Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 1 of 48 PageID #:
`5863
`
`Exhibit 2
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`
`
`

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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 2 of 48 PageID #:
`5864
`
`
`
`IN THE UNITED STATES DISTRICT COURT
`FOR THE EASTERN DISTRICT OF TEXAS
`SHERMAN DIVISION
`
`R2 SOLUTIONS LLC,
`
`
`Plaintiff,
`
`v.
`
`
`
`
`
`
`DATABRICKS, INC.,
`
`
`Civil Action No. 4:23-cv-01147-ALM
`
` JURY TRIAL DEMANDED
`
`Defendant.
`
`DEFENDANT’S INITIAL INVALIDITY CONTENTIONS
`
`I.
`
`INTRODUCTION
`
`Pursuant to the Court’s Scheduling Order (Dkt. 28) and P.R. 3-3, defendant Databricks,
`
`Inc. (“Defendant” or “Databricks”) provides the following initial invalidity contentions to plaintiff
`
`R2 Solutions, LLC (“Plaintiff” or “R2”) regarding the currently asserted claims of U.S. Patent No.
`
`8,190,610 (the “’610 patent” or the “Asserted Patent”).
`
`Plaintiff has asserted claims 1-5, and 17-21 of the ’610 patent against Defendant in this
`
`case (the “Asserted Claims”). See Plaintiff’s June 21, 2024 Infringement Contentions (“Cont.”) at
`
`2. Defendant provides these invalidity contentions for the above-identified Asserted Claims. To
`
`the extent Plaintiff later attempts to assert additional claims, Defendant reserves the right to amend
`
`its invalidity contentions and contend that any additional claims are also invalid. Defendant’s
`
`invalidity contentions are not an admission of validity as to any non-asserted claims of the Asserted
`
`Patent.
`
`Based on its investigation and knowledge developed to date, and pursuant to P.R. 3-3,
`
`Defendant: (a) identifies each currently known item of prior art that anticipates and/or renders
`
`1
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`

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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 3 of 48 PageID #:
`5865
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`
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`obvious the Asserted Claims; (b) states whether each such item of prior art anticipates the Asserted
`
`Claims or renders the Asserted Claims obvious (alone or in combination); (c) provides claim charts
`
`identifying where specifically in each item of prior art each element of the Asserted Claims is
`
`found; (d) identifies grounds of invalidity of the Asserted Claims under 35 U.S.C. § 112 based on
`
`indefiniteness, enablement, and/or written description; and (e) identifies claims that are directed
`
`to ineligible subject matter under 35 U.S.C. § 101. Contemporaneously with these invalidity
`
`contentions, Defendant produces the prior art referenced herein and documentation sufficient to
`
`show the operation of the functionalities accused in R2’s infringement contentions, as required
`
`under P.R. 3-4.
`
`Defendant’s invalidity contentions are based on information reasonably available at this
`
`time with respect to the Asserted Claims, and are necessarily preliminary and may require
`
`subsequent amendment, modification, and/or supplementation. Defendant reserves the right to
`
`amend, modify, and/or supplement these invalidity contentions based on, among other things,
`
`amendments, modifications or supplements
`
`to R2’s
`
`infringement contentions, further
`
`investigation, third-party discovery, fact or expert discovery and/or evaluation of the scope and
`
`content of the prior art (including, for example, the prior art from any other cases in which R2
`
`asserts the Asserted Patent), disclosure of the parties’ claim constructions, an order construing the
`
`Asserted Claims, new developments in the case or any inter partes review proceedings, or any
`
`other basis contemplated by the Federal Rules of Civil Procedure, the Court’s Local Rules, and
`
`any other applicable order entered by the Court.
`
`Moreover, fact discovery is ongoing, and Defendant has not obtained deposition testimony
`
`from any of the named inventors of the Asserted Patent or any third party, including, without
`
`limitation, deposition of any third party identified in these invalidity contentions. Defendant has
`
`2
`
`

