Case 4:23-cv-01147-ALM Document 53-2 Filed 10/29/24 Page 1 of 611 PageID #: 1671
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`Exhibit 2
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`Case 4:23-cv-01147-ALM Document 53-2 Filed 10/29/24 Page 2 of 611 PageID #: 1672
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`IN THE UNITED STATES DISTRICT COURT
`FOR THE EASTERN DISTRICT OF TEXAS
`SHERMAN DIVISION
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`Civil Action No. 4:23-cv-01147-ALM
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`Jury Trial Demanded
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`R2 Solutions LLC,
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` Plaintiff,
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`v.
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`Databricks, Inc.,
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` Defendant.
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`DECLARATION OF BILL DAVIS
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`I, Bill Davis, declare as follows:
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`1.
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`I am currently a Partner and Lead Engineer at Syscall 7. My work focuses on
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`computer information security, distributed analytical systems for network detection and response
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`platforms, and enterprise dataflow management for indexing and search. I have over 20 years of
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`experience building software for defense and industry applications.
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`2.
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`I have been retained on behalf of Plaintiff R2 Solutions LLC (“R2”) to provide
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`opinions and testimony in support of R2’s claim construction positions and briefing in this matter.
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`In that capacity I have been asked to provide opinions related to claim construction for certain
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`terms of U.S. Patent No. 8,190,610 (the “’610 patent”). When appropriate herein, I refer to that
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`patent as “the Asserted Patent.”
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`3.
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`I am being compensated for my work on this matter at my usual consulting rate of
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`$395/hour. My compensation is not dependent upon my opinions or testimony as set forth herein,
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`or on the outcome of this matter.
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`1
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`4.
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`The facts set forth below are known to me personally, and I have firsthand
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`knowledge of them. I am a U.S. permanent resident over eighteen years of age. I am fully
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`competent to testify as to the matters addressed in this Declaration.
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`I.
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`BACKGROUND AND QUALIFICATIONS
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`5.
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`In 1997, I received a Bachelor of Science with a double major in Mathematics and
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`Computer Science from the University of Maryland, graduating as part of the Gemstone Honors
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`Program. In 2006, I received my Master of Science in Computer Science from Johns Hopkins
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`University. Since 2011, I have been a member of the Association for Computing Machinery.
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`6.
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`I have been involved in research and technology in the area of big data analytics
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`since 2006, building cutting-edge analytic systems for defense intelligence analysts. This included
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`developing and deploying many classified Hadoop/MapReduce-based analytics systems. My
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`experience involves designing and implementing big data analytics systems using various
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`languages like Java, Scala, and Python, and I have also designed and implemented a number of
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`MapReduce abstraction layers utilizing, for example, Cascading, Hive, and Pig, and a number of
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`MapReduce alternatives such as Trino and Apache Spark. Analytics systems I have built are
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`capable of running software across databases holding billions of events. Most recently I have been
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`responsible for the design and implementation of an analytics architecture which operates over
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`computer network metadata. The analytics are designed to detect and classify anomalous or
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`malicious network activity without the use of signature-based detections. These systems rely
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`heavily on Kafka, Hadoop HDFS, Postgres, and Apache Spark for scalable and efficient
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`operations. I designed and coded a framework and testing architecture to migrate these workflows
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`to Kubernetes-based cluster deployments.
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`7.
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`I have additionally been the technical lead on small teams of 5 to 10 engineers in
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`the area of search engine technologies since 2008. I designed and built search engines and data
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`ingest pipelines to support large scale collections of intelligence reports for the purposes of user
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`free text and structured search queries. I have built both backend processing systems and front-end
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`user facing applications, which included designing custom query languages to facilitate user
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`queries. I have authored several internal papers related to graph search engines as applied to
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`document search and network community analytics.
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`8.
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`I have developed applications for the purposes of reverse engineering and software
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`analysis. For example, since 2012, I have built, run, and maintained disasembler.io, which is the
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`leading web application for reverse engineering binaries for over 50 CPU architectures.
