Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 1 of 52 PagelD #: 2410
`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 1 of 52 PageID #: 2410
`
`Exhibit 5
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 2 of 52 PageID #: 2411
`
`Trials@uspto.gov
`Tel: 571-272-7822
`
`Paper 16
`Entered: June 11, 2024
`
`
`
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`
`
`
`
`
`
`
`
`
`
`
`
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`
`DATABRICKS, INC.,
`Petitioner,
`v.
`R2 SOLUTIONS LLC,
`Patent Owner.
`
`IPR2024-00659
`Patent 8,190,610 B2
`
`
`
`
`Before KARL D. EASTHOM, KRISTEN L. DROESCH, and BRIAN P.
`MURPHY, Administrative Patent Judges.
`
`MURPHY, Administrative Patent Judge.
`
`
`
`DECISION
`Denying Institution of Inter Partes Review
`35 U.S.C. § 314
`
`
`
`
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 3 of 52 PageID #: 2412
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`
`I. INTRODUCTION
`Databricks, Inc. (“Petitioner”) filed a Petition pursuant to 35 U.S.C. § 311
`requesting institution of inter partes review of claims 1–46 of U.S. Patent No.
`8,190,610 B2 (Ex. 1001, “the ’610 patent”). Paper 1 (“Pet.”). R2 Solutions LLC
`(“Patent Owner”) timely filed a Patent Owner Preliminary Response. Paper 13
`(“Prelim. Resp.”). Pursuant to authorization from the Board, Petitioner filed a
`Reply to Patent Owner’s Preliminary Response addressing Patent Owner’s
`argument for discretionary denial under 35 U.S.C. § 325(d), and Patent Owner
`filed a Sur-Reply. Papers 14, 15.
` We have jurisdiction under 35 U.S.C. § 314, which provides that an inter
`partes review may not be instituted “unless . . . there is a reasonable likelihood that
`the petitioner would prevail with respect to at least 1 of the claims challenged in
`the petition.” Under § 314, the Board may not institute review on fewer than all
`claims challenged in the petition. SAS Inst., Inc. v. Iancu, 138 S. Ct. 1348, 1359–
`60 (2018). If the Board institutes a review, it will institute “on all of the
`challenged claims and on all grounds of unpatentability asserted for each claim.”
`37 C.F.R. § 42.108(a). Upon consideration of the Petition, the Preliminary
`Response, and the evidence of record, for the reasons set forth below, we conclude
`Petitioner does not demonstrate a reasonable likelihood that it would prevail in
`showing the unpatentability of at least one challenged claim of the ’610 patent.
`Accordingly, we do not institute an inter partes review of claims 1–46 of the ’610
`patent.
`A. Real Parties in Interest and Related Matters
`Petitioner, Databricks, Inc., identifies itself as the real party in interest.
`Pet. 81. Patent Owner, R2 Solutions LLC, identifies itself as the real party in
`
` 2
`
`
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 4 of 52 PageID #: 2413
`
`Court
`
`Filed
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`interest. Paper 6, 2. Patent Owner further states it is a subsidiary of Acacia
`Research Group LLC, whose parent is Acacia Research Corporation. Id.
`Petitioner identifies the following related matters:
`Name
`No.
`R2 Solutions LLC v. Databricks,
`Inc.
`
`4:23-cv-01147
`
`E.D. Tex.
`
`Dec. 28, 2023
`
`Cloudera, Inc. v. R2 Solutions
`LLC
`
`R2 Solutions LLC v. Cloudera,
`Inc.
`
`American Airlines, Inc. v. R2
`Solutions LLC
`
`R2 Solutions LLC v. Hilton
`Domestic Operating Co. Inc.
`
`FedEx Corporate Services, Inc.
`v. R2 Solutions LLC
`
`Allegiant Travel Company v. R2
`Solutions LLC
`
`R2 Solutions LLC v. American
`Airlines, Inc.
`
`R2 Solutions LLC v. CVS Health
`Corporation et al
`
`R2 Solutions LLC v. Hilton
`Worldwide Holdings Inc. et al
`
`R2 Solutions LLC v. Citigroup
`Inc.
