Systems and methods for discovery, classification, and indexing of data in a native computing system

Description

TECHNICAL FIELD

The present disclosure is generally related to computing systems and methods used for discovering, classifying, and indexing target data stored across various data assets.

BACKGROUND

A significant challenge encountered by many organizations is that requests for particular data may encompass a large number of computing systems which may, for example, store data in both structured and unstructured formats. Accordingly, an organization's processing of such requests can require a significant amount of computing resources, especially when the organization is required to process such requests in a relatively short period of time. Existing systems for complying with such requests can be inadequate for producing and providing the required information within the required timelines. This is especially the case for large corporations, which may store data on several different platforms in differing locations. Therefore, a need exists in the art for improved systems and methods for discovering and identifying data in order to process such requests.

SUMMARY

A system, according to some aspects, comprises a non-transitory computer-readable medium storing instructions; and a processing device communicatively coupled to the non-transitory computer-readable medium. In various aspects, the processing device is configured to execute the instructions and thereby perform operations comprising: (1) deploying, from a first computing system, via a public data network, a client application on a target computing system, the target computing system comprising a plurality of data sources in a private data network; (2) receiving, from the client application, at the first computing system, target computing system resource data for computing resources available to the target computing system; (3) causing, by the client application, the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network to discover target data stored on the plurality of data sources based on the target computing system resource data; and (4) responsive to discovering the target data stored on the plurality of data sources, generating and storing metadata for each of the plurality of data sources, the metadata indicating at least one of a type of the target data, a number of instances of the target data, or a location of the target data on each of the plurality of data sources.

In some aspects, the operations further comprise generating, by the first computing system, a catalog of data on the plurality of data sources; and creating, by the first computing system, a job schedule based on the catalog. In some aspects, causing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network comprises causing the target computing system to execute each job in the job schedule based on the target computing system resource data. In various aspects, each job in the job schedule comprises classifying, by the target computing system, data from each item in the catalog to identify the type of the target data stored on each item in the catalog. In some aspects, classifying the data from each item in the catalog comprises tokenizing the data from each item in the catalog to generate tokenized data, labeling the tokenized data to generate labelled data, and classifying the data as target data based on the labelled data.

In some aspects, receiving the target computing system resource data for the computing resources available to the target computing system comprises receiving current resource usage by the target computing system and total available resources for the target computing system; and causing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network based on the target computing system resource data comprises scheduling jobs to the target computing system such that executing the jobs does not cause the current resource usage to exceed the total available resources. In other aspects, causing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network based on the target computing system resource data comprises scheduling discovery to the target computing system during at least one of a particular time period, a particular part of the day, or during a particular day. In still other aspects, causing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network based on the target computing system resource data comprises scheduling jobs to the target computing system such that the target computing system does not execute more than a particular number of simultaneous jobs.

In particular aspects, the operations further comprise: (1) receiving an indication of a data subject access request received from a client device via a user interface that is accessible via the public data network and is configured for querying the plurality of data sources included in the private data network, the data subject access request identifying a data subject; and (2) responsive to receiving the indication, facilitating, by the target computing system, execution of processing operations or network communication for retrieving data responsive to the data subject access request from the plurality of data sources included in the private data network by accessing the metadata and identifying a subset of the plurality of data sources that store target data, and facilitating, by the target computing system, execution of processing operations or network communication for retrieving the data responsive to the data subject access request on the subset of the plurality of data sources that store target data.

A method, according to some aspects, comprises: (1) deploying, by computing hardware, via a public data network, a client application on a target computing system, the target computing system comprising a plurality of data sources in a private data network; (2) cataloging, by the computing hardware, data from the plurality of data sources in the private data network, the catalog identifying each file across the target computing system that requires scanning for target data; (3) creating, by the computing hardware, a plurality of jobs based on the catalog, each of the plurality of jobs corresponding to a respective file from the target computing system; (4) generating, by the computing hardware, a schedule for executing the plurality of jobs based on computing resource availability at the target computing system; (5) causing, by the computing hardware via the client application, the target computing system to use computing resources available to the target computing system to scan the plurality of data sources in the private data network to discover the target data stored on the plurality of data sources according to the schedule; and (6) responsive to discovering the target data stored on the plurality of data sources, generating and storing metadata, by the computing hardware, for each of the plurality of data sources, the metadata indicating at least one of a type of the target data, a number of instances of the target data, or a location of the target data on each of the plurality of data sources.

In some aspects, the method further comprises responsive a request to query the plurality of data sources included in the private data network via a user interface that is accessible via the public data network, facilitating, by the computing hardware, execution, by the target computing system, of processing operations or network communication for retrieving data responsive to the request from the plurality of data sources included in the private data network by: (1) accessing the metadata and identifying a subset of the plurality of data sources that store target data; and (2) facilitating, by the target computing system, execution of processing operations or network communication for retrieving the data responsive to the data subject access request on only the subset of the plurality of data sources that store target data.

In various aspects, causing the target computing system to use the computing resources available to the target computing system to scan the plurality of data sources in the private data network to discover the target data stored on the plurality of data sources according to the schedule comprises scheduling jobs to the target computing system such that executing the jobs does not cause a current resource usage at the target computing system to exceed a particular portion of the computing resource availability at the target computing system. In other aspects, causing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network based on the target computing system resource data comprises scheduling discovery to the target computing system during at least one of a particular time period, a particular part of the day, and during a particular day. In still other aspects, causing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network based on the target computing system resource data comprises scheduling jobs to the target computing system such that the target computing system does not execute more than a particular number of simultaneous jobs.

In some aspects, each of the plurality of jobs include causing the target computing system to classify data in the respective file as target data by: (1) tokenizing the data to generate tokenized data; (2) labeling the tokenized data to generate labelled data; and (3) classifying the data as target data based on the labelled data. In various aspects, the method further comprises generating a prediction of the type of the target data using a neural network based classification model and based on at least one of the tokenized data and the labelled data, and identifying the target data as the type of the persona data identifying based on the prediction satisfying a confidence threshold.

A method, in various aspects, comprises: (1) cataloging, by the computing hardware, data from a plurality of data sources that make up a target computing system over a private data network, the catalog identifying each file across the target computing system that requires scanning for target data; (2) creating, by the computing hardware, a plurality of jobs based on the catalog, each of the plurality of jobs corresponding to a respective file from the target computing system; (3) generating, by the computing hardware, a schedule for executing the plurality of jobs based on computing resource availability at the target computing system; (4) causing, by the computing hardware, the target computing system to use computing resources available to the target computing system to execute the plurality of jobs to scan the plurality of data sources in the private data network to discover the target data stored on the plurality of data sources according to the schedule; (5) causing, by the computing hardware, the target computing system to classify each piece of the target data according to a particular data type; and (6) responsive to discovering the target data stored on the plurality of data sources and classifying each piece of the target data according to the particular data type, generating and storing metadata, by the computing hardware, for each of the plurality of data sources, the metadata indicating at least one of the particular data type for the target data on each respective data source, a number of instances of the particular data type for the target data on each respective data source, and a location of the target data on each of the plurality of data sources.

In various aspects, classifying each piece of the target data according to the particular data type comprises: (1) causing, by the computing hardware, the target computing system to tokenize the data to generate tokenized data; (2) causing, by the computing hardware, the target computing system to label the tokenized data to generate labelled data; and (3) causing, by the computing hardware, the target computing system to classify each piece of the target data according to the particular data type based on the labelled data. In some aspects, the method further comprises: (1) causing, by the computing hardware, the target computing system to generate a prediction of a type of each piece of the target data using a neural network based classification model and based on at least one of the tokenized data and the labelled data; and (2) identifying each piece of the target data as the particular data type of the personal based on the prediction satisfying a confidence threshold. In other aspects, the method comprises: (1) causing, by the computing hardware, the target computing system to compare the particular data type of two adjacent pieces of target data in a particular document; and (2) modifying a confidence level for the prediction of at least one of the two adjacent pieces of target data.

A system, in various aspects, comprises a non-transitory computer-readable medium storing instructions; and a processing device communicatively coupled to the non-transitory computer-readable medium. In some aspects, the processing device is configured to execute the instructions and thereby perform operations comprising: (1) deploying, from a first computing system, via a public data network, a client application on a target computing system, the target computing system comprising a plurality of data sources in a private data network; (2) receiving, from the client application, at the first computing system, target computing system resource data for computing resources available to the target computing system; (3) causing, by the client application, the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network to discover target data stored on the plurality of data sources based on the target computing system resource data; and (4) causing, by the client application, the target computing system to generate an index for each respective piece of target data discovered on the plurality of data sources, the index identifying a respective file reference for each file in the plurality of data sources in which each respective piece of target data was discovered.

In some aspects, the operations further comprise: (1) providing a user interface that is accessible via the public data network and is configured for querying the plurality of data sources included in the private data network; (2) receiving, via the user interface and the public data network, a data subject access request from a client device, the data subject access request including a first piece of target data; and (3) facilitating, by the target computing system, execution of processing operations or network communication for retrieving data responsive to the data subject access request from the plurality of data sources included in the private data network by: (A) accessing the index to identify the first piece of target data; (B) identifying each respective file reference identified for the first piece of target data in the index; and (C) facilitating, by the target computing system, execution of processing operations or network communication for retrieving the data responsive to the data subject access request by limiting the processing operations or network communications for retrieving the data responsive to the data subject access requests to files on the plurality of data sources identified by each respective file reference identified for the first piece of target data in the index.

In some aspects, causing the target computing system to generate an index for each respective piece of target data discovered on the plurality of data sources occurs in response to receiving the data subject access request. In other aspects, the operations further comprise: (1) causing, by the client application, the target computing system to use the computing resources available on the target computing system to re-scan the plurality of data sources in the private data network to perform updated discovery of the target data stored on the plurality of data sources based on the target computing system resource data; (2) identifying, during the re-scan, a new instance of target data; and (3) causing, by the client application, the target computing system to modify the index to include the new instance of target data and a respective file reference for each file in the plurality of data sources in which the new piece of target data was discovered.

BRIEF DESCRIPTION OF THE DRAWINGS

In the course of this description, reference will be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

FIG. 2 depicts an example of a process for deploying a client application in a target computing system for facilitating and scheduling data discovery on the target computing system in accordance with various aspects of the present disclosure;

FIG. 3 depicts an example of a process for discovering target data on a target computing system in accordance with various aspects of the present disclosure;

FIG. 4 depicts an example of a process for classifying data discovered on a target computing system in accordance with various aspects of the present disclosure;

FIG. 5 depicts an example of a process for classifying data discovered on a target computing system in accordance with yet another aspect of the present disclosure;

FIG. 6 depicts an example of a process for indexing data discovered on a target computing system in accordance with yet another aspect of the present disclosure;

FIG. 7 depicts an example of a system architecture that may be used in accordance with various aspects of the present disclosure; and

FIG. 8 depicts an example of a computing entity that may be used in accordance with various aspects of the present disclosure.

DETAILED DESCRIPTION

Overview and Technical Contributions of Various Aspects

Many entities handling (e.g., collecting, receiving, transmitting, storing, processing, sharing, and/or the like) certain types of data, which may be associated with multiple data assets found within multiple computing systems, may be tasked with performing actions on the data that involve having to locate the data over the multiple data assets. A particular technical challenge related to performing these actions on the data includes identifying each instance of data across the multiple data assets and computing systems, and then determining which of those instances of data include particular target data related to the action to be performed.

Additionally, the cost of performing computations necessary to scan a large number of data assets and other data repositories may increase as the number of data assets and other computing systems on which the personally identifiable information or other target data may reside increases. An additional technical challenge related to discovering particular data across multiple data assets, computing systems, etc. may include risks related to transferring data out of a source or target data asset or other computing system for the purpose of scanning and analyzing that data in order to process such requests. For example, transferring data between computing systems (e.g., from a target computing system to a second system for the purposes of analyzing data from the target system) can expose the data to a signification risk of experiencing some type of data incident involving the data, such as a data breach leading to the unauthorized access of the data, a data loss event, etc. As such, it may be technically difficult to discover particular data on a target computing system without increasing the risk of such exposure.

As the quantity of data increases over time, and/or as the number of data assets and computing systems that may be potentially handling the data increases, determining where particular data has been stored (e.g., collected, received, transmitted, processed, shared, and/or the like) across all of the potential data assets, computing systems, data processing activities, and/or the like can be difficult. Additionally, discovering particular data (e.g., target data) across multiple data assets, computing systems, and/or the like may become even more challenging when each of the data assets, systems, data processing activities, and/or the like use their own, possibly unique, process or format for storing data. That is to say, where different file formats, attributes, procedures, techniques, and/or the like of storing target data are used across multiple computing systems, data sources, and/or the like, locating specific data, especially specific target data associated with a particular individual, may not be feasible by simply using a common mechanism for all the different systems.

Additionally, providing data responsive to one or more queries related to that data (e.g., when an individual entitled to the data responsive to the one or more queries submits such a query) may be technically challenging when processing the one or more queries in a tight timeline. These challenges may be amplified when an entity receives a large number of such queries from a large number of different data subjects, each of which may require discovering entirely different instances of data (e.g., data associated with each respective data subject for each respective query) across each of the systems, data sources, etc.

Accordingly, various aspects of the present disclosure overcome many of the technical challenges mentioned above using available (e.g., unused) computing resources on existing computing systems to perform data discovery computations on data stored on those existing computing systems, thereby reducing computing infrastructure footprint required to process data subject access requests. For example, according to particular aspects, a third-party computing system may connect, via a public data network, to an edge node inside a cluster network zone and schedule jobs to that cluster, harnessing existing computing power in the cluster network zone. In various aspects, by causing computing hardware that is native to a target computing system to perform operations to discover target data on the target computing system, the system may reduce infrastructure footprint, schedule jobs to the target computing system rather than transferring data out of the target computing system for computation purposes (e.g., thereby reducing cost, increasing performance, and minimizing or eliminating extra-system data transfers), and/or maintain data in the target computing system thus in same secure network zone, thereby reducing a chance of exposing the data to a significant risk of experiencing some type of data incident involving the data that may occur when transferring data out of the secure network zone of the target computing system.

More particularly, in order to reduce infrastructure footprint and schedule jobs to the target computing system, a third-party computing system (e.g., a data discovery system) may deploy a client application to a target computing system via a public data network. In some aspects, by deploying the client application to the target computing system (e.g., and using the client application to direct the target computing system to perform the operations), additional computing infrastructure may not be required to perform the data discovery process described herein (e.g., beyond the existing computing infrastructure that makes up the target computing system), thereby reducing the infrastructure footprint required to perform data discovery on the target computing system and the costs associated with acquiring and utilizing additional computing resources.

The target computing system may include data sources in a private data network, with the data sources storing target data. The third-party computing system may receive, from the client application, computing system resource data for the target computing system that is available on the target computing system. For example, the computing system resource data may include data describing the capacity or availability of any resource provided by one or more components of the target computing system. For instance, the computing system resource data includes data related to current resource usage, total available resources (e.g., total available resources across the target computing system, total available resources that are earmarked for use for data discovery processes, etc.). The third-party computing system may then cause the target computing system (e.g., instruct the target computing system) to scan the data sources in the private data network to discover target data stored on the data sources based on the computing system resource data. For example, the third party computing system can schedule the jobs based on the computing system resource data by determining a current resource usage by the target computing system and scheduling the jobs such that the total resources used by the scheduled jobs do not cause the target computing system to exceed the total available resources. In other aspects the third party computing system may schedule jobs to the target computing system according to any suitable schedule (e.g., at a particular time, during a particular day of the week, during a particular time period, during a particular part of the day, etc.), such as to avoid the target computing system executing jobs during peak processing times.

The third-party computing system and/or the target computing system may then generate and store metadata for each of the data sources that indicates, for example, a type of identified data, a location of the identified data, and/or an amount of the identified data at the location. The third-party computing system and/or the target computing system may then access and use the metadata when processing a query regarding target data for a particular individual (e.g., by limiting a search for the particular individual's particular data to computing systems, data assets, etc. on which the native computing system has previously identified storage locations that include target data).

It is noted that reference is made to target data throughout the remainder of the application. In various aspects, target data may include any data that is the target of the data discovery processes described herein (i.e., any data or type of data on the target computing system that the data discovery process is configured to discover). Target data may include, for example, personal or sensitive data (e.g., first and last name, email address, home address, social security number, date of birth, financial information, etc.) or other controlled data. However, targeted data is not necessarily limited to information that may be configured as personal and/or sensitive in nature but may also include other forms of data. For example, target data may include data that relates to an entity's business, operational procedures, legal obligations, and/or the like may need to be discovered for any reason. Further, targeted data may not necessarily be associated with an individual but may be associated with other entities such as a business, organization, government, association, and/or the like.

Example Computing Environment

FIG. 1 depicts an example of a computing environment that can be used for discovering, classifying, and/or indexing target data stored across data assets that may be spread over one or more data repositories in a target computing system according to various aspects. For example, a data asset may be a sub-system, software application, website, mobile application, data storage/repository, external system, and/or the like. A target computing system may, for example, store target data across computing systems in different locations in those computing systems. As discussed herein, an entity that collects, processes, accesses, stores, retrieves, deletes, or otherwise handles target data may be required to provide data responsive to one or more queries related to that data (e.g., when an individual entitled to the data responsive to the one or more queries submits such a query).

FIG. 1 depicts examples of hardware components of a target computing system 100 according to some aspects. In various aspects, the target computing system 100 may include any computing system 100 on which target data is stored for which the target computing system may receive one or more queries. The target computing system 100 is a specialized computing system that may be used for processing large amount of data using a large number of computer processing cycles. In various aspects, the target computing system 100 includes a specialized computing system that may be used for processing the large amount of data according to a job schedule determined based on computing system resource data (e.g., computing resource availability). In some aspects, the job schedule may enable the target computing system 100 to leverage available (e.g., unused) computing resources of the target computing system 100 while reducing a taxation of the computing resources by the target computing system 100.

The target computing system 100 may communicate with various computing systems, such as a user device 160. In various aspects, the target computing system 100 may receive one or more queries via a user interface 165 from the user device 160. For instance, many organizations provide a publicly accessible interface through which data subjects (or lawful representatives thereof) can submit requests (e.g., data subject access requests) related to their target data being processed by the organizations. For example, many organizations provide a website or software application that is accessible by data subjects over a public data network 142 such as the Internet, or through a publicly available application. Here, the website may include a web form that can be used by the data subjects to submit requests related to the data subjects' target data being processed by the organizations. Therefore, a data subject wishing to exercise their rights can simply visit an organization's website and use the webform to submit a data subject access request that includes a request related to a target data right that the organization must fulfil in a timely manner (e.g., a request for the target computing system 100 to provide data responsive to the request). Since the interface (e.g., website) is often publicly available, an organization can receive a considerable number of requests at any given time that then requires the organization to devote a significant number of computing resources to timely fulfill the requests. This can become even more of a substantial challenge as target data collected, stored, or otherwise processed by an organization increases in volume and/or is collected, stored, or otherwise processed over an increasing number of data sources involving multiple data storage systems that are in communication over one or more private data networks 144.

The target computing system 100 may further communicate with various other computing systems, such as a third-party computing system 140. For example, the third-party computing system 140 may send data to the target computing system 100 for processing or may send signals to a native computing system 104 at the target computing system 100 to control or influence operations of different aspects of the target computing system 100. For example, the third-party computing system 140 may deploy a client application to the native computing system 104 for execution by the native computing system 104. In various aspects, the native computing system 104 includes any computing system on the private data network 144. The client application may cause the native computing system 104 to execute processing operations related to discovering, classifying, and/or indexing data on the target computing system 100 (e.g., using computing resources available at the target computing system 100).

The third-party computing system 140 may interact, via one or more public data networks 142, with various external-facing subsystems 102 of the target computing system 100. Each of the various external-facing subsystems 102 may include one or more computing devices that provide a physical subnetwork that exposes certain online functions of the target computing system 100 to an untrusted network, such as the internet or another public data network 142. In various aspects, the various external-facing subsystems 102 may provide an interface between the public data network 142, operating as edge nodes in a cluster computing system used by the target computing system 100. In various aspects, the various external-facing subsystems 102 are in communication with the native computing system 104 (e.g., via one or more firewalls) and may, for example, be connected to one or more other computing systems within the target computing system 100 via a private data network 144. In some aspects, by using the private data network 144, the target computing system 100 may house one or more data repositories 120 that are not directly accessible via the public data network 142 (e.g., the Internet). In this way, the target computing system 100 may maintain data stored in the data repositories 120 in the private data network 144, and perform data discovery and other operations on the data, without having to transfer this data out of the target computing system 100 (e.g., to the third-party computing system 140) for analysis, processing, etc.

In some aspects, the third-party computing system 140 can include one or more third-party devices such as, for example, one or more servers operating in a distributed manner. The third-party computing system 140 can include any computing device or group of computing devices, and/or one or more server devices. The third-party computing system may include computing hardware performing different processes for deploying one or more client applications on the target computing system 100, and scheduling jobs to the target computing system 100 for using target computing system 100 resources to discover target data on the target computing system 100. For instance, the third-party computing system 140 executes a data discovery deployment and scheduling module 200 to schedule jobs to the native computing system 104.

The data discovery deployment and scheduling module 200 can deploy a client application on the native computing system 104 for scheduling discovery jobs on the native computing system 104. The third-party computing system 140 (e.g., when executing steps related to the data discovery deployment and scheduling module 200) receives computing resource data and job data from the native computing system 104 (e.g., via the client application over the public data network 142). The third-party computing system 140 uses the resource data and the job data to schedule jobs to the native computing system 104 (e.g., by optimizing computing resource use by the native computing system 104 during the discovery of target data on the target computing system 100).

The data repositories 120 may store structured data 122 and unstructured data 124. In some aspects, unstructured data may include data (e.g., stored in one or more documents, files, etc. of any type) that may include target data in a format other than a predefined or readily identifiable format. In various aspects, discovering, classifying, and indexing target data contained in unstructured data sources may provide an additional technical challenge (i.e., because the native computing system 104 may need to classify the data in the unstructured data sources prior to indexing it). Although the data repositories 120 are shown as separate components, these components 120 may include, in other aspects, a single server and/or repository, servers and/or repositories, one or more cloud-based servers and/or repositories, or any other suitable configuration.

According to various aspects, the target computing system 100 may include computing hardware performing a number of different processes in discovering data (e.g., target data), classifying the data, and indexing the data. Specifically, according to particular aspects, the native computing system 104 executes: (1) a data discovery module 300 to perform data discovery on the target computing system 100; (2) a data classification module 400 to classify data (e.g., by type) discovered on the target computing system; and/or (3) a data indexing module 500 to index the discovered, classified data.

The native computing system 104 (e.g., or other suitable system) may execute the data discovery module 300 to perform operations including initiating a data discovery scan across the target computing system 100 by: (1) cataloging data across the target computing system 100; (2) creating jobs based on the catalog; (3) performing data discovery based on a job schedule; and (4) generating and storing metadata regarding target data discovered on the target computing system 100. The metadata may include, for example, a type of identified data, a location of the identified data, an amount of the identified data at the location, etc.

The native computing system 104 or other suitable system may execute the data classification module 400 to perform operations including classifying data (e.g., as a particular type of target data) by: (1) executing one or more contextual analysis actions on data (e.g., and/or one or more schema associated with the data; (2) executing one or more advanced tokenization steps on the data; (3) executing one or more labeler steps on the data; (4) executing a confidence workflow on the tokenized, labelled data; and (5) executing one or more steps including applying an aggregate level reconciler to the data. These operations may vary, for example, based on whether a particular data source includes structured or unstructured data.

The native computing system 104 (e.g., or other suitable system) may execute the data indexing module 500 to perform operations including generating an index of data in unstructured documents and generating and storing metadata for the unstructured documents indicating an occurrence of a particular piece of target data in the unstructured documents.

The number of devices depicted in FIG. 1 are provided for illustrative purposes. It should be understood that, in some aspects, different number of devices may be used. In various aspects, for example, while certain devices or systems are shown as single devices in FIG. 1, multiple devices may instead be used to implement these devices or systems.

Data Discovery Deployment and Scheduling Module

FIG. 2 depicts an example of process performed by a data discovery deployment and scheduling module 200. This process include operations that the third-party computing system 140 may execute to deploy a client application on the target computing system 100 (e.g., the native computing system 104) for scheduling discovery jobs on the native computing system 104. For instance, the flow diagram shown in FIG. 2 may correspond to operations carried out, for example, by computing hardware found in the third-party computing system 140 as the computing hardware executes the data discovery deployment and scheduling module 200.