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`9.
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`From 2018 to the present, I have also assisted multiple companies transition their
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`enterprise Hadoop/Spark based workflows from on-premise data center deployments to modern
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`cloud service architectures utilizing both AWS and Microsoft Azure.
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`10.
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`During 2009 to 2014, I developed and deployed multiple classified information
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`systems utilizing Java/Groovy/MySQL/MongoDB and Accumulo Column Storage for the
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`Department of Defense.
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`11.
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`As part of earlier consulting work, I developed structured search tools which are
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`capable of indexing and storing key fields from a corpus of tens of millions of semi-structured
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`documents. These tools relied on a suite of open-source and commercial software like
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`ElasticSearch, Oracle, and Apache Solr to provide extensive search capabilities for data analysts.
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`12. My curriculum vitae, which is attached as Exhibit A also includes (1) a list of all
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`other cases in which, during at least the previous 4 years, the I have served as an expert, and (2) a
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`list of all publications I have authored in the previous 10 years.
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`3
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`II. MATERIALS CONSIDERED
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`13.
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`In connection with my study of this matter and reaching the opinions stated herein,
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`I have reviewed the Asserted Patent and its file history, as well as the papers filed in Databricks,
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`Inc. v. R2 Solutions LLC, No. IPR2024-00659 (PTAB March 6, 2024) and Cloudera, Inc. v. R2
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`Solutions LLC, No. IPR2024-00303 (PTAB December 18, 2023).
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`14.
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`I have also reviewed all of the extrinsic evidence and other materials cited herein.
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`III. RELEVANT LEGAL STANDARDS
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`15.
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`I understand that my assessment of the Asserted Patent and issues addressed in this
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`Declaration must be undertaken from the perspective of what would have been known or
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`understood by a person having ordinary skill in the art (POSITA) upon reading the Asserted Patent
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`as of its invention dates and in light of their respective specifications and file history.
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`16.
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`I understand that a patent is a fully integrated written instrument subject to a
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`presumption of validity. I also understand that by statute, a patent must provide a written
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`description of the invention that will enable one of ordinary skill in the art to make and use it. That
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`written description is often referred to as the “specification” of the patent. The patent concludes
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`with one or more “claims,” which define the patentee’s enforceable rights.
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`17.
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`I understand that claim construction is the process by which the meaning of the
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`claims is determined. To ascertain the scope and meaning of a claim, courts may consider intrinsic
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`evidence, such as claim language, the specification, the prosecution history, and IPRs, as well as
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`relevant extrinsic evidence, such as expert testimony, treatises, and dictionaries.
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`18.
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`I understand that the starting point in any claim construction is the language of the
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`claims themselves. I understand that claim terms are generally given their “ordinary and customary
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`meaning,” and that the ordinary and customary meaning of a claim term is the meaning that the
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`term would have to a POSITA at the time of the invention. Such a person is deemed to read the
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`words used in the patent documents with an understanding of their meaning in the field, and to
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`have knowledge of any special meaning and usage in the field. I further understand that the Federal
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`Circuit has held that the best source for understanding a technical term found in a patent’s claim
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`is the specification from which it arose because claims of a patent are always to be read or
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`interpreted in light of the specification.
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`19.
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`For this present Declaration, I was instructed to provide my analysis from the
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`perspective of a person of ordinary skill in the art as of the time that the original parent application
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`was filed.
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`Indefiniteness
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`20.
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`I understand from counsel that patent claims must particularly point out and
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`distinctly claim the subject matter regarded as the invention, which requires a claim, when viewed
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`in light of the intrinsic evidence, to “inform those skilled in the art about the scope of the invention
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`with reasonable certainty.” I also understand that the “reasonable certainty” standard mandates
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`clarity, while recognizing that absolute precision is unattainable.
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`21.
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`I am instructed that indefiniteness must be proven by “clear and convincing
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`evidence,” which is more rigorous to meet than the “preponderance of evidence” standard. The
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`“clear and convincing” legal standard means that the evidence being presented must be highly and
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`substantially more probable to be true rather than untrue, while “preponderance of evidence”
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`requires that the evidence be “more likely than not” to prove the matter at hand.