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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 4 of 48 PageID #:
`5866
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`
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`served third-party subpoenas to Yahoo, Microsoft, and the Apache Software Foundation and is
`
`awaiting discovery in response to its requests. Defendant may subpoena additional third parties,
`
`including Google, for information. Defendant expects further discovery will reveal additional
`
`prior art, including related disclosures and corresponding evidence for many of the prior art
`
`references identified herein. As such, Defendant has not yet completed its investigation, discovery
`
`or analysis of matters relating to the validity or enforceability of the Asserted Claims, including,
`
`without limitation, invalidity due to on-sale statutory bars, public use statutory bars, improper
`
`inventorship, or unenforceability due to inequitable conduct. The disclosures herein are not and
`
`should not be construed as a statement that no other persons have discoverable information, that
`
`no other documents, data compilations, and/or tangible things exist that Defendant may use to
`
`support its claims or defenses, or that no other legal theories or factual bases will be pursued.
`
`Accordingly, Defendant reserves the right to amend, modify and/or supplement these invalidity
`
`contentions as additional information is discovered, identified or otherwise appreciated, including
`
`testimony about the scope and content of the prior art and the claimed inventions.
`
`These initial invalidity contentions are based on the Defendant’s present understanding of
`
`R2’s infringement contentions, served on June 21, 2024. Nothing in these initial invalidity
`
`contentions should be regarded as conceding that R2’s infringement contentions are legally or
`
`factually adequate or as necessarily reflecting the proper interpretation of the claims or an
`
`interpretation of the claims that Defendant agrees with or proposes. Defendant disputes R2’s
`
`apparent claim interpretations and will identify claim constructions for specific claim terms under
`
`the scheduling order governing this case. To the extent additional information regarding R2’s
`
`infringement contentions becomes available, Defendant anticipates that it will provide
`
`corresponding invalidity contentions correlating R2’s interpretation of the claims with the prior art
`
`3
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`

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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 5 of 48 PageID #:
`5867
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`
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`and Defendant may thus amend its invalidity contentions accordingly as applicable to the claims
`
`asserted by R2.
`
`Nothing in these invalidity contentions shall be treated as an admission that Defendant’s
`
`accused products meet any limitation of the Asserted Claims. Defendant denies that it infringes
`
`any claim of the Asserted Patent. To the extent that any prior art reference identified by Defendant
`
`contains a claim element that is the same as or similar to an element in an accused product,
`
`inclusion of that reference in Defendant’s invalidity contentions shall not be deemed a waiver of
`
`any claim construction or non-infringement position. Any use of these invalidity contentions to
`
`support any allegation of infringement would be misleading, false, and wrong as a matter of law
`
`and fact.
`
`Unless otherwise specified, the invalidity contentions set forth herein are based on the
`
`alleged priority date of the ’610 patent asserted by R2 in its infringement contentions. To the
`
`extent R2 asserts entitlement to an earlier priority date for prior art purposes, Defendant reserves
`
`the right to amend these contentions. Further, nothing in these contentions constitutes an
`
`admission concerning the priority dates, conception date, or reduction to practice of the Asserted
`
`Claims.
`
`II.
`
`IDENTIFICATION OF PRIOR ART
`
`The concepts disclosed and claimed in the Asserted Patent are not new, and had been
`
`disclosed, and actively practiced by others prior to the claimed invention date. The prior art
`
`includes various documents, products, patents, and inventions that separately and together render
`
`the Asserted Claims invalid. In addition, as described in more detail below, claims of the Asserted
`
`Patent are invalid under 35 U.S.C. §§ 101 and 112.
`
`Defendant asserts that the prior art listed in Exhibit A, individually or in combination,
`
`invalidate the Asserted Claims. Defendant identifies patents, publications, and the products and
`
`4
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`

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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 6 of 48 PageID #:
`5868
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`
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`systems it describes as prior art under 35 U.S.C. §§ 102 (a), (b), (e), (g) and § 103. The Asserted
`
`Patent has an earliest filing or priority date before March 16, 2013 and is therefore not subject to
`
`the AIA rules relating to what qualifies as prior art. Defendant asserts that as of the date of these
`
`invalidity contentions, these products and systems were: (1) known or used in this country before
`
`the alleged invention of the claimed subject matter of the Asserted Claims; (2) in public use and/or
`
`on sale in this country more than one year before the filing date of the patent; (3)invented in this
`
`country by another who did not abandon, suppress, or conceal, before the alleged invention of the
`
`claimed subject matter of the asserted claim; (4) patented or described in a printed publication in
`
`this or a foreign country; before the alleged invention of the claimed subject matter of the Asserted
`
`Claims; and/or (5) patented or described in a printed publication in this or a foreign country more
`
`than one year prior to the filing date of the patent. These prior art products and systems and their
`
`associated patents and/or printed publications individually anticipate and/or collectively render
`
`obvious each of the Asserted Claims.
`
`These prior art references and products disclose each and every element of one or more of
`
`the Asserted Claims either explicitly, inherently, or via an obvious combination and may also be
`
`relied upon to show the state of the art in the relevant timeframe. The date these prior art items
`
`were offered for sale or publicly used or known, is at least as early as the date the related
`
`publications were published. Defendant anticipates that the actual dates, circumstances, and
`
`identities of individuals will be the subject of third-party discovery during this lawsuit. Defendant
`
`therefore reserves the right to modify, amend, or supplement these invalidity contentions if
`
`additional information becomes available during the course of discovery.
`
`5
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`

