Introduction
This article is the first of a five-part series of articles dealing with what patentability of machine learning looks like in 2019. This article begins the series by describing the USPTOs 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) in the context of the U.S. patent system. Then, this article and the four following articles will describe one of five cases in which Examiners rejections under Section 101 were reversed by the PTAB under this new 2019 PEG. Each of the five cases discussed deal with machine-learning patents, and may provide some insight into how the 2019 PEG affects the patentability of machine-learning, as well as software more broadly.
Patent Eligibility Under the U.S. Patent System
The US patent laws are set out in Title 35 of the United States Code (35 U.S.C.). Section 101 of Title 35 focuses on several things, including whether the invention is classified as patent-eligible subject matter. As a general rule, an invention is considered to be patent-eligible subject matter if it falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter).[1] This, on its own, is an easy hurdle to overcome. However, there are exceptions (judicial exceptions). These include (1) laws of nature; (2) natural phenomena; and (3) abstract ideas. If the subject matter of the claimed invention fits into any of these judicial exceptions, it is not patent-eligible, and a patent cannot be obtained. The machine-learning and software aspects of a claim face 101 issues based on the abstract idea exception, and not the other two.
Section 101 is applied by Examiners at the USPTO in determining whether patents should be issued; by district courts in determining the validity of existing patents; in the Patent Trial and Appeal Board (PTAB) in appeals from Examiner rejections, in post-grant-review (PGR) proceedings, and in covered-business-method-review (CBM) proceedings; and in the Federal Circuit on appeals. The PTAB is part of the USPTO, and may hear an appeal of an Examiners rejection of claims of a patent application when the claims have been rejected at least twice.
In determining whether a claim fits into the abstract idea category at the USPTO, the Examiners and the PTAB must apply the 2019 PEG, which is described in the following section of this paper. In determining whether a claim is patent-ineligible as an abstract idea in the district courts and the Federal Circuit, however, the courts apply the Alice/Mayo test; and not the 2019 PEG. The definition of abstract idea was formulated by the Alice and Mayo Supreme Court cases. These two cases have been interpreted by a number of Federal Circuit opinions, which has led to a complicated legal framework that the USPTO and the district courts must follow.[2]
The 2019 PEG
The USPTO, which governs the issuance of patents, decided that it needed a more practical, predictable, and consistent method for its over 8,500 patent examiners to apply when determining whether a claim is patent-ineligible as an abstract idea.[3] Previously, the USPTO synthesized and organized, for its examiners to compare to an applicants claims, the facts and holdings of each Federal Circuit case that deals with section 101. However, the large and still-growing number of cases, and the confusion arising from similar subject matter [being] described both as abstract and not abstract in different cases,[4] led to issues. Accordingly, the USPTO issued its 2019 Revised Patent Subject Matter Eligibility Guidance on January 7, 2019 (2019 PEG), which shifted from the case-comparison structure to a new examination structure.[5] The new examination structure, described below, is more patent-applicant friendly than the prior structure,[6] thereby having the potential to result in a higher rate of patent issuances. The 2019 PEG does not alter the federal statutory law or case law that make up the U.S. patent system.
The 2019 PEG has a structure consisting of four parts: Step 1, Step 2A Prong 1, Step 2A Prong 2, and Step 2B. Step 1 refers to the statutory categories of patent-eligible subject matter, while Step 2 refers to the judicial exceptions. In Step 1, the Examiners must determine whether the subject matter of the claim is a process, machine, manufacture, or composition of matter. If it is, the Examiner moves on to Step 2.
In Step 2A, Prong 1, the Examiners are to determine whether the claim recites a judicial exception including laws of nature, natural phenomenon, and abstract ideas. For abstract ideas, the Examiners must determine whether the claim falls into at least one of three enumerated categories: (1) mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations); (2) certain methods of organizing human activity (fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people); and (3) mental processes (concepts performed in the human mind: encompassing acts people can perform using their mind, or using pen and paper). These three enumerated categories are not mere examples, but are fully-encompassing. The Examiners are directed that [i]n the rare circumstance in which they believe[] a claim limitation that does not fall within the enumerated groupings of abstract ideas should nonetheless be treated as reciting an abstract idea, they are to follow a particular procedure involving providing justifications and getting approval from the Technology Center Director.