`
`IPR2024-00303
`
`PTAB
`
`Dec. 18, 2023
`
`1-23-cv-01205 W.D. Tex. Oct. 5, 2023
`
`IPR2023-00689
`
`PTAB
`
`Mar. 7, 2023
`
`3-22-cv-02761
`
`N.D. Tex.
`
`Dec. 12, 2022
`
`IPR2022-01405
`
`PTAB
`
`Aug. 15, 2022
`
`2-22-cv-00828
`
`D. Nev.
`
`May. 24, 2022
`
`4-22-cv-00353
`
`E.D. Tex.
`
`Apr. 28, 2022
`
`4-22-cv-00354
`
`E.D. Tex.
`
`Apr. 28, 2022
`
`4-22-cv-00356
`
`E.D. Tex.
`
`Apr. 28, 2022
`
`4-22-cv-00357
`
`E.D. Tex.
`
`Apr. 28, 2022
`
` 3
`
`
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 5 of 52 PageID #: 2414
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`
`R2 Solutions LLC v. FedEx
`Corporate Services, Inc.
`
`4-21-cv-00940
`
`E.D. Tex.
`
`Nov. 29, 2021
`
`R2 Solutions LLC v. State Farm
`Mutual Automobile Insurance
`Company
`
`
`
`4-21-cv-00941
`
`
`
`E.D. Tex.
`
`
`
`Nov. 29, 2021
`
`R2 Solutions LLC v.
`Booking.com BV
`
`4-21-cv-00942
`
`E.D. Tex.
`
`Nov. 29, 2021
`
`R2 Solutions LLC v.
`Booking.com Transport Limited 4-21-cv-00943
`R2 Solutions LLC v. Agoda
`Company Pte. Ltd.
`
`4-21-cv-00944
`
`E.D. Tex.
`
`Nov. 29, 2021
`
`E.D. Tex.
`
`Nov. 29, 2021
`
`R2 Solutions LLC v. Expedia
`Group, Inc.
`
`R2 Solutions LLC v.
`iHeartMedia, Inc.
`
`R2 Solutions LLC v. JPMorgan
`Chase & Co.
`
`R2 Solutions LLC v. The
`Charles Schwab Corporation
`
`R2 Solutions LLC v. Fidelity
`Brokerage Services LLC
`
`6-21-cv-00628 W.D. Tex.
`
`Jun. 17, 2021
`
`6-21-cv-00552 W.D. Tex.
`
`Jun. 01, 2021
`
`4-21-cv-00174
`
`E.D. Tex. Mar. 02, 2021
`
`4-21-cv-00122
`
`E.D. Tex.
`
`Feb. 09, 2021
`
`4-21-cv-00123
`
`E.D. Tex.
`
`Feb. 09, 2021
`
`R2 Solutions LLC v. Deezer SA
`
`4-21-cv-00090
`
`E.D. Tex.
`
`Jan. 29, 2021
`
`R2 Solutions LLC v. Walmart
`Inc.
`
`R2 Solutions LLC v. Target
`Corporation
`
`R2 Solutions LLC v. Workday,
`Inc.
`
`4-21-cv-00091
`
`E.D. Tex.
`
`Jan. 29, 2021
`
`4-21-cv-00092
`
`E.D. Tex.
`
`Jan. 29, 2021
`
`4-21-cv-00093
`
`E.D. Tex.
`
`Jan. 29, 2021
`
` 4
`
`
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 6 of 52 PageID #: 2415
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`
`
`Pet. 81–83. Petitioner and Patent Owner both represent that the only currently
`pending matters are the first three proceedings listed above: R2 Solutions LLC v.
