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The FDA granted approvals for the generic versions of Lyrica to Alembic Pharmaceuticals, Alkem Laboratories, Amneal Pharmaceuticals, Dr. Reddy’s Laboratories, InvaGen Pharmaceuticals, MSN Laboratories Ltd., Rising Pharmaceuticals, Inc., Sciegen Pharmaceuticals Inc., and Teva Pharmaceuticals. On July 19, the U.S. Food and Drug Administration approved multiple applications for first generics of Lyrica (pregabalin): for the management of neuropathic pain associated with diabetic peripheral neuropathy, for the management of postherpetic neuralgia, as an adjunctive therapy for the treatment of partial onset seizures in patients 17 years of age and older, for the management of fibromyalgia, and for the management of neuropathic pain associated with spinal cord injury.

It was developed as a successor to Gabapentin. Gabapentin and Lyrica are both anticonvulsant medications used to treat seizures, post-herpetic neuralgia (pain from shingles), and certain other types of neuropathic pain. Both drugs also have some potential for abuse and the development of physical dependence. In terms of differences, Lyrica appears to be more rapidly absorbed, may be relatively more effective for certain treatments, and produces fewer side effects. In 2016, it was the 83rd most prescribed medication in the United States with more than 9 million prescriptions. In the US, Pregabalin is a Schedule V controlled substance under the Controlled Substances Act of 1970.

Original content: https://www.fda.gov/news-events/press-announcements/fda-approves-first-generics-lyrica

One of the most common questions that comes to people’s minds is if Blockchain and Cryptocurrencies are the same thing or if they are different.

Bitcoin and Blockchain

Bitcoin and Blockchain are different things. While Bitcoin is the first ever cryptocurrency invented in 2008 by Satoshi Nakamoto, Blockchain is the technology behind Bitcoin. It can be said that Bitcoin is one of the first innovations of Blockchain. The launch of Bitcoin has been an attempt to create alternative cryptocurrencies that is controlled by a decentralized network of users and which is not directly subject to the laws of central banking authorities or governments, with additional benefits of faster transactions and lesser fees. The confusion between the two was created as the sudden growth of Bitcoin led people to start using the terms ‘Bitcoin’, and ‘Blockchain’ interchangeably.

Blockchain goes beyond cryptocurrency

Though the best-known example of blockchain technology is cryptocurrency Bitcoin, there are many more use cases ranging from real estate, food products, cars, logistics, and more. The global emerging technology enterprise Everledger developed the pioneering idea of how blockchain can play a role in preventing frauds in luxury goods. Blockchain modified use of Bitcoin to make systemic flaws less likely in the future. Wide ranges of sectors are being identified like banking and finance which could benefit from blockchain.
Meanwhile being the technology behind Bitcoin, blockchain carries a huge disparity from it, with several features, other than secure and regulated digital cash transactions.

Cyptocurriencies – alternative money?

Cryptocurrencies’ nature of being decentralized alternative money that can’t be controlled, making it unacceptable in many countries around the world, while the blockchain is an emerging technology which is rapidly evolving and touching different industries like logistics, education and health.

SATA and Peripheral Component Interconnect Express are the interfaces that are broadly utilized by SSDs that are accessible today in the market.

Serial ATA (SATA) is the most widespread interface used for connecting SSDs today. This interface has been around for a significant long time now. Big players like #Intel, #Samsung, #Micron have already spent significant amount of time and efforts to develop this technology for SSD’s. A study on Bus transfer protocol technology for SSD after 2015 indicates a sharp rise in technologies related to protocol development. More specifically the NVMe protocol.

Future of SSD interface – SATA and PCI

Speaking at the A3 TechLive conference in London, Eyal Shani – Director #WesternDigital, technical product marketing said that SATA interfaces for SSDS are on their way out and the NVMe SSD interface will take over in 2020.

A graph presented by Eyal Shani shows declining popularity of SATA based SSD’s in data centers.

SATA to NVMe transition

Based on the trends, it seems like NVMe will become a de-facto interface in future majorly due to scalability and lowest latency benefits.

However, the latest filing trend in SATA based SSD domain indicate a sharp increase in patent filings on SATA based SSD.

SATA to NVMe transition

The major contributors to this sharp rise are #Samsung and #Intel who have filed patents related to physical arrangements of SSD’s and power management.