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`Means-Plus-Function
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`22.
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`I understand that the Patent Act provides a special type of claim formatting that
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`permits a patent owner to express a claim element as a “means” or “step” for performing a certain
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`function whereby:
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`An element in a claim for a combination may be expressed as a means or step for
`performing a specified function without the recital of structure, material, or acts in support
`thereof, and such claim shall be construed to cover the corresponding structure, material,
`or acts described in the specification and equivalents thereof.
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`35 U.S.C. § 112 ¶ 6.
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`23.
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`I understand that when a term is alleged to be a means-plus-function term, the
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`analysis involves a two-step process: (1) determine whether the claim includes sufficiently definite
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`structure, and (2) if it does not, determine what structure, if any, disclosed in the specification
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`corresponds to the claimed function. I understand that if the court determines that the claim term
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`is not a means-plus-function limitation, then the claim construction analysis for that term does not
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`advance to the second step of analysis.
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`24.
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`I also understand that the absence of the word “means” from a claim term creates a
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`presumption that § 112 ¶ 6 does not apply. When a claim term lacks the word “means,” however,
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`the presumption can be overcome and § 112 ¶ 6 will apply if a challenger demonstrates that the
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`claim term fails to recite sufficiently definite structure or else recites a function without reciting
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`sufficient structure for performing that function. Sufficiency of the structure is viewed through the
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`lens of a POSITA and without the need to disclose structures well known in the art. I understand
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`that if a claim term connotes sufficiently definite structure to a POSITA, then the claim term is not
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`a “means” term.
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`25.
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`I further understand that if the court determines that a term is subject to 35 U.S.C.
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`§ 112 ¶ 6, the court must follow a two-step process in order to construe a means-plus-function
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`claim term. First, the court must identify the claimed function. Second, the court must determine
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`what structure, if any, disclosed in the specification corresponds to the claimed function. I also
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`understand that the law does not permit incorporation of structure from the written description
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`beyond that necessary to perform the claimed function, meaning that a court may not import into
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`the claim features that are unnecessary to perform the claimed function. Features that do not
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`perform the recited function do not constitute corresponding structure and thus do not serve as
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`claim limitations.
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`26.
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`I further understand that a claim with a term subject to 35 U.S.C. § 112 ¶ 6 is invalid
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`as indefinite if the patent’s specification fails to disclose adequate corresponding structure to
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`perform the claimed function(s). The patent’s disclosure is deemed inadequate when a POSITA
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`would be unable to recognize the corresponding structure disclosed in the specification and
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`associate it with the corresponding function in the claim.
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`IV.
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`LEVEL OF SKILL IN THE ART
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`27. My assessment of the claim terms addressed herein assumes that they are being
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`interpreted by a POSITA. In that regard, I have been asked to provide my opinion as to the
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`educational level and professional experience of such a POSITA.
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`28.
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`It is my understanding that the ’610 patent has an earliest effective filing date of
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`October 5, 2006. Based on this relevant time frame, my review of the ’610 patent, and my
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`experience in the field of the art applicable to those patents, I believe that a POSITA would have
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`at least a bachelor’s degree in electrical engineering, computer engineering, computer science, or
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`a related field, and two years of experience in the design or development of big data analytics
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`systems, devices, hardware, or software/firmware, or the equivalent. This description is
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`approximate and additional educational experience would make up for less work experience and
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`vice versa.
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`29.
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`I note that I would have met or exceeded the qualifications of a POSITA. My
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`opinions provided herein are from the perspective of such a person.
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`V.
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`RELEVANT TECHNOLOGY
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`30.