`Databricks, Inc. (E.D. Tex.), Cloudera, Inc. v. R2 Solutions LLC, IPR2024-00303
`(PTAB), and R2 Solutions LLC v. Cloudera, Inc. (W.D. Tex.). Id. at 2; Paper 4, 2–
`3. Petitioner represents that the American Airlines and FedEx IPR proceedings
`listed above “were both terminated prior to any decision on institution.” Pet. 81;
`see also IPR2022-01405, Paper 9 (PTAB Dec. 9, 2022) and IPR2023-00666, Paper
`21 (PTAB Oct. 4, 2023) (granting joint motions to terminate prior to a decision on
`institution). Petitioner further represents that the Petition in this proceedings “is
`substantively identical to the petition submitted in IPR2024-00303.” Pet. 81.
`B. The ’610 Patent
`1. Background Regarding MapReduce Processing
`The ’610 patent, titled “MapReduce for Distributed Database Processing,” is
`directed to a claimed enhancement of MapReduce data processing. “MapReduce
`is a programming methodology to perform parallel computations over distributed
`(typically, very large) data sets.” Ex. 1001, 1:5–7. Conventional map and reduce
`processing can be performed by multiple computers, thereby “enabl[ing] the use of
`massive clusters of commodity computers to provide a simplified programming
`and execution model for processing large sets of data in parallel.” Id. at 3:9–12;
`see also Ex. 1005 ¶ 36 (citing Ex. 1001, 3:9–12).
`MapReduce involves a “map” function followed by a “reduce” function, as
`depicted in the figure below:
`
` 5
`
`
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 7 of 52 PageID #: 2416
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`
`
`
`Ex. 2002, 4. Each Map Task or map function, shown above, “maps [input] key-
`value pairs to new (intermediate) key-value pairs.” Ex. 1001, 1:17–18. The new
`intermediate key-value pairs generated by the map function are referred to as
`“intermediate data” that is input to Reduce Tasks (reduce functions). Id. at 4:4–13;
`see also Ex. 1005 ¶ 36 (citing Ex. 1001, 1:6–27). Each reduce function “combines
`the intermediate data into smaller data sets or lists of values.” Ex. 1005 ¶ 36
`(citing Ex. 1001, 1:6–27).
`MapReduce, for example, can be used to count how often a word occurs in a
`large collection of documents. In the example reproduced below, “the input data
`consists of a collection of words (Deer, Bear, River, and Car)”:
`
`
`
` 6
`
`
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 8 of 52 PageID #: 2417
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`Prelim. Resp. 4 (citing Ex. 2002, 5). In the above example, MapReduce divides
`the input into three splits (“Splitting”), each one supplied to a separate map
`function for parallel processing. Id. Each map function generates a list of
`intermediate key-value pairs, where each word is a “key” and the “value” for each
`occurrence of a word is 1 (e.g., “Deer, 1” or “Car, 1”). Id.1 All intermediate key-
`value pairs having a common key (same word) are grouped together (“Shuffling”)
`so that intermediate data having “the same key are sent to the corresponding
`reducer.” Ex. 2002, 5. “[E]ach [r]educer counts the values … present in that list
`of values” and generates new “reduced” key-value pairs that reflect the total count
`for each word (e.g., “Bear, 2,” “Car, 3,” “Deer, 2” or “River, 2”). Id.
`2. The claimed “data group” enhancement to MapReduce
`The ’610 patent states that “conventional MapReduce implementations do
`not have facility to efficiently process data from heterogeneous sources.”
`Ex. 1001, 3:15–17. The ’610 patent describes an enhancement where “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).”2 Id.
`at 1:34–36. Input data is “partitioned” into two or more “data groups” where “data
`sets within the same group are characterized by the same schema; and data sets
`within different groups are characterized by different schemas.” Id. at 3:52–55.
`
`
`1 The input data also may be provided as a list of key-value pairs, in which case the
`map function generates new key-value pairs as intermediate data. Prelim. Resp. 5–
`6 (citing Ex. 2003, 8).
`2 The word “schema” (short for logical schema) is a term of art in computer
`science meaning “[t]he encoding of the data model of a database in the relevant
`database language. It is sometimes simply referred to as the schema of a
`database.” Ex. 3002 (A Dictionary of Computer Science, “logical schema,” 166
`(Oxford Univ. Press., 7 ed.) (2016)). Patent Owner also argues that “[s]chema can
`refer to, e.g., ‘fields’ in a particular collection of data.” Prelim. Resp. 7.