Next, if the claim limitation recites one of the enumerated categories of abstract ideas under Prong 1 of Step 2A, the Examiner is instructed to proceed to Prong 2 of Step 2A. In Step 2A, Prong 2, the Examiners are to determine if the claim is directed to the recited abstract idea. In this step, the claim does not fall within the exception, despite reciting the exception, if the exception is integrated into a practical application. The 2019 PEG provides a non-exhaustive list of examples for this, including, among others: (1) an improvement in the functioning of a computer; (2) a particular treatment for a disease or medical condition; and (3) an application of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Finally, even if the claim recites a judicial exception under Step 2A Prong 1, and the claim is directed to the judicial exception under Step 2A Prong 2, it might still be patent-eligible if it satisfies the requirement of Step 2B. In Step 2B, the Examiner must determine if there is an inventive concept: that the additional elements recited in the claims provide[] significantly more than the recited judicial exception. This step attempts to distinguish between whether the elements combined to the judicial exception (1) add[] a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field; or alternatively (2) simply append[] well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality. Furthermore, the 2019 PEG indicates that where an additional element was insignificant extra-solution activity, [the Examiner] should reevaluate that conclusion in Step 2B. If such reevaluation indicates that the element is unconventional . . . this finding may indicate that an inventive concept is present and that the claim is thus eligible.
In summary, the 2019 PEG provides an approach for the Examiners to apply, involving steps and prongs, to determine if a claim is patent-ineligible based on being an abstract idea. Conceptually, the 2019-PEG method begins with categorizing the type of claim involved (process, machine, etc.); proceeds to determining if an exception applies (e.g., abstract idea); then, if an exception applies, proceeds to determining if an exclusion applies (i.e., practical application or inventive concept). Interestingly, the PTAB not only applies the 2019 PEG in appeals from Examiner rejections, but also applies the 2019 PEG in its other Section-101 decisions, including CBM review and PGRs.[7] However, the 2019 PEG only applies to the Examiners and PTAB (the Examiners and the PTAB are both part of the USPTO), and does not apply to district courts or to the Federal Circuit.
Case 1: Appeal 2018-007443[8] (Decided October 10, 2019)
This case involves the PTAB reversing the Examiners Section 101 rejections of claims of the 14/815,940 patent application. This patent application relates to applying AI classification technologies and combinational logic to predict whether machines need to be serviced, and whether there is likely to be equipment failure in a system. The Examiner contended that the claims fit into the judicial exception of abstract idea because monitoring the operation of machines is a fundamental economic practice. The Examiner explained that the limitations in the claims that set forth the abstract idea are: a method for reading data; assessing data; presenting data; classifying data; collecting data; and tallying data. The PTAB disagreed with the Examiner. The PTAB stated:
Specifically, we do not find monitoring the operation of machines, as recited in the instant application, is a fundamental economic principle (such as hedging, insurance, or mitigating risk). Rather, the claims recite monitoring operation of machines using neural networks, logic decision trees, confidence assessments, fuzzy logic, smart agent profiling, and case-based reasoning.
As explained in the previous section of this paper, the 2019 PEG set forth three possible categories of abstract ideas: mathematical concepts, certain methods of organizing human activity, and mental processes. Here, the PTAB addressed the second of these categories. The PTAB found that the claims do not recite a fundamental economic principle (one method of organizing human activity) because the claims recite AI components like neural networks in the context of monitoring machines. Clearly, economic principles and AI components are not always mutually exclusive concepts.[9] For example, there may be situations where these algorithms are applied directly to mitigating business risks. Accordingly, the PTAB was likely focusing on the distinction between monitoring machines and mitigating risk; and not solely on the recitation of the AI components. However, the recitation of the AI components did not seem to hurt.
Then, moving on to another category of abstract ideas, the PTAB stated:
Claims 1 and 8 as recited are not practically performed in the human mind. As discussed above, the claims recite monitoring operation of machines using neural networks, logic decision trees, confidence assessments, fuzzy logic, smart agent profiling, and case-based reasoning. . . . [Also,] claim 8 recites an output device that transforms the composite prediction output into human-readable form.
. . . .
In other words, the classifying steps of claims 1 and modules of claim 8 when read in light of the Specification, recite a method and system difficult and challenging for non-experts due to their computational complexity. As such, we find that one of ordinary skill in the art would not find it practical to perform the aforementioned classifying steps recited in claim 1 and function of the modules recited in claim 8 mentally.