In various aspects, the third-party computing system 140 (e.g., when executing steps related to the data discovery deployment and scheduling module 200) receives computing resource data and job data from the native computing system 104 (e.g., via the client application over the public data network 142) and uses the resource data and the job data to schedule jobs to the native computing system 104 (e.g., by optimizing computing resources used by the native computing system 104 to discover target data on the target computing system 100).

At operation 210, the data discovery deployment and scheduling module 200 deploys a client application on the native computing system 104. In various aspects, the client application may execute on the native computing system 104 and, for example, execute one or more operations included in the data discovery module 300, the data classification module 400, and the data indexing module 500 discussed more fully below.

In still other aspects, the client application communicates information and/or instructions with the third-party computing system 140 (e.g., via the public data network 142 between the third-party computing system 140 and the native computing system 104).

At operation 212, the data discovery deployment and scheduling module 200 catalogs data across the target computing system 100. In various aspects, cataloging the data across the target computing system 100 includes identifying each file stored across the target computing system 100. Such files may include target data such that each file needs to be scanned to identify target data as part of the data discovery process. In some aspects, cataloging the data may involve identifying, for each identified file, data related to a file type, file format, etc. In other aspects, cataloging the data includes identifying and cataloging each data storage location across the target computing system 100 (e.g., cluster computing system). In various aspects, initiating the scan of the target computing system 100 begins with cataloging the data across the target computing system because the data on the target computing system 100 may change, expand, contract, etc. frequently as the target computing system 100 stores new data, moves data within the system, etc. As such, it may be desirable to begin a fresh data discovery process with a fresh data catalog. In some aspects, the third-party computing system 140 may catalog data from the target computing system as part of the data discovery process on the target computing system 100. In some aspects, as discussed below with respect to the data discovery module 30), the target computing system 100 (e.g., in conjunction with and/or instead of the third-party computing system 140, etc.) may catalog the data on the target computing system 100 for use in scheduling jobs to the target computing system 100.

At operation 214, the data discovery deployment and scheduling module 200 creates jobs based on the catalog. For instance, the third-party computing system 140 generates job data that includes identified and/or created jobs that must be executed in order to perform data discovery on the target computing system 100. The third-party computing system 140 may create a job for each file identified in the catalog. The third-party computing system 140 may, for example, create each job based on a file type or file format of the associated file. Each job may, for example, include a particular script for execution by a system (e.g., by the native computing system 104). In various aspects, the created jobs may include one or more classification or indexing steps discussed herein. In this way, the third-party computing system 140 may generate a set of jobs that, when executed (e.g., by the native computing system 104), cause the native computing system 104 to perform the data discovery process discussed herein across the entire catalog.

Continuing to operation 220, the data discovery deployment and scheduling module 200 receives target computing system resource data from the native computing system 104 in the target computing system 100. The computing system resource data may include data describing the capacity or availability of any resource provided by one or more components of the target computing system. For instance, the computing system resource data includes data related to current resource usage, total available resources (e.g., total available resources across the target computing system 100, total available resources that are earmarked for use for data discovery processes, etc.).

At operation 230, the third-party computing system 140 schedules jobs to the native computing system 104 based on the target computing system resource data. For example, the third-party computing system 140 may transmit instructions to the native computing system 104 (e.g., to the client application executing on the native computing system 104) to execute each identified job according to a determined schedule. Scheduling the jobs based on the target computing system resource data may include, for example determining a current and/or future computing resource availability based on the current usage and the total available computing resources and scheduling the jobs such that the total resources used by the scheduled jobs do not exceed the current and/or future computing resources availability. In a particular example, the third-party computing system 140 may schedule jobs such that the native computing system 104 does not execute more than a particular number of jobs simultaneously. In another example, the third-party computing system 140 may schedule jobs such that native computing system resource use by data discovery processes does not exceed a particular portion of the total available resources at the target computing system 100.

In other aspects, the third-party computing system 140 schedules jobs to the target computing system 100 according to any suitable schedule (e.g., at a particular time, during a particular day of the week, during a particular time period, during a particular part of the day, etc.) As such, the third-party computing system 140 may schedule jobs on the target computing system 100 such that a number of simultaneous jobs executed by the target computing system 100 varies based on the currently available computing resources.

In one example involving a cluster computing system, the third-party computing system 140 schedules jobs on the native cluster (e.g., at the target computing system 100) based on the resource availability of that system. For instance, the third-party computing system 140 could query a Yarn Job manager to see the resource availability for a given Yarn Queue or Queues and then, based on a threshold, start a job or waits for the necessary resources to open up prior to starting a job. The third-party computing system 140 can schedule one or more jobs at the native cluster on the target computing system 100 based on a maximum parallel number of jobs. For example, at any given time the third-party computing system 140 can run not more than N jobs (e.g., any suitable number N) simultaneously on the cluster computing system.

In this way, the third-party computing system 140 may direct the target computing system 100 (e.g., the native computing system 104) to leverage available (e.g., unused) computing resources of the target computing system 100 for data discovery processes, without overtaxing those computing resources.

For illustrative purposes, the data discovery deployment and scheduling module 200 is described with reference to implementations described above with respect to one or more examples described herein. Other implementations, however, are possible. In some aspects, the steps in FIG. 2 may be implemented in program code that is executed by one or more computing devices such as the third-party computing system 140, the native computing system 104, or other system in FIG. 1. In some aspects, one or more operations shown in FIG. 2 may be omitted or performed in a different order. Similarly, additional operations not shown in FIG. 2 may be performed.

Data Discovery Module

FIG. 3 depicts an example of a process performed by a data discovery module 300. This process may, for example, include operations that the native computing system 104 may execute to initiate a data discovery scan across the target computing system 100 by: (1) cataloging data across the target computing system 100; (2) creating jobs based on the catalog; (3) performing data discovery based on a job schedule; and (4) generating and storing metadata regarding target data discovered on the target computing system 100. For instance, the flow diagram shown in FIG. 3 may correspond to operations performed by computing hardware found in the native computing system 104 that executes the data discovery module 300.

At operation 310, the data discovery module 300 initiates a scan of the target computing system 100. In various aspects, the native computing system 104 initiates the scan of the target computing system 100 to discover target data stored across data assets within different data repositories 120 in the target computing system 100. The target computing system 100 may, for example, store target data across computing systems (e.g., in the native computing system 104) in different locations in those computing systems (e.g., on one or more of the data repositories 120, or in any other suitable location on the target computing system 100). target data

In operation 320, the data discovery deployment module 300 catalogs data across the target computing system 100. Cataloging the data across the target computing system 100 can include identifying each file stored across the target computing system 100. These files may include target data such that each file needs to be scanned to identify target data as part of the data discovery process. Cataloging the target computing system 100 can also include identifying and cataloging each data storage location across the target computing system 100 (e.g., cluster computing system). In various aspects, initiating the scan of the target computing system 100 begins with cataloging the data across the target computing system because the data on the target computing system 100 may change, expand, contract, etc. frequently as the target computing system 100 stores new data, moves data within the system, etc. As such, it may be desirable to begin a fresh data discovery process with a fresh data catalog. In some aspects, the third-party computing system 140 may catalog the target computing system as part of the data discovery process on the target computing system 100.

At operation 330, the data discovery deployment module 300 creates jobs based on the catalog. The native computing system 104 generates job data that includes identified and/or created jobs that must be executed in order to perform data discovery on the target computing system 100. When creating jobs, the native computing system 104 may create a job for each file identified in the catalog. For instance, the third native computing system 104 may create each job based on a file type or file format of the associated file. Each job may include a particular script for execution by the native computing system 104. In this way, the native computing system 104 may generate a set of jobs that, when executed (e.g., by the native computing system 104), cause the native computing system 104 to perform the data discovery discuss herein across the entire catalog. In some aspects, the native computing system 104 may generate jobs that include the jobs generated by the third party computing system 140 (e.g., discussed with respect to operation 230 in FIG. 2). In other aspects, the native computing system 104 may generate jobs to supplement jobs generated by the third party computing system 140 (e.g., discussed with respect to operation 230 in FIG. 2). In still other aspects (e.g., aspects in which the third party computing system omits a job creating step at operation 230 of FIG. 2), the native computing system 104 may create jobs based on the catalog in lieu of the third party computing system 140 creating the jobs.

At operation 340, the data discovery deployment module 300 performs data discovery on the data repositories 120 based on a job schedule. In some aspects, the native computing system 104 receives the job schedule from the third-party computing system 140 (e.g., the job schedule described with respect to the data discovery deployment and scheduling module 200 in FIG. 2). In additional or alternative aspects, the native computing system 104 may generate the job schedule. When performing the data discovery on the data repositories 120 (e.g., on the target computing system 100) based on the job schedule, the native computing system 104 may execute one or more scripts associated with each job according to the schedule. A timing of execution of each job may vary based on current resource availability, time, or any other suitable factor.

At operations 350, the data discovery deployment module 300 generates and stores metadata regarding the target data discovered on one or more of the data repositories 120. The metadata includes, for example, a type of identified data, a location of the identified data, an amount of the identified data at the location, etc. The third-party computing system 140 and/or the native computing system 104 may then access the metadata when processing a query regarding target data for a particular individual (e.g., by limiting a search for the particular individual's particular data to computing systems, data assets, etc. which the native computing system 104 has identified as storage locations that include target data).

In this way, the process may identify locations on the target computing system 100 that include target data (e.g., which may be relevant to such queries) without transferring any of the actual data out of the target computing system. By only transferring or making available metadata relating to the discovered data (e.g., the location of the data, the type of data at the location, etc.) as opposed to the data itself, the process may alleviate technical problems that arise from transferring data out of a source data asset or other computing system for the purpose of scanning and analyzing that data in order to process data subject access requests and other queries (e.g., by avoiding triggering regulations related to such transfers).

For illustrative purposes, the data discovery module 300 is described with reference to implementations described above with respect to one or more examples described herein. Other implementations, however, are possible. In some aspects, the steps in FIG. 3 may be implemented in program code that is executed by one or more computing devices such as the target computing system 100, the native computing system 104, or other system in FIG. 1. In some aspects, one or more operations shown in FIG. 3 may be omitted or performed in a different order. Similarly, additional operations not shown in FIG. 3 may be performed.

Data Classification Module

FIGS. 4 and 5 are flow charts depicting examples of processes performed by a data classification module 400 module according to various aspects. A data discovery process described herein involves identifying target data in a target computing system 100. When scanning a target computing system 100 for target data, the process may involve classifying identified data (e.g., based on data type) in order to determine whether particular data in a document, on a data store, etc. includes target data. For example, a nine digit number may represent any suitable type of data (e.g., a page view count for a web page, a filename, etc.). In some instances, a nine digit number may represent a social security number, which would fall in the category of target data. The processes depicted in FIGS. 4 and 5 may include operations related to classifying data (e.g., as a particular type of target data) by: (1) executing one or more contextual analysis actions on data (e.g., and/or one or more schema associated with the data; (2) executing one or more advanced tokenization steps on the data; (3) executing one or more labeler steps on the data; (4) executing a confidence workflow on the tokenized, labelled data; and (5) executing one or more steps including applying an aggregate level reconciler to the data. In some aspects, the steps of the process may vary, for example, based on whether a particular data source includes structured or unstructured data.

FIG. 4 depicts an example of a process 401 performed by a data classification module 400 for classifying structured data during a data discovery process are shown. For instance, operations depicted in FIG. 4 can be performed by computing hardware found in the native computing system 104 (e.g., or other computing system) as described herein 401 (e.g., in conjunction with executing the data discovery module 300).

At operation 410A, the native computing system 104 accesses data. In various aspects, that native computing system 104 may access a data set (e.g., on one or more of the data repositories 120) as the native computing system 104 performs data discovery on the target computing system 100. In some aspects, the data may include any suitable data format (e.g., such as a table, string, email, document, etc.).

At operation 420A, the native computing system 104 tokenizes the data set. In various aspects, the native computing system 104 may tokenize the dataset by separating the text found in the dataset (e.g., in one or more data elements that make up the data set) into tokens, i.e., smaller units (e.g., words, characters, sub-words, and/or the like). The tokenized data may be utilized in the labelling process described herein.

At operation 402A, the native computing system 104 reads the schema for the data to determine the structure of the data (e.g., column headings, data configuration, etc.). The schema for the data can include a structure or organization of the data. The schema may define relationships between one or more data elements that make up the data. The schema may, for example, define labels for data in a particular column or row of a table. The native computing system 104, at operation 404A then performs context analysis on the schema. In one example, the native computing system 104 system performs a context analysis by using one or more fuzzy matchers and/or machine-learning based classifiers to identify a particular schema category and/or label for the data (e.g., which may include a data type). For instance, in the context of a structured data set, the native computing system 104 may apply a fuzzy matcher to identify data labels based on any suitable fuzzy matching threshold. The native computing system 104 may further use the machine learning based classifier to categorize one or more pieces of data in the data set. Examples of suitable machine learning classifiers include: (1) one or more linear classifiers; (2) one or more nearest neighbor classifiers; (3) one or more support vector machines; (4) one or more decision trees; (5) one or more boosted tress; (6) one or more random forests; (7) one or more neural networks; and/or (8) any other suitable machine learning based classification technique. In some aspects, the native computing system 104 uses one or more neural network machine-learning based classification models to perform character embedding and feature extraction processes. For instance, the system may use a 1m_1b language model. The native computing system 104 may use the neural network model to train and make a prediction related to the context analysis. The prediction may include a prediction of a data type associated with a particular label in the schema and may include a confidence level for the prediction. In some aspects, the native computing system 104 evaluates an output from the fuzzy matcher and machine learning based calculator to assign a determined data label for one or more data elements as either the output from the fuzzy matcher or the output from the machine learning based calculator. (e.g., by assigning the output from whichever of the fuzzy matcher and the machine learning based calculator generated the prediction with the highest confidence).

Returning to operation 430A, the native computing system 104 labels the tokenized data. For instance, the native computing system 104 labels various data elements in the data based on an identified type of the target data in each data element. In some aspects, the native computing system 104 may use the results of the context analysis to improve the computation performance of the labeler. For example, the native computing system 104 may limit potential labels for data to the predicted labels (e.g., data types) generated through the machine learning based calculator and/or the fuzzy matcher). In various aspects, the native computing system 104, when labelling the data, uses one or more predicate chains in the labelling process. For example, when using predicate chains, the native computing system 104 utilizes predicates as a predictive tool to eliminate different labels (e.g., categories) as potential labels for the tokenized data. The native computing system 104 progresses through the predicate chain until the native computing system 104 identifies the label for the data. For example, a phone number may include a three digit area code followed by a seven digit number. When progressing through a predicate chain for a particular piece of data, the native computing system 104 may apply a predicate to determine whether the data may be properly labelled as a phone number by determining whether the tokenized data begins with a three digit number. The native computing system 104 may chain and apply other predicates to the date eliminate other potential data labels prior to finalizing a label. For example, other data types may also include data that begins with three digits (e.g., such as an e-mail address, employee ID number, SSN, credit card number etc.). In order to use predicate chains to assign a label of ‘phone number’ to a piece of data that begins with a three digit number, the native computing system 104 may chain together and progress through additional predicates to eliminate other potential labels that would also begin with a three digit number (e.g., a predicate to identify an (@) in the data to eliminate e-mail addresses beginning with three digits).

In various aspects, the native computing system 104 may utilize predicates such as, for example: (1) one or more mask predicates, which may, for example determine whether a particular chain should continue executing and make one or more N-gram determinations; (2) one or more label predicates, which may, for example, include fuzzy matching, regular expressions, look up, and one or more entity identifications based on a named-entity recognition model; (3) one or more windowing predicates, which may, for example, identify one or more child labels in a given label, identify user names and passwords, adult content, or other sensitive information in one or more URLs; (4) one or more custom predicates, which may, for example, support domain-specific labelling scenarios; and/or (5) any other suitable labelling predicate or combination of predicates.

At operation 440A, the native computing system 104 executes a confidence workflow on the tokenized, labelled data. For example, the native computing system 104 may use any validator and/or context analysis discussed herein to compute a confidence of a determined label. In various aspects, the one or more validators may include, for example: (1) one or more Luhn validators to check for credit card or other numbers; (2) one or more Modulus checks to check for social security numbers, social insurance numbers, etc.; (3) and/or any other custom domain value check. The native computing system 104 may determine a confidence level for a predicted label (e.g., using a neural network based classification model). The native computing system 104 may apply a label to a particular data element in response to determining that the confidence level exceeds a particular threshold.

In the context of a structured data classification, at operation 445A, the native computing system may apply one or more aggregate level reconcilers to the analyzed data. In various aspects, the native computing system 104 can compute label distribution statistics for a given column in a data set and weed out false positive matches. For example, in a particular column, the native computing system may generate a label prediction for each data element in the column as a first name. The native computing system 104 may further generate a label prediction of country name for a particular data element in the column that includes the word “China.” In this example, the native computing system 104 may modify the false positive label by reclassifying the label prediction for “China” to “first name.” In some aspects, the native computing system 104 may reconcile conflicts in labels for adjacent data (e.g., data in a single column of a table, row of a table, etc.) based on one or more variables such as, for example, confidence score, number of occurrences, etc. For example, the native computing system 104 can assign a respective label to a plurality of pieces of adjacent data (e.g., data in a particular column). The native computing system 104 can then compare the respective labels for the adjacent data to identify differences in the labelling. In one example, if the native computing system 104 assigns a first label to nine out of ten pieces of data in a column but assigns a second label to one of the ten pieces of data, the native computing system 104 may reassign the label for the one piece of data to the first label (i.e., because the number of occurrences of the first label in the column exceeds the number of occurrences of the second label). In other aspects, the native computing system 104 analyzes a confidence score of each assigned label for a set of adjacent data, and modifies the assigned label for each piece of data in the set to include the highest confidence label applied to any piece of the adjacent data.

The native computing system may finalize a predicted label and produce a result at operation 450A. In various aspects, the final result may drive the metadata generation discussed herein with respect to the data discovery module 300. For example, referring back to FIG. 3, when generating the metadata regarding the target data discovered on the one or more data repositories 120 at operation 350, the data discovery module 300 may assign the finalized, predicted label as a data type for the target data. The data discovery module 300 may then store metadata for a particular data storage location on which the target data resides indicating that data of the type assigned to the target data is stored thereon. In some aspects, the native computing system 104 may assign a particular data type to date identified in a particular location based on the result produced at operation 450A.

FIG. 5 depicts an example of a process 501 for classifying unstructured data during a data discovery process are shown. For instance, process 501 includes operations performed by computing hardware found in the native computing system 104 as the computing hardware executes the data classification module 400.

At operation 410B, the data classification module 400 accesses data. For instance, that native computing system 104 may access a data set (e.g., on one or more of the data repositories 120) as the native computing system 104 performs data discovery on the target computing system 100. In some aspects, the data may include any suitable data format (e.g., such as a table, string, email, document, etc.).

At operation 420B, the data classification module 400 performs advanced tokenization on the data set. This operation can be performed in the same manner as operation 420A described above.

At operation 430B, the native computing system 104 labels the tokenized data. This operation can be performed in the same manner as operation 430A described above.

At operation 435B, the native computing system 104 may use one or more validators on the labeled data (e.g., to improve a confidence of the predicted label). For example, in various aspects, the one or more validators may include, for example: (1) one or more Luhn validators to check for credit card or other numbers; (2) one or more Modulus checks to check for social security numbers, social insurance numbers, etc.; (3) and/or any other custom domain value check

At operation 440B, the native computing system 104 executes a confidence workflow on the tokenized, labelled data. The native computing system 104 may determine a confidence level for a predicted label (e.g., using the machine-learning model). The native computing system 104 may apply a label to a particular data element in response to determining that the confidence level exceeds a particular threshold.

In some aspects, such as when the native computing system 104 is classifying data in an unstructured data source, the native computing system 104 may use one or more natural language processing techniques to identify particular attributes in the data such as country, name, etc. In other aspects, native computing system 104 is configured to apply natural language processing to identify any particular desired attribute within a data set (e.g., names, organizations, locations, etc.). In a particular aspect, the native computing system 104, at operation 442B is configured to apply Bidirectional Encoder Representations from Transformers (BERT) to the dataset (e.g., one or more data elements in the data set). In various aspects, BERT may include pre-trained bidirectional representations from the unlabeled text by jointly conditioning on both left and right contexts. In other aspects, the native computing system may use any other suitable machine-learning based natural language processing technique on the data elements to improve or modify a confidence score of a generated label prediction. The native computing system 104 may then finalize a predicted label and produce a result at operation 450B. In various aspects, the final result may drive the metadata generation discussed herein with respect to the data discovery module 300. In some aspects, the native computing system 104 may assign a particular data type to data identified in a particular location based on the result produced at operation 450B. For example, the native computing system 104 may assign the finalized, predicted label produced at operation 450B as the particular data type for the data identified in the particular location. The native computing system 104 may store metadata for the data identified in the particular location that indicates the finalized predicted label produced at operation 450B as the data type (e.g., name, country, address, e-mail address, etc.) for the particular data.

Data Indexing Module

FIG. 6 depicts an example of a process, performed by a data indexing module 500, for generating an index of data in unstructured (e.g., and/or structured) documents and generating and storing metadata for the unstructured documents indicating an occurrence of a particular piece of target data in the unstructured documents. The native computing system 104 may index data (e.g., in unstructured documents) according to one or more processes described herein, as the native computing system 104 performs data discovery (e.g., according to the data discovery module 300) on a target computing system. Each job in a job schedule described herein may include indexing the target data identified in a particular data source (e.g., by updating and maintaining an index). The operations shown in FIG. 6 can be performed by computing hardware found in the native computing system 104 that executes the data indexing module 500 (e.g., in conjunction with executing the data discovery module 300).

At operation 510, the data indexing module 500 identifies unstructured documents. When performing data discovery on a target computing system 100, at least a portion of the data on the system may reside in one or more unstructured documents. A technical challenge related to discovering target data in a particular computing system includes discovering instances of target data that exist in unstructured data on the system. As such, when encountering unstructured data during a data discovery process, it may be necessary to classify, label, and index the data in the unstructured documents to provide a mechanism for: (1) identifying target data in the unstructured documents; and (2) producing particular data from the unstructured documents as part of a response to a query requesting the target data (e.g., in response to a data subject access request).

At Operation 520, the native computing system 104 may generate an index of data in the unstructured documents. For example, the native computing system 104: may (1) index all data within one or more unstructured documents; (2) perform one or more real-time searches of all of the data in each of the one or more unstructured documents; (3) perform one or more hybrid searching and indexing techniques; (4) perform one or more smart indexing techniques; and/or (5) perform any other suitable searching and indexing technique on the one or more unstructured documents. In various aspects, the native computing system 104 may perform optical character recognition on the one or more unstructured documents.

When indexing all data in unstructured documents, the native computing system 104, at operation 522 builds and maintains an index of all data within the one or more unstructured documents. The native computing system 104 may, for example, maintain all data across all data sources (e.g., each of the one or more unstructured documents) in an index on each scan. In various aspects, in response to the native computing system 104 indexing all data within the one or more unstructured documents, the native computing system 104 replicates each piece of data (e.g., replicates each piece of existing data in the generated index). In some aspects, the creation of an index of all the data may enable a system to instantaneously find search terms within the index. For example, a system serving a data subject access request using the index may be able to search the index substantially instantaneously in order to identify search terms such as e-mail address, etc. (e.g., when serving data subject access requests on a targeted computing system 100 that includes the one or more unstructured documents for which the native computing system 104 generated the index). In various aspects, the native computing system 104 may generate the index during any suitable discovery process described herein.

In some aspects, as the native computing system 104 builds and maintains an index of all the data within each of the one or more unstructured documents (e.g., as more unstructured documents are identified and added to the index), the size of the index may grow with the size of the data (e.g., grow linearly with the data). In various aspects, the cost of maintaining and building the index may be relatively high (i.e., in terms of computing resources required to build and maintain the index). In such aspects, the cost of fulfilling a particular data subject access request, however, may be relatively low, as the native computing system 104 (e.g., or other computing system which may be processing such a request) may fulfil the data subject access request using the existing index (e.g., and/or provide the index for use by the other system for fulfilling the data subject access request).