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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 7 of 48 PageID #:
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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 8 of 48 PageID #:
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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 9 of 48 PageID #:
`5871
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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 10 of 48 PageID #:
`5872
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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 11 of 48 PageID #:
`5873
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`

`

`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 12 of 48 PageID #:
`5874
`
`
`
`Applying Lucene to the
`Web
`Nutch, Open-Source Web
`Search
`Free Search: Lucene &
`Nutch
`Nutch: Open Source Web
`Search Software
`Scalable Computing with
`MapReduce
`Open Source Search
`Scaling Nutch
`Hadoop version source code
`and documentation, hadoop-
`0.4.0
`Hadoop version source code
`and documentation, hadoop-
`0.1.0
`Hadoop Map/Reduce
`
`200417
`
`200418
`
`200419
`
`200420
`
`200521
`
`200522
`200523
`200624
`
`200625
`
`200626
`
`Doug Cutting
`
`Cutting VIII
`
`Doug Cutting
`
`Cutting IX
`
`Doug Cutting
`
`Cutting X
`
`Doug Cutting
`
`Cutting XI
`
`Doug Cutting
`
`Cutting XII
`
`Doug Cutting
`Doug Cutting
`Doug Cutting /
`Apache Software
`Foundation
`Doug Cutting /
`Apache Software
`Foundation
`Owen O’Malley
`
`Cutting XIII
`Cutting XIV
`Hadoop I
`
`Hadoop II
`
`Hadoop III
`
`
`17 Presented May 7, 2004, The Server Side Java Symposium, Las Vegas, NV. Available at
`https://cwiki.apache.org/confluence/display/NUTCH/Presentation
`18 Presented May 22, 2004, WWW2004, New York, NY. Available at
`https://cwiki.apache.org/confluence/display/NUTCH/Presentation
`19 Presented June, 10 2004, Wizards of OS, Berlin. Available at
`https://cwiki.apache.org/confluence/display/NUTCH/Presentation Audio link available at:
`http://www.archive.org/audio/audio-details-db.php?collectionid=3 do t2 17h 2-
`Cutting_a&collection=conference_proceedings; video link available at:
`http://www.archive.org/details/3_do_t2_17h_2-Cutting
`20 Presented November 26, 2004, University of Pisa, Italy. Available at
`https://cwiki.apache.org/confluence/display/NUTCH/Presentation
`21 Presented August 3, 2005 at OSCON in Portland, OR. Available at
`https://cwiki.apache.org/confluence/display/NUTCH/Presentation
`22 Presented December 5, 2005, at the IBM Information Retrieval Seminar in Haifa, Israel.
`Available at https://cwiki.apache.org/confluence/display/NUTCH/Presentation
`23 Presented September 22, 2005, at the 5th International Web Archiving Workshop in Vienna,
`Austria. Available at
`24 Hadoop-0.4.0 was publicly available no later than June 28, 2006.
`25 Hadoop-0.1.0 was publicly available no later than April 1, 2006.
`26 Presented in July 2006. Available at
`https://cwiki.apache.org/confluence/display/HADOOP2/HadoopPresentations.
`
`11
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`