In the language above, the PTAB addressed the third category of abstract ideas: mental processes. The PTAB provided that the claim does not recite a mental process because the AI algorithms, based on the context in which they are applied, are computationally complex.
The PTAB also addressed the first of the three categories of abstract ideas (mathematical concepts), and found that it does not apply because the specific mathematical algorithm or formula is not explicitly recited in the claims. Requiring that a mathematical concept be explicitly recited seems to be a narrow interpretation of the 2019 PEG. The 2019 PEG does not require that the recitation be explicit, and leaves the math category open to relationships, equations, or calculations. From this, the PTAB might have meant that the claims list a mathematical concept (the AI algorithm) by its name, as a component of the process, rather than trying to claim the steps of the algorithm itself. Clearly, the names of the algorithms are explicitly recited; the steps of the AI algorithms, however, are not recited in the claims.
Notably, reciting only the name of an algorithm, rather than reciting the steps of the algorithm, seems to indicate that the claims are not directed to the algorithms (i.e., the claims have a practical application for the algorithms). It indicates that the claims include an algorithm, but that there is more going on in the claim than just the algorithm. However, instead of determining that there is a practical application of the algorithms, or an inventive concept, the PTAB determined that the claim does not even recite the mathematical concepts.
Additionally, the PTAB found that even if the claims had been classified as reciting an abstract idea, as the Examiner had contended the claims are not directed to that abstract idea, but are integrated into a practical application. The PTAB stated:
Appellants claims address a problem specifically using several artificial intelligence classification technologies to monitor the operation of machines and to predict preventative maintenance needs and equipment failure.
The PTAB seems to say that because the claims solve a problem using the abstract idea, they are integrated into a practical application. The PTAB did not specify why the additional elements are sufficient to integrate the invention. The opinion actually does not even specifically mention that there are additional elements. Instead, the PTABs conclusion might have been that, based on a totality of the circumstances, it believed that the claims are not directed to the algorithms, but actually just apply the algorithms in a meaningful way. The PTAB could have fit this reasoning into the 2019 PEG structure through one of the Step 2A, Prong 2 examples (e.g., that the claim applies additional elements in some other meaningful way), but did not expressly do so.
Conclusion
This case illustrates:
(1) the monitoring of machines was held to not be an abstract idea, in this context;(2) the recitation of AI components such as neural networks in the claims did not seem to hurt for arguing any of the three categories of abstract ideas;(3) complexity of algorithms implemented can help with the mental processes category of abstract ideas; and(4) the PTAB might not always explicitly state how the rule for practical application applies, but seems to apply it consistently with the examples from the 2019 PEG.
The next four articles will build on this background, and will provide different examples of how the PTAB approaches reversing Examiner 101-rejections of machine-learning patents under the 2019 PEG. Stay tuned for the analysis and lessons of the next case, which includes methods for overcoming rejections based on the mental processes category of abstract ideas, on an application for a probabilistic programming compiler that performs the seemingly 101-vulnerable function of generat[ing] data-parallel inference code.
FOOTNOTES
[1] MPEP 2106.04.[2] Accordingly, the USPTO must follow both the Federal Circuits case law that interprets Title 35 of the United States Code, and must follow the 2019 PEG. The 2019 PEG is not the same as the Federal Circuits standard the 2019 PEG does not involve distinguishing case law (the USPTO, in its 2019 PEG, has declared the Federal Circuits case law to be too clouded to be practically applied by the Examiners. 84 Fed. Reg. 52.). The USPTO practically could not, and actually did not, synthesize the holdings of each of the Federal Circuit opinions regarding Section 101 into the standard of the 2019 PEG. Therefore, logically, the only way to ensure that the 2019 PEG does not impinge on the statutory rights (provided by 35 U.S.C.) of patent applicants, as interpreted by the Federal Circuit, is for the 2019 PEG to define the scope of the 101 judicial exceptions more narrowly than the Statutory requirement. However, assuming there are instances where the 2019 PEG defines the 101 judicial exceptions more broadly than the statutory standard (if the USPTO rejects claims that the Federal Circuit would not have), that patent applicant may have additional arguments for eligibility.[3] 84 Fed. Reg. 50, 52.[4] Id.[5] The USPTO also, on October 17 of 2019, issued an update to the 2019 PEG. The October update is consistent with the 2019 PEG, and merely provides clarification to some of the terms used in the 2019 PEG, and clarification as to the scope of the 2019 PEG. October 2019 Update: Subject Matter Eligibility (October 17, 2019), https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_update.pdf.%5B6%5D See Frequently Asked Questions (FAQs) on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), C-6 (https://www.uspto.gov/sites/default/files/documents/faqs_on_2019peg_20190107.pdf) (Any claim considered patent eligible under the current version of the MPEP and subsequent guidance should be considered patent eligible under the 2019 PEG. Because the claim at issue was considered eligible under the current version of the MPEP, the Examiner should not make a rejection under 101 in view of the 2019 PEG.).[7] See American Express v. Signature Systems, CBM2018-00035 (Oct. 30, 2019); Supercell Oy v. Gree, Inc., PGR2018-00061 (Oct. 15, 2019).[8] https://e-foia.uspto.gov/Foia/RetrievePdf?system=BPAI&flNm=fd2018007443-10-10-2019-0.%5B9%5D Notably, the mental process category and not the certain methods of organizing human activity category is the one that focuses on the complexity of the process. Furthermore, as shown in the following paragraph, the mental process category was separately discussed by the PTAB, again mentioning the algorithms. Accordingly, the PTAB is likely not mentioning the algorithms for the purpose of describing the complexity of the method.