At operation 524, the native computing system 104 performs one or more real-time searches of data in each unstructured document in order to process each received data subject access request. In operation 524, the native computing system 104 may not actively index data in the documents during the discovery process. In various aspects, such as when the native computing system 104 does not actively index unstructured documents, the native computing system may not replicate any data when relying on real-time searching (i.e., because the documents may be searched in response to a data subject access request rather than indexing the data on discovery). In such aspects, the process depicted in FIG. 6 may preserve computing resources by not creating additional copies of the data. In some aspects, relying on real-time searching to respond to a data subject access request may be slow, as the native computing system 104 may need to rescan a large number of identified documents for every data subject access request that the native computing system 104 receives. In some aspects, when relying on real-time searching, the native computing system 104 may preserve computing resources related to producing, maintaining, and storing an index, but the native computing system 104 may need to perform a full scan in response to each data subject access request received. As such, the native computing system 104 may optimize data subject access request response time by processing the requests in batches (e.g., weekly, monthly, etc.). In some aspects, batch frequency may be based on statutory response requirements.

When indexing the unstructured documents using a hybrid searching technique, the native computing system 104, at operation 526, may approximate a location of personally identifiable information or other target data in the unstructured documents. In such aspects, the native computing system 104 may maintain an index with a reduced file size (e.g., as compared to a complete index). In some aspects, hybrid searching and indexing may require partial data replication by the native computing system 104.

When indexing the unstructured documents using a smart searching technique, the native computing system 104, at operation 528, may only index personally identifiable information and other target data across all data sources on each scan. For example, the native computing system may only store a reference to discovered files for each piece of personally identifiable information (e.g., or other target data) that is discovered. In various aspects, smart indexing may only require data replication for unique target data (i.e., by supplementing the index upon discovery of a new piece of target data). For example, smart indexing may include generating, by the native computing system 104, an initial index on an initial scan of the target computing system 100. The initial index may include an index indicating, for each identified piece of target data, every location on the target computing system 100 in which the identified piece of target data is located (e.g., using a respective file reference or other location reference in the index). On subsequent scans of the target computing system 100, the native computing system 104 can supplement the index to include newly identified pieces of target data, and modify the index to include just a location reference for instances of target data that have been identified in a new location, but were already included in the index. In some aspects, smart indexing may increase a size of the generated index at rate of newly discovered target data elements. In some respects, when serving a data subject access request or other query on the data set that includes the unstructured documents, a system may utilize OCR for each request, but only for documents that are known to contain the target data.

At operation 530, the native computing system 104 generates and stores metadata for the unstructured documents based on the index. For example, the native computing system 104 may store metadata for the unstructured documents that identifies a location of particular instances of target data across all of the documents. For example, the metadata may define a respective file reference for each instance of the particular piece of target data discovered across the target computing system 100 and store the file reference in association with the piece of target data in the index. For a particular e-mail address (e.g., user@mail.com) that appears in 25 different files in the target computing system 100, the native computing system may store, in the index, the e-mail address user@mail.com in association with a respective file reference for each of the 25 different files. In this way, when serving a data subject access request for the e-mail address user@gmail.com, the native computing system 104 may use the index to limit a search for target data for the data subject associated with that e-mail to those 25 different files identified by the index.

Example Technical Platforms

Aspects of the present disclosure may be implemented in various ways, including as computer program products that include articles of manufacture. Such computer program products may include one or more software components including, for example, software objects, methods, data structures, and/or the like. A software component may be coded in any of a variety of programming languages. An illustrative programming language may be a lower-level programming language such as an assembly language associated with a particular hardware architecture and/or operating system platform. A software component comprising assembly language instructions may require conversion into executable machine code by an assembler prior to execution by the hardware architecture and/or platform. Another example programming language may be a higher-level programming language that may be portable across multiple architectures. A software component comprising higher-level programming language instructions may require conversion to an intermediate representation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to, a macro language, a shell or command language, a job control language, a script language, a database query, or search language, and/or a report writing language. In one or more example aspects, a software component comprising instructions in one of the foregoing examples of programming languages may be executed directly by an operating system or other software component without having to be first transformed into another form. A software component may be stored as a file or other data storage construct. Software components of a similar type or functionally related may be stored together such as, for example, in a particular directory, folder, or library. Software components may be static (e.g., pre-established, or fixed) or dynamic (e.g., created or modified at the time of execution).

A computer program product may include a non-transitory computer-readable storage medium storing applications, programs, program modules, scripts, source code, program code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like (also referred to herein as executable instructions, instructions for execution, computer program products, program code, and/or similar terms used herein interchangeably). Such non-transitory computer-readable storage media include all computer-readable media (including volatile and non-volatile media).

According to various aspects, a non-volatile computer-readable storage medium may include a floppy disk, flexible disk, hard disk, solid-state storage (SSS) (e.g., a solid-state drive (SSD), solid state card (SSC), solid state module (SSM)), enterprise flash drive, magnetic tape, or any other non-transitory magnetic medium, and/or the like. A non-volatile computer-readable storage medium may also include a punch card, paper tape, optical mark sheet (or any other physical medium with patterns of holes or other optically recognizable indicia), compact disc read only memory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc (DVD), Blu-ray disc (BD), any other non-transitory optical medium, and/or the like. Such a non-volatile computer-readable storage medium may also include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory (e.g., Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC), secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF) cards, Memory Sticks, and/or the like. Further, a non-volatile computer-readable storage medium may also include conductive-bridging random access memory (CBRAM), phase-change random access memory (PRAM), ferroelectric random-access memory (FeRAM), non-volatile random-access memory (NVRAM), magnetoresistive random-access memory (MRAM), resistive random-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory (SONOS), floating junction gate random access memory (FJG RAM), Millipede memory, racetrack memory, and/or the like.

According to various aspects, a volatile computer-readable storage medium may include random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), fast page mode dynamic random access memory (FPM DRAM), extended data-out dynamic random access memory (EDO DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), double data rate type two synchronous dynamic random access memory (DDR2 SDRAM), double data rate type three synchronous dynamic random access memory (DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), Twin Transistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM), Rambus in-line memory module (RIMM), dual in-line memory module (DIMM), single in-line memory module (SIMM), video random access memory (VRAM), cache memory (including various levels), flash memory, register memory, and/or the like. It will be appreciated that where various aspects are described to use a computer-readable storage medium, other types of computer-readable storage media may be substituted for or used in addition to the computer-readable storage media described above.

Various aspects of the present disclosure may also be implemented as methods, apparatuses, systems, computing devices, computing entities, and/or the like. As such, various aspects of the present disclosure may take the form of a data structure, apparatus, system, computing device, computing entity, and/or the like executing instructions stored on a computer-readable storage medium to perform certain steps or operations. Thus, various aspects of the present disclosure also may take the form of entirely hardware, entirely computer program product, and/or a combination of computer program product and hardware performing certain steps or operations.

Various aspects of the present disclosure are described below with reference to block diagrams and flowchart illustrations. Thus, each block of the block diagrams and flowchart illustrations may be implemented in the form of a computer program product, an entirely hardware aspect, a combination of hardware and computer program products, and/or apparatuses, systems, computing devices, computing entities, and/or the like carrying out instructions, operations, steps, and similar words used interchangeably (e.g., the executable instructions, instructions for execution, program code, and/or the like) on a computer-readable storage medium for execution. For example, retrieval, loading, and execution of code may be performed sequentially such that one instruction is retrieved, loaded, and executed at a time. In some examples of aspects, retrieval, loading, and/or execution may be performed in parallel such that multiple instructions are retrieved, loaded, and/or executed together. Thus, such aspects can produce specially configured machines performing the steps or operations specified in the block diagrams and flowchart illustrations. Accordingly, the block diagrams and flowchart illustrations support various combinations of aspects for performing the specified instructions, operations, or steps.

Example System Architecture

FIG. 7 depicts an example of a computing environment that can be used for discovering, classifying, and/or indexing target data stored across data assets that may be spread over one or more data repositories in a target computing system in accordance with various aspects of the present disclosure. Components of the system architecture 700 are configured according to various aspects to discover target data on a target computing system 100.

As may be understood from FIG. 7, the system architecture 700 according to various aspects may include a target computing system that includes a native computing system 104 and one or more data repositories 120. The native computing system 104 further includes one or more native servers 704. Although the target computing system 100, native computing system 104, and one or more data repositories 120 are shown as separate components, according to other aspects, these components may include a single server and/or repository, servers and/or repositories, one or more cloud-based servers and/or repositories, or any other suitable configuration.

In addition, the system architecture 700 according to various aspects may include a third-party computing system 140 that includes one or more third party servers 740. Although the third party server 740 and third-party computing system 140 are shown as separate components, according to other aspects, these components 140, 740 may include a single server and/or repository, servers and/or repositories, one or more cloud-based servers and/or repositories, or any other suitable configuration.

The native server(s) 704, third party servers 740, and/or other components may communicate with, access, and/or the like with each other over one or more networks, such as via a public data network 142 and/or a private data network 144. According to particular aspects, the third party server 740 may provide one or more graphical user interfaces through which personnel of an entity can interact with the native computing system 104 (e.g., via a client application). Furthermore, the third party server 740, and/or native server 704, may provide one or more interfaces that allow the target computing system 100, the native computing system 104, and/or the third-party computing system to communicate with each other such as one or more suitable application programming interfaces (APIs), direct connections, and/or the like.

Example Computing Hardware

FIG. 8 illustrates a diagrammatic representation of a computing hardware device 800 that may be used in accordance with various aspects of the disclosure. For example, the hardware device 800 may be computing hardware such as a third party server 740, and/or native server 704 as described in FIG. 7. According to particular aspects, the hardware device 800 may be connected (e.g., networked) to one or more other computing entities, storage devices, and/or the like via one or more networks such as, for example, a LAN, an intranet, an extranet, and/or the Internet. As noted above, the hardware device 800 may operate in the capacity of a server and/or a client device in a client-server network environment, or as a peer computing device in a peer-to-peer (or distributed) network environment. According to various aspects, the hardware device 800 may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a mobile device (smartphone), a web appliance, a server, a network router, a switch or bridge, or any other device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single hardware device 800 is illustrated, the term “hardware device,” “computing hardware,” and/or the like shall also be taken to include any collection of computing entities that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

A hardware device 800 includes a processor 802, a main memory 804 (e.g., read-only memory (ROM), flash memory, dynamic random-access memory (DRAM) such as synchronous DRAM (SDRAM), Rambus DRAM (RDRAM), and/or the like), a static memory 806 (e.g., flash memory, static random-access memory (SRAM), and/or the like), and a data storage device 818, that communicate with each other via a bus 832.

The processor 802 may represent one or more general-purpose processing devices such as a microprocessor, a central processing unit, and/or the like. According to some aspects, the processor 802 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, processors implementing a combination of instruction sets, and/or the like. According to some aspects, the processor 802 may be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, and/or the like. The processor 802 can execute processing logic 826 for performing various operations and/or steps described herein.

The hardware device 800 may further include a network interface device 808, as well as a video display unit 810 (e.g., a liquid crystal display (LCD), a cathode ray tube (CRT), and/or the like), an alphanumeric input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse, a trackpad), and/or a signal generation device 816 (e.g., a speaker). The hardware device 800 may further include a data storage device 818. The data storage device 818 may include a non-transitory computer-readable storage medium 830 (also known as a non-transitory computer-readable storage medium or a non-transitory computer-readable medium) on which is stored one or more modules 822 (e.g., sets of software instructions) embodying any one or more of the methodologies or functions described herein. For instance, according to particular aspects, the modules 822 include data discovery deployment and scheduling module 200, the data discovery module 300, the data classification module 400, and the data indexing module 500 as described herein. The one or more modules 822 may also reside, completely or at least partially, within main memory 804 and/or within the processor 802 during execution thereof by the hardware device 800—main memory 804 and processor 802 also constituting computer-accessible storage media. The one or more modules 822 may further be transmitted or received over a network 140 via the network interface device 808.

While the computer-readable storage medium 830 is shown to be a single medium, the terms “computer-readable storage medium” and “machine-accessible storage medium” should be understood to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” should also be understood to include any medium that is capable of storing, encoding, and/or carrying a set of instructions for execution by the hardware device 800 and that causes the hardware device 800 to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” should accordingly be understood to include, but not be limited to, solid-state memories, optical and magnetic media, and/or the like.

System Operation

The logical operations described herein may be implemented (1) as a sequence of computer implemented acts or one or more program modules running on a computing system and/or (2) as interconnected machine logic circuits or circuit modules within the computing system. The implementation is a matter of choice dependent on the performance and other requirements of the computing system. Accordingly, the logical operations described herein are referred to variously as states, operations, steps, structural devices, acts, or modules. These states, operations, steps, structural devices, acts, and modules may be implemented in software, in firmware, in special purpose digital logic, and any combination thereof. Greater or fewer operations may be performed than shown in the figures and described herein. These operations also may be performed in a different order than those described herein.

CONCLUSION

While this specification contains many specific aspect details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features that may be specific to particular aspects of particular inventions. Certain features that are described in this specification in the context of separate aspects also may be implemented in combination in a single aspect. Conversely, various features that are described in the context of a single aspect also may be implemented in multiple aspects separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination may in some cases be excised from the combination, and the claimed combination may be a sub-combination or variation of a sub-combination.

Similarly, while operations are described in a particular order, this should not be understood as requiring that such operations be performed in the particular order described or in sequential order, or that all described operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various components in the various aspects described above should not be understood as requiring such separation in all aspects, and the described program components (e.g., modules) and systems may be integrated together in a single software product or packaged into multiple software products.

Many modifications and other aspects of the disclosure will come to mind to one skilled in the art to which this disclosure pertains having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the disclosure is not to be limited to the specific aspects disclosed and that modifications and other aspects are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for the purposes of limitation.

Claims

1. A system comprising: a non-transitory computer-readable medium storing instructions; anda processing device communicatively coupled to the non-transitory computer-readable medium,wherein, the processing device is configured to execute the instructions and thereby perform operations comprising: deploying, from a first computing system, via a public data network, a client application on a target computing system, the target computing system comprising a plurality of data sources in a private data network;receiving, from the client application, at the first computing system, target computing system resource data for computing resources available to the target computing system;causing, by the client application, the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network to discover target data stored on the plurality of data sources based on the target computing system resource data;causing, by the client application, the target computing system to classify each piece of the target data according to a type by: causing, by the client application, the target computing system to generate a prediction of a type of each piece of the target data using a neural network based classification model and based on at least one of tokenized data corresponding to the target data or labelled data corresponding to the target data; andidentifying each piece of the target data as the type for the target data based on the prediction satisfying a confidence threshold; andresponsive to discovering the target data stored on the plurality of data sources, generating and storing metadata for each of the plurality of data sources, the metadata indicating at least one of the type of the target data, a number of instances of the target data, or a location of the target data on each of the plurality of data sources.
2. The system of claim 1, wherein: the operations further comprise: generating, by the first computing system, a catalog of data on the plurality of data sources; andcreating, by the first computing system, a job schedule based on the catalog; andcausing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network comprises causing the target computing system to execute each job in the job schedule based on the target computing system resource data.
3. The system of claim 2, wherein causing the target computing system to classify each piece of target data comprises: tokenizing data from each item in the catalog of data to generate the tokenized data;labeling the tokenized data to generate the labelled data; andclassifying the data as target data based on the labelled data.
4. The system of claim 1, wherein: receiving the target computing system resource data for the computing resources available to the target computing system comprises receiving current resource usage by the target computing system and total available resources for the target computing system; andcausing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network based on the target computing system resource data comprises scheduling jobs to the target computing system such that executing the jobs does not cause the current resource usage to exceed the total available resources.
5. The system of claim 1, wherein causing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network based on the target computing system resource data comprises scheduling discovery to the target computing system during at least one of a particular time period, a particular part of a day, or during a particular day.
6. The system of claim 1, wherein causing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network based on the target computing system resource data comprises scheduling jobs to the target computing system such that the target computing system does not execute more than a particular number of simultaneous jobs.
7. The system of claim 1, wherein the operations further comprise: receiving an indication of a data subject access request received from a client device via a user interface that is accessible via the public data network and is configured for querying the plurality of data sources included in the private data network, the data subject access request identifying a data subject; andresponsive to receiving the indication, facilitating, by the target computing system, execution of processing operations or network communication for retrieving data responsive to the data subject access request from the plurality of data sources included in the private data network by: accessing the metadata and identifying a subset of the plurality of data sources that store target data; andfacilitating, by the target computing system, execution of processing operations or network communication for retrieving the data responsive to the data subject access request on the subset of the plurality of data sources that store target data.
8. A method comprising: deploying, by computing hardware, via a public data network, a client application on a target computing system, the target computing system comprising a plurality of data sources in a private data network;cataloging, by the computing hardware, data from the plurality of data sources in the private data network into a catalog, the catalog identifying each file across the target computing system that requires scanning for target data;creating, by the computing hardware, a plurality of jobs based on the catalog, each of the plurality of jobs corresponding to a respective file from the target computing system;generating, by the computing hardware, a schedule for executing the plurality of jobs based on computing resource availability at the target computing system;causing, by the computing hardware via the client application, the target computing system to use computing resources available to the target computing system to scan the plurality of data sources in the private data network to discover the target data stored on the plurality of data sources according to the schedule;causing, by the computing hardware, the target computing system to classify each piece of the target data according to a type by: causing, by the computing hardware, the target computing system to generate a prediction of a type of each piece of the target data using a neural network based classification model and based on at least one of tokenized data corresponding to the target data or labelled data corresponding to the target data; andidentifying each piece of the target data as the type for the target data based on the prediction satisfying a confidence threshold; andresponsive to discovering the target data stored on the plurality of data sources, generating and storing metadata, by the computing hardware, for each of the plurality of data sources, the metadata indicating at least one of the type of the target data, a number of instances of the target data, or a location of the target data on each of the plurality of data sources.
9. The method of claim 8, further comprising: responsive a request to query the plurality of data sources included in the private data network via a user interface that is accessible via the public data network, facilitating, by the computing hardware, execution, by the target computing system, of processing operations or network communication for retrieving data responsive to the request from the plurality of data sources included in the private data network by: accessing the metadata and identifying a subset of the plurality of data sources that store target data; andfacilitating, by the target computing system, execution of processing operations or network communication for retrieving the data responsive to the request on only the subset of the plurality of data sources that store target data.
10. The method of claim 8, wherein: causing the target computing system to use the computing resources available to the target computing system to scan the plurality of data sources in the private data network to discover the target data stored on the plurality of data sources according to the schedule comprises scheduling the plurality of jobs to the target computing system such that executing the plurality of jobs does not cause a current resource usage at the target computing system to exceed a particular portion of the computing resource availability at the target computing system.
11. The method of claim 8, wherein causing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network comprises scheduling discovery to the target computing system during at least one of a particular time period, a particular part of a day, or during a particular day.
12. The method of claim 8, wherein causing the target computing system to use the computing resources available on the target computing system to scan the plurality of data sources in the private data network comprises scheduling jobs to the target computing system such that the target computing system does not execute more than a particular number of simultaneous jobs.
13. The method of claim 8, wherein each of the plurality of jobs include causing the target computing system to classify data in the respective file as target data by: tokenizing the data to generate the tokenized data;labeling the tokenized data to generate the labelled data; andclassifying the data as target data based on the labelled data.
14. A method comprising: cataloging, by computing hardware, data from a plurality of data sources that make up a target computing system over a private data network into a catalog, the catalog identifying each file across the target computing system that requires scanning for target data;creating, by the computing hardware, a plurality of jobs based on the catalog, each of the plurality of jobs corresponding to a respective file from the target computing system;generating, by the computing hardware, a schedule for executing the plurality of jobs based on computing resource availability at the target computing system;causing, by the computing hardware, the target computing system to use computing resources available to the target computing system to execute the plurality of jobs to scan the plurality of data sources in the private data network to discover the target data stored on the plurality of data sources according to the schedule;causing, by the computing hardware, the target computing system to classify each piece of the target data according to a particular data type by: causing, by the computing hardware, the target computing system to generate a prediction of a type of each piece of the target data using a neural network based classification model and based on at least one of tokenized data corresponding to the target data or labelled data corresponding to the target data; andidentifying each piece of the target data as the particular data type for the target data based on the prediction satisfying a confidence threshold; andresponsive to discovering the target data stored on the plurality of data sources and classifying each piece of the target data according to the particular data type, generating and storing metadata, by the computing hardware, for each of the plurality of data sources, the metadata indicating at least one of: the particular data type for the target data on each respective data source,a number of instances of the particular data type for the target data on each respective data source, and/ora location of the target data on each of the plurality of data sources.
15. The method of claim 14, wherein classifying each piece of the target data according to the particular data type comprises: causing, by the computing hardware, the target computing system to tokenize the data to generate the tokenized data;causing, by the computing hardware, the target computing system to label the tokenized data to generate the labelled data; andcausing, by the computing hardware, the target computing system to classify each piece of the target data according to the particular data type based on the labelled data.
16. The method of claim 14, the method further comprising: causing, by the computing hardware, the target computing system to compare the particular data type of two adjacent pieces of target data in a particular document; andmodifying a confidence level for the prediction of at least one of the two adjacent pieces of target data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent Application Ser. No. 63/141,216, filed Jan. 25, 2021, which is hereby incorporated herein by reference in its entirety.