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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 13 of 48 PageID #:
`5875
`
`
`
`Hadoop Distributed File
`System: Architecture and
`Design
`Lucene-Hadoop
`
`Hadoop Overview
`
`Grep – Lucene-hadoop
`Wiki
`Lucene-Hadoop FAQ
`
`Hadoop MapReduce
`
`How Many Maps and
`Reduces
`Lucene-Hadoop DFS
`Requirements
`Lucene-Hadoop IO
`
`200527
`
`200628
`
`200629
`
`200630
`
`200631
`
`200632
`
`200633
`
`200634
`
`200635
`
`Dhruba Borthakur
`/ Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`
`Hadoop IV
`
`Hadoop V
`
`Hadoop VI
`
`Hadoop VII
`
`Hadoop VIII
`
`Hadoop IX
`
`Hadoop X
`
`Hadoop XI
`
`Hadoop XII
`
`
`27 Published in 2005. Available at: https://web.mit.edu/mriap/hadoop/hadoop-
`0.13.1/docs/hdfs_design.pdf
`28 Publicly available at least as of September 2006. Available at:
`https://web.archive.org/web/20060901172519/http:/wiki.apache.org/lucene-hadoop/
`29 Publicly available at least as of June 26, 2006. Available at:
`https://web.archive.org/web/20060626012900/http:/wiki.apache.org:80/lucene-
`hadoop/HadoopOverview
`30 Publicly available at least as of June 26, 2006. Available at:
`https://web.archive.org/web/20061124055719/http:/wiki.apache.org/lucene-hadoop/Grep
`31 Publicly available at least as of August 23, 2006. Available at:
`https://web.archive.org/web/20060823192520/http:/wiki.apache.org/lucene-hadoop/FAQ
`32 Publicly available at least as of September 1, 2006. Available at:
`https://web.archive.org/web/20060901172147/http:/wiki.apache.org/lucene-
`hadoop/HadoopMapReduce
`33 Publicly available at least as of August 23, 2006. Available at:
`https://web.archive.org/web/20060823193735/http://wiki.apache.org/lucene-
`hadoop/HowManyMapsAndReduces
`34 Publicly available at least as of September 1, 2006. Available at:
`https://web.archive.org/web/20060901050023/http://wiki.apache.org/lucene-
`hadoop/DFS_requirements
`35 Publicly available at least as of June 26, 2006. Available at:
`https://web.archive.org/web/20060626010843/http://wiki.apache.org:80/lucene-hadoop/io
`
`12
`
`

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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 14 of 48 PageID #:
`5876
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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 15 of 48 PageID #:
`5877
`
`
`
`prior
`Yahoo
`implementations
`MapReduce
`and
`functionality42
`prior
`Microsoft
`implementations
`MapReduce
`and
`functionality43
`The Hadoop System44
`
`art
`of
`similar
`
`art
`of
`similar
`
`The Nutch System45
`
`Prior to
`October 5,
`2006
`
`Prior to
`October 5,
`2006
`
`Prior to
`October 5,
`2006
`Prior to
`October 5,
`2006
`
`Yahoo
`
`The Yahoo
`MapReduce System
`
`Microsoft
`
`The Microsoft
`MapReduce System
`
`Apache Software
`Foundation
`
`Apache Software
`Foundation
`
`Hadoop System
`
`Nutch System
`
`
`
`3.
`
`IDENTIFICATION OF INVALIDITY DUE TO OBVIOUSNESS
`
`The tables below list prior art references that render one or more of the Asserted Claims
`
`invalid as obvious under 35 U.S.C. § 103, alone or in combination with the knowledge of a person
`
`having ordinary skill in the art and/or other prior art. The attached claim charts in Exhibit A
`
`demonstrate where each limitation of the Asserted Claims is found in certain of the references
`
`listed below, either expressly or inherently in the larger context of the passage, as understood by a
`
`person having ordinary skill in the art. The following references are prior art under at least 35
`
`U.S.C. §§ 102(a), (b), (e), or (g).
`
`
`42 Databricks is seeking discovery from Yahoo for non-public information concerning their
`implementations of MapReduce and similar functionality including related projects, services,
`or products.
`43 Databricks is seeking discovery from Microsoft for non-public information concerning their
`implementations of MapReduce and similar functionality including related projects, services,
`or products.
`44 The Hadoop System includes but is not limited to: Cutting I-XIV, Hadoop I-XII, including all
`prior art versions of the Hadoop source code. The Hadoop System is the subject of on-going
`third-party discovery.
`45 The Nutch System includes but is not limited to: Cutting I-XIV, including all prior art versions
`of the Nutch source code. The Nutch System is the subject of on-going third-party discovery.
`
`
`
`14
`
`

`

`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 16 of 48 PageID #:
`5878
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`