#Toshiba also seems to join the party by continuing their effort to provide efficient data transmission for SATA based SSD’s. It seems like #Toshiba is actively working to improve data transmission technologies with their research and development in connection establishment, read-write operations for SATA based SSD’s.

Captivatingly, in 2016-17 #Alibaba group filed a patent portfolio related to data deduplication technique for SATA based SSD using signature matching technique. However, the possible use of patented technology in #Alibaba’s Data Center is still a question for the outer world.

On the one hand, SATA SSDs have better hardware compatibility. While on the other hand they have worse relative performance. Unlike SATA based SSDs, PCIe can allow more bandwidth through faster signaling and multiple lanes. Due to the direct connection to peripherals, SSDs based on PCIe perform much better than the SATA counterparts that uses cables to connect to the motherboard, which in turn results in high latency.

Well, the recent trend shows growing NVMe market response, however corporates like #samsung #Toshiba and #intel hasn’t left the SATA based SSD domain

We will be discussing the growth trends of NVMe in our next article

Let us know your thoughts and the one you are going for in the comments section below.

The litigation issues involving patents and trade secrets related to artificial intelligence (AI) and machine learning is going to increase as they are being integrated into an increasing number of products and services. Some of the legal experts expressed their views in recently published interviews where they reflected on the evolution of AI and machine learning in a broad range of industries, their likely impacts on intellectual property (IP) protection, the preparedness companies should do now to prepare for future legal issues.

Why patents haven’t come into litigation yet?

According to one of the industry expert, AI and machine learning are very new and that’s why patents haven’t come into litigation yet. He says an application takes about three years to get through the U.S. patent office to issuance and thus one may start seeing enforcement activity ramp up in another year or two.

Another expert says that some clients have the realization that several legal issues may be coming down the road. She opines that new issues in patent law may include who is the inventor if an artificially intelligent system discovers a new innovation. According to her, one of the major concerns is how liability will be ascertained if an artificially intelligent system makes a decision which causes monetary damage or harm. Another concern is spelling out in agreements, responsibility for the decision-making or results they get from automated or AI systems clients may be using now or in the future. The ownership of data, information, or results that may be generated by the artificially intelligent system is also a concern for the clients which will be required to be spelled out in an agreement.

Ownership of the outputs of AI systems that are able to engage in the act of creation is going to change to the future litigation scenario. The act of creation can involve anything with potentially traditionally copyrightable subject matter, including music, or solving particular problems or creating engineering solutions that might be patentable subject matter.

It is opined that the legal risk involving AI and IP is not just for the tech, automotive, and transportation companies but will extend to industrials companies, energy companies, agricultural, and chemical companies, as they will also enter into development and use of artificially intelligent systems.

On the difference between machine learning and AI, it is said that machine learning is a subset of artificial intelligence which involves a large set of training data. On the infringement issues companies may face if they use AI or machine learning in their products, it is opined that one of the main issues is determining whether to seek patent protection or keep a machine learning innovation as a trade secret. At this time there is no easy way to determine if one’s machine learning patent is being infringed. There is almost no way to determine other machine learning process and algorithm without suing them, which can be very expensive and risky. So the client has to decide whether to file for a patent or go through all that expense of trying to figure out if there is infringement.

Patents and Trade secrets

On the distinction between patents and trade secrets, one of the panelists said that trade secret is something that is defined as information that is not public and that has independent economic value from the secret not being known and it lasts as long as it’s kept secret. The infringement of trade secret depends on the wrongful taking of it. But the Patents are protected for a limited period of time and its disclosure should enable somebody to make and use the invention. The inventor, in exchange for disclosing the invention, gets the exclusive right to practice the invention for a limited period of time during which its more of a strict liability right, where if someone wanders into the space protected by a patent becomes liable for infringement.

On the types of information clients should include in their AI-related patents, it is observed that a simple thing which is being done for a long period of time can’t be patented unless the process of machine learning is defined. If the client has to disclose the specifics and detailed implementation choices that client has selected, and it can start to limit the value because there may be many different ways a particular problem can be tackled. The panelist is of the view that the question from the perspective of inventors is whether the inventor wants to disclose how the AI goes about doing things, although it all happens inside the algorithm, but the flip side of this is that description in the patent is necessary in determining infringement.

Refining patent data (specially Assignee/Inventor names) is one of the most time consuming and crucial task involved in patent analytics. There are multiple methods available to solve the need but in this blog we are going to talk about ‘OpenRefine’.