US Referenced Citations (1538)

Number	Name	Date	Kind
4536866	Jerome et al.	Aug 1985	A
4574350	Starr	Mar 1986	A
5193162	Bordsen et al.	Mar 1993	A
5276735	Boebert et al.	Jan 1994	A
5329447	Leedom, Jr.	Jul 1994	A
5404299	Tsurubayashi et al.	Apr 1995	A
5535393	Reeve et al.	Jul 1996	A
5560005	Hoover et al.	Sep 1996	A
5668986	Nilsen et al.	Sep 1997	A
5710917	Musa et al.	Jan 1998	A
5761529	Raji	Jun 1998	A
5764906	Edelstein et al.	Jun 1998	A
5872973	Mitchell et al.	Feb 1999	A
5913041	Ramanathan et al.	Jun 1999	A
5913214	Madnick et al.	Jun 1999	A
6016394	Walker	Jan 2000	A
6122627	Carey et al.	Sep 2000	A
6148297	Swor et al.	Nov 2000	A
6148342	Ho	Nov 2000	A
6240416	Immon et al.	May 2001	B1
6243816	Fang et al.	Jun 2001	B1
6253203	Oflaherty et al.	Jun 2001	B1
6263335	Paik et al.	Jul 2001	B1
6272631	Thomlinson et al.	Aug 2001	B1
6275824	Oflaherty et al.	Aug 2001	B1
6282548	Burner et al.	Aug 2001	B1
6330562	Boden et al.	Dec 2001	B1
6363488	Ginter et al.	Mar 2002	B1
6374237	Reese	Apr 2002	B1
6374252	Althoff et al.	Apr 2002	B1
6408336	Schneider et al.	Jun 2002	B1
6427230	Goiffon et al.	Jul 2002	B1
6442688	Moses et al.	Aug 2002	B1
6446120	Dantressangle	Sep 2002	B1
6463488	San Juan	Oct 2002	B1
6484149	Jammes et al.	Nov 2002	B1
6484180	Lyons et al.	Nov 2002	B1
6516314	Birkler et al.	Feb 2003	B1
6516337	Tripp et al.	Feb 2003	B1
6519571	Guheen et al.	Feb 2003	B1
6574631	Subramanian et al.	Jun 2003	B1
6591272	Williams	Jul 2003	B1
6601233	Underwood	Jul 2003	B1
6606744	Mikurak	Aug 2003	B1
6611812	Hurtado et al.	Aug 2003	B2
6625602	Meredith et al.	Sep 2003	B1
6629081	Cornelius et al.	Sep 2003	B1
6633878	Underwood	Oct 2003	B1
6662192	Rebane	Dec 2003	B1
6662357	Bowman-Amuah	Dec 2003	B1
6697824	Bowman-Amuah	Feb 2004	B1
6699042	Smith et al.	Mar 2004	B2
6701314	Conover et al.	Mar 2004	B1
6721713	Guheen et al.	Apr 2004	B1
6725200	Rost	Apr 2004	B1
6732109	Lindberg et al.	May 2004	B2
6754665	Futagami et al.	Jun 2004	B1
6755344	Mollett et al.	Jun 2004	B1
6757685	Raffaele et al.	Jun 2004	B2
6757888	Knutson et al.	Jun 2004	B1
6816944	Peng	Nov 2004	B2
6826693	Yoshida et al.	Nov 2004	B1
6850252	Hoffberg	Feb 2005	B1
6886101	Glazer et al.	Apr 2005	B2
6901346	Tracy et al.	May 2005	B2
6904417	Clayton et al.	Jun 2005	B2
6909897	Kikuchi	Jun 2005	B2
6925443	Baggei, Jr. et al.	Aug 2005	B1
6938041	Brandow et al.	Aug 2005	B1
6956845	Baker et al.	Oct 2005	B2
6978270	Carty et al.	Dec 2005	B1
6980927	Tracy et al.	Dec 2005	B2
6980987	Kaminer	Dec 2005	B2
6983221	Tracy et al.	Jan 2006	B2
6985887	Sunstein et al.	Jan 2006	B1
6990454	Mcintosh	Jan 2006	B2
6993448	Tracy et al.	Jan 2006	B2
6993495	Smith, Jr. et al.	Jan 2006	B2
6996807	Vardi et al.	Feb 2006	B1
7003560	Mullen et al.	Feb 2006	B1
7003662	Genty et al.	Feb 2006	B2
7013290	Ananian	Mar 2006	B2
7017105	Flanagin et al.	Mar 2006	B2
7023979	Wu et al.	Apr 2006	B1
7039594	Gersting	May 2006	B1
7039654	Eder	May 2006	B1
7047517	Brown et al.	May 2006	B1
7051036	Rosnow et al.	May 2006	B2
7051038	Yeh et al.	May 2006	B1
7058970	Shaw	Jun 2006	B2
7069427	Adler et al.	Jun 2006	B2
7076558	Dunn	Jul 2006	B1
7093200	Schreiber et al.	Aug 2006	B2
7095854	Ginter et al.	Aug 2006	B1
7100195	Underwood	Aug 2006	B1
7120800	Ginter et al.	Oct 2006	B2
7124101	Mikurak	Oct 2006	B1
7124107	Pishevar et al.	Oct 2006	B1
7127705	Christfort et al.	Oct 2006	B2
7127741	Bandini et al.	Oct 2006	B2
7133845	Ginter et al.	Nov 2006	B1
7139999	Bowman-Amuah	Nov 2006	B2
7143091	Charnock et al.	Nov 2006	B2
7149698	Guheen et al.	Dec 2006	B2
7165041	Guheen et al.	Jan 2007	B1
7167842	Josephson, II et al.	Jan 2007	B1
7167844	Leong et al.	Jan 2007	B1
7171379	Menninger et al.	Jan 2007	B2
7181438	Szabo	Feb 2007	B1
7203929	Vinodkrishnan et al.	Apr 2007	B1
7213233	Vinodkrishnan et al.	May 2007	B1
7216340	Vinodkrishnan et al.	May 2007	B1
7219066	Parks et al.	May 2007	B2
7223234	Stupp et al.	May 2007	B2
7225460	Barzilai et al.	May 2007	B2
7234065	Breslin et al.	Jun 2007	B2
7247625	Zhang et al.	Jul 2007	B2
7251624	Lee et al.	Jul 2007	B1
7260830	Sugimoto	Aug 2007	B2
7266566	Kennaley et al.	Sep 2007	B1
7272818	Ishimitsu et al.	Sep 2007	B2
7275063	Horn	Sep 2007	B2
7281020	Fine	Oct 2007	B2
7284232	Bates et al.	Oct 2007	B1
7284271	Lucovsky et al.	Oct 2007	B2
7287280	Young	Oct 2007	B2
7290275	Baudoin et al.	Oct 2007	B2
7293119	Beale	Nov 2007	B2
7299299	Hollenbeck et al.	Nov 2007	B2
7302569	Betz et al.	Nov 2007	B2
7313575	Carr et al.	Dec 2007	B2
7313699	Koga	Dec 2007	B2
7313825	Redlich et al.	Dec 2007	B2
7315826	Guheen et al.	Jan 2008	B1
7315849	Bakalash et al.	Jan 2008	B2
7322047	Redlich et al.	Jan 2008	B2
7330850	Seibel et al.	Feb 2008	B1
7340447	Ghatare	Mar 2008	B2
7340776	Zobel et al.	Mar 2008	B2
7343434	Kapoor et al.	Mar 2008	B2
7346518	Frank et al.	Mar 2008	B1
7353204	Liu	Apr 2008	B2
7356559	Jacobs et al.	Apr 2008	B1
7367014	Griffin	Apr 2008	B2
7370025	Pandit	May 2008	B1
7376835	Olkin et al.	May 2008	B2
7380120	Garcia	May 2008	B1
7382903	Ray	Jun 2008	B2
7383570	Pinkas et al.	Jun 2008	B2
7391854	Salonen et al.	Jun 2008	B2
7398393	Mont et al.	Jul 2008	B2
7401235	Mowers et al.	Jul 2008	B2
7403942	Bayliss	Jul 2008	B1
7409354	Putnam et al.	Aug 2008	B2
7412402	Cooper	Aug 2008	B2
7424680	Carpenter	Sep 2008	B2
7428546	Nori et al.	Sep 2008	B2
7430585	Sibert	Sep 2008	B2
7454457	Lowery et al.	Nov 2008	B1
7454508	Mathew et al.	Nov 2008	B2
7478157	Bohrer et al.	Jan 2009	B2
7480755	Herrell et al.	Jan 2009	B2
7487170	Stevens	Feb 2009	B2
7493282	Manly et al.	Feb 2009	B2
7512987	Williams	Mar 2009	B2
7516882	Cucinotta	Apr 2009	B2
7523053	Pudhukottai et al.	Apr 2009	B2
7529836	Bolen	May 2009	B1
7533113	Haddad	May 2009	B1
7548968	Bura et al.	Jun 2009	B1
7552480	Voss	Jun 2009	B1
7562339	Racca et al.	Jul 2009	B2
7565685	Ross et al.	Jul 2009	B2
7567541	Karimi et al.	Jul 2009	B2
7584505	Mondri et al.	Sep 2009	B2
7584508	Kashchenko et al.	Sep 2009	B1
7587749	Leser et al.	Sep 2009	B2
7590705	Mathew et al.	Sep 2009	B2
7590972	Axelrod et al.	Sep 2009	B2
7603356	Schran et al.	Oct 2009	B2
7606783	Carter	Oct 2009	B1
7606790	Levy	Oct 2009	B2
7607120	Sanyal et al.	Oct 2009	B2
7613700	Lobo et al.	Nov 2009	B1
7617136	Lessing et al.	Nov 2009	B1
7617167	Griffis et al.	Nov 2009	B2
7620644	Cote et al.	Nov 2009	B2
7627666	Degiulio et al.	Dec 2009	B1
7630874	Fables et al.	Dec 2009	B2
7630998	Zhou et al.	Dec 2009	B2
7636742	Olavarrieta et al.	Dec 2009	B1
7640322	Wendkos et al.	Dec 2009	B2
7650497	Thornton et al.	Jan 2010	B2
7653592	Flaxman et al.	Jan 2010	B1
7657476	Barney	Feb 2010	B2
7657694	Mansell et al.	Feb 2010	B2
7665073	Meijer et al.	Feb 2010	B2
7665125	Heard et al.	Feb 2010	B2
7668947	Hutchinson et al.	Feb 2010	B2
7673282	Amaru et al.	Mar 2010	B2
7676034	Wu et al.	Mar 2010	B1
7681034	Lee et al.	Mar 2010	B1
7681140	Ebert	Mar 2010	B2
7685561	Deem et al.	Mar 2010	B2
7685577	Pace et al.	Mar 2010	B2
7693593	Ishibashi et al.	Apr 2010	B2
7698398	Lai	Apr 2010	B1
7702639	Stanley et al.	Apr 2010	B2
7707224	Chastagnol et al.	Apr 2010	B2
7711995	Morris	May 2010	B1
7712029	Ferreira et al.	May 2010	B2
7716242	Pae et al.	May 2010	B2
7725474	Tamai et al.	May 2010	B2
7725875	Waldrep	May 2010	B2
7729940	Harvey et al.	Jun 2010	B2
7730142	Levasseur et al.	Jun 2010	B2
7752124	Green et al.	Jul 2010	B2
7756826	Bots et al.	Jul 2010	B2
7756987	Wang et al.	Jul 2010	B2
7761586	Olenick et al.	Jul 2010	B2
7774745	Fildebrandt et al.	Aug 2010	B2
7788212	Beckmann et al.	Aug 2010	B2
7788222	Shah et al.	Aug 2010	B2
7788632	Kuester et al.	Aug 2010	B2
7788726	Teixeira	Aug 2010	B2
7801758	Gracie et al.	Sep 2010	B2
7801826	Labrou et al.	Sep 2010	B2
7801912	Ransil et al.	Sep 2010	B2
7802305	Leeds	Sep 2010	B1
7805349	Yu et al.	Sep 2010	B2
7805451	Hosokawa	Sep 2010	B2
7813947	Deangelis et al.	Oct 2010	B2
7822620	Dixon et al.	Oct 2010	B2
7827523	Ahmed et al.	Nov 2010	B2
7836078	Dettinger et al.	Nov 2010	B2
7844640	Bender et al.	Nov 2010	B2
7849143	Vuong	Dec 2010	B2
7853468	Callahan et al.	Dec 2010	B2
7853470	Sonnleithner et al.	Dec 2010	B2
7853925	Kemmler	Dec 2010	B2
7860816	Fokoue-Nkoutche et al.	Dec 2010	B2
7870540	Zare et al.	Jan 2011	B2
7870608	Shraim et al.	Jan 2011	B2
7873541	Klar et al.	Jan 2011	B1
7877327	Gwiazda et al.	Jan 2011	B2
7877812	Koved et al.	Jan 2011	B2
7885841	King	Feb 2011	B2
7890461	Oeda et al.	Feb 2011	B2
7895260	Archer et al.	Feb 2011	B2
7904360	Evans	Mar 2011	B2
7904478	Yu et al.	Mar 2011	B2
7904487	Ghatare	Mar 2011	B2
7917888	Chong et al.	Mar 2011	B2
7917963	Goyal et al.	Mar 2011	B2
7921152	Ashley et al.	Apr 2011	B2
7930197	Ozzie et al.	Apr 2011	B2
7930753	Mellinger et al.	Apr 2011	B2
7953725	Burris et al.	May 2011	B2
7954150	Croft et al.	May 2011	B2
7958087	Blumenau	Jun 2011	B2
7958494	Chaar et al.	Jun 2011	B2
7962900	Barraclough et al.	Jun 2011	B2
7966310	Sullivan et al.	Jun 2011	B2
7966599	Malasky et al.	Jun 2011	B1
7966663	Strickland et al.	Jun 2011	B2
7974992	Fastabend et al.	Jul 2011	B2
7975000	Dixon et al.	Jul 2011	B2
7991559	Dzekunov et al.	Aug 2011	B2
7991747	Upadhyay et al.	Aug 2011	B1
7996372	Rubel, Jr.	Aug 2011	B2
8005891	Knowles et al.	Aug 2011	B2
8010612	Costea et al.	Aug 2011	B2
8010720	Iwaoka et al.	Aug 2011	B2
8019881	Sandhu et al.	Sep 2011	B2
8020206	Hubbard et al.	Sep 2011	B2
8024384	Prabhakar et al.	Sep 2011	B2
3041749	Beck	Oct 2011	A1
3041913	Chin-Yu	Oct 2011	A1
8032721	Murai	Oct 2011	B2
8036374	Noble, Jr.	Oct 2011	B2
8037409	Jacob et al.	Oct 2011	B2
3069161	Bugir et al.	Nov 2011	A1
8069471	Boren	Nov 2011	B2
8082539	Schelkogonov	Dec 2011	B1
8090754	Schmidt et al.	Jan 2012	B2
8095923	Harvey et al.	Jan 2012	B2
8099709	Baikov et al.	Jan 2012	B2
8099765	Parkinson	Jan 2012	B2
8103962	Embley et al.	Jan 2012	B2
8117441	Kurien et al.	Feb 2012	B2
8135815	Mayer	Mar 2012	B2
8146054	Baker et al.	Mar 2012	B2
8146074	Ito et al.	Mar 2012	B2
8150717	Whitmore	Apr 2012	B2
8156105	Altounian et al.	Apr 2012	B2
8156158	Rolls et al.	Apr 2012	B2
8156159	Ebrahimi et al.	Apr 2012	B2
8166406	Goldfeder et al.	Apr 2012	B1
8176061	Swanbeck et al.	May 2012	B2
8176177	Sussman et al.	May 2012	B2
8176334	Vainstein	May 2012	B2
8176470	Klumpp et al.	May 2012	B2
8180759	Hamzy	May 2012	B2
8181151	Sedukhin et al.	May 2012	B2
8185409	Putnam et al.	May 2012	B2
8185497	Vermeulen et al.	May 2012	B2
8196176	Berteau et al.	Jun 2012	B2
8205093	Argott	Jun 2012	B2
8205140	Hafeez et al.	Jun 2012	B2
8214362	Djabarov	Jul 2012	B1
8214803	Horii et al.	Jul 2012	B2
8234377	Cohn	Jul 2012	B2
8239244	Ginsberg et al.	Aug 2012	B2
8250051	Bugir et al.	Aug 2012	B2
8255468	Vitaldevara et al.	Aug 2012	B2
8260262	Ben Ayed	Sep 2012	B2
8261362	Goodwin et al.	Sep 2012	B2
8266231	Golovin et al.	Sep 2012	B1
8275632	Awaraji et al.	Sep 2012	B2
8275793	Ahmad et al.	Sep 2012	B2
8286239	Sutton	Oct 2012	B1
8312549	Goldberg et al.	Nov 2012	B2
8316237	Felsher et al.	Nov 2012	B1
8332908	Hatakeyama et al.	Dec 2012	B2
8340999	Kumaran et al.	Dec 2012	B2
8341405	Meijer et al.	Dec 2012	B2
8346929	Lai	Jan 2013	B1
8364713	Pollard	Jan 2013	B2
8370224	Grewal	Feb 2013	B2
8370794	Moosmann et al.	Feb 2013	B2
8380630	Felsher	Feb 2013	B2
8380743	Converting et al.	Feb 2013	B2
8381180	Rostoker	Feb 2013	B2
8381297	Touboul	Feb 2013	B2
8386314	Kirkby et al.	Feb 2013	B2
8392982	Harris et al.	Mar 2013	B2
8418226	Gardner	Apr 2013	B2
8423954	Ronen et al.	Apr 2013	B2
8429179	Mirhaji	Apr 2013	B1
8429597	Prigge	Apr 2013	B2
8429630	Nickolov et al.	Apr 2013	B2
8429758	Chen et al.	Apr 2013	B2
8438644	Watters et al.	May 2013	B2
8448252	King et al.	May 2013	B1
8463247	Misiag	Jun 2013	B2
8464311	Ashley et al.	Jun 2013	B2
8468244	Redlich et al.	Jun 2013	B2
8473324	Alvarez et al.	Jun 2013	B2
8474012	Ahmed et al.	Jun 2013	B2
8494894	Jaster et al.	Jul 2013	B2
8504481	Motahari et al.	Aug 2013	B2
8510199	Erlanger	Aug 2013	B1
8515988	Jones et al.	Aug 2013	B2
8516076	Thomas	Aug 2013	B2
8527337	Lim et al.	Sep 2013	B1
8533746	Nolan et al.	Sep 2013	B2
8533844	Mahaffey et al.	Sep 2013	B2
8538817	Wilson	Sep 2013	B2
8539359	Rapaport et al.	Sep 2013	B2
8539437	Finlayson et al.	Sep 2013	B2
8560645	Linden et al.	Oct 2013	B2
8560841	Chin et al.	Oct 2013	B2
8560956	Curtis et al.	Oct 2013	B2
8561100	Hu et al.	Oct 2013	B2
8561153	Grason et al.	Oct 2013	B2
8565729	Moseler et al.	Oct 2013	B2
8566726	Dixon et al.	Oct 2013	B2
8566938	Prakash et al.	Oct 2013	B1
8571909	Miller et al.	Oct 2013	B2
8572717	Narayanaswamy	Oct 2013	B2
8578036	Holfelder et al.	Nov 2013	B1
8578166	De Monseignat et al.	Nov 2013	B2
8578481	Rowley	Nov 2013	B2
8578501	Ogilvie	Nov 2013	B1
8583694	Siegel et al.	Nov 2013	B2
8583766	Dixon et al.	Nov 2013	B2
8589183	Awaraji et al.	Nov 2013	B2
8589372	Krislov	Nov 2013	B2
8601467	Hofhansl et al.	Dec 2013	B2
8601591	Krishnamurthy et al.	Dec 2013	B2
8606746	Yeap et al.	Dec 2013	B2
8612420	Sun et al.	Dec 2013	B2
8612993	Grant et al.	Dec 2013	B2
8615549	Knowles et al.	Dec 2013	B2
8615731	Doshi	Dec 2013	B2
8620952	Bennett et al.	Dec 2013	B2
8621637	Al-Harbi et al.	Dec 2013	B2
8626671	Federgreen	Jan 2014	B2
8627114	Resch et al.	Jan 2014	B2
8630961	Beilby et al.	Jan 2014	B2
8631048	Davis et al.	Jan 2014	B1
8640110	Kopp et al.	Jan 2014	B2
8646072	Savant	Feb 2014	B1
8650399	Le Bihan et al.	Feb 2014	B2
8655939	Redlich et al.	Feb 2014	B2
8656265	Paulin et al.	Feb 2014	B1
8656456	Maxson et al.	Feb 2014	B2
8661036	Turski et al.	Feb 2014	B2
8667074	Farkas	Mar 2014	B1
8667487	Boodman et al.	Mar 2014	B1
8677472	Dotan et al.	Mar 2014	B1
8681984	Lee et al.	Mar 2014	B2
8682698	Cashman et al.	Mar 2014	B2
8683502	Shkedi et al.	Mar 2014	B2
8688601	Jaiswal	Apr 2014	B2
8689292	Williams et al.	Apr 2014	B2
8693689	Belenkiy et al.	Apr 2014	B2
8700524	Williams et al.	Apr 2014	B2
8700699	Shen et al.	Apr 2014	B2
8706742	Ravid et al.	Apr 2014	B1
8707451	Ture et al.	Apr 2014	B2
8712813	King	Apr 2014	B2
8713098	Adya et al.	Apr 2014	B1
8713638	Hu et al.	Apr 2014	B2
8719366	Mathew et al.	May 2014	B2
8732839	Hohl	May 2014	B2
8744894	Christiansen et al.	Jun 2014	B2
8751285	Deb et al.	Jun 2014	B2
8762406	Ho et al.	Jun 2014	B2
8762413	Graham, Jr. et al.	Jun 2014	B2
8763071	Sinha et al.	Jun 2014	B2
8763082	Huber et al.	Jun 2014	B2
8763131	Archer et al.	Jun 2014	B2
8767947	Ristock et al.	Jul 2014	B1
8769242	Tkac et al.	Jul 2014	B2
8769412	Gill et al.	Jul 2014	B2
8769671	Shraim et al.	Jul 2014	B2
8776241	Zaitsev	Jul 2014	B2
8788935	Hirsch et al.	Jul 2014	B1
8793614	Wilson et al.	Jul 2014	B2
8793650	Hilerio et al.	Jul 2014	B2
8793781	Grossi et al.	Jul 2014	B2
8793809	Falkenburg et al.	Jul 2014	B2
8799984	Ahn	Aug 2014	B2
8805707	Schumann, Jr. et al.	Aug 2014	B2
8805806	Amarendran et al.	Aug 2014	B2
8805925	Price et al.	Aug 2014	B2
8812342	Barcelo et al.	Aug 2014	B2
8812752	Shih et al.	Aug 2014	B1
8812766	Kranendonk et al.	Aug 2014	B2
8813028	Farooqi	Aug 2014	B2
8819253	Simeloff et al.	Aug 2014	B2
8819617	Koenig et al.	Aug 2014	B1
8819800	Gao et al.	Aug 2014	B2
8826446	Liu et al.	Sep 2014	B1
8832649	Bishop et al.	Sep 2014	B2
8832854	Staddon et al.	Sep 2014	B1
8839232	Taylor et al.	Sep 2014	B2
8839346	Murgia	Sep 2014	B2
8843487	Mcgraw et al.	Sep 2014	B2
8843745	Roberts, Jr.	Sep 2014	B2
8849757	Kruglick	Sep 2014	B2
8856534	Khosravi et al.	Oct 2014	B2
8856936	Datta Ray et al.	Oct 2014	B2
8862507	Sandhu et al.	Oct 2014	B2
8863261	Yang	Oct 2014	B2
8875232	Blom et al.	Oct 2014	B2
8893078	Schaude et al.	Nov 2014	B2
8893286	Oliver	Nov 2014	B1
8893297	Eversoll et al.	Nov 2014	B2
8904494	Kindler et al.	Dec 2014	B2
8914263	Shimada et al.	Dec 2014	B2
8914299	Pesci-Anderson et al.	Dec 2014	B2
8914342	Kalaboukis et al.	Dec 2014	B2
8914902	Moritz et al.	Dec 2014	B2
8918306	Cashman et al.	Dec 2014	B2
8918392	Brooker	Dec 2014	B1
8918632	Sartor	Dec 2014	B1
8924388	Elliot	Dec 2014	B2
8930364	Brooker	Jan 2015	B1
8930896	Wiggins	Jan 2015	B1
8930897	Nassar	Jan 2015	B2
8935198	Phillips et al.	Jan 2015	B1
8935266	Wu	Jan 2015	B2
8935342	Patel	Jan 2015	B2
8935804	Clark et al.	Jan 2015	B1
8938221	Brazier et al.	Jan 2015	B2
8943076	Stewart et al.	Jan 2015	B2
8943548	Drokov et al.	Jan 2015	B2
8949137	Crapo et al.	Feb 2015	B2
8955038	Nicodemus et al.	Feb 2015	B2
8959568	Hudis et al.	Feb 2015	B2
8959584	Piliouras	Feb 2015	B2
8966575	Mcquay et al.	Feb 2015	B2
8966597	Saylor et al.	Feb 2015	B1
8973108	Roth et al.	Mar 2015	B1
8977234	Chava	Mar 2015	B2
8977643	Schindlauer et al.	Mar 2015	B2
8978158	Rajkumar et al.	Mar 2015	B2
8983972	Kriebel et al.	Mar 2015	B2
8984031	Todd	Mar 2015	B1
8990933	Magdalin	Mar 2015	B1
8996417	Channakeshava	Mar 2015	B1
8996480	Agarwala et al.	Mar 2015	B2
8997213	Papakipos et al.	Mar 2015	B2
9002939	Laden et al.	Apr 2015	B2
9003295	Baschy	Apr 2015	B2
9003552	Goodwin et al.	Apr 2015	B2
9009851	Droste et al.	Apr 2015	B2
9014661	Decharms	Apr 2015	B2
9015796	Fujioka	Apr 2015	B1
9021469	Hilerio et al.	Apr 2015	B2
9026526	Bau et al.	May 2015	B1
9030987	Bianchei et al.	May 2015	B2
9032067	Prasad et al.	May 2015	B2
9043217	Cashman et al.	May 2015	B2
9043480	Barton et al.	May 2015	B2
9047463	Porras	Jun 2015	B2
9047582	Hutchinson et al.	Jun 2015	B2
9047583	Patton et al.	Jun 2015	B2
9047639	Quintiliani et al.	Jun 2015	B1
9049244	Prince et al.	Jun 2015	B2
9049314	Pugh et al.	Jun 2015	B2
9055071	Gates et al.	Jun 2015	B1
9058590	Criddle et al.	Jun 2015	B2
9064033	Jin et al.	Jun 2015	B2
9069940	Hars	Jun 2015	B2
9076231	Hill et al.	Jul 2015	B1
9077736	Werth et al.	Jul 2015	B2
9081952	Sagi et al.	Jul 2015	B2
9087090	Cormier et al.	Jul 2015	B1
9092478	Vaitheeswaran et al.	Jul 2015	B2
9092796	Eversoll et al.	Jul 2015	B2
9094434	Williams et al.	Jul 2015	B2
9098515	Richter et al.	Aug 2015	B2
9100778	Stogaitis et al.	Aug 2015	B2
9106691	Burger et al.	Aug 2015	B1
9106710	Feimster	Aug 2015	B1
9110918	Rajaa et al.	Aug 2015	B1
9111105	Barton et al.	Aug 2015	B2
9111295	Tietzen et al.	Aug 2015	B2
9123339	Shaw et al.	Sep 2015	B1
9129311	Schoen et al.	Sep 2015	B2