`
` 7
`
`
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 9 of 52 PageID #: 2418
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`The different “data group” schemas may reflect data originating from heterogenous
`data sources, such as the Employee table and Department table exemplified in the
`’610 patent. Id. at 3:15–28. Thus, the fundamental organizing principle of the
`claimed invention is to provide at least two input “data groups” characterized by
`different schemas or data types for separate MapReduce processing.
`Importantly, the claimed method also provides a mechanism to identify data
`being processed as corresponding to its original input “data group.” Id. at 4:4–22,
`6:29–48, 7:40–58. A common key, in contrast, permits data from different “data
`groups” to be related and reduced in a MapReduce process. The ’610 patent
`describes the claimed “data group” improvement as follows:
`
`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 data sets with (group)
`identifiers. It is noted that the output group identifiers may differ from
`the input/intermediate group identifiers.
`Id. at 3:58–64 (emphasis added). As an example, Figure 5 of the ’610 patent
`depicts an embodiment of claim 1 where different “data group” schemas (the
`Employee table of employee names and department IDs and the Department table
`of department names and department IDs), their associated “data group” identifiers
`(“Emp” and “Dept”), and common keys (the department ID numbers) are used in
`MapReduce to join the data in the Employee table and Department table. Id.
`at 3:19–34, 8:14–19, Figs. 3, 5.
`Patent Owner provides a helpful summary of the invention supported by the
`
` 8
`
`
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 10 of 52 PageID #: 2419
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`record in an annotated version of Figure 5, reproduced below:
`
`
`Prelim. Resp. 10. Starting from the left in annotated Figure 5, above, the “full data
`of the Employee table and the Department table” is the input data partitioned into
`two “data groups” labeled Group:Emp (502, green box at top) and Group:Dept
`(502, green box below). Ex. 1001, 8:15–24; see also Prelim. Resp. 10–11 (citing
`Ex. 1001, 8:15–24). Each input “data group” is “characterized by its own
`schema,” e.g., the data in the Group:Emp group is of a different type (employee
`names) and source (Employee table) than the data in the Group:Dept group
`(Department table with department names), such that the two “data groups” have
`
` 9
`
`
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 11 of 52 PageID #: 2420
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`different attributes.3 Ex. 1001, 3:66–4:1; see also Ex. 1005 ¶ 56 (“The ’610 patent
`therefore specifies a different data format or ‘schema’ in different input data
`groups.”).
`The Group:Emp data group has key-value pairs of DeptID[key]-
`LastName[value] for each employee, i.e., 34, Smith; 33, Jones; 34, Robinson etc.
`Id. at 3:23–28. The Group:Dept data group has key-value pairs of DeptID[key]-
`DeptName[value] for each department, i.e., 31, Sales; 33, Engineering; 34,
`Clerical etc. Id. The two input “data groups” share common keys—DeptID
`numbers 31, 33, 34, and 35—but their different values—LastName v.
`DeptName—and sources reflect different schemas.
`Annotated Figure 5 depicts each data group as being further partitioned into
`two “data partitions” (502), and each “data partition” provides a set of key-value
`pairs to separate MAP functions (504). Ex. 1001, 8:14–23, Fig. 5. For example,
`the Group:Emp “data group” has a first “data partition” for key-value pairs: 34,
`Smith; 33, Jones; and 34, Robinson; which are input into one MAP function. The
`Group:Emp “data group” has a second “data partition” for key-value pairs: 34,
`Jasper; 33, Stone; and 31, Rosen; which are input into a separate MAP function.
`Id. The independent claims 1, 17, 33, and 40 of the ’610 patent recite “a plurality
`of data groups” and “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,” or similar
`language, exemplified in annotated Figure 5 above.