See the original post here:
Machine Learning Patentability in 2019: 5 Cases Analyzed and Lessons Learned Part 1 - JD Supra
- Microsoft reveals how it caught mutating Monero mining malware with machine learning - The Next Web [Last Updated On: December 1st, 2019] [Originally Added On: December 1st, 2019]
- The role of machine learning in IT service management - ITProPortal [Last Updated On: December 1st, 2019] [Originally Added On: December 1st, 2019]
- Workday talks machine learning and the future of human capital management - ZDNet [Last Updated On: December 1st, 2019] [Originally Added On: December 1st, 2019]
- Verification In The Era Of Autonomous Driving, Artificial Intelligence And Machine Learning - SemiEngineering [Last Updated On: December 1st, 2019] [Originally Added On: December 1st, 2019]
- Synthesis-planning program relies on human insight and machine learning - Chemical & Engineering News [Last Updated On: December 1st, 2019] [Originally Added On: December 1st, 2019]
- Here's why machine learning is critical to success for banks of the future - Tech Wire Asia [Last Updated On: December 1st, 2019] [Originally Added On: December 1st, 2019]
- The 10 Hottest AI And Machine Learning Startups Of 2019 - CRN: The Biggest Tech News For Partners And The IT Channel [Last Updated On: December 1st, 2019] [Originally Added On: December 1st, 2019]
- Onica Showcases Advanced Internet of Things, Artificial Intelligence, and Machine Learning Capabilities at AWS re:Invent 2019 - PR Web [Last Updated On: December 3rd, 2019] [Originally Added On: December 3rd, 2019]
- Machine Learning Answers: If Caterpillar Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes [Last Updated On: December 3rd, 2019] [Originally Added On: December 3rd, 2019]
- Amazons new AI keyboard is confusing everyone - The Verge [Last Updated On: December 5th, 2019] [Originally Added On: December 5th, 2019]
- Exploring the Present and Future Impact of Robotics and Machine Learning on the Healthcare Industry - Robotics and Automation News [Last Updated On: December 5th, 2019] [Originally Added On: December 5th, 2019]
- 3 questions to ask before investing in machine learning for pop health - Healthcare IT News [Last Updated On: December 5th, 2019] [Originally Added On: December 5th, 2019]
- Amazon Wants to Teach You Machine Learning Through Music? - Dice Insights [Last Updated On: December 5th, 2019] [Originally Added On: December 5th, 2019]
- Measuring Employee Engagement with A.I. and Machine Learning - Dice Insights [Last Updated On: December 6th, 2019] [Originally Added On: December 6th, 2019]
- The NFL And Amazon Want To Transform Player Health Through Machine Learning - Forbes [Last Updated On: December 11th, 2019] [Originally Added On: December 11th, 2019]
- Scientists are using machine learning algos to draw maps of 10 billion cells from the human body to fight cancer - The Register [Last Updated On: December 11th, 2019] [Originally Added On: December 11th, 2019]
- Appearance of proteins used to predict function with machine learning - Drug Target Review [Last Updated On: December 11th, 2019] [Originally Added On: December 11th, 2019]
- Google is using machine learning to make alarm tones based on the time and weather - The Verge [Last Updated On: December 11th, 2019] [Originally Added On: December 11th, 2019]
- 10 Machine Learning Techniques and their Definitions - AiThority [Last Updated On: December 11th, 2019] [Originally Added On: December 11th, 2019]
- Taking UX and finance security to the next level with IBM's machine learning - The Paypers [Last Updated On: December 12th, 2019] [Originally Added On: December 12th, 2019]
- Government invests 49m in data analytics, machine learning and AI Ireland, news for Ireland, FDI,Ireland,Technology, - Business World [Last Updated On: December 12th, 2019] [Originally Added On: December 12th, 2019]
- Machine Learning Answers: If Nvidia Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes [Last Updated On: December 12th, 2019] [Originally Added On: December 12th, 2019]
- Bing: To Use Machine Learning; You Have To Be Okay With It Not Being Perfect - Search Engine Roundtable [Last Updated On: December 12th, 2019] [Originally Added On: December 12th, 2019]
- IQVIA on the adoption of AI and machine learning - OutSourcing-Pharma.com [Last Updated On: December 12th, 2019] [Originally Added On: December 12th, 2019]