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`The Asserted Patent relates to the field of big data analytics, specifically to
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`MapReduce. The ’610 patent is directed to an enhanced MapReduce architecture that can be
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`applied to affect mapping and reducing across diverse data sets, including data sets comprising
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`different schema. The specification summarizes these enhancements:
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`In accordance with an aspect, an input data set is treated as a plurality of grouped
`sets of key/value pairs, which enhances the utility of the MapReduce programming
`methodology. Utilizing such grouping, map processing is carried out independently
`on two or more related datasets (e.g., related by each being characterized by a
`schema with a key in common). The intermediate results of the map processing
`(key/value pairs) for a particular key are processed together in a single reduce
`function by applying a different iterator to intermediate values for each group.
`Different iterators can be composed inside reduce functions in ways however
`desired.
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`Thus, for example, the enhanced MapReduce programming methodology may be
`easily employed to carry out distributed relational database processing.
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`’610 patent at 1:31-44.
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`31.
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`The specification goes on to explain how implementation of “data groups”
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`realizes these improvements:
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`In general, partitioning the data sets into data groups enables a mechanism to
`associate (group) identifiers with data sets, map functions and iterators (useable
`within reduce functions to access intermediate data) and, also, to produce output
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`data sets with (group) identifiers. It is noted that the output group identifiers may
`differ from the input/intermediate group identifiers.
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`’610 patent at 3:58-64.
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`32.
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`This solution described in the specification is embodied, for example, in Claims 1
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`and 17 of the ’610 patent:
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`VI. OPINIONS ON DISPUTED TERMS FOR U.S. PATENT NO. 8,190,610
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`33.
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`I understand that Defendant Databricks, Inc. has proposed a number of terms for
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`construction as to the ’610 patent.
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`34.
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`I have been asked to provide in this Declaration opinion and analysis concerning
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`the phrase “at least one processor and memory that are operable to perform the following
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`operations…” in Claim 17 of the ’610 patent, which Defendant contends is indefinite.
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`35.
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`I understand that the parties have offered competing proposals for other terms and
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`phrases at issue. Although I have reviewed the parties’ competing proposals for such terms and
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`phrases, I have not been asked to provide opinions on the scope or meaning of those terms and do
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`not intend to offer an opinion on those terms or phrases in connection with the claim construction
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`proceedings in these cases.
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`A. “at least one processor and memory that are operable to perform the following
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`operations…”
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`36.
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`Claim 17 of the ’610 patent requires “at least one processor and memory that are
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`operable to perform the following operations…” I understand that Databricks contends that this
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`phrase is governed by 35 U.S.C. § 112 ¶ 6 and that the structure is indefinite. I understand that
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`Databricks bears the burden of proof on these issues but has not yet explained either position.
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`And based on my review of Databricks’ Petition filed against the ’610 patent in IPR2024-00659
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`(attached hereto as Exhibit B) and Databricks’ accompanying expert declaration of Dr. Jimmy
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`Lin, 1 I understand that Databricks and Dr. Lin applied constructions for some terms, but did not
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`assert that “processor” or “memory” required construction. Instead, Databricks and Dr. Lin
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`afforded these terms their “ordinary and customary meaning[s] as understood by one of skill in
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`the art in light of the specification and the prosecution history” in the IPR proceeding. Ex. C at
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`¶¶ 65-66; Ex. B at 9-10.
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`37.
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`In any event, it is my opinion that this term is not governed by 35 U.S.C. § 112 ¶ 6.
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`Based on my review of the intrinsic and extrinsic evidence, as well as the educational background
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`and professional experience of a POSITA, in my opinion the terms “processor” and “memory”
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`would have connoted sufficiently definite hardware structures to a POSITA.
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`38.
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`First of all, the claim itself recites specific objectives and operations of the
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`“processor” and “memory.” Claim 17 is reproduced below with such specific objectives and
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`operations in bold:
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`17. A computer system including a plurality of computing devices, the computer
`system configured to process data of a data set, wherein the data set comprises a plurality
`of data groups, the computer system comprises at least one processor and memory that
`are operable to perform the following operations:
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`partitioning the data of each one of the data groups into a plurality of data
`partitions that each have a plurality of key-value pairs and providing each data
`partition to a selected one of a plurality of mapping functions that are each user-
`configurable to independently output a plurality of lists of values for each of a set of
`keys found in such map function's corresponding data partition to form
`corresponding intermediate data for that data group and identifiable to that data
`group, wherein the data of a first data group has a different schema than the data of a
`second data group and the data of the first data group is mapped differently than the data
`of the second data group so that different lists of values are output for the corresponding
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`1 In IPR2024-00659, Databricks filed two declarations from Dr. Lin. The first declaration (Ex.