`MAP functions (504) generate intermediate data in the form of new key-
`value pairs (506), which are identified with their corresponding input “data group”
`
`
`3 “[T]he schema of each data set … includes a set of attributes (such as DeptID,
`LastName, DeptName) and their properties (such as their data types: integer
`DeptID, string LastName, string DeptName).” Ex. 1001, 3:37–41.
`
`10
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 12 of 52 PageID #: 2421
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`by “being emitted into files with an ‘emp’ extension [that] identifies the data with
`the ‘emp group,” or “being emitted into files with a ‘dept’ extension” that
`identifies the data with the “dept” group. Id. at 7:40–58, 8:24–37. In annotated
`Figure 5, Patent Owner highlights the “data group” identifiers “Emp” and “Dept”
`(green boxes) that “follow[] the data through the mapping phase 504 and into the
`reducing phase 510.” Prelim. Resp. 11, 13. Providing a mechanism to identify
`processed data as corresponding to its input “data group” is a critical component of
`the claimed enhancement to MapReduce data processing. The independent claims
`recite a map function that forms “corresponding intermediate data for that data
`group and identifiable to that data group” (claims 1 and 17), or similar language.
`The description of the claimed invention concludes as follows:
`[T]he MapReduce concept may be utilized to carry out map processing
`independently on two or more related datasets … even when the related
`data sets are heterogenous with respect to each other, such as data
`tables organized according to different schema. The intermediate
`results of the map processing (key value pairs) for a particular key can
`be processed together in a single reduce function by applying a
`different iterator4 to intermediate values for each group. In this way,
`operations on the two or more related datasets [“data groups”] may be
`carried out more efficiently or in a way not even possible with the
`conventional MapReduce architecture.
`Ex. 1001, 8:46–57 (emphasis added). Thus, in the example of Figure 5,
`heterogenous data sets (Employee and Department tables) are organized
`(partitioned) into separate “data groups,” where data in one “data group” has the
`same schema (same type of data or data format) that is different from the schema
`
`
`4 “Iterator” is defined as a “self-contained software object[] that accepts[] a stream
`of rows from null or n-ary data sets. The role of an iterator is to process many
`iterations of a data set across many nodes in serial or parallel. . . . For example,
`scanning rows from a table on disk can be the behavior of one type of iterator.”
`Ex. 1003 ¶ 61; see also Ex. 1005 ¶ 76 (citing same).
`
`11
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 13 of 52 PageID #: 2422
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`characterizing the data in the other “data group.” Each “data group” is further
`partitioned into a plurality of “data partitions” that are separately map-processed to
`form intermediate data (key-value pairs), and the map-processed intermediate data
`is identified as corresponding to one of the input “data groups” using an “emp” or
`“dept” file extension. Such “data group” identification permits intermediate data
`having a common key, e.g., key-value pairs having the same DeptID number but
`originating from different “data groups”—such as 34, Smith; 34, Robinson; 34,
`Jasper (Employee data group); and 34, Clerical (Department data group) (see
`Fig. 5, 508, 509a, above)—to be reduced (combined) in a single reduce function by
`applying a separate iterator to each corresponding “data group.”
`C. Illustrative Claim
`Claims 1, 17, 33, and 40 are the independent claims. Claim 1, reproduced
`below, illustrates the claimed subject matter:
`1. A method of processing data of a data set over a distributed
`system, wherein the data set comprises a plurality of data groups, the
`method comprising:
`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 different intermediate data, wherein the different
`schema and corresponding different intermediate data have a key in
`common; and
`
`
`
`
`12
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 14 of 52 PageID #: 2423
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`
`reducing 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,
`wherein the mapping and reducing operations are performed by
`a distributed system.
`Ex. 1001, 8:60–9:19 (emphasis added).
` In summary, the claims are directed to a method for enhanced MapReduce
`data processing by organizing (partitioning) input data into “data groups” having
`different schemas. The data in each “data group” is further partitioned for separate
`processing in MapReduce to generate intermediate data (“mapping”) and reduced
`data (“reducing”) “identifiable” to a “corresponding” “data group.” The claimed
`method, therefore, enables MapReduce processing on a “data group” basis.