This Tool Started Its life as Google refine but now we all know it as the OpenRefine. OpenRefine makes data cleaning simpler and more efficient while keeping the data secure and private with in our workstation.

It is most helpful in the cases where we are stuck with inventor names such as ‘Jim, Daniels’ and ‘Daniels, Jim’. Searching and correcting such errors are important to have a proper and accurate analysis of the patent data.

In order to proceed, we first need to download OpenRefine. Once you download and install the application you can open the applications (BTW it runs in a web browser and also without any active internet connection)

It can accept a lot of input file types, but for this example I took the same good old Excel sheet. You can perform your basic cleaning (trimming/replacing and stuff) either in Excel itself or OpenRefine can do that for you. Cleaning data through OpenRefine can be found in more details here.

We all know there can be multiple Inventors for a single patent, so the first thing we will do is to split those columns.

We need to separate the columns with a “|” separator

In OpenRefine we work with facets, for applying cleaning algorithms and rules to sort and simplify our patent data.
To apply a facet > Text Facet

A cluster would be formed at the left side of the dashboard.

Click on the cluster and apply the required algorithm for data cleaning. You can find the details about these algorithms and use the one you like or the one meets your needs.

Now, we can see how this algorithm has captured all the discrepancies within the inventor’s name. We can select and merge the one we need and can cluster and match again using different algorithms.

We can also use customs facets using Open Refine Expression Language (GREL) to use functions which are not defined in the facet settings.

Once you are done with clustering and refine all your patent data (Inventors/Assignee) you need to concatenate the data which we had split into several columns. This is the phase open refine need to develop a bit. Even though it has a concatenate function it doesn’t concatenate well for our case where there are blank cells.

 

Continued on page 2

  • Analyzing the formula to make a general mathematical relationship and simplify the equation by analyzing the variables in a formula so that the formula can be expressed in a simpler form
  • Understanding a relation between the variables given in the formula or equation in the subject patent. For example: If we consider gravitation force law which is stated as:
  • Now in this formula gravitational force (g) is directly proportional to mass, therefore the search can be conducted based on the relation of gravitational force with mass using keywords like proportionality, ratio and equality etc.
  • As equations are not written directly in the prior arts, but the text implies the relation between the variables. So, during a search the words like ‘equal to’, ‘equals’, ‘proportional’, ‘proportionately’ and others relate to the expression logic can be of help while interpreting the required concept
  • In a reference, variables can be rearranged that may yield the same equation. This is because sometimes the expression mentioned in the search reference is illustrated from a particular point which if rearranged can gives the same expression as required
  • If the formula or equation claimed in the subject patent is based on some standard equations and includes one or more novel factor along with the standard equations to establish a formula having inventive part. Then we can also use the standard equations to derive the formula up to some extent. Further, the search can be focused on relation of the remaining factors in the formula or equation

Before reading this article you must be familiar with the term patent. In brief a patent is a right granted to an inventor by the federal government that permits the inventor to exclude others from making, selling or using the invention for a period of time. Now when you know what patent is, let’s dig deeper into it.

The paramount part of a whole patent document is its CLAIMS!

What is a Claim?

A Claim in a patent defines its scope i.e. the area of protection (right to exclude everyone or to solely have right on claimed technology) a patent gets or it can be define as the bounds of what the inventor is claiming as their invention. Claims are the most critical part of a patent as all the prosecutions and litigations are mainly done on claims only. Claims lead a patent. If a patent is a championship belt, then Claims is no doubt the heavy-weight that keeps throwing knock-out blows.
“If patent is body, claim is its soul!”

How to Identify a Claim?

  • Claims are written as a single sentence.
  • A claim starts with an identifier i.e. “Claim 1”.
  • Claims are heavily punctuated (, : ; .).
  • [“,” Is used after preamble.
    “:” is used after transitional phase.
    “;” is used to separate paragraphs within the body.
    “.” Is used to end the claim.]

  • Claims happen to occur in the end of a granted patent or a patent application.

Parts of Claim

Let’s look into it with an example:

An apparatus, comprising:

a plurality of printed pages;
a binding configured to hold the printed pages
together;and
a cover attached to the binding.

Where:

The preamble, which tells category and objective of invention.

Transitional Phrase, which joins preamble with body.

The Body, which tells what the invention is in a proper sentence.