`1005 to Databricks’ Petition, attached hereto as Exhibit C) was a copy of the declaration that Dr.
`Lin provided for a co-pending proceeding involving Cloudera, Inc. (see IPR2024-00303). The
`second declaration (Ex. 1027 to Databricks’ Petition, attached hereto as Exhibit D) memorialized
`that Dr. Lin was adopting the opinions he expressed in the Cloudera proceeding for the Databricks
`proceeding. References to Dr. Lin’s declaration throughout my declaration refer to the substantive
`declaration that Dr. Lin offered as Exhibit 1005 to each of those Petitions.
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`different intermediate data, wherein the different schema and corresponding different
`intermediate data have a key in common; and
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`reduce the intermediate data for the data groups to at least one output data
`group, including processing the intermediate data for each data group in a manner
`that is defined to correspond to that data group so as to result in a merging of the
`corresponding different intermediate data based on the key in common.
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`39.
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`It is clear from the claim that the processor and memory must, for example, partition
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`data in a specific way, provide partitions to mapping functions to create intermediate data in a
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`specific way, and reduce the intermediate data via processing that corresponds to the data groups
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`in a specific way. This explains how the “processor” and “memory” operate within the context of
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`the claimed invention, denoting sufficient structure to a POSITA.
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`40.
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`Additionally, the specification itself confirms that both “processor” and “memory”
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`are structures of well-known classes of hardware devices. In the background section, the ’610
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`patent cites specifically to a reference describing MapReduce programming methodology, which
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`the ’610 patent explicitly seeks to enhance: “MapReduce: Simplified Data Processing on Large
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`Clusters,” by Jeffrey Dean and Sanjay Ghemawat, appearing in OSDI’04: Sixth Symposium on
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`Operating System Design and Implementation, San Francisco, Calif., December, 2004
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`(hereinafter, “the Dean and Ghemawat paper”).2 See ’610 patent at 1:6-16, 31-45. The Dean and
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`Ghemawat paper contains significant discussions of both processors and memory, going so far as
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`to provide specific examples of each that could be used in MapReduce programming. See, e.g.,
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`Ex. E at 3 (“For example, one implementation may be suitable for a small shared-memory machine,
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`another for a large NUMA multi-processor, and yet another for an even larger collection of
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`networked machines.”; Machines are typically dual-processor x86 processors running Linux, with
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`2-4 GB of memory per machine.”); 4 (“The intermediate key/value pairs produced by the Map
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`2 This reference was attached as Exhibit 1009 to Databricks’ IPR petition. It is attached as Exhibit
`E to this declaration.
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`function are buffered in memory.”; “If the amount of intermediate data is too large to fit in
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`memory, an external sort is used.”); 8 (“Each machine had two 2GHz Intel Xeon processors with
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`Hyper-Threading enabled, 4GB of memory, two 160GB IDE disks, and a gigabit Ethernet link.”);
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`11 (“We run on commodity processors to which a small number of disks are directly connected
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`instead of running directly on disk controller processors, but the general approach is similar.”).
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`Such discussion in the prior art (particularly prior art that the patent itself is explicitly improving
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`upon) emphasizes that, to a POSITA at the time of the ’610 patent, “processor” and “memory” had
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`well-understood and definite structures.
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`41.
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`The fact that “processor” and “memory” are well-known classes of hardware
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`devices is further apparent from Dr. Lin’s discussion of the art that Databricks cited in its IPR
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`Petition. Dr. Lin equated the processor and memory discussed in the reference “Pike” to the
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`processor and memory discussed in the ’610 patent. See Ex. C, ¶ 154. Dr. Lin also discussed the
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`“exemplary computer system” disclosed by Pike, which included processing units and memory.