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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 17 of 48 PageID #:
`5879
`
`
`
`Intranet Search With Nutch
`Applying Lucene to the
`Web
`Nutch, Open-Source Web
`Search
`Free Search: Lucene &
`Nutch
`Nutch: Open Source Web
`Search Software
`Scalable Computing with
`MapReduce
`Open Source Search
`Scaling Nutch
`Hadoop version source code
`and documentation, hadoop-
`0.4.0
`Hadoop version source code
`and documentation, hadoop-
`0.1.0
`Hadoop Map/Reduce
`Hadoop Distributed File
`System: Architecture and
`Design
`Lucene-Hadoop
`
`Hadoop Overview
`
`Grep – Lucene-hadoop
`Wiki
`Lucene-Hadoop FAQ
`
`Hadoop MapReduce
`
`How Many Maps and
`Reduces
`Lucene-Hadoop DFS
`Requirements
`
`Doug Cutting
`Doug Cutting
`
`Cutting VII
`Cutting VIII
`
`Doug Cutting
`
`Cutting IX
`
`Doug Cutting
`
`Cutting X
`
`Doug Cutting
`
`Cutting XI
`
`Doug Cutting
`
`Cutting XII
`
`Doug Cutting
`Doug Cutting
`Doug Cutting /
`Apache Software
`Foundation
`Doug Cutting /
`Apache Software
`Foundation
`Owen O’Malley
`Dhruba Borthakur
`/ Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`Apache Software
`Foundation
`
`Cutting XIII
`Cutting XIV
`Hadoop I
`
`Hadoop II
`
`Hadoop III
`Hadoop IV
`
`Hadoop V
`
`Hadoop VI
`
`Hadoop VII
`
`Hadoop VIII
`
`Hadoop IX
`
`Hadoop X
`
`Hadoop XI
`
`2004
`2004
`
`2004
`
`2004
`
`2004
`
`2005
`
`2005
`2005
`2006
`
`2006
`
`2006
`2005
`
`2006
`
`2006
`
`2006
`
`2006
`
`2006
`
`2006
`
`2006
`
`16
`
`

`

`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 18 of 48 PageID #:
`5880
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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 19 of 48 PageID #:
`5881
`
`
`
`d.
`
`Motivation for Combining Prior Art
`
`A person having ordinary skill in the art at the time of filing of the Asserted Patent would
`
`have understood the references listed above, alone or in combination, to contain explicit and/or
`
`implicit teaching, suggestion, and/or rationales to combine them for at least the following
`
`exemplary reasons.46
`
`The combinations of references provided in the accompanying prior art reference charts
`
`for each Asserted Claim are examples and are not intended to be exhaustive. Additional
`
`obviousness combinations of the references identified here are possible, and Defendant may rely
`
`on such combination(s) in this litigation. In particular, Defendant is unaware of the extent, if any,
`
`to which R2 may contend that limitations of the claims at issue are not disclosed in the prior art
`
`identified by Defendant as anticipatory, and the extent to which R2 will contend that elements not
`
`disclosed in the specification of the Asserted Patent would have been known to persons of skill in
`
`the art. Defendant is continuing its investigation of the large universe of prior art to identify
`
`potential prior art systems, publications related to those systems, and third-parties that may have
`
`information about those systems. Defendant reserves the right to supplement these contentions to
`
`identify other prior art and combinations that would have made such limitations obvious.
`
`A POSITA would have been motivated to combine the teachings of any of the references
`
`related to MapReduce—i.e., Pike, Dean I-III, McClosky, Nutch System, Hadoop System, GFS,
`
`and/or The Google MapReduce System, Yahoo MapReduce System, and/or Microsoft MapReduce
`
`
`46 In KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 418 (2007), the Supreme Court held that prior
`art need not disclose the precise teachings of a patented invention to render it obvious,
`because a court “can take account of the inferences and creative steps that a person of
`ordinary skill in the art would employ.” Under KSR, an explanation for why a combination
`of prior art items renders a claim obvious may be found in the “interrelated teachings of
`multiple patents; the effects of demands known to the design community or present in the
`marketplace; and the background knowledge possessed by a person having ordinary skill in
`the art.” Id. at 418.
`
`18
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`