`D. Asserted Grounds of Unpatentability
`Petitioner asserts claims 1–46 are unpatentable on the following grounds:
`Claim(s) Challenged
`35 U.S.C. §
`Reference(s)/ Basis
`1–46 (Ground 1)
`103(a)5
`Pike6
`1–46 (Ground 2)
`103(a)
`Pike, Chowdhuri7
`3, 4, 9–11, 19, 20, 26–28, 37–39,
`103 (a)
`Pike, Chowdhuri, MacLeod8
`44–46 (Ground 3)
`
`
`
`5 The Leahy-Smith America Invents Act, Pub. L. No. 112-29, 125 Stat. 284 (2011)
`(“AIA”), includes revisions to 35 U.S.C. § 103 that became effective after the
`filing of the application that led to the challenged patent.
`6 US Patent No. 7,590,620 B1 issued Sep. 15, 2009, filed Sep. 29, 2004 (Ex. 1002,
`“Pike”).
`7 US 2006/0218123 A1, Pub. Sep. 28, 2006 (Ex. 1003, “Chowdhuri”).
`8 US Patent No. 6,343,295 B1, Jan. 29, 2002 (Ex. 1024, “MacLeod”).
`
`13
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 15 of 52 PageID #: 2424
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`Pet. 2.9 Petitioner relies on the Declaration testimony of Jimmy Lin, Ph.D. in
`support of the asserted grounds of obviousness. Ex. 1005; Ex. 1027.
`II. ANALYSIS
`A. Level of Ordinary Skill in the Art
`Petitioner describes a person of ordinary skill in the art (“POSITA”) as
`someone having “at least a bachelor’s degree in computer science or similar field,
`and approximately two years of industry or academic experience in a field related
`[to] performing data analytics and/or related data processing tasks.” Pet. 9 (citing
`Ex. 1005 ¶¶ 67–69; In re GPAC Inc., 57 F.3d 1573, 1579 (Fed. Cir. 1995)).
`Petitioner further asserts that work experience can substitute for formal education
`and additional formal education can substitute for work experience. Id. Patent
`Owner does not contest Petitioner’s definition for purposes of its Preliminary
`Response. Prelim. Resp. 15.
`In determining the level of ordinary skill in the art, various factors may be
`considered, including the “type of problems encountered in the art; prior art
`solutions to those problems; rapidity with which innovations are made;
`sophistication of the technology; and educational level of active workers in the
`field.” In re GPAC, Inc., 57 F.3d 1573, 1579 (Fed. Cir. 1995) (citing Custom
`Accessories, Inc. v. Jeffrey-Allan Indus., Inc., 807 F.2d 955, 962 (Fed. Cir. 1986)).
`“These factors are not exhaustive but are merely a guide to determining the level of
`
`
`9 The Petition analyzes dependent claims 3, 4, 19, and 20 with reference to
`Ground 3, although claim 20 is not listed in the Unpatentability Grounds.
`Compare Pet. 2 with Pet. 58, 59. Similarly, the Petition includes substantive
`analysis of MacLeod (Ground 3) in connection with only limitation 9[b] regarding
`dependent claims 9–11, 26–28, 37–39, 44–46, but Petitioner’s list of
`Unpatentability Grounds omits claim 28 from Ground 3. Compare Pet. 2 with Pet.
`66–68, 80–81.
`
`
`
`
`
`14
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 16 of 52 PageID #: 2425
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`ordinary skill in the art.” Daiichi Sankyo Co. v. Apotex, Inc., 501 F.3d 1254, 1256
`(Fed. Cir. 2007). There is uncontested evidence in the record before us that
`reflects the knowledge and experience of a POSITA.
`We determine no express finding is necessary, on this record, and that the
`level of ordinary skill in the art is reflected by the prior art of record. See Okajima
`v. Bourdeau, 261 F.3d 1350, 1355 (Fed. Cir. 2001); In re GPAC Inc., 57 F.3d
`1573, 1579 (Fed. Cir. 1995); In re Oelrich, 579 F.2d 86, 91 (CCPA 1978).