Types of Transitions

Role of Transitional Phrases

Transitional phrases are the key of drafting a patent. Let’s see how this key is going to work for you.

CLOSE
  • Narrowest
  • e.g. comprising of, including, consisting of, et cetera.
  • Least monetary benifits
  • Less vulnurable to litigation
OPEN
  • Broadest
  • e.g. comprises, consists, et cetera.
  • Greatest monetary benifits
  • More vulnurable to litigation
HYBRID
  • Midway
  • e.g. consisting essentially of, et cetera.
  • Moderate monetary benifits
  • Moderately vulnurable to litigation

Prior Art Search is to identify all the coinciding inventions. Prior arts include issued patents, research paper, patent applications, presentations, videos, press releases and blogs. Anything related to your invention that is derived from public domain is fall under prior art.
Prior arts related to similar invention may perturb the decision of the examiner whether to grant the patent or not. It would always be better to perform the prior art search to know whether your invention is novel or not. The search can be performed before or after filing the provisional application. Prior art search attains you the professional guidance whether to move forward with your invention or not.

  • Search before filing will save money
  • Search before filing will give you number of prior art references to design around
  • Search before filing will help you to identify potential competitors
  • Search before filing will give you new insights for your invention

A prior art search requires a search strategy using multiple logics which includes phrase based search, keywords based search, classification based search and f-term based search.

Relevancy of Semantic Search

Semantic search will help to identify the concept and find the relevant prior arts. Semantic search cover all the results that could be missed with keyword based searching. Semantic search is the simplest search that can be performed by anyone by just pasting the sentence or paragraphs to find the most relevant or similar documents.

Relevancy of Keyword Based Search

Keyword based searching is a type of searching that help you to identify the relevant or similar documents using various synonyms for one relevant keyword. Combination of multiple different keywords must make logic to locate relevant documents. One missed keyword could skip the relevant documents.
All the keywords are extracted based on the key-features identified by the professional searcher from the invention disclosure. Keywords can also be selected from the additional information given in the disclosure e.g. Applications.

Relevancy of Patent Classification Search

Classes are segregated on the basis of technology. There are broader classes (base-class) as well as narrow classes (sub-class). Challenge is to extract the most relevant classes related to the invention. Classes include thousands of patents and patent applications so it would be suggested to use classes with appropriate keywords to extract relevant dataset to analyze. Patent classification includes IPC, CPC, F-Term, USPC, and ECLA.

Relevancy of Inventor Based Search

List of prolific inventors related to the technology of your invention will also help to extract the relevant documents. Easiest way to find the relevant inventors is using semantic search. Paid database like Derwent Innovation, Orbit etc. help to get you most active inventors.

Relevancy of Organization Based Search

Organization/ Assignee that is active in a particular domain related to your invention can also help to get most relevant documents. Paid database like Derwent Innovation, Orbit etc. help to get you most active assignee.

Patent trolls gather huge attention either because of the number of lawsuits they launch or probably because of the number of high-profile companies that these patent troll target upon. Either way, patent trolls always manage to garner much publicity.

One such livid instance is of Sportbrain – a company that was virtually out of business at one time which has now emerged as an NPE and managed to sue over 100 companies by laying claims to a broad software patent, US patent no. 7,454,002, titled ‘Integrating personal data capturing functionality into a portable computing device and a wireless communication device.’

The patent relates to integration of personal data capturing functionality into a wireless communication device for analyzing and supplying feedback information to a user. The personal data is captured using a wireless communication device and is periodically transmitted to a network server for comparison of personal data for said user with personal data for at least one other different user and posting the feedback information to a web site that is accessible to said user.

Sportbrain has been filing lawsuits against many high profile companies that own range of ‘wearable’ devices and software products that gather user fitness information. Adidas, Fitbit, Nike, Apple, Samsung, HP and Microsoft have all fallen prey to these lawsuits. It has also sued watchmaking companies such as Timex, Tag Heuer, Nixon and Swatch, to name a few.

A petition for inter partes review (IPR) against all claims 1-16 of the ‘002 patent was filed last year by Unified Patents, a defense-oriented patent company that calls itself as ‘The Anti-Troll.’ It was part of Unified’s campaign to challenge the “three most prolific patent trolls” of 2016 – Sportbrain being one of them. Together, these three NPEs sued more than 200 companies in 2016, accounting for almost 15% of patent cases filed against high-tech companies.

