Exhibit 11

 

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Exhibit 11 to Complaint 

Intellectual Ventures I LLC and Intellectual Ventures II LLC 

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Example Chase Count IV Systems and Services 

U.S. Patent No. 7,712,080 (“the ’080 Patent”) 

The Accused Systems and Services include, without limitation systems that utilize Apache Spark (“Spark”); all past, current, 

and future systems and services that operate in the same or substantially similar manner as the specifically identified systems and 

services; and all past, current, and future Chase’s systems and services that have the same or substantially similar features as the 

specifically identified systems and services (“Example Chase Count IV Systems and Services”). 

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U.S. Patent No. 7,712,080 

Example Claim 9 Example Chase Count IV Systems and Services 

9. A distributed 

parallel computing 

system, having at 

least one memory 

area and at least one 

processor, 

comprising: 

Upon information and belief, Chase’s systems include “[a] distributed parallel computing system, having at least

one memory area and at least one processor[.]” Upon information and belief, Chase’s systems consist of a

distributed parallel computing system having many memory areas and multiple processors. Upon information

and belief, Chase's distributed parallel computing system is implemented, at least in part, using Spark. 

See https://spark.apache.org/docs/latest/rdd-programming-guide.html (last accessed on November 9, 2023). 

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9[a] 

at least one 

distributed shared 

variable capable of 

loading into the at 

least one memory 

area, wherein the at 

least one distributed 

shared variable is a 

single variable that 

includes several 

variables that may be 

physically loaded into 

the at least one 

memory area; and 

Upon information and belief, Chase job postings confirm that Chase implements Spark in its data processing 

framework. 

See

https://in.linkedin.com/jobs/search?keywords=Spark&location=India&geoId=102713980&f_C=1068&

currentJobId=3756690144&position=1&pageNum=0 (last accessed on November 11, 2023). 

See https://builtin.com/job/engineer/software-engineer-ii-java-apache-spark/1883999 (last accessed on 

November 9, 2023). 

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See also

https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1002/requisitions/preview/21044516

9/?keyword=spark&mode=location (last accessed on November 13, 2023); 

https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1002/job/210442431/?keyword=spar

k&mode=location (last accessed on November 13, 2023). 

Upon information and belief, Chase implements Spark. Upon information and belief, Chase’s systems include

“at least one distributed shared variable capable of loading into the at least one memory area, wherein the at least

one distributed shared variable is a single variable that includes several variables that may be physically loaded 

into the at least one memory area[.]”. Spark collects large volumes of data that cannot fit on a single node and

distributes slices of the data across multiple nodes. Spark automatically partitions the data into Resilient

Distributed Datasets that operate in parallel. The Resilient distributed datasets are partitioned across multiple

nodes and processed across at least one memory area. 

See https://blog.k2datascience.com/batch-processing-apache-spark-a67016008167 (last accessed on November 

11, 2023)

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See https://spark.apache.org/docs/latest/rdd-programming-guide.html (last accessed on November 9, 2023).

The RDDs contain variables that allow the nodes to process slides of data in parallel. RDDs are processed using 

functions and multiple variables that can be used to create other RDDs to further process the data set. 

See https://spark.apache.org/docs/latest/rdd-programming-guide.html (last accessed on November 9, 2023).

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See https://docs.cloudera.com/documentation/enterprise/5-6-x/topics/cdh_ig_spark_apps.html (last accessed on 

November 9, 2023). 

9[b][i] 

at least one 

distributed sequential 

computing program, 

configured to operate 

in the at least one 

processor, configured 

to access the at least 

one distributed shared 

variable, 

Upon information and belief, Chase’s systems include “at least one distributed sequential computing program, 

configured to operate in the at least one processor, configured to access the at least one distributed shared 

variable[.]” Spark contains a driver program that is configured to operate in the at least one processor and is 

configured to access the RDDs. Spark jobs are authored in a language such as Java, Scala, Phthon, or R. Spark 

is configured to access the at least one distributed shared variable (the RDDs) through the access of RDDs by a 

Spark job. 

 See https://spark.apache.org/docs/1.2.1/programming-guide.html#:~:text=Go%20from%20Here-

,Overview,parallel%20operations%20on%20a%20cluster. (last accesed November 9, 2023).

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9[b][ii] 

and configured to 

transform into at least 

one distributed 

parallel computing 

program by spawning 

at least one child 

distributed sequential 

computing system 

program 

Upon information and belief, Chase’s systems are “configured to transform into at least one distributed parallel 

computing program by spawning at least one child distributed sequential computing system program[.]” The 

Spark Execution Model transforms the distributed sequential computing program into a series of stages and sets 

of parallel tasks that run in parallel on the Spark cluster. This includes Spark tasks operating on a set of RDDs 

in parallel in a given stage. 

See https://blog.k2datascience.com/batch-processing-apache-spark-a67016008167 (last accessed on November 

9, 2023). 

See https://spark.apache.org/docs/2.0.1/ (last accessed on November 9, 2023). 

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9[b][iii] 

when at least one 

intermediate 

condition occurs 

within the at least one 

distributed sequential 

program 

Upon information and belief, Chase’s systems include “at least one intermediate condition occurs within the at 

least one distributed sequential program[.]” Spark implements shuffle boundary where stages/tasks must wait for 

the previous stage to finish before they fetch map outputs. 

See https://mallikarjuna_g.gitbooks.io/spark/content/spark-dagscheduler-stages.html (last accessed on 

November 9, 2023). 

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9[b][iv] 

wherein the at least 

one distributed 

parallel computing 

program concurrently 

uses the at least one 

distributed sequential 

computing program 

and the at least one 

spawned child 

distributed sequential 

computing program 

to perform parallel 

processing and/or 

operations, 

Upon information and belief, Chase’s systems are configured such that “wherein the at least one distributed 

parallel computing program concurrently uses the at least one distributed sequential computing program and the 

at least one spawned child distributed sequential computing program to perform parallel processing and/or 

operations[.]” Spark implements a set of tasks running in parallel for a given stage. The distributed parallel 

computing program includes the Spark job and driver program (above). The spawned child distributed sequential 

computing program is the sequential code in a task for a given stage (above). 

See https://blog.k2datascience.com/batch-processing-apache-spark-a67016008167

(last accessed on November 9, 2023).

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9[b][v] 

wherein the at least 

one intermediate 

condition comprising 

one intermediate 

result that will be 

required by the at 

least one spawned 

child distributed 

sequential computing 

program to continue 

computation. 

Upon information and belief, Chase’s systems are configured such that “wherein the at least one intermediate 

condition comprising one intermediate result that will be required by the at least one spawned child distributed 

sequential computing program to continue computation.” Spark implements shuffle boundary where stages/tasks 

must wait for the previous stage to finish before they fetch map outputs. 

See https://mallikarjuna_g.gitbooks.io/spark/content/spark-dagscheduler-stages.html (last accessed on 

November 10, 2023)

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