncology, gastroenterology and central nervous system disorders


    Benevolent AI

    Deep-learning and natural language processing of research literature

    Janssen Pharmaceutica

    Multiple therapeutic areas


    Table 1: AI and Pharmaceutical Companies

    In addition to collaborations between AI firms and Big Pharma, there have developed consortiums aimed at sharing data between big pharma companies. For a long time, the main barrier to sharing the vast amount of data between pharmaceutical companies has been the fear of compromising intellectual property. Once such consortium, MELLODDY, aims to obviate the trade-off between data sharing and security, thus encouraging pharma companies to overcome their fears. The consortium seeks to use Owkin’s block-chain architecture technology to extract insight from multiple datasets, without having to first pool the data. MELLODDY brings together the following partners: a) 10 leading pharmaceutical companies - Amgen, Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, GSK, Janssen Pharmaceutica NV, Merck KgaA, Novartis, and Institut de Recherches Servier, b) 2 academic universities - KU Leuven, Budapesti Muszaki es Gazdasagtudomanyi Egyetem, c) 4 subject matter experts - Owkin, Substra Foundation, Loodse, Iktos, and d) 1 large AI computing company – NVIDIA.

    A recent market study estimates that the AI in drug discovery market will reach $4.4 billion by 2025. Although there is a rush of excitement in using AI for drug discovery, we will have to wait until AI companies deliver on their promises. One of the challenges that AI companies face is the capability to make accurate predictions from available data. There is also a skills gap issue that needs to be addressed to train pharmaceutical researchers and scientists in the capabilities of AI and the requirements for implementing the same in their organization. Addressing these specific concerns can offer breakthroughs in time, costs and resources involved in the drug discovery process.

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    Featured Patent Applications of the Week: October 24, 2019
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