9135261	Maunder et al.	Sep 2015	B2
9135444	Carter et al.	Sep 2015	B2
9141823	Dawson	Sep 2015	B2
9141911	Zhao et al.	Sep 2015	B2
9152818	Hathaway et al.	Oct 2015	B1
9152820	Pauley, Jr. et al.	Oct 2015	B1
9154514	Prakash	Oct 2015	B1
9154556	Dotan et al.	Oct 2015	B1
9158655	Wadhwani et al.	Oct 2015	B2
9165036	Mehra	Oct 2015	B2
9170996	Lovric et al.	Oct 2015	B2
9172706	Krishnamurthy et al.	Oct 2015	B2
9177293	Gagnon et al.	Nov 2015	B1
9178901	Xue et al.	Nov 2015	B2
9183100	Gventer et al.	Nov 2015	B2
9189642	Perlman	Nov 2015	B2
9201572	Lyon et al.	Dec 2015	B2
9201770	Duerk	Dec 2015	B1
9202026	Reeves	Dec 2015	B1
9202085	Mawdsley et al.	Dec 2015	B2
9215076	Roth et al.	Dec 2015	B1
9215252	Smith et al.	Dec 2015	B2
9218596	Ronca et al.	Dec 2015	B2
9224009	Liu et al.	Dec 2015	B1
9230036	Davis	Jan 2016	B2
9231935	Bridge et al.	Jan 2016	B1
9232040	Barash et al.	Jan 2016	B2
9235476	Mchugh et al.	Jan 2016	B2
9240987	Barrett-Bowen et al.	Jan 2016	B2
9241259	Daniela et al.	Jan 2016	B2
9245126	Christodorescu et al.	Jan 2016	B2
9245266	Hardt	Jan 2016	B2
9253609	Hosier, Jr.	Feb 2016	B2
9258116	Moskowitz	Feb 2016	B2
9264443	Weisman	Feb 2016	B2
9274858	Milliron et al.	Mar 2016	B2
9280581	Grimes et al.	Mar 2016	B1
9286149	Sampson et al.	Mar 2016	B2
9286282	Ling, III et al.	Mar 2016	B2
9288118	Pattan	Mar 2016	B1
9288556	Kim et al.	Mar 2016	B2
9294498	Yampolskiy et al.	Mar 2016	B1
9299050	Stiffler et al.	Mar 2016	B2
9306939	Chan et al.	Apr 2016	B2
9317697	Maier et al.	Apr 2016	B2
9317715	Schuette et al.	Apr 2016	B2
9325731	Mcgeehan	Apr 2016	B2
9336184	Mital et al.	May 2016	B2
9336220	Li et al.	May 2016	B2
9336324	Lomme et al.	May 2016	B2
9336332	Davis et al.	May 2016	B2
9336400	Milman et al.	May 2016	B2
9338188	Ahn	May 2016	B1
9342706	Chawla et al.	May 2016	B2
9344297	Shah et al.	May 2016	B2
9344424	Tenenboym et al.	May 2016	B2
9344484	Ferris	May 2016	B2
9348802	Massand	May 2016	B2
9348862	Kawecki, III	May 2016	B2
9348929	Eberlein	May 2016	B2
9349016	Brisebois et al.	May 2016	B1
9350718	Sondhi et al.	May 2016	B2
9355157	Mohammed et al.	May 2016	B2
9356961	Todd et al.	May 2016	B1
9361446	Demirjian et al.	Jun 2016	B1
9369488	Woods et al.	Jun 2016	B2
9372869	Joseph	Jun 2016	B2
9374693	Olincy et al.	Jun 2016	B1
9384199	Thereska et al.	Jul 2016	B2
9384357	Patil et al.	Jul 2016	B2
9386078	Reno et al.	Jul 2016	B2
9386104	Adams et al.	Jul 2016	B2
9395959	Hatfield et al.	Jul 2016	B2
9396332	Abrams et al.	Jul 2016	B2
9401900	Levasseur et al.	Jul 2016	B2
9411967	Parecki et al.	Aug 2016	B2
9411982	Dippenaar et al.	Aug 2016	B1
9417859	Gounares et al.	Aug 2016	B2
9424021	Zamir	Aug 2016	B2
9424414	Demirjian et al.	Aug 2016	B1
9426177	Wang et al.	Aug 2016	B2
9450940	Belov et al.	Sep 2016	B2
9460136	Todd et al.	Oct 2016	B1
9460171	Marrelli et al.	Oct 2016	B2
9460307	Breslau et al.	Oct 2016	B2
9461876	Van Dusen et al.	Oct 2016	B2
9462009	Kolman et al.	Oct 2016	B1
9465702	Gventer et al.	Oct 2016	B2
9465800	Lacey	Oct 2016	B2
9473446	Vijay et al.	Oct 2016	B2
9473505	Asano et al.	Oct 2016	B1
9473535	Sartor	Oct 2016	B2
9477523	Warman et al.	Oct 2016	B1
9477660	Scott et al.	Oct 2016	B2
9477685	Leung et al.	Oct 2016	B1
9477942	Adachi et al.	Oct 2016	B2
9483659	Bao et al.	Nov 2016	B2
9489366	Scott et al.	Nov 2016	B2
9495547	Schepis et al.	Nov 2016	B1
9501523	Hyatt et al.	Nov 2016	B2
9507960	Bell et al.	Nov 2016	B2
9509674	Nasserbakht et al.	Nov 2016	B1
9509702	Grigg et al.	Nov 2016	B2
9514231	Eden	Dec 2016	B2
9516012	Chochois et al.	Dec 2016	B2
9521166	Wilson	Dec 2016	B2
9524500	Dave et al.	Dec 2016	B2
9529989	Kling et al.	Dec 2016	B2
9536108	Powell et al.	Jan 2017	B2
9537546	Cordeiro et al.	Jan 2017	B2
9542568	Francis et al.	Jan 2017	B2
9549047	Fredinburg et al.	Jan 2017	B1
9552395	Bayer et al.	Jan 2017	B2
9552470	Turgeman et al.	Jan 2017	B2
9553918	Manion et al.	Jan 2017	B1
9558497	Carvalho	Jan 2017	B2
9569752	Deering et al.	Feb 2017	B2
9571509	Satish et al.	Feb 2017	B1
9571526	Sartor	Feb 2017	B2
9571559	Raleigh et al.	Feb 2017	B2
9571991	Brizendine et al.	Feb 2017	B1
9576289	Henderson et al.	Feb 2017	B2
9578060	Brisebois et al.	Feb 2017	B1
9578173	Sanghavi et al.	Feb 2017	B2
9582681	Mishra	Feb 2017	B2
9584964	Pelkey	Feb 2017	B2
9589110	Carey et al.	Mar 2017	B2
9600181	Patel et al.	Mar 2017	B2
9602529	Jones et al.	Mar 2017	B2
9606971	Seolas et al.	Mar 2017	B2
9607041	Himmelstein	Mar 2017	B2
9619652	Slater	Apr 2017	B2
9619661	Finkelstein	Apr 2017	B1
9621357	Williams et al.	Apr 2017	B2
9621566	Gupta et al.	Apr 2017	B2
9626124	Lipinski et al.	Apr 2017	B2
9626680	Ryan et al.	Apr 2017	B1
9629064	Graves et al.	Apr 2017	B2
9642008	Wyatt et al.	May 2017	B2
9646095	Gottlieb et al.	May 2017	B1
9647949	Varki et al.	May 2017	B2
9648036	Seiver et al.	May 2017	B2
9652314	Mahiddini	May 2017	B2
9654506	Barrett	May 2017	B2
9654541	Kapczynski et al.	May 2017	B1
9665722	Nagasundaram et al.	May 2017	B2
9665733	Sills et al.	May 2017	B1
9665883	Roullier et al.	May 2017	B2
9672053	Tang et al.	Jun 2017	B2
9672355	Titonis et al.	Jun 2017	B2
9678794	Barrett et al.	Jun 2017	B1
9691090	Barday	Jun 2017	B1
9699209	Ng et al.	Jul 2017	B2
9703549	Dufresne	Jul 2017	B2
9704103	Suskind et al.	Jul 2017	B2
9705840	Pujare et al.	Jul 2017	B2
9705880	Siris	Jul 2017	B2
9721078	Cornick et al.	Aug 2017	B2
9721108	Krishnamurthy et al.	Aug 2017	B2
9727751	Oliver et al.	Aug 2017	B2
9729583	Barday	Aug 2017	B1
9734148	Bendersky et al.	Aug 2017	B2
9734255	Jiang	Aug 2017	B2
9736004	Jung et al.	Aug 2017	B2
9740985	Byron et al.	Aug 2017	B2
9740987	Dolan	Aug 2017	B2
9749408	Subramani et al.	Aug 2017	B2
9753796	Mahaffey et al.	Sep 2017	B2
9754091	Kode et al.	Sep 2017	B2
9756059	Demirjian et al.	Sep 2017	B2
9760620	Nachnani et al.	Sep 2017	B2
9760635	Bliss et al.	Sep 2017	B2
9760697	Walker	Sep 2017	B1
9760849	Vinnakota et al.	Sep 2017	B2
9762553	Ford et al.	Sep 2017	B2
9767202	Darby et al.	Sep 2017	B2
9767309	Patel et al.	Sep 2017	B1
9769124	Yan	Sep 2017	B2
9773269	Lazarus	Sep 2017	B1
9785795	Grondin et al.	Oct 2017	B2
9787671	Bogrett	Oct 2017	B1
9798749	Saner	Oct 2017	B2
9798826	Wilson et al.	Oct 2017	B2
9798896	Jakobsson	Oct 2017	B2
9800605	Baikalov et al.	Oct 2017	B2
9800606	Yumer	Oct 2017	B1
9804649	Cohen et al.	Oct 2017	B2
9804928	Davis et al.	Oct 2017	B2
9805381	Frank et al.	Oct 2017	B2
9811532	Parkison et al.	Nov 2017	B2
9817850	Dubbels et al.	Nov 2017	B2
9817978	Marsh et al.	Nov 2017	B2
9819684	Cernoch et al.	Nov 2017	B2
9825928	Lelcuk et al.	Nov 2017	B2
9830563	Paknad	Nov 2017	B2
9832633	Gerber, Jr. et al.	Nov 2017	B2
9836598	Iyer et al.	Dec 2017	B2
9838407	Oprea et al.	Dec 2017	B1
9838839	Vudali et al.	Dec 2017	B2
9841969	Seibert, Jr. et al.	Dec 2017	B2
9842042	Chhatwal et al.	Dec 2017	B2
9842349	Sawczuk et al.	Dec 2017	B2
9848005	Ardeli et al.	Dec 2017	B2
9848061	Jain et al.	Dec 2017	B1
9852150	Sharpe et al.	Dec 2017	B2
9853959	Kapczynski et al.	Dec 2017	B1
9860226	Thormaehlen	Jan 2018	B2
9864735	Lamprecht	Jan 2018	B1
9876825	Amar et al.	Jan 2018	B2
9877138	Franklin	Jan 2018	B1
9880157	Levak et al.	Jan 2018	B2
9882935	Barday	Jan 2018	B2
9887965	Kay et al.	Feb 2018	B2
9888377	Mccorkendale et al.	Feb 2018	B1
9892441	Barday	Feb 2018	B2
9892442	Barday	Feb 2018	B2
9892443	Barday	Feb 2018	B2
9892444	Barday	Feb 2018	B2
9894076	Li et al.	Feb 2018	B2
9898613	Swerdlow et al.	Feb 2018	B1
9898739	Monastyrsky et al.	Feb 2018	B2
9898769	Barday	Feb 2018	B2
9912625	Mutha et al.	Mar 2018	B2
9912677	Chien	Mar 2018	B2
9912810	Segre et al.	Mar 2018	B2
9916703	Levinson et al.	Mar 2018	B2
9922124	Rathod	Mar 2018	B2
9923927	Mcclintock et al.	Mar 2018	B1
9928379	Hoffer	Mar 2018	B1
9934406	Khan et al.	Apr 2018	B2
9934493	Castinado et al.	Apr 2018	B2
9934544	Whitfield et al.	Apr 2018	B1
9936127	Todasco	Apr 2018	B2
9942214	Burciu et al.	Apr 2018	B1
9942244	Lahoz et al.	Apr 2018	B2
9942276	Sartor	Apr 2018	B2
9946897	Lovin	Apr 2018	B2
9948652	Yu et al.	Apr 2018	B2
9948663	Wang et al.	Apr 2018	B1
9953189	Cook et al.	Apr 2018	B2
9954879	Sadaghiani et al.	Apr 2018	B1
9954883	Ahuja et al.	Apr 2018	B2
9959551	Schermerhorn et al.	May 2018	B1
9959582	Sukman et al.	May 2018	B2
9961070	Tang	May 2018	B2
9973518	Lee et al.	May 2018	B2
9973585	Ruback et al.	May 2018	B2
9977904	Khan et al.	May 2018	B2
9977920	Danielson et al.	May 2018	B2
9983936	Dornemann et al.	May 2018	B2
9984252	Pollard	May 2018	B2
9990499	Chan et al.	Jun 2018	B2
9992213	Sinnema	Jun 2018	B2
10001975	Bharthulwar	Jun 2018	B2
10002064	Muske	Jun 2018	B2
10007895	Vanasco	Jun 2018	B2
10013577	Beaumont et al.	Jul 2018	B1
10015164	Hamburg et al.	Jul 2018	B2
10019339	Von Hanxleden et al.	Jul 2018	B2
10019588	Garcia et al.	Jul 2018	B2
10019591	Beguin	Jul 2018	B1
10019741	Hesselink	Jul 2018	B2
10021143	Cabrera et al.	Jul 2018	B2
10025804	Vranyes et al.	Jul 2018	B2
10028226	Ayyagari et al.	Jul 2018	B2
10032172	Barday	Jul 2018	B2
10044761	Ducatel et al.	Aug 2018	B2
10055426	Arasan et al.	Aug 2018	B2
10055869	Borrelli et al.	Aug 2018	B2
10061847	Mohammed et al.	Aug 2018	B2
10069858	Robinson et al.	Sep 2018	B2
10069914	Smith	Sep 2018	B1
10073924	Karp et al.	Sep 2018	B2
10075437	Costigan et al.	Sep 2018	B1
10075451	Hall et al.	Sep 2018	B1
10084817	Saher et al.	Sep 2018	B2
10091214	Godlewski et al.	Oct 2018	B2
10091312	Khanwalkar et al.	Oct 2018	B1
10102533	Barday	Oct 2018	B2
10108409	Pirzadeh et al.	Oct 2018	B2
10122663	Hu et al.	Nov 2018	B2
10122760	Terrill et al.	Nov 2018	B2
10127403	Kong et al.	Nov 2018	B2
10129211	Heath	Nov 2018	B2
10140666	Wang et al.	Nov 2018	B1
10142113	Zaidi et al.	Nov 2018	B2
10152560	Potiagalov et al.	Dec 2018	B2
10158676	Barday	Dec 2018	B2
10165011	Barday	Dec 2018	B2
10169762	Ogawa	Jan 2019	B2
10176503	Barday et al.	Jan 2019	B2
10181043	Pauley, Jr. et al.	Jan 2019	B1
10181051	Barday et al.	Jan 2019	B2
10187363	Smirnoff et al.	Jan 2019	B2
10187394	Bar et al.	Jan 2019	B2
10204154	Barday et al.	Feb 2019	B2
10205994	Splaine et al.	Feb 2019	B2
10212134	Rai	Feb 2019	B2
10212175	Seul et al.	Feb 2019	B2
10223533	Dawson	Mar 2019	B2
10230571	Rangasamy et al.	Mar 2019	B2
10230711	Kohli	Mar 2019	B2
10250594	Chathoth et al.	Apr 2019	B2
10255602	Wang	Apr 2019	B2
10257127	Dotan-Cohen et al.	Apr 2019	B2
10257181	Sherif et al.	Apr 2019	B1
10268838	Yadgiri et al.	Apr 2019	B2
10275221	Thattai et al.	Apr 2019	B2
10275614	Barday et al.	Apr 2019	B2
10282370	Barday et al.	May 2019	B1
10282559	Barday et al.	May 2019	B2
10284604	Barday et al.	May 2019	B2
10289584	Chiba	May 2019	B2
10289857	Brinskelle	May 2019	B1
10289866	Barday et al.	May 2019	B2
10289867	Barday et al.	May 2019	B2
10289870	Barday et al.	May 2019	B2
10296504	Hock et al.	May 2019	B2
10304442	Rudden et al.	May 2019	B1
10310723	Rathod	Jun 2019	B2
10311042	Kumar	Jun 2019	B1
10311475	Yuasa	Jun 2019	B2
10311492	Gelfenbeyn et al.	Jun 2019	B2
10318761	Barday et al.	Jun 2019	B2
10320940	Brennan et al.	Jun 2019	B1
10324960	Skvortsov et al.	Jun 2019	B1
10326768	Verweyst et al.	Jun 2019	B2
10326798	Lambert	Jun 2019	B2
10326841	Bradley et al.	Jun 2019	B2
10331689	Sorrentino et al.	Jun 2019	B2
10331904	Sher-Jan et al.	Jun 2019	B2
10333975	Soman et al.	Jun 2019	B2
10339470	Dutta et al.	Jul 2019	B1
10346186	Kalyanpur	Jul 2019	B2
10346635	Kumar et al.	Jul 2019	B2
10346637	Barday et al.	Jul 2019	B2
10346638	Barday et al.	Jul 2019	B2
10346849	Ionescu et al.	Jul 2019	B2
10348726	Caluwaert	Jul 2019	B2
10348775	Barday	Jul 2019	B2
10353673	Barday et al.	Jul 2019	B2
10361857	Woo	Jul 2019	B2
10366241	Sartor	Jul 2019	B2
10373119	Driscoll et al.	Aug 2019	B2
10373409	White et al.	Aug 2019	B2
10375115	Mallya	Aug 2019	B2
10387559	Wendt et al.	Aug 2019	B1
10387577	Hill et al.	Aug 2019	B2
10387657	Belfiore, Jr. et al.	Aug 2019	B2
10387952	Sandhu et al.	Aug 2019	B1
10395201	Vescio	Aug 2019	B2
10402545	Gorfein et al.	Sep 2019	B2
10404729	Turgeman	Sep 2019	B2
10417401	Votaw et al.	Sep 2019	B2
10417621	Cassel et al.	Sep 2019	B2
10419476	Parekh	Sep 2019	B2
10423985	Dutta et al.	Sep 2019	B1
10425492	Comstock et al.	Sep 2019	B2
10430608	Peri et al.	Oct 2019	B2
10435350	Ito et al.	Oct 2019	B2
10437412	Barday et al.	Oct 2019	B2
10437860	Barday et al.	Oct 2019	B2
10438016	Barday et al.	Oct 2019	B2
10438273	Burns et al.	Oct 2019	B2
10440062	Barday et al.	Oct 2019	B2
10445508	Sher-Jan et al.	Oct 2019	B2
10445526	Barday et al.	Oct 2019	B2
10452864	Barday et al.	Oct 2019	B2
10452866	Barday et al.	Oct 2019	B2
10453076	Parekh et al.	Oct 2019	B2
10453092	Wang et al.	Oct 2019	B1
10454934	Parimi et al.	Oct 2019	B2
10460322	Williamson et al.	Oct 2019	B2
10481763	Bartkiewicz et al.	Nov 2019	B2
10489454	Chen	Nov 2019	B1
10503926	Barday et al.	Dec 2019	B2
10510031	Barday et al.	Dec 2019	B2
10521623	Rodriguez et al.	Dec 2019	B2
10534851	Chan et al.	Jan 2020	B1
10535081	Ferreira et al.	Jan 2020	B2
10536475	Mccorkle, Jr. et al.	Jan 2020	B1
10536478	Kirti et al.	Jan 2020	B2
10540212	Feng	Jan 2020	B2
10541938	Timmerman et al.	Jan 2020	B1
10546135	Kassoumeh et al.	Jan 2020	B1
10552462	Hart	Feb 2020	B1
10558809	Joyce et al.	Feb 2020	B1
10558821	Barday et al.	Feb 2020	B2
10564815	Soon-Shiong	Feb 2020	B2
10564935	Barday et al.	Feb 2020	B2
10564936	Barday et al.	Feb 2020	B2
10565161	Barday et al.	Feb 2020	B2
10565236	Barday et al.	Feb 2020	B1
10567439	Barday	Feb 2020	B2
10567517	Weinig et al.	Feb 2020	B2
10572684	Lafever et al.	Feb 2020	B2
10572686	Barday et al.	Feb 2020	B2
10574705	Barday et al.	Feb 2020	B2
10581825	Poschel et al.	Mar 2020	B2
10592648	Barday et al.	Mar 2020	B2
10592692	Brannon et al.	Mar 2020	B2
10606916	Brannon et al.	Mar 2020	B2
10613971	Vasikarla	Apr 2020	B1
10614365	Sathish et al.	Apr 2020	B2
10628553	Murrish et al.	Apr 2020	B1
10645102	Hamdi	May 2020	B2
10645548	Reynolds et al.	May 2020	B2
10649630	Vora et al.	May 2020	B1
10650408	Andersen et al.	May 2020	B1
10657469	Bade et al.	May 2020	B2
10657504	Zimmerman et al.	May 2020	B1
10659566	Luah et al.	May 2020	B1
10671749	Felice-Steele et al.	Jun 2020	B2
10671760	Esmailzadeh et al.	Jun 2020	B2
10678945	Barday et al.	Jun 2020	B2
10685140	Barday et al.	Jun 2020	B2
10706176	Brannon	Jul 2020	B2
10706226	Byun et al.	Jul 2020	B2
10708305	Barday et al.	Jul 2020	B2
10713387	Brannon et al.	Jul 2020	B2
10726145	Duminy et al.	Jul 2020	B2
10726153	Nerurkar et al.	Jul 2020	B2
10726158	Brannon et al.	Jul 2020	B2
10732865	Jain et al.	Aug 2020	B2
10735388	Rose et al.	Aug 2020	B2
10740487	Barday et al.	Aug 2020	B2
10747893	Kiriyama et al.	Aug 2020	B2
10747897	Cook	Aug 2020	B2
10749870	Brouillette et al.	Aug 2020	B2
10762213	Rudek et al.	Sep 2020	B2
10762230	Ancin	Sep 2020	B2
10762236	Brannon et al.	Sep 2020	B2
10769302	Barday et al.	Sep 2020	B2
10769303	Brannon et al.	Sep 2020	B2
10776510	Antonelli et al.	Sep 2020	B2
10776518	Barday et al.	Sep 2020	B2
10778792	Handy Bosma et al.	Sep 2020	B1
10783256	Brannon et al.	Sep 2020	B2
10785173	Willett et al.	Sep 2020	B2
10785299	Gupta et al.	Sep 2020	B2
10791150	Barday et al.	Sep 2020	B2
10795527	Legge et al.	Oct 2020	B1
10796020	Barday et al.	Oct 2020	B2
10796260	Brannon et al.	Oct 2020	B2
10798133	Barday et al.	Oct 2020	B2
10803196	Bodegas Martinez et al.	Oct 2020	B2
10805331	Boyer et al.	Oct 2020	B2
10831831	Greene	Nov 2020	B2
10834590	Turgeman et al.	Nov 2020	B2
10846433	Brannon et al.	Nov 2020	B2
10853356	McPherson	Dec 2020	B1
10853501	Brannon	Dec 2020	B2
10860721	Gentile	Dec 2020	B1
10860742	Joseph et al.	Dec 2020	B2
10860979	Geffen et al.	Dec 2020	B2
10878127	Brannon et al.	Dec 2020	B2
10885485	Brannon et al.	Jan 2021	B2
10891393	Currier et al.	Jan 2021	B2
10893074	Sartor	Jan 2021	B2
10896394	Brannon et al.	Jan 2021	B2
10902490	He et al.	Jan 2021	B2
10909488	Hecht et al.	Feb 2021	B2
10924514	Altman et al.	Feb 2021	B1
10929557	David	Feb 2021	B2
10949555	Rattan et al.	Mar 2021	B2
10949565	Barday et al.	Mar 2021	B2
10957326	Bhaya et al.	Mar 2021	B2
10963571	Bar Joseph et al.	Mar 2021	B2
10963572	Belfiore, Jr. et al.	Mar 2021	B2
10965547	Esposito et al.	Mar 2021	B1
10970418	Durvasula et al.	Apr 2021	B2
10972509	Barday et al.	Apr 2021	B2
10976950	Trezzo et al.	Apr 2021	B1
10983963	Venkatasubramanian et al.	Apr 2021	B1
10984458	Gutierrez	Apr 2021	B1
10997318	Barday et al.	May 2021	B2
11003748	Oliker et al.	May 2021	B2
11012475	Patnala et al.	May 2021	B2
11023528	Lee et al.	Jun 2021	B1
11037168	Lee et al.	Jun 2021	B1
11057356	Malhotra et al.	Jul 2021	B2
11057427	Wright et al.	Jul 2021	B2
11062051	Barday et al.	Jul 2021	B2
11068318	Kuesel et al.	Jul 2021	B2
11068584	Burriesci et al.	Jul 2021	B2
11068618	Brannon et al.	Jul 2021	B2
11068797	Bhide et al.	Jul 2021	B2
11068847	Boutros et al.	Jul 2021	B2
11093950	Hersh et al.	Aug 2021	B2
11138299	Brannon et al.	Oct 2021	B2
11144622	Brannon et al.	Oct 2021	B2
11144678	Dondini et al.	Oct 2021	B2
11144862	Jackson et al.	Oct 2021	B1
11195134	Brannon et al.	Dec 2021	B2
11201929	Dudmesh et al.	Dec 2021	B2
11210420	Brannon et al.	Dec 2021	B2
11238390	Brannon et al.	Feb 2022	B2
11240273	Barday et al.	Feb 2022	B2
11252159	Kannan et al.	Feb 2022	B2
11256777	Brannon et al.	Feb 2022	B2
11263262	Chen	Mar 2022	B2
11327996	Reynolds et al.	May 2022	B2
11443062	Latka	Sep 2022	B2
20020004736	Roundtree et al.	Jan 2002	A1
20020049907	Woods et al.	Apr 2002	A1
20020055932	Wheeler et al.	May 2002	A1
20020077941	Halligan et al.	Jun 2002	A1
20020103854	Okita	Aug 2002	A1
20020129216	Collins	Sep 2002	A1
20020161594	Bryan et al.	Oct 2002	A1
20020161733	Grainger	Oct 2002	A1
20030041250	Proudler	Feb 2003	A1
20030065641	Chaloux	Apr 2003	A1
20030093680	Astley et al.	May 2003	A1
20030097451	Bjorksten et al.	May 2003	A1
20030097661	Li et al.	May 2003	A1
20030115142	Brickell et al.	Jun 2003	A1
20030130893	Farmer	Jul 2003	A1
20030131001	Matsuo	Jul 2003	A1
20030131093	Aschen et al.	Jul 2003	A1
20030140150	Kemp et al.	Jul 2003	A1
20030167216	Brown et al.	Sep 2003	A1