`

`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 20 of 48 PageID #:
`5882
`
`
`
`Systems (the “MapReduce References”). Each of the MapReduce references concerns the same
`
`subject matter—MapReduce—or various systems or products implementing it or similar parallel
`
`programming architectures. For example, each of those references disclose partitioning data
`
`groups into key value pairs, providing the partitions to mapping functions, and reducing the
`
`intermediate data based on a common key, all within a distributed system. See e.g., Pike at 2:14-
`
`39, 2:41-3:35; MapReduce in Nutch; Dean I at Fig. 1, 2:40-60; McClosky at 1-5. It would have
`
`been obvious to consider and combine the teachings of a publication, patent, patent application, or
`
`system that relates to a certain product or service with the features of that product or service as
`
`known and/or provided commercially. Similarly, where multiple publications discuss the same
`
`underlying product, service, standard, or project, it was obvious to combine the discussions and
`
`disclosures of the publications as they would be understood to describe features or potential
`
`features of the underlying subject matter. Further, where one publication discusses another
`
`publication or standard, it was obvious to consider and combine the teachings of each publication
`
`in combination with each other. Indeed, the inventors of the ’610 patent refer to teachings from
`
`such references interchangeably. See Map-Reduce-Merge: Simplified Relational Data Processing
`
`on Large Clusters at 1029 (classifying Google’s File System (GFS), Map-Reduce, Microsoft’s
`
`Dryad, Yahoo’s internal infrastructure and open-source Hadoop as comparable systems).
`
`Combining the teachings of these references would have involved nothing more than the use of
`
`known features to yield predictable results—mapping and reducing data groups using key-value
`
`pairs.
`
`Moreover, these prior art references or other evidence of prior art identify and address the
`
`same technical issues and suggest similar solutions to those issues. The prior art references,
`
`actions, knowledge, and/or prior inventions for the Asserted Patent are directed to the same or
`
`19
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`