`B. Claim Construction
`We construe claim terms using “the same claim construction standard that
`would be used to construe the claim in a civil action under 35 U.S.C. 282(b).” 37
`C.F.R. § 42.100(b). In this context, claim terms “are generally given their ordinary
`and customary meaning” as understood by a POSITA at the time of the invention.
`Phillips v. AWH Corp., 415 F.3d 1303, 1312–13 (Fed. Cir. 2005) (en banc).
`“Importantly, the person of ordinary skill in the art is deemed to read the claim
`term not only in the context of the particular claim in which the disputed term
`appears, but in the context of the entire patent, including the specification.” Id.
`at 1313. The “specification ‘is always highly relevant to the claim construction
`analysis. Usually, it is dispositive; it is the single best guide to the meaning of a
`disputed term.’” Id. at 1315 (citation omitted). Accordingly, when construing a
`disputed claim limitation, “we look principally to the intrinsic evidence of record,
`examining the claim language itself, the written description, and the prosecution
`history, if in evidence.” DePuy Spine, Inc. v. Medtronic Sofamor Danek, Inc., 469
`F.3d 1005, 1014 (Fed. Cir. 2006) (citing Phillips, 415 F.3d at 1312–17).
`Petitioner and Patent Owner both apply the same district court construction
`of “data group” to support their respective arguments. Pet. 9–10 (citing Ex. 1007,
`13–31; Ex. 1008, 10–19); Prelim. Resp. 11 n.2, 17–18 (citing Pet. 10; Ex. 1007,
`
`
`
`
`15
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 17 of 52 PageID #: 2426
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`24–25); see also Pet. 81–83 (list of pending and terminated district court actions).
`The parties each provided proposed constructions of “data group” and related
`arguments to the court. Id. The district court construed “data group” as “a group
`of data and a mechanism for identifying data from that group (e.g., a group
`identifier).” Ex. 1007, 23–25.
`Having reviewed the district court’s construction of “data group,” and given
`the parties’ application of that construction here, we apply the district court’s
`construction of “data group” for purposes of this Decision. See Wellman, Inc. v.
`Eastman Chem. Co., 642 F.3d 1355, 1361 (Fed. Cir. 2011) (claim terms “need only
`be construed ‘to the extent necessary to resolve the controversy’”) (quoting Vivid
`Techs., Inc. v. Am. Sci. & Eng’g, Inc., 200 F.3d 795, 803 (Fed. Cir. 1999)).
`C. Asserted Obviousness of Claims 1–46 Over Pike or Pike in View of
`Chowdhuri (Grounds 1 and 2)
`
`Petitioner argues that independent claim 1 of the ’610 patent would have
`been obvious over Pike alone (Ground 1) or Pike in view of Chowdhuri
`(Ground 2). Pet. 15–53. Given the similarities in claim language, Petitioner
`addresses independent claims 17, 33, and 40 by providing brief cross-citations to
`its argument and evidence regarding claim 1, with some limited additional analysis
`provided for claim 33. See Pet. 53–54 (claim 17), 73–79 (claim 33), 79 (claim 40).
`All four independent claims recite similar language for map and reduce processing
`“a plurality of data groups” in a manner “corresponding” and/or “identifiable” to
`each “data group” or to a data partition of each “data group.” We apply the
`district court’s claim construction of “data group” and address whether Petitioner
`has provided sufficient evidence and argument to establish a reasonable likelihood
`that Pike or Pike-Chowdhuri renders obvious the claimed MapReduce data
`processing method recited in claim 1 of the ’610 patent.