20030212604	Cullen	Nov 2003	A1
20040002818	Kulp et al.	Jan 2004	A1
20040025053	Hayward	Feb 2004	A1
20040088235	Ziekle et al.	May 2004	A1
20040098366	Sinclair et al.	May 2004	A1
20040098493	Rees	May 2004	A1
20040111359	Hudock	Jun 2004	A1
20040128508	Wheeler et al.	Jul 2004	A1
20040186912	Harlow et al.	Sep 2004	A1
20040193907	Patanella	Sep 2004	A1
20050022198	Olapurath et al.	Jan 2005	A1
20050033616	Vavul et al.	Feb 2005	A1
20050076294	Dehamer et al.	Apr 2005	A1
20050114343	Wesinger et al.	May 2005	A1
20050144066	Cope et al.	Jun 2005	A1
20050197884	Mullen, Jr.	Sep 2005	A1
20050198177	Black	Sep 2005	A1
20050198646	Kortela	Sep 2005	A1
20050246292	Sarcanin	Nov 2005	A1
20050278538	Fowler	Dec 2005	A1
20060031078	Pizzinger et al.	Feb 2006	A1
20060035204	Lamarche et al.	Feb 2006	A1
20060041507	Novack et al.	Feb 2006	A1
20060075122	Lindskog et al.	Apr 2006	A1
20060149730	Curtis	Jul 2006	A1
20060156052	Bodnar et al.	Jul 2006	A1
20060190280	Hoebel et al.	Aug 2006	A1
20060206375	Scott et al.	Sep 2006	A1
20060224422	Cohen	Oct 2006	A1
20060253597	Mujica	Nov 2006	A1
20060259416	Johnson	Nov 2006	A1
20070011058	Dev	Jan 2007	A1
20070027715	Gropper et al.	Feb 2007	A1
20070061125	Bhatt et al.	Mar 2007	A1
20070061393	Moore	Mar 2007	A1
20070130101	Anderson et al.	Jun 2007	A1
20070130323	Landsman et al.	Jun 2007	A1
20070157311	Meier	Jul 2007	A1
20070173355	Klein	Jul 2007	A1
20070179793	Bagchi et al.	Aug 2007	A1
20070180490	Renzi et al.	Aug 2007	A1
20070192438	Goei	Aug 2007	A1
20070266420	Hawkins et al.	Nov 2007	A1
20070283171	Breslin et al.	Dec 2007	A1
20080005194	Smolen	Jan 2008	A1
20080015927	Ramirez	Jan 2008	A1
20080028065	Caso et al.	Jan 2008	A1
20080028435	Strickland et al.	Jan 2008	A1
20080047016	Spoonamore	Feb 2008	A1
20080120699	Spear	May 2008	A1
20080140696	Mathuria	Jun 2008	A1
20080189306	Hewett et al.	Aug 2008	A1
20080195436	Whyte	Aug 2008	A1
20080222271	Spires	Sep 2008	A1
20080235177	Kim et al.	Sep 2008	A1
20080270203	Holmes et al.	Oct 2008	A1
20080270351	Thomsen	Oct 2008	A1
20080270381	Thomsen	Oct 2008	A1
20080270382	Thomsen et al.	Oct 2008	A1
20080270451	Thomsen et al.	Oct 2008	A1
20080270462	Thomsen	Oct 2008	A1
20080281649	Morris	Nov 2008	A1
20080282320	Denovo et al.	Nov 2008	A1
20080288271	Faust	Nov 2008	A1
20080288299	Schultz	Nov 2008	A1
20090012896	Arnold	Jan 2009	A1
20090022301	Mudaliar	Jan 2009	A1
20090037975	Ishikawa et al.	Feb 2009	A1
20090119500	Roth et al.	May 2009	A1
20090132419	Grammer et al.	May 2009	A1
20090138276	Hayashida et al.	May 2009	A1
20090140035	Miller	Jun 2009	A1
20090144702	Atkin et al.	Jun 2009	A1
20090158249	Tomkins et al.	Jun 2009	A1
20090172705	Cheong	Jul 2009	A1
20090182818	Krywaniuk	Jul 2009	A1
20090187764	Astakhov et al.	Jul 2009	A1
20090204452	Iskandar et al.	Aug 2009	A1
20090204820	Brandenburg et al.	Aug 2009	A1
20090210347	Sarcanin	Aug 2009	A1
20090216610	Chorny	Aug 2009	A1
20090249076	Reed et al.	Oct 2009	A1
20090303237	Liu et al.	Dec 2009	A1
20100010912	Jones et al.	Jan 2010	A1
20100010968	Redlich et al.	Jan 2010	A1
20100077484	Paretti et al.	Mar 2010	A1
20100082533	Nakamura et al.	Apr 2010	A1
20100094650	Tran et al.	Apr 2010	A1
20100100398	Auker et al.	Apr 2010	A1
20100121773	Currier et al.	May 2010	A1
20100192201	Shimoni et al.	Jul 2010	A1
20100205057	Hook et al.	Aug 2010	A1
20100223349	Thorson	Sep 2010	A1
20100228786	Tibor	Sep 2010	A1
20100234987	Benschop et al.	Sep 2010	A1
20100235297	Mamorsky	Sep 2010	A1
20100235915	Memon et al.	Sep 2010	A1
20100262624	Pullikottil	Oct 2010	A1
20100268628	Pitkow et al.	Oct 2010	A1
20100268932	Bhattacharjee	Oct 2010	A1
20100281313	White et al.	Nov 2010	A1
20100287114	Bartko et al.	Nov 2010	A1
20100333012	Adachi et al.	Dec 2010	A1
20110006996	Smith et al.	Jan 2011	A1
20110010202	Neale	Jan 2011	A1
20110082794	Blechman	Apr 2011	A1
20110137696	Meyer et al.	Jun 2011	A1
20110145154	Rivers et al.	Jun 2011	A1
20110153396	Marcuvitz et al.	Jun 2011	A1
20110191664	Sheleheda et al.	Aug 2011	A1
20110208850	Sheleheda et al.	Aug 2011	A1
20110209067	Bogess et al.	Aug 2011	A1
20110231896	Tovar	Sep 2011	A1
20110238573	Varadarajan	Sep 2011	A1
20110252456	Hatakeyama	Oct 2011	A1
20110302643	Pichna et al.	Dec 2011	A1
20120041939	Amsterdamski	Feb 2012	A1
20120084151	Kozak et al.	Apr 2012	A1
20120084349	Lee et al.	Apr 2012	A1
20120102411	Sathish	Apr 2012	A1
20120102543	Kohli et al.	Apr 2012	A1
20120110674	Belani et al.	May 2012	A1
20120116923	Irving et al.	May 2012	A1
20120131438	Li	May 2012	A1
20120143650	Crowley et al.	Jun 2012	A1
20120144499	Tan et al.	Jun 2012	A1
20120191596	Kremen et al.	Jul 2012	A1
20120226621	Petran et al.	Sep 2012	A1
20120239557	Weinflash et al.	Sep 2012	A1
20120254320	Dove et al.	Oct 2012	A1
20120259752	Agee	Oct 2012	A1
20120323700	Aleksandrovich et al.	Dec 2012	A1
20120330769	Arceo	Dec 2012	A1
20120330869	Durham	Dec 2012	A1
20130004933	Bhaskaran	Jan 2013	A1
20130018954	Cheng	Jan 2013	A1
20130085801	Sharpe et al.	Apr 2013	A1
20130091156	Raiche et al.	Apr 2013	A1
20130103485	Postrel	Apr 2013	A1
20130111323	Taghaddos et al.	May 2013	A1
20130124257	Schubert	May 2013	A1
20130159351	Hamann et al.	Jun 2013	A1
20130166573	Vaitheeswaran et al.	Jun 2013	A1
20130171968	Wang	Jul 2013	A1
20130179982	Bridges et al.	Jul 2013	A1
20130179988	Bekker et al.	Jul 2013	A1
20130185806	Hatakeyama	Jul 2013	A1
20130211872	Cherry et al.	Aug 2013	A1
20130218829	Martinez	Aug 2013	A1
20130219459	Bradley	Aug 2013	A1
20130254649	Michael	Sep 2013	A1
20130254699	Bashir et al.	Sep 2013	A1
20130262328	Federgreen	Oct 2013	A1
20130282466	Hampton	Oct 2013	A1
20130290169	Bathula et al.	Oct 2013	A1
20130298071	Wine	Nov 2013	A1
20130311224	Heroux et al.	Nov 2013	A1
20130318207	Dotter	Nov 2013	A1
20130326112	Park et al.	Dec 2013	A1
20130332362	Ciurea	Dec 2013	A1
20130340086	Blom	Dec 2013	A1
20140006355	Kirihata	Jan 2014	A1
20140006616	Aad et al.	Jan 2014	A1
20140012833	Humprecht	Jan 2014	A1
20140019561	Belity et al.	Jan 2014	A1
20140032259	Lafever et al.	Jan 2014	A1
20140032265	Paprocki	Jan 2014	A1
20140040134	Peter	Feb 2014	A1
20140040161	Jason	Feb 2014	A1
20140040979	Barton et al.	Feb 2014	A1
20140041048	Goodwin et al.	Feb 2014	A1
20140047551	Nagasundaram et al.	Feb 2014	A1
20140052463	Cashman et al.	Feb 2014	A1
20140067973	Eden	Mar 2014	A1
20140074645	Ingram	Mar 2014	A1
20140089027	Brown	Mar 2014	A1
20140089039	McClellan	Mar 2014	A1
20140108173	Cooper et al.	Apr 2014	A1
20140108968	Eric	Apr 2014	A1
20140137257	Martinez et al.	May 2014	A1
20140142988	Grosso et al.	May 2014	A1
20140143011	Mudugu et al.	May 2014	A1
20140143844	Goertzen	May 2014	A1
20140164476	Thomson	Jun 2014	A1
20140188956	Subba et al.	Jul 2014	A1
20140196143	Fliderman et al.	Jul 2014	A1
20140208418	Libin	Jul 2014	A1
20140222468	Araya et al.	Aug 2014	A1
20140244309	Francois	Aug 2014	A1
20140244325	Cartwright	Aug 2014	A1
20140244375	Kim	Aug 2014	A1
20140244399	Orduna et al.	Aug 2014	A1
20140257917	Spencer et al.	Sep 2014	A1
20140258093	Gardiner et al.	Sep 2014	A1
20140278539	Edwards	Sep 2014	A1
20140278663	Samuel et al.	Sep 2014	A1
20140278730	Muhart et al.	Sep 2014	A1
20140283027	Orona et al.	Sep 2014	A1
20140283106	Stahura et al.	Sep 2014	A1
20140288971	Whibbs, III	Sep 2014	A1
20140289681	Wielgosz	Sep 2014	A1
20140289862	Gorfein et al.	Sep 2014	A1
20140317171	Fox et al.	Oct 2014	A1
20140324480	Dufel et al.	Oct 2014	A1
20140337041	Madden et al.	Nov 2014	A1
20140337466	Li et al.	Nov 2014	A1
20140344015	Puértolas-Montañés et al.	Nov 2014	A1
20150006514	Hung	Jan 2015	A1
20150012363	Grant et al.	Jan 2015	A1
20150019530	Felch	Jan 2015	A1
20150026056	Calman et al.	Jan 2015	A1
20150026260	Worthley	Jan 2015	A1
20150033112	Norwood et al.	Jan 2015	A1
20150066577	Christiansen et al.	Mar 2015	A1
20150066865	Yara et al.	Mar 2015	A1
20150088598	Acharyya et al.	Mar 2015	A1
20150089585	Novack	Mar 2015	A1
20150106264	Johnson	Apr 2015	A1
20150106867	Liang	Apr 2015	A1
20150106948	Holman et al.	Apr 2015	A1
20150106949	Holman et al.	Apr 2015	A1
20150121462	Courage et al.	Apr 2015	A1
20150143258	Carolan et al.	May 2015	A1
20150149362	Baum et al.	May 2015	A1
20150154520	Federgreen et al.	Jun 2015	A1
20150169318	Nash	Jun 2015	A1
20150172296	Fujioka	Jun 2015	A1
20150178740	Borawski et al.	Jun 2015	A1
20150199534	Francis et al.	Jul 2015	A1
20150199541	Koch et al.	Jul 2015	A1
20150199702	Singh	Jul 2015	A1
20150229664	Hawthorn et al.	Aug 2015	A1
20150235049	Cohen et al.	Aug 2015	A1
20150235050	Wouhaybi et al.	Aug 2015	A1
20150235283	Nishikawa	Aug 2015	A1
20150242778	Wilcox et al.	Aug 2015	A1
20150242858	Smith et al.	Aug 2015	A1
20150248391	Watanabe	Sep 2015	A1
20150254597	Jahagirdar	Sep 2015	A1
20150261887	Joukov	Sep 2015	A1
20150262189	Vergeer	Sep 2015	A1
20150264417	Spitz et al.	Sep 2015	A1
20150269384	Holman et al.	Sep 2015	A1
20150271167	Kalai	Sep 2015	A1
20150309813	Patel	Oct 2015	A1
20150310227	Ishida et al.	Oct 2015	A1
20150310575	Shelton	Oct 2015	A1
20150348200	Fair et al.	Dec 2015	A1
20150356362	Demos	Dec 2015	A1
20150379430	Dirac et al.	Dec 2015	A1
20160006760	Lala et al.	Jan 2016	A1
20160012465	Sharp	Jan 2016	A1
20160026394	Goto	Jan 2016	A1
20160034918	Bjelajac et al.	Feb 2016	A1
20160048700	Stransky-Heilkron	Feb 2016	A1
20160050213	Storr	Feb 2016	A1
20160063523	Nistor et al.	Mar 2016	A1
20160063567	Srivastava	Mar 2016	A1
20160071112	Unser	Mar 2016	A1
20160080405	Schler et al.	Mar 2016	A1
20160087957	Shah et al.	Mar 2016	A1
20160099963	Mahaffey et al.	Apr 2016	A1
20160103963	Mishra	Apr 2016	A1
20160125550	Joao et al.	May 2016	A1
20160125749	Delacroix et al.	May 2016	A1
20160125751	Barker et al.	May 2016	A1
20160140466	Sidebottom et al.	May 2016	A1
20160143570	Valacich et al.	May 2016	A1
20160148143	Anderson et al.	May 2016	A1
20160162269	Pogorelik et al.	Jun 2016	A1
20160164915	Cook	Jun 2016	A1
20160180386	Konig	Jun 2016	A1
20160188450	Appusamy et al.	Jun 2016	A1
20160189156	Kim et al.	Jun 2016	A1
20160196189	Miyagi et al.	Jul 2016	A1
20160225000	Glasgow	Aug 2016	A1
20160232465	Kurtz et al.	Aug 2016	A1
20160232534	Lacey et al.	Aug 2016	A1
20160234319	Griffin	Aug 2016	A1
20160253497	Christodorescu et al.	Sep 2016	A1
20160255139	Rathod	Sep 2016	A1
20160261631	Vissamsetty et al.	Sep 2016	A1
20160262163	Gonzalez Garrido et al.	Sep 2016	A1
20160292453	Patterson et al.	Oct 2016	A1
20160292621	Ciccone et al.	Oct 2016	A1
20160321582	Broudou et al.	Nov 2016	A1
20160321748	Mahatma et al.	Nov 2016	A1
20160330237	Edlabadkar	Nov 2016	A1
20160335531	Mullen et al.	Nov 2016	A1
20160342811	Whitcomb et al.	Nov 2016	A1
20160364736	Maugans, III	Dec 2016	A1
20160370954	Burningham et al.	Dec 2016	A1
20160378762	Rohter	Dec 2016	A1
20160381064	Chan et al.	Dec 2016	A1
20160381560	Margaliot	Dec 2016	A1
20170004055	Horan et al.	Jan 2017	A1
20170032395	Kaufman et al.	Feb 2017	A1
20170032408	Kumar et al.	Feb 2017	A1
20170034101	Kumar et al.	Feb 2017	A1
20170041324	Ionutescu et al.	Feb 2017	A1
20170046399	Sankaranarasimhan et al.	Feb 2017	A1
20170046753	Deupree, IV	Feb 2017	A1
20170061501	Horwich	Mar 2017	A1
20170068785	Experton et al.	Mar 2017	A1
20170070495	Cherry et al.	Mar 2017	A1
20170093917	Chandra et al.	Mar 2017	A1
20170115864	Thomas et al.	Apr 2017	A1
20170124570	Nidamanuri et al.	May 2017	A1
20170140174	Lacey et al.	May 2017	A1
20170140467	Neag et al.	May 2017	A1
20170142158	Laoutaris et al.	May 2017	A1
20170142177	Hu	May 2017	A1
20170154188	Meier et al.	Jun 2017	A1
20170161520	Lockhart, III et al.	Jun 2017	A1
20170171235	Mulchandani et al.	Jun 2017	A1
20170171325	Perez	Jun 2017	A1
20170177324	Frank et al.	Jun 2017	A1
20170180378	Tyler et al.	Jun 2017	A1
20170180505	Shaw et al.	Jun 2017	A1
20170193017	Migliori	Jul 2017	A1
20170193624	Tsai	Jul 2017	A1
20170201518	Holmqvist et al.	Jul 2017	A1
20170206707	Guay et al.	Jul 2017	A1
20170208084	Steelman et al.	Jul 2017	A1
20170220685	Yan et al.	Aug 2017	A1
20170220964	Datta Ray	Aug 2017	A1
20170249710	Guillama et al.	Aug 2017	A1
20170269791	Meyerzon et al.	Sep 2017	A1
20170270318	Ritchie	Sep 2017	A1
20170278004	Mcelhinney et al.	Sep 2017	A1
20170278117	Wallace et al.	Sep 2017	A1
20170286719	Krishnamurthy et al.	Oct 2017	A1
20170287031	Barday	Oct 2017	A1
20170289199	Barday	Oct 2017	A1
20170308875	O'Regan et al.	Oct 2017	A1
20170316400	Venkatakrishnan et al.	Nov 2017	A1
20170330197	DiMaggio et al.	Nov 2017	A1
20170353404	Hodge	Dec 2017	A1
20180032757	Michael	Feb 2018	A1
20180039975	Hefetz	Feb 2018	A1
20180041498	Kikuchi	Feb 2018	A1
20180046503	Feng	Feb 2018	A1
20180046753	Shelton	Feb 2018	A1
20180046939	Meron et al.	Feb 2018	A1
20180063174	Grill et al.	Mar 2018	A1
20180063190	Wright et al.	Mar 2018	A1
20180082368	Weinflash et al.	Mar 2018	A1
20180083843	Sambandam	Mar 2018	A1
20180091476	Jakobsson et al.	Mar 2018	A1
20180131574	Jacobs et al.	May 2018	A1
20180131658	Bhagwan et al.	May 2018	A1
20180165637	Romero et al.	Jun 2018	A1
20180198614	Neumann	Jul 2018	A1
20180204281	Painter et al.	Jul 2018	A1
20180219917	Chiang	Aug 2018	A1
20180239500	Allen et al.	Aug 2018	A1
20180248914	Sartor	Aug 2018	A1
20180285887	Crispen	Oct 2018	A1
20180301222	Dew, Sr. et al.	Oct 2018	A1
20180307859	Lafever et al.	Oct 2018	A1
20180336509	Guttmann	Nov 2018	A1
20180349583	Turgeman et al.	Dec 2018	A1
20180351888	Howard	Dec 2018	A1
20180352003	Winn et al.	Dec 2018	A1
20180357243	Yoon	Dec 2018	A1
20180365040	Lissack	Dec 2018	A1
20180365720	Goldman et al.	Dec 2018	A1
20180374030	Barday et al.	Dec 2018	A1
20180375814	Hart	Dec 2018	A1
20190005210	Wiederspohn et al.	Jan 2019	A1
20190012211	Selvaraj	Jan 2019	A1
20190012672	Francesco	Jan 2019	A1
20190019184	Lacey et al.	Jan 2019	A1
20190050547	Welsh et al.	Feb 2019	A1
20190087570	Sloane	Mar 2019	A1
20190096020	Barday et al.	Mar 2019	A1
20190108353	Sadeh et al.	Apr 2019	A1
20190130132	Barbas et al.	May 2019	A1
20190138496	Yamaguchi	May 2019	A1
20190139087	Dabbs et al.	May 2019	A1
20190148003	Van Hoe	May 2019	A1
20190156053	Vogel et al.	May 2019	A1
20190156058	Van Dyne et al.	May 2019	A1
20190171801	Barday et al.	Jun 2019	A1
20190179652	Hesener et al.	Jun 2019	A1
20190180051	Barday et al.	Jun 2019	A1
20190182294	Rieke et al.	Jun 2019	A1
20190188402	Wang et al.	Jun 2019	A1
20190266200	Francolla	Aug 2019	A1
20190266201	Barday et al.	Aug 2019	A1
20190266350	Barday et al.	Aug 2019	A1
20190268343	Barday et al.	Aug 2019	A1
20190268344	Barday et al.	Aug 2019	A1
20190272492	Elledge et al.	Sep 2019	A1
20190294818	Barday et al.	Sep 2019	A1
20190332802	Barday et al.	Oct 2019	A1
20190332807	Lafever et al.	Oct 2019	A1
20190333118	Crimmins et al.	Oct 2019	A1
20190354709	Brinskelle	Nov 2019	A1
20190356684	Sinha et al.	Nov 2019	A1
20190362169	Lin et al.	Nov 2019	A1
20190362268	Fogarty et al.	Nov 2019	A1
20190377901	Balzer et al.	Dec 2019	A1
20190378073	Lopez et al.	Dec 2019	A1
20190384934	Kim	Dec 2019	A1
20190392162	Stern et al.	Dec 2019	A1
20190392170	Barday et al.	Dec 2019	A1
20190392171	Barday et al.	Dec 2019	A1
20200020454	Mcgarvey et al.	Jan 2020	A1
20200050966	Enuka et al.	Feb 2020	A1
20200051117	Mitchell	Feb 2020	A1
20200057781	Mccormick	Feb 2020	A1
20200074471	Adjaoute	Mar 2020	A1
20200081865	Farrar et al.	Mar 2020	A1
20200082270	Gu et al.	Mar 2020	A1
20200090197	Rodriguez et al.	Mar 2020	A1
20200092179	Chieu et al.	Mar 2020	A1
20200110589	Bequet et al.	Apr 2020	A1
20200110904	Shinde et al.	Apr 2020	A1
20200117737	Gopalakrishnan et al.	Apr 2020	A1
20200137097	Zimmermann et al.	Apr 2020	A1
20200143301	Bowers	May 2020	A1
20200143797	Manoharan et al.	May 2020	A1
20200159952	Dain et al.	May 2020	A1
20200159955	Barlik et al.	May 2020	A1
20200167653	Manjunath et al.	May 2020	A1
20200175424	Kursun	Jun 2020	A1
20200183655	Barday et al.	Jun 2020	A1
20200186355	Davies	Jun 2020	A1
20200193018	Van Dyke	Jun 2020	A1
20200193022	Lunsford et al.	Jun 2020	A1
20200210558	Barday et al.	Jul 2020	A1
20200210620	Haletky	Jul 2020	A1
20200211002	Steinberg	Jul 2020	A1
20200220901	Barday et al.	Jul 2020	A1
20200226156	Borra et al.	Jul 2020	A1
20200226196	Brannon et al.	Jul 2020	A1
20200242259	Chirravuri et al.	Jul 2020	A1
20200242719	Lee	Jul 2020	A1
20200250342	Miller et al.	Aug 2020	A1
20200252413	Buzbee et al.	Aug 2020	A1
20200252817	Brouillette et al.	Aug 2020	A1
20200272764	Brannon et al.	Aug 2020	A1
20200285755	Kassoumeh et al.	Sep 2020	A1
20200293679	Handy Bosma et al.	Sep 2020	A1
20200296171	Mocanu et al.	Sep 2020	A1
20200302089	Barday et al.	Sep 2020	A1
20200310917	Tkachev et al.	Oct 2020	A1
20200311310	Barday et al.	Oct 2020	A1
20200344243	Brannon et al.	Oct 2020	A1
20200356695	Brannon et al.	Nov 2020	A1
20200364369	Brannon et al.	Nov 2020	A1
20200372178	Barday et al.	Nov 2020	A1
20200394327	Childress et al.	Dec 2020	A1
20200401380	Jacobs et al.	Dec 2020	A1
20200401962	Gottemukkala et al.	Dec 2020	A1
20200410117	Barday et al.	Dec 2020	A1
20200410131	Barday et al.	Dec 2020	A1
20200410132	Brannon et al.	Dec 2020	A1
20210012341	Garg et al.	Jan 2021	A1
20210056569	Silberman et al.	Feb 2021	A1
20210081567	Park et al.	Mar 2021	A1
20210099449	Frederick et al.	Apr 2021	A1
20210110047	Victor	Apr 2021	A1
20210125089	Nickl et al.	Apr 2021	A1
20210136065	Liokumovich et al.	May 2021	A1
20210152496	Kim et al.	May 2021	A1
20210182940	Gupta	Jun 2021	A1
20210233157	Crutchfield, Jr.	Jul 2021	A1
20210243595	Buck et al.	Aug 2021	A1
20210248247	Poothokaran et al.	Aug 2021	A1
20210256163	Fleming et al.	Aug 2021	A1
20210279360	Gimenez Palop et al.	Sep 2021	A1
20210297441	Olalere	Sep 2021	A1
20210303828	Lafreniere et al.	Sep 2021	A1
20210312061	Schroeder et al.	Oct 2021	A1
20210326786	Sun et al.	Oct 2021	A1
20210328969	Gaddam et al.	Oct 2021	A1
20210382949	Yastrebenetsky et al.	Dec 2021	A1
20210397735	Samatov et al.	Dec 2021	A1
20210400018	Vettaikaran et al.	Dec 2021	A1
20210406712	Bhide et al.	Dec 2021	A1
20220171759	Jindal	Jun 2022	A1