- Schneider Electric Wins 'AI/ Machine Learning Innovation' and 'Edge Project of the Year' at the 2019 SDC Awards - PRNewswire [Last Updated On: December 12th, 2019] [Originally Added On: December 12th, 2019]
- Industry Call to Define Universal Open Standards for Machine Learning Operations and Governance - MarTech Series [Last Updated On: December 12th, 2019] [Originally Added On: December 12th, 2019]
- Qualitest Acquires AI and Machine Learning Company AlgoTrace to Expand Its Offering - PRNewswire [Last Updated On: December 12th, 2019] [Originally Added On: December 12th, 2019]
- Automation And Machine Learning: Transforming The Office Of The CFO - Forbes [Last Updated On: December 12th, 2019] [Originally Added On: December 12th, 2019]
- Machine learning results: pay attention to what you don't see - STAT [Last Updated On: December 12th, 2019] [Originally Added On: December 12th, 2019]
- The challenge in Deep Learning is to sustain the current pace of innovation, explains Ivan Vasilev, machine learning engineer - Packt Hub [Last Updated On: December 15th, 2019] [Originally Added On: December 15th, 2019]
- Israelis develop 'self-healing' cars powered by machine learning and AI - The Jerusalem Post [Last Updated On: December 15th, 2019] [Originally Added On: December 15th, 2019]
- Theres No Such Thing As The Machine Learning Platform - Forbes [Last Updated On: December 15th, 2019] [Originally Added On: December 15th, 2019]
- Global Contextual Advertising Markets, 2019-2025: Advances in AI and Machine Learning to Boost Prospects for Real-Time Contextual Targeting -... [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- Machine Learning Answers: If Twitter Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- Tech connection: To reach patients, pharma adds AI, machine learning and more to its digital toolbox - FiercePharma [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- Machine Learning Answers: If Seagate Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- MJ or LeBron Who's the G.O.A.T.? Machine Learning and AI Might Give Us an Answer - Built In Chicago [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- Amazon Releases A New Tool To Improve Machine Learning Processes - Forbes [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- AI and machine learning platforms will start to challenge conventional thinking - CRN.in [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- What is Deep Learning? Everything you need to know - TechRadar [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- Machine Learning Answers: If BlackBerry Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- QStride to be acquired by India-based blockchain, analytics, machine learning consultancy - Staffing Industry Analysts [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- Dotscience Forms Partnerships to Strengthen Machine Learning - Database Trends and Applications [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- The Machines Are Learning, and So Are the Students - The New York Times [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- Kubernetes and containers are the perfect fit for machine learning - JAXenter [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- Data science and machine learning: what to learn in 2020 - Packt Hub [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- What is Machine Learning? A definition - Expert System [Last Updated On: December 20th, 2019] [Originally Added On: December 20th, 2019]
- Want to dive into the lucrative world of deep learning? Take this $29 class. - Mashable [Last Updated On: December 24th, 2019] [Originally Added On: December 24th, 2019]
- Another free web course to gain machine-learning skills (thanks, Finland), NIST probes 'racist' face-recog and more - The Register [Last Updated On: December 24th, 2019] [Originally Added On: December 24th, 2019]
- TinyML as a Service and machine learning at the edge - Ericsson [Last Updated On: December 24th, 2019] [Originally Added On: December 24th, 2019]
- Machine Learning in 2019 Was About Balancing Privacy and Progress - ITPro Today [Last Updated On: December 24th, 2019] [Originally Added On: December 24th, 2019]
- Ten Predictions for AI and Machine Learning in 2020 - Database Trends and Applications [Last Updated On: December 25th, 2019] [Originally Added On: December 25th, 2019]