`
`16
`
`
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 18 of 52 PageID #: 2427
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`
`A patent claim is unpatentable under 35 U.S.C. § 103(a) if the differences
`between the subject matter sought to be patented and the prior art are such that the
`subject matter as a whole would have been obvious to a person of ordinary skill in
`the art at the time the invention was made. See KSR Int’l Co. v. Teleflex Inc., 550
`U.S. 398, 406 (2007) (quoting 35 U.S.C. § 103(a)). Obviousness is resolved based
`on underlying factual determinations, including: (1) the scope and content of the
`prior art; (2) any differences between the claimed subject matter and the prior art;
`(3) the level of ordinary skill in the art; and (4) objective evidence of
`nonobviousness, i.e., secondary considerations. Graham v. John Deere Co., 383
`U.S. 1, 17–18 (1966). A party who petitions the Board for a determination of
`obviousness must show that “a skilled artisan would have been motivated to
`combine the teachings of the prior art references to achieve the claimed invention,
`and that the skilled artisan would have had a reasonable expectation of success in
`doing so.” Procter & Gamble Co. v. Teva Pharms. USA, Inc., 566 F.3d 989, 994
`(Fed. Cir. 2009) (quoting Pfizer, Inc. v. Apotex, Inc., 480 F.3d 1348, 1361 (Fed.
`Cir. 2007)). We assess Petitioner’s evidence and argument according to the above-
`referenced standard and in view of Patent Owner’s argument and evidence.
`We begin with a discussion of Pike and Chowdhuri.
`1. The Pike Reference (Ex. 1002)
`Pike’s MapReduce system is designed to “efficiently handle the analysis of
`data records” in a large-scale, distributed data processing system that “includes
`allocating groups of records to respective processes of a first plurality of processes
`executing in parallel.” Ex. 1002, Abstract (code 57), 1:32–41. Pike discloses a
`MapReduce method to process groups of data records “in a parallel and distributed
`processing environment.” Id. at 1:22–25. Pike’s system utilizes a set of
`“application-specific map(), reduce() and, optionally, partition() operators,” and
`
`
`
`
`17
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 19 of 52 PageID #: 2428
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`the work queue master “determines the number of map tasks and reduce tasks to be
`performed to process the input data.” Id. at 5:40–44.
`Petitioner provides a color-coded form of Pike Figure 3, reproduced below:
`
`
`See Pet. 10–11. In Pike Figure 3, above, input files (302, highlighted in red) “‘can
`include a variety of data types,’ including ‘text files’ and ‘tables.’” Id. at 10 (citing
`Ex. 1002, 9:56–10:7). The input files are split into “data blocks” (Split 0, Split-1 .
`
`
`
`
`18
`
`

`

`Case 4:23-cv-01147-ALM Document 53-5 Filed 10/29/24 Page 20 of 52 PageID #: 2429
`
`Case IPR2024-00659
`Patent 8,190,610 B2
`
`. . Split N-1) “of either a specified or predefined size (e.g., 64 MB). Alternately, in
`some embodiments the input files have a predefined maximum size (e.g., 1 GB),
`and the individual files are the data blocks.” Ex. 1002, 4:54–60; see also id. at
`9:58–62 (user may “control the size of the data blocks”). Pike further discloses
`that a data block may comprise “a portion of an input file (e.g., where the portion
`comprises a data block) to be processed … [or] portions of two o[r] more input
`files.” Id. at 6:59–62. In summary, Pike discloses a MapReduce parallel
`processing system where a “data block” to be processed is either i) an input file,
`e.g., a data table, having a predetermined maximum file size, ii) a portion of one
`input file that has been split into two or more data blocks of a specified or
`predefined size, or iii) comprised of portions of two or more input files of a
`specified, predefined, or maximum size.
`Pike’s input data blocks, moreover, “may in some embodiments be treated as
`key/value pairs,” and each worker process (304-0, 304-1 . . . , highlighted in blue)
`“applies the application-specific map( ) operator to the respective input data block
`so as [to] generate intermediate data values.” Id. at 9:65–66, 10:4–7; see also Pet.
`10 (citing and partially quoting same). The intermediate data values “are collected
`and written to one or more intermediate files” (306a, b, and c, highlighted in
`green). Ex. 1002, 10:8–9. Each worker process (308-3, 308-4 . . . , highlighted in
`yellow) “sorts the intermediate data values . . . in accordance with the key of the
`key/value pair