Foreign Referenced Citations (15)

Number	Date	Country
111496802	Aug 2020	CN
112115859	Dec 2020	CN
1394698	Mar 2004	EP
2031540	Mar 2009	EP
20130062500	Jun 2013	KR
2001033430	May 2001	WO
20020067158	Aug 2002	WO
20030050773	Jun 2003	WO
2005008411	Jan 2005	WO
2007002412	Jan 2007	WO
2008134203	Nov 2008	WO
2012174659	Dec 2012	WO
2015116905	Aug 2015	WO
2020146028	Jul 2020	WO
2022006421	Jan 2022	WO

Non-Patent Literature Citations (892)

Entry
International Search Report, dated Feb. 11, 2022, from corresponding International Application No. PCT/US2021/053518.
Jiahao Chen et al. “Fairness Under Unawareness: Assessing Disparity when Protected Class is Unobserved,” arxiv.org, Cornell University Library, 201 Olin Library Cornell University, Ithaca, NY 14853, Nov. 27, 2018 (Nov. 27, 2018), Section 2, Figure 2. (Year 2018).
Notice of Allowance, dated Feb. 1, 2022, from corresponding U.S. Appl. No. 17/346,509.
Notice of Allowance, dated Feb. 14, 2022, from corresponding U.S. Appl. No. 16/623,157.
Notice of Allowance, dated Feb. 22, 2022, from corresponding U.S. Appl. No. 17/535,065.
Notice of Allowance, dated Feb. 4, 2022, from corresponding U.S. Appl. No. 17/520,272.
Notice of Allowance, dated Feb. 8, 2022, from corresponding U.S. Appl. No. 17/342,153.
Notice of Allowance, dated Jan. 31, 2022, from corresponding U.S. Appl. No. 17/472,948.
Office Action, dated Feb. 16, 2022, from corresponding U.S. Appl. No. 16/872,031.
Office Action, dated Feb. 9, 2022, from corresponding U.S. Appl. No. 17/543,546.
Office Action, dated Jan. 31, 2022, from corresponding U.S. Appl. No. 17/493,290.
Sarkar et al., “Towards Enforcement of the EU GDPR: Enabling Data Erasure,” 2018 IEEE Confs on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology, Congress on Cybermatics, 2018, pp. 222-229, IEEE (Year: 2018).
Written Opinion of the International Searching Authority, dated Feb. 11, 2022, from corresponding International Application No. PCT/US2021/053518.
Choi et al., “A Survey on Ontology Mapping,” ACM, pp. 34-41 (Year: 2006).
Cui et al., “Domain Ontology Management Environment,” IEEE, pp. 1-9 (Year 2000).
Falbo et al., “An Ontological Approach to Domain Engineering,” ACM, pp. 351-358 (Year 2002).
Final Office Action, dated Jun. 10, 2022, from corresponding U.S. Appl. No. 17/161,159.
Final Office Action, dated Jun. 9, 2022, from corresponding U.S. Appl. No. 17/494,220.
International Search Report, dated Jun. 1, 2022, from corresponding International Application No. PCT/US2022/016930.
International Search Report, dated Jun. 22, 2022, from corresponding International Application No. PCT/US2022/019358.
International Search Report, dated Jun. 24, 2022, from corresponding International Application No. PCT/US2022/019882.
Nemec et al., “Assessment of Query Execution Performance Using Selected Business Intelligence Tools and Experimental Agile Oriented Data Modeling Approach,” Sep. 16, 2015, IEEE, pp. 1327-1333. (Year 2015).
Notice of Allowance, dated Jun. 14, 2022, from corresponding U.S. Appl. No. 17/679,734.
Notice of Allowance, dated Jun. 16, 2022, from corresponding U.S. Appl. No. 17/119,080.
Notice of Allowance, dated Jun. 2, 2022, from corresponding U.S. Appl. No. 17/493,290.
Notice of Allowance, dated Jun. 23, 2022, from corresponding U.S. Appl. No. 17/588,645.
Notice of Allowance, dated Jun. 8, 2022, from corresponding U.S. Appl. No. 17/722,551.
Notice of Allowance, dated May 27, 2022, from corresponding U.S. Appl. No. 17/543,546.
Notice of Allowance, dated May 31, 2022, from corresponding U.S. Appl. No. 17/679,715.
Office Action, dated Jun. 1, 2022, from corresponding U.S. Appl. No. 17/306,496.
Office Action, dated Jun. 14, 2022, from corresponding U.S. Appl. No. 17/346,586.
Office Action, dated Jun. 16, 2022, from corresponding U.S. Appl. No. 17/689,683.
Ozdikis et al., “Tool Support for Transformation from an OWL Ontology to an HLA Object Model,” ACM, pp. 1-6 (Year: 2010).
Vukovic et al., “Managing Enterprise IT Systems Using Online Communities,” Jul. 9, 2011, IEEE, pp. 552-559. (Year: 2011).
Wong et al., “Ontology Mapping for the Interoperability Problem in Network Management,” IEEE, pp. 2058-2068 (Year: 2005).
Written Opinion of the International Searching Authority, dated Jun. 1, 2022, from corresponding International Application No. PCT/US2072/016930.
Written Opinion of the International Searching Authority, dated Jun. 22, 2022, from corresponding International Application No. PCT/US2022/019358.
Written Opinion of the International Searching Authority, dated Jun. 24, 2022, from corresponding International Application No. PCT/US2022/019882.
Bansal et al., “Integrating Big Data: A Semantic Extract-Transform-Load Framework,” IEEE, pp. 42-50 (Year: 2015).
Bao et al., “Performance Modeling and Workflow Scheduling of Microservice-Based Applications in Clouds,” IEEE Transactions on Parallel and Distributed Systems, vol. 30, No. 9, Sep. 2019, pp. 2101-2116 (Year: 2019).
Bindschaedler et al., “Privacy Through Fake Yet Semantically Real Traces,” arxiv.org, Cornell University Library, 201 Olin Library Cornell University Ithaca, NY 14853, May 27, 2015 (Year: 2015).
Castro et al., “Creating Lightweight Ontologies for Dataset Description,” IEEE, pp. 1-4 (Year: 2014).
Ex Parte Quayle Action, dated May 10, 2022, from corresponding U.S. Appl. No. 17/668,714.
Final Office Action, dated May 12, 2022, from corresponding U.S. Appl. No. 17/499,624.
Final Office Action, dated May 16, 2022, from corresponding U.S. Appl. No. 17/480,377.
Final Office Action, dated May 2, 2022, from corresponding U.S. Appl. No. 17/499,595.
Final Office Action, dated May 24, 2022, from corresponding U.S. Appl. No. 17/499,582.
International Search Report, dated May 12, 2022, from corresponding International Application No. PCT/US2022/015929.
International Search Report, dated May 17, 2022, from corresponding International Application No. PCT/US2022/015241.
International Search Report, dated May 19, 2022, from corresponding International Application No. PCT/US2022/015637.
Lasierra et al., “Data Management in Home Scenarios Using an Autonomic Ontology-Based Approach,” IEEE, pp. 94-99 (Year: 2012).
Lenzerini et al., “Ontology-based Data Management,” ACM, pp. 5-6 (Year: 2011).
Niu, et al., “Achieving Data Truthfulness and Privacy Preservation in Data Markets”, IEEE Transactions on Knowledge and Data Engineering, IEEE Service Centre, Los Alamitos, CA, US, vol. 31, No. 1, Jan. 1, 2019, pp. 105-119 (Year 2019).
Notice of Allowance, dated May 11, 2022, from corresponding U.S. Appl. No. 17/395,759.
Notice of Allowance, dated May 18, 2022, from corresponding U.S. Appl. No. 17/670,354.
Notice of Allowance, dated May 25, 2022, from corresponding U.S. Appl. No. 16/872,031.
Notice of Allowance, dated May 6, 2022, from corresponding U.S. Appl. No. 17/666,886.
Office Action, dated May 12, 2022, from corresponding U.S. Appl. No. 17/509,974.
Office Action, dated May 16, 2022, from corresponding U.S. Appl. No. 17/679,750.
Office Action, dated May 24, 2022, from corresponding U.S. Appl. No. 17/674,187.
Office Action, dated May 9, 2022, from corresponding U.S. Appl. No. 16/840,943.
Preuveneers et al, “Access Control with Delegated Authorization Policy Evaluation for Data-Driven Microservice Workflows,” Future Internet 2017, MDPI, pp. 1-21 (Year: 2017).
Thomas et al., “MooM—A Prototype Framework for Management of Ontology Mappings,” IEEE, pp. 548-555 (Year: 2011).
Written Opinion of the International Searching Authority, dated May 12, 2022, from corresponding International Application No. PCT/US2022/015929.
Written Opinion of the International Searching Authority, dated May 17, 2022, from corresponding International Application No. PCT/US2022/015241.
Written Opinion of the International Searching Authority, dated May 19, 2022, from corresponding International Application No. PCT/US2022/015637.
Final Office Action, dated Apr. 1, 2022, from corresponding U.S. Appl. No. 17/370,650.
Final Office Action, dated Apr. 5, 2022, from corresponding U.S. Appl. No. 17/013,756.
International Search Report, dated Apr. 12, 2022, from corresponding International Application No. PCT/US2022/016735.
International Search Report, dated Feb. 14, 2022, from corresponding International Application No. PCT/US2021/058274.
International Search Report, dated Mar. 18, 2022, from corresponding International Application No. PCT/US2022/013733.
Lewis, James et al., “Microservices,” Mar. 25, 2014 (Mar. 25, 2014),XP055907494, Retrieved from the Internet: https://martinfowler.com/articles/micr oservices.html [retrieved on Mar. 31, 2022].
Notice of Allowance, dated Apr. 4, 2022, from corresponding U.S. Appl. No. 17/493,332.
Notice of Allowance, dated Apr. 4, 2022, from corresponding U.S. Appl. No. 17/572,298.
Notice of Allowance, dated Mar. 31, 2022, from corresponding U.S. Appl. No. 17/476,209.
Office Action, dated Apr. 8, 2022, from corresponding U.S. Appl. No. 16/938,509.
Written Opinion of the International Searching Authority, dated Apr. 12, 2022, from corresponding International Application No. PCT/US2022/016735.
Written Opinion of the International Searching Authority, dated Feb. 14, 2022, from corresponding International Application No. PCT/US2021/058274.
Written Opinion of the International Searching Authority, dated Mar. 18, 2022, from corresponding International Application No. PCT/US2022/013733.
Ali et al, “Age Estimation from Facial Images Using Biometric Ratios and Wrinkle Analysis,” IEEE, 2015, pp. 1-5 (Year: 2015).
Chang et al., “A Ranking Approach for Human Age Estimation Based on Face Images,” IEEE, 2010, pp. 3396-3399 (Year: 2010).
Edinger et al., “Age and Gender Estimation of Unfiltered Faces,” IEEE, 2014, pp. 2170-2179 (Year: 2014).
Final Office Action, dated Apr. 25, 2022, from corresponding U.S. Appl. No. 17/149,421.
Han et al., “Demographic Estimation from Face Images: Human vs. Machine Performance,” IEEE, 2015, pp. 1148-1161 (Year: 2015).
Huettner, “Digital Risk Management: Protecting Your Privacy, Improving Security, and Preparing for Emergencies,” IEEE, pp. 136-138 (Year: 2006).
Jayasinghe et al., “Matching Facial Images Using Age Related Morphing Changes,” ISSRI, 2009, pp. 2901-2907 (Year: 2009).
Khan et al., “Wrinkles Energy Based Age Estimation Using Discrete Cosine Transform,” IEEE, 2015, pp. 1-4 (Year: 2015).
Kristian et al., “Human Facial Age Classification Using Active Shape Module, Geometrical Feature, and Support Vendor Machine on Early Growth Stage,” ISICO, 2015, pp. 1-8 (Year: 2015).
Liu et al., “Overview on Ontology Mapping and Approach,” IEEE, pp. 592-595 (Year: 2011).
Milic et al., “Comparative Analysis of Metadata Models on e-Government Open Data Platforms,” IEEE, pp. 119-130 (Year: 2021).
Notice of Allowance, dated Apr. 12, 2022, from corresponding U.S. Appl. No. 17/479,807.
Notice of Allowance, dated Apr. 14, 2022, from corresponding U.S. Appl. No. 17/572,276.
Notice of Allowance, dated Apr. 20, 2022, from corresponding U.S. Appl. No. 17/573,808.
Notice of Allowance, dated Apr. 27, 2022, from corresponding U.S. Appl. No. 17/573,999.
Notice of Allowance, dated Apr. 28, 2022, from corresponding U.S. Appl. No. 17/670,352.
Office Action, dated Apr. 12, 2022, from corresponding U.S. Appl. No. 17/670,341.
Office Action, dated Apr. 18, 2022, from corresponding U.S. Appl. No. 17/670,349.
Office Action, dated Apr. 25, 2022, from corresponding U.S. Appl. No. 17/588,645.
Office Action, dated Apr. 26, 2022, from corresponding U.S. Appl. No. 17/151,334.
Qu et al., “Metadata Type System: Integrate Presentation, Data Models and Extraction to Enable Exploratory Browsing Interfaces,” ACM, pp. 107-116 (Year: 2014).
Shulz et al., “Generative Data Models for Validation and Evaluation of Visualization Techniques,” ACM, pp. 1-13 (Year: 2016).
Final Office Action, dated Apr. 28, 2022, from corresponding U.S. Appl. No. 16/925,550.
Notice of Allowance, dated Apr. 28, 2022, from corresponding U.S. Appl. No. 17/592,922.
Notice of Allowance, dated Apr. 29, 2022, from corresponding U.S. Appl. No. 17/387,421.
Czeskis et al., “Lightweight Server Support for Browser-based CSRF Protection,” Proceedings of the 22nd International Conference on World Wide Web, 2013, pp. 273-284 (Year: 2013).
Final Office Action, dated Feb. 25, 2022, from corresponding U.S. Appl. No. 17/346,586.
Final Office Action, dated Mar. 21, 2022, from corresponding U.S. Appl. No. 17/373,444.
Final Office Action, dated Mar. 22, 2022, from corresponding U.S. Appl. No. 17/380,485.
Matte et al., “Do Cookie Banners Respect my Choice?: Measuring Legal Compliance of Banners from IAB Europe's Transparency and Consent Framework,” 2020 IEEE Symposium on Security and Privacy (SP), 2020, pp. 791-809 (Year: 2020).
Notice of Allowance, dated Feb. 24, 2022, from corresponding U.S. Appl. No. 17/234,205.
Notice of Allowance, dated Feb. 24, 2022, from corresponding U.S. Appl. No. 17/549,170.
Notice of Allowance, dated Mar. 16, 2022, from corresponding U.S. Appl. No. 17/486,350.
Notice of Allowance, dated Mar. 2, 2022, from corresponding U.S. Appl. No. 16/872,130.
Notice of Allowance, dated Mar. 2, 2022, from corresponding U.S. Appl. No. 17/535,098.
Notice of Allowance, dated Mar. 21, 2022, from corresponding U.S. Appl. No. 17/366,754.
Notice of Allowance, dated Mar. 22, 2022, from corresponding U.S. Appl. No. 17/475,244.
Notice of Allowance, dated Mar. 22, 2022, from corresponding U.S. Appl. No. 17/504,102.
Notice of Allowance, dated Mar. 28, 2022, from corresponding U.S. Appl. No. 17/499,609.
Notice of Allowance, dated Mar. 4, 2022, from corresponding U.S. Appl. No. 17/409,999.
Office Action, dated Mar. 1, 2022, from corresponding U.S. Appl. No. 17/119,080.
Office Action, dated Mar. 2, 2022, from corresponding U.S. Appl. No. 17/020,275.
Office Action, dated Mar. 2, 2022, from corresponding U.S. Appl. No. 17/161,159.
Office Action, dated Mar. 2, 2022, from corresponding U.S. Appl. No. 17/200,698.
Office Action, dated Mar. 21, 2022, from corresponding U.S. Appl. No. 17/571,871.
Office Action, dated Mar. 22, 2022, from corresponding U.S. Appl. No. 17/187,329.
Sanchez-Rola et al., “Can I Opt Out Yet?: GDPR and the Global Illusion of Cookie Control,” Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security, 2019, pp. 340-351 (Year: 2019).
Abdullah et al., “The Mapping Process of Unstructured Data to the Structured Data”, ACM, pp. 151-155 (Year: 2013).
Acar, Gunes, et al., The Web Never Forgets, Computerand Communications Security, ACM, Nov. 3, 2014, pp. 674-689.
Aghasian, Erfan, et al., Scoring Users' Privacy Disclosure Across Multiple Online Social Networks,IEEE Access, Multidisciplinary Rapid Review Open Access Journal, Jul. 31, 2017, vol. 5, 2017.
Agosti et al., “Access and Exchange of Hierarchically Structured Resources on the Web with the NESTOR Framework”, IEEE, pp. 659-662 (Year: 2009).
Agrawal et al, “Securing Electronic Health Records Without Impeding the Flow of Information,” International Journal of Medical Informatics 76, 2007, pp. 471-479 (Year: 2007).
Ahmad et al., “Task-Oriented Access Model for Secure Data Sharing Over Cloud,” ACM, pp. 1-7 (Year: 2015).
Ahmad, et al., “Performance of Resource Management Algorithms for Processable Bulk Data Transfer Tasks in Grid Environments,” ACM, pp. 177-188 (Year: 2008).
Alaa et al., “Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes,” Apr. 27, 2017, IEEE, vol. 65, issue 1, pp. 207-217 (Year: 2017).
Aman et al., “Detecting Data Tampering Attacks in Synchrophasor Networks using Time Hopping,” IEEE, pp. 1-6 (Year: 2016).
Amar et al., “Privacy-Aware Infrastructure for Managing Personal Data,” ACM, pp. 571-572, Aug. 22-26, 2016 (Year: 2016).
Antunes et al., “Preserving Digital Data in Heterogeneous Environments”, ACM, pp. 345-348, 2009 (Year: 2009).
Ardagna, et al., “A Privacy-Aware Access Control System,” Journal of Computer Security, 16:4, pp. 369-397 (Year: 2008).
Avepoint, Automating Privacy Impact Assessments, AvePoint, Inc.
Avepoint, AvePoint Privacy Impact Assessment 1: User Guide, Cumulative Update 2, Revision E, Feb. 2015, AvePoint, Inc.
Avepoint, Installing and Configuring the APIA System, International Association of Privacy Professionals, AvePoint, Inc.
Ball, et al., “Aspects of the Computer-Based Patient Record,” Computers in Healthcare, Springer-Verlag New York Inc., pp. 1-23 (Year: 1992).
Banerjee et al., “Link Before You Share: Managing Privacy Policies through Blockchain,” IEEE, pp. 4438-4447 (Year: 2017).
Bang et al., “Building an Effective and Efficient Continuous Web Application Security Program,” 2016 International Conference on Cyber Security Situational Awareness, Data Analytics and Assessment (CyberSA), London, 2016, pp. 1-4 (Year: 2016).
Barker, “Personalizing Access Control by Generalizing Access Control,” ACM, pp. 149-158 (Year: 2010).
Barr, “Amazon Rekognition Update—Estimated Age Range for Faces,” AWS News Blog, Feb. 10, 2017, pp. 1-5 (Year: 2017).
Bayardo et al, “Technological Solutions for Protecting Privacy,” Computer 36.9 (2003), pp. 115-118, (Year: 2003).
Berezovskiy et al, “A framework for dynamic data source identification and orchestration on the Web”, ACM, pp. 1-8 (Year: 2010).
Bertino et al., “On Specifying Security Policies for Web Documents with an XML-based Language,” ACM, pp. 57-65 (Year: 2001).
Bertino et al., “Towards Mechanisms for Detection and Prevention of Data Exfiltration by Insiders,” Mar. 22, 2011, ACM, pp. 10-19 (Year: 2011).
Bhargav-Spantzel et al., Receipt Management—Transaction History based Trust Establishment, 2007, ACM, p. 82-91.
Bhuvaneswaran et al., “Redundant Parallel Data Transfer Schemes for the Grid Environment”, ACM, pp. 18 (Year: 2006).
Bieker, et al, “Privacy-Preserving Authentication Solutions—Best Practices for Implementation and EU Regulatory Perspectives,” Oct. 29, 2014, IEEE, pp. 1-10 (Year: 2014).
Bin, et al., “Research on Data Mining Models for the Internet of Things,” IEEE, pp. 1-6 (Year: 2010).
Binns, et al., “Data Havens, or Privacy Sans Frontiéres? A Study of International Personal Data Transfers,” ACM, pp. 273-274 (Year: 2002).
Bjorn Greif, “Cookie Pop-up Blocker: Cliqz Automatically Denies Consent Requests,” Cliqz.com, pp. 1-9, Aug. 11, 2019 (Year: 2019).
Borgida, “Description Logics in Data Management,” IEEE Transactions on Knowledge and Data Engineering, vol. 7, No. 5, Oct. 1995, pp. 671-682 (Year: 1995).
Brandt et al, “Efficient Metadata Management in Large Distributed Storage Systems,” IEEE, pp. 1-9 (Year: 2003).
Bujlow et al., “Web Tracking: Mechanisms, Implications, and Defenses,” Proceedings of the IEEE, Aug. 1, 2017, vol. 5, No. 8, pp. 1476-1510 (Year: 2017).
Byun, Ji-Won, Elisa Bertino, and Ninghui Li. “Purpose based access control of complex data for privacy protection.” Proceedings of the tenth ACM symposium on Access control models and technologies. ACM, 2005. (Year: 2005).
Carminati et al., “Enforcing Access Control Over Data Streams,” ACM, pp. 21-30 (Year: 2007).
Carpineto et al., “Automatic Assessment of Website Compliance to the European Cookie Law with CooLCheck,” Proceedings of the 2016 ACM on Workshop on Privacy in the Electronic Society, 2016, pp. 135-138 (Year: 2016).
Cerpzone, “How to Access Data on Data Archival Storage and Recovery System”, https://www.saj.usace.army.mil/Portals/44/docs/Environmental/Lake%20O%20Watershed/15February2017/How%20To%20Access%20Model%20Data%20on%20DASR.pdf?ver=2017-02-16-095535-633, Feb. 16, 2017.
Cha et al., “A Data-Driven Security Risk Assessment Scheme for Personal Data Protection,” IEEE, pp. 50510-50517 (Year: 2018).