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`Case 4:23-cv-01147-ALM Document 84-3 Filed 02/12/25 Page 21 of 48 PageID #:
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`similar fields and are directed to solving the same or similar problems such that one of ordinary
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`skill in the art would have been motivated to consider the techniques and systems disclosed or
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`involved in those items of prior art and to combine them to arrive at the alleged inventions in the
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`Asserted Claims.
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`Additionally it would have been obvious to combine any of MacLeod I-III, Matsumoto,
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`Chowdhuri, DeWitt, Hellerstein, and Ozsu (the Relational Database References), as each of which
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`describe performing processing operations on data in relational databases, merging or other
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`functions on heterogeneous datasets and identifying and tracking data throughout processing, with
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`the MapReduce References. Each of the Relational Database References concerns data in a
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`distributed database, and explicitly refers to performing the same functions within that database
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`system. See e.g., MacLeod I at 1:19-29 (“A relational database is a collection of related data that
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`is organized in related two-dimensional tables of columns and rows wherein information can be
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`derived by performing set operations on the tables, such as join, sort, merge, and so on. The data
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`stored in a relational database is typically accessed by way of a user-defined query that is
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`constructed in a query language such as Structured Query Language (“SQL”). A SQL query is
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`non-procedural in that it specifies the objective or desired result of the query in a language
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`meaningful to a user but does not define the steps to be performed, or the order of the steps in order
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`to accomplish the query.”); Matsumoto at 1:13-14 (“FIG.22 and FIG. 23 are block diagrams
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`showing a related art join processing system [i]n a relational database), 2:58-61 (“An object of this
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`invention is to provide a join processing system and method, which includes high speed join
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`processing in a relational database configured in a multiprocessor system.”); Chowdhuri at ¶ 108
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`(“Data in a relational database is stored as a series of tables, also called relations. Typically resident
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`on the server 230, each table itself comprises one or more “rows' or “records” (tuples) (e.g., row
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`255 as shown at FIG. 2). A typical database will contain many tables, each of which stores
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`information about a particular type of entity. A table in a typical relational database may contain
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`anywhere from a few rows to millions of rows.”), ¶ 109 (“Most relational databases implement a
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`variant of the Structured Query Language (SQL), which is a language allowing users and
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`administrators to create, manipulate, and access data stored in the database.”); DeWitt at Abstract
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`(“This paper describes the design of the Gamma database machine and the techniques employed
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`in its implementation. Gamma is a relational database machine currently operating on an Intel
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`iPSC /2 hypercube with 32 processors and 32 disk drives. Gamma employs three key technical
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`ideas which enable the architecture to be scaled to hundreds of processors.”), id. (“Second, novel
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`parallel algorithms based on hashing are used to implement the complex relational operators such
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`as join and aggregate functions.”)
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`A POSITA would have been motivated to combine the MapReduce Reference with the
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`Relational Database References. For example, Matsumoto and Chowdhuri disclose performing
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`join operations on heterogeneous data having different schema. A POSITA also would have been
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`motivated to map and reduce the heterogeneous data sources disclosed in Choudhuri and
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`Matsumoto in the systems of the MapReduce References because those references teach and
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`suggest performing similar MapReduce operations on their data / data tables. For example, the
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`Nutch and Hadoop Systems disclose joining heterogeneous data sets using mapping and reducing
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`functions. See Cutting II at 13 (describing how the Nutch System uses MapReduce to merge data
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`from heterogeneous data files “ParseData,” “ParseText,” “Inlinks,” “CrawlDatum.”).
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`Moreover, a POSITA would have recognized, for example, that Chowdhuri’s scanning and
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`hashjoin iterators correspond to the MapReduce References’ map and reduce tasks, and thus
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`Chowdhuri’s tables and processing techniques were compatible with the MapReduce References’
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`systems. Indeed, Chowdhuri’s scan, hashjoin, GroupBy, and sort iterators correspond to the ’610
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`patent’s description of map and reduce functions. ’610 patent at 1:20-27 (“[t]he map function
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`iterates” and “[t]he reduce operation … combines elements”), 5:19-39 (sorting), 2:64-66 (“a sort
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`and group-by-key task”), 5:48-6:7 (group iterator), 6:34-7:16 (“hashtable” iterators), 10:1-7 (“the
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`reducers include a sort, group-by-key and combine task”); see also Matsumoto at Abstract (“This
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`invention provides a join processing system and method, which operates efficiently without a
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`burden to a master processor, in a relational database on a multiprocessor.”); id. (“Each of the slave
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`processors retrieves a first sub-table and transfers the table to the master processor, and the master
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`processor creates a first main table. Each of the slave processors retrieves the second data
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`selectively with reference to the first main table, and creates a second sub-table. The master
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`processor merges the second sub-tables, and creates a second main table. Then, a join processing
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`of the first main table and the second main table is performed.”); id. at 11:8-19 (“The command
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`orders the system to retrieve employee names from the employee data and to select the sales
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`amount and merchandise from the sales data. In this example, a condition that the employee's
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`family names are Tanaka is given. Furthermore, when join conditions of the employee data and
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`the sales data are matched according to a key called a join key, the employee data and the sales
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`data are selected and joined. In FIG. 2, the join condition is that the data is selected when man
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`numbers of the employee data and the man numbers of the sales data are matched. The man
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`numbers are used as the join keys to join the data.”).
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`A POSITA would have looked to the MapReduce References to map and merge data sets
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`or tables from Chowdhuri and Matsumoto using the MapReduce References’ distributed system
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`to, for example, leverage the MapReduce References’ distributed system and its fault tolerant task
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`scheduling. E.g., Pike, 4:28-53, 6:37-54, 6:55-7:25, 11:57-13:3. This would have been nothing
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`more than combining known elements in the prior art to achieve predictable results. The resulting
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`combination would have processed Chowdhuri’s or Matsumoto’s tables using, for example,
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`Chowdhuri’s scanning, hashjoin, GroupBy, and sort iterators as map and reduce tasks in the
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`MapReduce References, while employing the MapReduce References’ fault-tolerant architecture
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`to do so. Alternatively, the resulting combination would have processed Chowdhuri’s or
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`Matsumoto’s tables using, for example the map and reduce operations described by the
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`MapReduce References.
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`A POSITA would have been motivated to combine, e.g., Chowdhuri’s partitioning with
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`the MapReduce References large-scale data processing systems to take advantage of the
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`MapReduce References parallel processing system and fault tolerant task scheduling. E.g., Pike,
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`4:28-53, 6:55-7:25, 11:57-13:3. As the MapReduce architecture already includes partitioning
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`input data sets, see e.g., Dean I at Fig. 2, the combination would have been nothing more than
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`combining known elements in the prior art to yield the predictable result of efficiently processing
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`Chowdhuri’s input tables by partitioning each table into a plurality of partitions and processing
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`them in a distributed, fault-tolerant system. The resulting combination would have processed
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`Chowdhuri’s or Matsumoto’s tables using, for example, Chowdhuri’s scanning, hashjoin,
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`GroupBy, and sort iterators as map and reduce tasks in the MapReduce References, while
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`employing the MapReduce References’ fault-tolerant architecture to do so. Alternatively, the
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`resulting combination would have processed Chowdhuri’s or Mat