Recently, the Patent Office has decided to institute an investigation over all 16 of the claims in the ‘002 patent. News says that the Patent Office is already aware of two earlier patents that ‘collectively’ teach “collecting, storing, and compiling performance data at a web server.”

But what if, there exists a single prior art document that disclosed each and every element of the ‘002 patent – wouldn’t that have been even better!?

Since we love to challenge our limits at IDS-IP, we got into action and soon found exactly what we were looking for! We were able to identify multiple prior art references that reveals all the limitations of the ‘002 patent! One of the prior art identified discloses a system that transfers feedback data of athlete’s performance to a remote station which is further compared with performance data of one or more athletes. The outcomes of this comparison data is then displayed over an internet website.

If you want your copy of one of such prior art you may reach us at ip.support@idsil.com. You may also share your views.

We are all aware of the fact that industries such as IoT (Internet of things) & automobile are witnessing continuous transformations and are considered to be the primary trends in today’s time. IoT, among other varied applications, is used to manage and monitor electronic devices from remote places. The automobile industry is observing a continuous need for electronic components like ICs, microprocessors and sensors that work at faster speeds and have better performing capabilities.

For such applications there remains a constant requirement for sensor-enabled devices that use flash-based storage devices to store collected data. These storage devices must be very reliable with fast boost speed and shall be packed with ability to perform efficiently even during extreme conditions. The demand for NOR flash memories is expected to increase, even further, in the coming times, due to the growing demands of IoT based applications and wearables.

Intel was the first to introduce NOR flash back in 1988. NOR Flash had revolutionized a market that was then dominated by EPROM and EEPROM devices. NOR Flash could perform without the need for any external power source and was best used for code storage and execution, usually in small capacities. At one time, NOR flash was a booming industry due to their usage in feature phones.

However, as time passed, feature phones began witnessing a stagnation in their sales growth. Also with the entry of lower-capacity NAND flash memories as substitutes, market demands for NOR flash continued to shrink. Due to very less gross margins, even the Semiconductor wafer fabs declined production orders for NOR flash memories. Several major suppliers of NOR flash have plans to reduce production or gradually exit the market. Cypress, for instance, is reducing the portion of NOR Flash as the company shifts its focus towards automotive and industrial IC markets. As GigaDevice adjusts to the domestic semiconductor policies, the company is expected to supply less NOR Flash products than before.

Today, AMOLED screens need NOR flash to supplement the brightness and electric current; Full HD models demand the installation of 8Mb NOR flash, QHD models need 16Mb NOR flash. To further add to the list, NOR flash is increasingly being applied to automotive electronics and industrial control devices. As a result, due to heavy rush because of continuous demand of NOR flash within the markets, the global supply remains constrained.

The growingly tight supply of NOR flash is driving many smaller NOR suppliers in Taiwan and China to expand production capacity. Macronix International and Winbond Electronics, both Taiwan-based firms, have shown keen interest in developing, designing and producing NOR Flash products. This is evident from the fact that their sales are up from 2% in 2012 to 20%. Winbond who is a leading IC memory company in Taiwan that manufactures serial flash memories will expand its monthly capacity for NOR flash from 44,000 to 48,000 wafers at its 12-inch wafer fab in Taichung, central Taiwan by the end of 2017, and further to 53,000 wafers by the end of 2018. Market observers indicate that the global sales of NOR flash products will experience a CAGR of 15% in the next few years to reach US$4.7 billion by 2020!

It is interesting to see that companies are stepping up the production of NOR flash at commendable rates. Winbond is producing additional wafer wholly designated for NOR Flash; Powerchip plans to resume production of NOR flash memory chips to take advantage of supply crunch; China’s Semiconductor Manufacturing International Corp (SMIC) reportedly has also seen sharp increases in orders from GigaDevice Semiconductor (Beijing) for NOR flash products. Etron Technology Inc. now plans to focus on production of specialized DRAM products.

It is still uncertain whether these measures will be enough to overcome the extreme scarcity of NOR flash memories, or whether the NOR flash will be replaced by something else in the coming future. Yet one thing remains true for sure – competition, R&D, and IP markets are expected to witness huge boom in the NOR Flash memory chip sector. This market is full of opportunities and threats and if investigated through right kind of amalgam of machine technology and human intelligence, the threats and the challenges can be converted into profitable business ventures!

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