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`See id., ¶ 84. This demonstrates that Dr. Lin understood what the ’610 patent meant by the terms
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`“processor” and “memory,” as he was able to identify portions of Pike that he alleged disclosed
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`those structures.
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`42.
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`Furthermore, the prior art discussed during prosecution of the ’610 patent3
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`demonstrates that “processor” and “memory” have well-understood and definite structures. The
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`Burroughs reference4 explicitly discusses processors and memory. See Ex. G at 6:43-51
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`(“Processor 110 performs computation and control functions of computer system 100, and
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`comprises a suitable central processing unit (CPU). Processor 10 may comprise a single integrated
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`3 The file history of the ’610 patent is attached hereto as Exhibit F.
`4 U.S. Pat. No. 6,341,289, attached hereto as Exhibit G.
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`circuit, such as a microprocessor, or may comprise any suitable number of integrated circuit
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`devices and/or circuit boards working in cooperation to accomplish the functions of a processor.
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`Processor 110 suitably executes object-oriented computer programs within main memory 120.”);
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`7:26-41 (“It should be understood that for purposes of this application, in memory 120 is used in
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`its broadest sense, and can include Dynamic Random Access Memory (DRAM), Static RAM
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`(SRAM), flash memory, cache memory, etc. While not explicitly shown in FIG. 1, memory 120
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`may be a single type of memory component or may be composed of many different types of
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`memory components. For example, memory 120 and CPU 110 may be distributed across several
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`different computers that collectively comprise system 100. It should also be understood that
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`programs in memory 120 can include any and all forms of computer programs, including Source
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`code, intermediate code, machine code, and any other representation of a computer program.”).
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`The Cruanes reference5 contains similar discussions. See generally Ex. H, ¶ 61.
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`43.
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`To further inform my opinion that “processor” and “memory” had well understood
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`and definite meaning to a POSITA, dictionaries from 2001 or earlier describe both a “processor”
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`and a “memory” as well-known structures. See Ex I at 5-6.
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`44.
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`For at least the reasons detailed above, I am of the opinion that the terms
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`“processor” and “memory” recited in Claim 17 of the ’610 patent are not “means” words, but
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`instead describe classes of structures that were well known to a POSITA by 2006.
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`5 U.S. Pat. Pub. No. 2006/0117036A1, attached hereto as Exhibit H.
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`I declare under penalty of perjury under the laws of the United States that the foregoing is
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`true and correct.
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`Executed this____ day of September, 2024.
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`By:______________________________
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`Bill Davis
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`Exhibit A
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`Case 4:23-cv-01147-ALM Document 53-2 Filed 10/29/24 Page 17 of 611 PageID #: 1687
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`Bill Davis
`bill.davis@syscall7.com
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`SUMMARY
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`Mr. Davis is a highly experienced software developer and cloud architect with over 20 years of
`expertise in large-scale distributed systems, real-time embedded software, and networked
`applications. He has supported a variety of patent litigation matters, with extensive experience in
`source code review, claim chart development, and forensic analysis.
`He has served as an expert witness in numerous high-profile cases, providing critical insights into
`software patent infringement, trade secret disputes, and copyright issues. He brings a strong
`technical background in cloud infrastructure, security solutions, and endpoint data loss
`prevention (DLP) across various platforms, including AWS and Azure.
`He is proficient in a wide range of programming languages and technologies, and he has a proven
`track record of delivering mission-critical applications and consulting on complex litigation
`matters.
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`TECHNICAL SKILLS
`• Cloud application development including using AWS, and Azure.
`• Development of security and endpoint DLP solutions
`• Kubernetes development including developing a helm charts, system monitoring and
`maintenance, upgrade solutions
`• System-level and application-level software development in Linux, Android,
`Windows, and Mac OS
`• Configuring and developing custom and commercial networking protocols, including
`the development of Wireshark dissectors in C and Lua
`• Scripting languages (Python, Perl, Bash, Ruby)
`• Higher level languages, including Python, C++, C#, and Java development in Windows and
`Linux environments.