- The Value of Machine-Driven Initiatives for K12 Schools - EdTech Magazine: Focus on Higher Education [Last Updated On: December 25th, 2019] [Originally Added On: December 25th, 2019]
- CMSWire's Top 10 AI and Machine Learning Articles of 2019 - CMSWire [Last Updated On: December 25th, 2019] [Originally Added On: December 25th, 2019]
- Machine Learning Market Accounted for US$ 1,289.5 Mn in 2016 and is expected to grow at a CAGR of 49.7% during the forecast period 2017 2025 - The... [Last Updated On: December 27th, 2019] [Originally Added On: December 27th, 2019]
- Are We Overly Infatuated With Deep Learning? - Forbes [Last Updated On: December 27th, 2019] [Originally Added On: December 27th, 2019]
- Can machine learning take over the role of investors? - TechHQ [Last Updated On: December 27th, 2019] [Originally Added On: December 27th, 2019]
- Dr. Max Welling on Federated Learning and Bayesian Thinking - Synced [Last Updated On: December 28th, 2019] [Originally Added On: December 28th, 2019]
- 2010 2019: The rise of deep learning - The Next Web [Last Updated On: January 4th, 2020] [Originally Added On: January 4th, 2020]
- Machine Learning Answers: Sprint Stock Is Down 15% Over The Last Quarter, What Are The Chances It'll Rebound? - Trefis [Last Updated On: January 4th, 2020] [Originally Added On: January 4th, 2020]
- Sports Organizations Using Machine Learning Technology to Drive Sponsorship Revenues - Sports Illustrated [Last Updated On: January 4th, 2020] [Originally Added On: January 4th, 2020]
- What is deep learning and why is it in demand? - Express Computer [Last Updated On: January 4th, 2020] [Originally Added On: January 4th, 2020]
- Byrider to Partner With PointPredictive as Machine Learning AI Partner to Prevent Fraud - CloudWedge [Last Updated On: January 4th, 2020] [Originally Added On: January 4th, 2020]
- Stare into the mind of God with this algorithmic beetle generator - SB Nation [Last Updated On: January 5th, 2020] [Originally Added On: January 5th, 2020]
- US announces AI software export restrictions - The Verge [Last Updated On: January 5th, 2020] [Originally Added On: January 5th, 2020]
- How AI And Machine Learning Can Make Forecasting Intelligent - Demand Gen Report [Last Updated On: January 5th, 2020] [Originally Added On: January 5th, 2020]
- Fighting the Risks Associated with Transparency of AI Models - EnterpriseTalk [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- NXP Debuts i.MX Applications Processor with Dedicated Neural Processing Unit for Advanced Machine Learning at the Edge - GlobeNewswire [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- Cerner Expands Collaboration with Amazon Web as its Preferred Machine Learning Provider - Story of Future [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- Can We Do Deep Learning Without Multiplications? - Analytics India Magazine [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- Machine learning is innately conservative and wants you to either act like everyone else, or never change - Boing Boing [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- Pear Therapeutics Expands Pipeline with Machine Learning, Digital Therapeutic and Digital Biomarker Technologies - Business Wire [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- FLIR Systems and ANSYS to Speed Thermal Camera Machine Learning for Safer Cars - Business Wire [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- SiFive and CEVA Partner to Bring Machine Learning Processors to Mainstream Markets - PRNewswire [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- Tiny Machine Learning On The Attiny85 - Hackaday [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- Finally, a good use for AI: Machine-learning tool guesstimates how well your code will run on a CPU core - The Register [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- AI, machine learning, and other frothy tech subjects remained overhyped in 2019 - Boing Boing [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- Chemists are training machine learning algorithms used by Facebook and Google to find new molecules - News@Northeastern [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- AI and machine learning trends to look toward in 2020 - Healthcare IT News [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]
- What Is Machine Learning? | How It Works, Techniques ... [Last Updated On: January 7th, 2020] [Originally Added On: January 7th, 2020]