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`Case 4:23-cv-01147-ALM Document 53-2 Filed 10/29/24 Page 18 of 611 PageID #: 1688
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`May 2007
`Baltimore, MD
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`May 2002
`College Park, MD
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`EDUCATION
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`M.S. IN COMPUTER SCIENCE
` Johns Hopkins University
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`B.S. IN MATHEMATICS AND COMPUTER SCIENCE
` University Of Maryland
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`LITIGATION EXPERIENCE
`• Croga v. IBM
`Croga v. Fortinet
`Croga v. Palo Alto Networks
`Croga v. Cisco Networks
`Serving as an expert for patent assertion, source code review for networking and VOIP
`related patents. 2024
`• DALE LEFEBVRE v. EXTRABUX, INC. Performed source code review of online
`platform for rebate redemption. 2024
`• HeroDevs, inc v. DevIntent LLC. Serving as an expert for forensic analysis code history to
`identify potential copyright infringement. 2023
`• Balaji K. Srinivasan v. Hashflow Foundatiojns. Source code forensic analysis and opinion.
`2023
`• BEIJING MEISHE NETWORK TECHNOLOGY CO. LTD. V. TIKTOC INC. Clean
`room source code replacement analysis for copyright infringement. 2022-2023
`• R2 Solutions LLC v. American Airlines
`R2 Solutions LLC v. FedEx Corporate Services, Inc.
`R2 Solutions LLC v. Deezer
`R2 Solutions LLC v. Charles Schwab Corp
`Source code review and analysis to identity patent infringement for multiple patents
`related to authentication, MapReduce processing and structured search. 2022
`• Clinicomp International v. Cerner Corporation. Provided source code review for cloud
`health care electronics record system for the purposes of identifying patent infringement.
`2022-2023
`• Century Capital Partners IV, L.P. v. Marsh USA, INC. Provided system design review and
`consultation for AWS reference network architecture. Written opinion regarding
`standalone evaluation of deployed AWS system. 2021-2023
`• Teradata Corp v. SAP SE, SAP Am. INC. Provided source code review for database and
`enterprise resource planning (ERP) products. Developed presentations and documentation
`identifying potential trade secret infringement. Performed reverse engineering on
`linux/windows applications to identify executed code paths. 2019-2021
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`Case 4:23-cv-01147-ALM Document 53-2 Filed 10/29/24 Page 19 of 611 PageID #: 1689
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`EMPLOYMENT
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`SYSCALL 7, LLC WOODBINE, MD
`Principal Security Researcher JANUARY 2016 - PRESENT
`• Developed custom DLP tools integrated with Microsoft platform for endpoint data
`collection. Also integrated proofpoint and Microsoft Sentinel data into the solution.
`Developed customized reporting pipeline to automate workflow and enable analysts
`to search and analyze DLP data.
`• Consulted for a large fortune 50 company on enterprise security architecture utilizing
`a full suite of Microsoft based technologies including Azure Information Protection,
`Azure Defender for Cloud, and Microsoft Cloud App Security platform to ensure
`information security for high value internal documents.
`• Expert Witness for multiple patent infringement and trade secret lawsuits. Consulting
`for cases required a mix of in-depth trade secret review, analysis of run-time
`behavior, in-person and remote code review of large codebases.
`• Led UI developer for a high performance real-time dataflow analytics platform,
`backed by the open source postgres database and processing 10M-100M transactions
`per day. Principally responsible for UI migration from a Rails 2 application to
`angular based front end utilizing Rails 4 and ROAR rails library. Additionally
`migrated back-end processing to integrate ElasticSearch and custom event processing
`code and included user interface unit testing through webpack and jasmine libraries.
`• Co-founded the Online Disassembler (ODA) at onlinedisassembler.com and worked
`with a team of remote developers to develop the fundamental feature s