Cha, et al., “Process-Oriented Approach for Validating Asset Value for Evaluating Information Security Risk,” IEEE, Aug. 31, 2009, pp. 379-385 (Year: 2009).
Chapados et al., “Scoring Models for Insurance Risk Sharing Pool Optimization,” 2008, IEEE, pp. 97-105 (Year: 2008).
Cheng, Raymond, et al., “Radiatus: A Shared-Nothing Server-Side Web Architecture,” Proceedings of the Seventh ACM Symposium on Cloud Computing, Oct. 5, 2016, pp. 237-250 (Year: 2016).
Choi et al., “Retrieval Effectiveness of Table of Contents and Subject Headings,” ACM, pp. 103-104 (Year: 2007).
Chowdhury et al., “A System Architecture for Subject-Centric Data Sharing”, ACM, pp. 1-10 (Year: 2018).
Chowdhury et al., “Managing Data Transfers in Computer Clusters with Orchestra,” ACM, pp. 98-109 (Year: 2011).
Civili et al., “Mastro Studio: Managing Ontology-Based Data Access Applications,” ACM, pp. 1314-1317, Aug. 26-30, 2013 (Year: 2013).
Decision Regarding Institution of Post-Grant Review in Case PGR2018-00056 for U.S. Pat. No. 9,691,090 B1, Oct. 11, 2018.
Degeling et al., “We Value Your Privacy . . . Now Take Some Cookies: Measuring the GDPRs Impact on Web Privacy,” arxiv.org, Cornell University Library, 201 Olin Library Cornell University, Ithaca, NY 14853, Aug. 15, 2018, pp. 1-15 (Year: 2019).
Dimou et al., “Machine-Interpretable Dataset and Service Descriptions for Heterogeneous Data Access and Retrieval”, ACM, pp. 145-152 (Year: 2015).
Dokholyan et al., “Regulatory and Ethical Considerations for Linking Clinical and Administrative Databases,” American Heart Journal 157.6 (2009), pp. 971-982 (Year: 2009).
Dunkel et al., “Data Organization and Access for Efficient Data Mining”, IEEE, pp. 522-529 (Year: 1999).
Office Action, dated Feb. 3, 2021, from corresponding U.S. Appl. No. 17/013,757.
Office Action, dated Feb. 5, 2020, from corresponding U.S. Appl. No. 16/586,202.
Office Action, dated Feb. 6, 2020, from corresponding U.S. Appl. No. 16/707,762.
Office Action, dated Feb. 8, 2021, from corresponding U.S. Appl. No. 17/139,650.
Office Action, dated Feb. 9, 2021, from corresponding U.S. Appl. No. 16/808,493.
Office Action, dated Jan. 14, 2022, from corresponding U.S. Appl. No. 17/499,595.
Office Action, dated Jan. 18, 2019, from corresponding U.S. Appl. No. 16/055,984.
Office Action, dated Jan. 21, 2022, from corresponding U.S. Appl. No. 17/499,624.
Office Action, dated Jan. 22, 2021, from corresponding U.S. Appl. No. 17/099,270.
Office Action, dated Jan. 24, 2020, from corresponding U.S. Appl. No. 16/505,426.
Office Action, dated Jan. 24, 2020, from corresponding U.S. Appl. No. 16/700,049.
Office Action, dated Jan. 25, 2022, from corresponding U.S. Appl. No. 17/494,220.
Office Action, dated Jan. 27, 2020, from corresponding U.S. Appl. No. 16/656,895.
Office Action, dated Jan. 28, 2020, from corresponding U.S. Appl. No. 16/712,104.
Office Action, dated Jan. 29, 2021, from corresponding U.S. Appl. No. 17/101,106.
Office Action, dated Jan. 4, 2019, from corresponding U.S. Appl. No. 16/159,566.
Office Action, dated Jan. 4, 2019, from corresponding U.S. Appl. No. 16/159,628.
Office Action, dated Jan. 4, 2021, from corresponding U.S. Appl. No. 17/013,756.
Office Action, dated Jan. 4, 2022, from corresponding U.S. Appl. No. 17/480,377.
Office Action, dated Jan. 7, 2020, from corresponding U.S. Appl. No. 16/572,182.
Office Action, dated Jan. 7, 2022, from corresponding U.S. Appl. No. 17/387,421.
Office Action, dated Jul. 13, 2021, from corresponding U.S. Appl. No. 17/306,496.
Office Action, dated Jul. 15, 2021, from corresponding U.S. Appl. No. 17/020,275.
Office Action, dated Jul. 18, 2019, from corresponding U.S. Appl. No. 16/410,762.
Office Action, dated Jul. 19, 2021, from corresponding U.S. Appl. No. 17/316,179.
Office Action, dated Jul. 21, 2017, from corresponding U.S. Appl. No. 15/256,430.
Office Action, dated Jul. 21, 2021, from corresponding U.S. Appl. No. 16/901,654.
Office Action, dated Jul. 23, 2019, from corresponding U.S. Appl. No. 16/436,616.
Office Action, dated Jul. 24, 2020, from corresponding U.S. Appl. No. 16/404,491.
Office Action, dated Jul. 27, 2020, from corresponding U.S. Appl. No. 16/595,342.
Office Action, dated Jun. 1, 2020, from corresponding U.S. Appl. No. 16/862,952.
Office Action, dated Jun. 24, 2019, from corresponding U.S. Appl. No. 16/410,336.
Office Action, dated Jun. 24, 2021, from corresponding U.S. Appl. No. 17/234,205.
Office Action, dated Jun. 27, 2019, from corresponding U.S. Appl. No. 16/404,405.
Office Action, dated Jun. 7, 2021, from corresponding U.S. Appl. No. 17/200,698.
Office Action, dated Jun. 9, 2021, from corresponding U.S. Appl. No. 17/222,523.
Office Action, dated Mar. 11, 2019, from corresponding U.S. Appl. No. 16/220,978.
Office Action, dated Mar. 12, 2019, from corresponding U.S. Appl. No. 16/221,153.
Office Action, dated Mar. 15, 2021, from corresponding U.S. Appl. No. 17/149,421.
Office Action, dated Mar. 16, 2020, from corresponding U.S. Appl. No. 16/719,488.
Office Action, dated Mar. 17, 2020, from corresponding U.S. Appl. No. 16/565,395.
Office Action, dated Mar. 17, 2020, from corresponding U.S. Appl. No. 16/719,071.
Office Action, dated Mar. 20, 2020, from corresponding U.S. Appl. No. 16/778,709.
Office Action, dated Mar. 23, 2020, from corresponding U.S. Appl. No. 16/671,444.
Office Action, dated Mar. 25, 2019, from corresponding U.S. Appl. No. 16/278,121.
Office Action, dated Mar. 25, 2020, from corresponding U.S. Appl. No. 16/701,043.
Office Action, dated Mar. 25, 2020, from corresponding U.S. Appl. No. 16/791,006.
Office Action, dated Mar. 27, 2019, from corresponding U.S. Appl. No. 16/278,120.
Office Action, dated Mar. 30, 2018, from corresponding U.S. Appl. No. 15/894,890.
Office Action, dated Mar. 30, 2018, from corresponding U.S. Appl. No. 15/896,790.
Salim et al., “Data Retrieval and Security using Lightweight Directory Access Protocol”, IEEE, pp. 685-688 (Year: 2009).
Santhisree, et al., “Web Usage Data Clustering Using Dbscan Algorithm and Set Similarities,” IEEE, pp. 220-224 (Year: 2010).
Sanzo et al., “Analytical Modeling of Lock-Based Concurrency Control with Arbitrary Transaction Data Access Patterns,” ACM, pp. 69-78 (Year: 2010).
Schwartz, Edward J., et al., 2010 IEEE Symposium on Security and Privacy: All You Ever Wanted to Know About Dynamic Analysis and forward Symbolic Execution (but might have been afraid to ask), Carnegie Mellon University, IEEE Computer Society, 2010, p. 317-331.
Sedinic et al., “Security Risk Management in Complex Organization,” May 29, 2015, IEEE, pp. 1331-1337 (Year: 2015).
Shahriar et al., “A Model-Based Detection of Vulnerable and Malicious Browser Extensions,” IEEE, pp. 198-207 (Year: 2013).
Shankar et al., “Doppleganger: Better Browser Privacy Without the Bother,” Proceedings of the 13th ACM Conference on Computer and Communications Security; [ACM Conference on Computer and Communications Security], New York, NY: ACM, US, Oct. 30, 2006, pp. 154-167 (Year: 2006).
Singh, et al., “A Metadata Catalog Service for Data Intensive Applications,” ACM, pp. 1-17 (Year: 2003).
Sjosten et al., “Discovering Browser Extensions via Web Accessible Resources,” ACM, pp. 329-336, Mar. 22, 2017 (Year: 2017).
Slezak, et al., “Brighthouse: An Analytic Data Warehouse for Ad-hoc Queries,” ACM, pp. 1337-1345 (Year: 2008).
Soceanu, et al., “Managing the Privacy and Security of eHealth Data,” May 29, 2015, IEEE, pp. 1-8 (Year: 2015).
Srinivasan et al., “Descriptive Data Analysis of File Transfer Data,” ACM, pp. 1-8 (Year: 2014).
Srivastava, Agrima, et al., Measuring Privacy Leaks in Online Social Networks, International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013.
Stack Overflow, “Is there a way to force a user to scroll to the bottom of a div?,” Stack Overflow, pp. 1-11, Nov. 2013. [Online], Available: https://stackoverflow.com/questions/2745935/is-there-a-way-to-force-a-user-to-scroll-to-the-bottom-of-a-div (Year: 2013).
Stern, Joanna, “iPhone Privacy Is Broken . . . and Apps Are to Blame”, The Wall Street Journal, wsj.com, May 31, 2019.
Strodl, et al., “Personal & SOHO Archiving,” Vienna University of Technology, Vienna, Austria, JCDL '08, Jun. 16-20, 2008, Pittsburgh, Pennsylvania, USA, pp. 115-123 (Year: 2008).
Sukumar et al., “Review on Modern Data Preprocessing Techniques in Web Usage Mining (WUM),” IEEE, 2016, pp. 34-69 (Year: 2016).
Symantec, Symantex Data Loss Prevention—Discover, monitor, and protect confidential data; 2008; Symantec Corporation; http://www.mssuk.com/images/Symantec%2014552315_IRC_BR_DLP_03.09_sngl.pdf.
Tanasa et al., “Advanced Data Preprocessing for Intersites Web Usage Mining,” IEEE, Mar. 2004, pp. 59-65 (Year: 2004).
Tanwar, et al., “Live Forensics Analysis: Violations of Business Security Policy,” 2014 International Conference on Contemporary Computing and Informatics (IC31), 2014, pp. 971-976 (Year: 2014).
The Cookie Collective, Optanon Cookie Policy Generator, The Cookie Collective, Year 2016, http://web.archive.org/web/20160324062743/https:/optanon.com/.
Thuraisingham, “Security Issues for the Semantic Web,” Proceedings 27th Annual International Computer Software and Applications Conference, COMPSAC 2003, Dallas, TX, USA, 2003, pp. 633-638 (Year: 2003).
TRUSTe Announces General Availability of Assessment Manager for Enterprises to Streamline Data Privacy Management with Automation, PRNewswire, Mar. 4, 2015.
Tsai et al., “Determinants of Intangible Assets Value: The Data Mining Approach,” Knowledge Based System, pp. 67-77 http://www.elsevier.com/locate/knosys (Year: 2012).
Tuomas Aura et al., Scanning Electronic Documents for Personally Identifiable Information, ACM, Oct. 30, 2006, retrieved online on Jun. 13, 2019, pp. 41-49. Retrieved from the Internet: URL: http://delivery.acm.org/10.1145/1180000/1179608/p41-aura.pdf? (Year: 2006).
Van Eijk et al., “The Impact of User Location on Cookie Notices (Inside and Outside of the European Union,” IEEE Security & Privacy Workshop on Technology and Consumer Protection (CONPRO '19), Jan. 1, 2019 (Year: 2019).
Wang et al., “Revealing Key Non-Financial Factors for Online Credit-Scoring in E-Financing,” 2013, IEEE, pp. 1-6 (Year: 2013).
Wang et al., “Secure and Efficient Access to Outsourced Data,” ACM, pp. 55-65 (Year: 2009).
Weaver et al., “Understanding Information Preview in Mobile Email Processing”, ACM, pp. 303-312, 2011 (Year: 2011).
Wu et al., “Data Mining with Big Data,” IEEE, Jan. 2014, pp. 97-107, vol. 26, No. 1, (Year: 2014).
www.truste.com (1), 200150207, Internet Archive Wayback Machine, www.archive.org,2_7_2015.
Xu, et al., “GatorShare: A File System Framework for High-Throughput Data Management,” ACM, pp. 776-786 (Year: 2010).
Yang et al., “DAC-MACS: Effective Data Access Control for Multiauthority Cloud Storage Systems,” IEEE, pp. 1790-1801 (Year: 2013).
Yang et al, “Mining Web Access Sequence with Improved Apriori Algorithm,” IEEE, 2017, pp. 780-784 (Year: 2017).
Ye et al, “An Evolution-Based Cache Scheme for Scalable Mobile Data Access,” ACM, pp. 1-7 (Year: 2007).
Yin et al, “Multibank Memory Optimization for Parallel Data Access in Multiple Data Arrays”, ACM, pp. 1-8 (Year: 2016).
Yiu et al, “Outsourced Similarity Search on Metric Data Assets”, IEEE, pp. 338-352 (Year: 2012).
Yu, “Using Data from Social Media Websites to Inspire the Design of Assistive Technology”, ACM, pp. 1-2 (Year: 2016).
Yu, et al, “Performance and Fairness Issues in Big Data Transfers,” ACM, pp. 9-11 (Year: 2014).
Yue et al, “An Automatic HTTP Cookie Management System,” Computer Networks, Elsevier, Amsterdam, NL, vol. 54, No. 13, Sep. 15, 2010, pp. 2182-2198 (Year: 2010).
Zannone, et al., “Maintaining Privacy on Derived Objects,” ACM, pp. 10-19 (Year: 2005).
Zeldovich, Nickolai, et al, Making Information Flow Explicit in HiStar, OSDI '06: 7th USENIX Symposium on Operating Systems Design and Implementation, USENIX Association, p. 263-278.
Zhang et al, “Data Transfer Performance Issues for a Web Services Interface to Synchrotron Experiments”, ACM, pp. 59-65 (Year: 2007).
Zhang et al., “Dynamic Topic Modeling for Monitoring Market Competition from Online Text and Image Data”, ACM, pp. 1425-1434 (Year: 2015).
Zheng, et al, “Methodologies for Cross-Domain Data Fusion: An Overview,” IEEE, pp. 16-34 (Year: 2015).
Zheng, et al, “Toward Assured Data Deletion in Cloud Storage,” IEEE, vol. 34, No. 3, pp. 101-107 May/Jun. 2020 (Year: 2020).
Zhu, et al, “Dynamic Data Integration Using Web Services,” IEEE, pp. 1-8 (Year: 2004).
Krol, Kat, et al, Control versus Effort in Privacy Warnings for Webforms, ACM, Oct. 24, 2016, pp. 13-23.
Lamb et al, “Role-Based Access Control for Data Service Integration”, ACM, pp. 3-11 (Year: 2006).
Leadbetter, et al, “Where Big Data Meets Linked Data: Applying Standard Data Models to Environmental Data Streams,” IEEE, pp. 2929-2937 (Year: 2016).
Lebeau, Franck, et al, “Model-Based Vulnerability Testing for Web Applications,” 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation Workshops, pp. 445-452, IEEE, 2013 (Year: 2013).
Li, Ninghui, et al, t-Closeness: Privacy Beyond k-Anonymity and I-Diversity, IEEE, 2014, p. 106-115.
Liu et al, “A Novel Approach for Detecting Browser-based Silent Miner,” IEEE, pp. 490-497 (Year: 2018).
Liu et al, “Cross-Geography Scientific Data Transferring Trends and Behavior,” ACM, pp. 267-278 (Year: 2018).
Liu, Kun, et al, A Framework for Computing the Privacy Scores of Users in Online Social Networks, ACM Transactions an Knowledge Discovery from Data, vol. 5, No. 1, Article 6, Dec. 2010, 30 pages.
Liu, Yandong, et al, “Finding the Right Consumer: Optimizing for Conversion in Display Advertising Campaigns,” Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, Feb. 2, 2012, pp. 473-428 (Year: 2012).
Lizar et al, “Usable Consents: Tracking and Managing Use of Personal Data with a Consent Transaction Receipt,” Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014, pp. 647-652 (Year: 2014).
Lu et al, “An HTTP Flooding Detection Method Based on Browser Behavior,” IEEE, pp. 1151-1154 (Year: 2006).
Lu, “How Machine Learning Mitigates Racial Bias in the US Housing Market,” Available as SSRN 3489519, pp. 1-73, Nov. 2019 (Year: 2019).
Luu, et al, “Combined Local and Holistic Facial Features for Age-Determination,” 2010 11th Int. Conf. Control, Automation, Robotics and Vision, Singapore, Dec. 7, 2010, IEEE, pp. 900-904 (Year: 2010).
Ma Ziang, et al, “LibRadar: Fast and Accurate Detection of Third-Party Libraries in Android Apps,” 2016 IEEE/ACM 38th IEEE International Conference on Software Engineering Companion (ICSE-C), ACM, May 14, 2016, pp. 653-656, DOI: http://dx.doi.org/10.1145/2889160.2889178, p. 653, r.col, par. 1-3; figure 3 (Year: 2016).
Mandal, et al, “Automated Age Prediction Using Wrinkles Features of Facial Images and Neural Network,” International Journal of Emerging Engineering Research and Technology, vol. 5, Issue 2, Feb. 2017, pp. 12-20 (Year: 2017).
Maret et al, “Multimedia Information Interchange: Web Forms Meet Data Servers”, IEEE, pp. 499-505 (Year: 1999).
Martin, et al, “Hidden Surveillance by Web Sites: Web Bugs in Contemporary Use,” Communications of the ACM, vol. 46, No. 12, Dec. 2003, pp. 258-264. Internet source https://doi.org/10.1145/953460.953509 (Year: 2003).
McGarth et al, “Digital Library Technology for Locating and Accessing Scientific Data”, ACM, pp. 188-194 (Year: 1999).
Mesbah et al, “Crawling Ajax-Based Web Applications Through Dynamic Analysis of User Interface State Changes,” ACM Transactions on the Web (TWEB) vol. 6, No. 1, Article 3, Mar. 2012, pp. 1-30 (Year: 2012).
Moiso et al, “Towards a User-Centric Personal Data Ecosystem The Role of the Bank of Individual's Data,” 2012 16th International Conference on Intelligence in Next Generation Networks, Berlin, 2012, pp. 202-209 (Year: 2012).
Moscoso-Zea et al, “Datawarehouse Design for Educational Data Mining,” IEEE, pp. 1-6 (Year: 2016).
Mudepalli et al, “An efficient data retrieval approach using blowfish encryption on cloud CipherText Retrieval in Cloud Computing” IEEE, pp. 267-271 (Year: 2017).
Mundada et al, “Half-Baked Cookies: Hardening Cookie-Based Authentication for the Modern Web,” Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, 2016, pp. 675-685 (Year: 2016).
Newman et al, “High Speed Scientific Data Transfers using Software Defined Networking,” ACM, pp. 1-9 (Year 2015).
Newman, “Email Archive Overviews using Subject Indexes”, ACM, pp. 652-653, 2002 (Year: 2002).
Nishikawa, Taiji, English Translation of JP 2019154505, Aug. 27, 2019 (Year: 2019).
Notice of Filing Date for Petition for Post-Grant Review of related U.S. Pat. No. 9,691,090 dated Apr. 12, 2018.
Nouwens et al, “Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence,” ACM, pp. 1-13, Apr. 25, 2020 (Year: 2020).
D'KEEFE et al, “Privacy-Preserving Data Linkage Protocols,” Proceedings of the 2004 ACM Workshop on Privacy in the Electronic Society, 2004, pp. 94-102 (Year: 2004).
Olenski, Steve, For Consumers, Data is a Matter of Trust, CMO Network, Apr. 18, 2016, https://www.forbes.com/sites/steveolenski/2016/04/18/for-consumers-data-is-a-matter-of-trust/#2e48496278b3.
Paes, “Student Research Abstract: Automatic Detection of Cross-Browser Incompatibilities using Machine Learning and Screenshot Similarity,” ACM, pp. 697-698, Apr. 3, 2017 (Year: 2017).
Pearson, et al, “A Model-Based Privacy Compliance Checker,” IJEBR, vol. 5, No. 2, pp. 63-83, 2009, Nov. 21, 2008. [Online]. Available: http://dx.doi.org/10.4018/jebr.2009040104 (Year: 2008).
Pechenizkiy et al, “Process Mining Online Assessment Data,” Educational Data Mining, pp. 279-288 (Year: 2009).
Petition for Post-Grant Review of related U.S. Pat. No. 9,691,090 dated Mar. 27, 2018.
Petrie et al, “The Relationship between Accessibility and Usability of Websites”, ACM, pp. 397-406 (Year: 2007).
Pfeifle, Sam, The Privacy Advisor, IAPP and AvePoint Launch New Free PIA Tool, International Association of Privacy Professionals, Mar. 5, 2014.
Pfeifle, Sam, The Privacy Advisor, IAPP Heads to Singapore with APIA Template in Tow, International Association of Privacy Professionals, https://iapp.org/news/a/iapp-heads-to-singapore-with-apia-template_in_tow/, Mar. 28, 2014, p. 1-3.
Ping et al, “Wide Area Placement of Data Replicas for Fast and Highly Available Data Access,” ACM, pp. 1-8 (Year 2011).
Popescu-Zeletin, “The Data Access and Transfer Support in a Local Heterogeneous Network (HMINET)”, IEEE, pp. 147-152 (Year: 1979).
Porter, “De-Identified Data and Third Party Data Mining: The Risk of Re-Identification of Personal Information,” Shidler JL Com. & Tech. 5, 2008, pp. 1-9 (Year: 2008).
Pretorius, et al, “Attributing Users Based on Web Browser History,” 2017 IEEE Conference on Application, Information and Network Security (AINS), 2017, pp. 69-74 (Year: 2017).
Qing-Jiang et al, “The (P, a, K) Anonymity Model for Privacy Protection of Personal Information in the Social Networks,” 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, vol. 2 IEEE, 2011, pp. 420-423 (Year: 2011).
Qiu, et al, “Design and Application of Data Integration Platform Based on Web Services and XML,” IEEE, pp. 253-256 (Year: 2016).
Radu, et al, “Analyzing Risk Evaluation Frameworks and Risk Assessment Methods,” IEEE, Dec. 12, 2020, pp. 1-6 (Year: 2020).
Rakers, “Managing Professional and Personal Sensitive Information,” ACM, pp. 9-13, Oct. 24-27, 2010 (Year: 2010).
Reardon et al, User-Level Secure Deletion on Log-Structured File Systems, ACM, 2012, retrieved online on Apr. 22, 2021, pp. 1-11. Retrieved from the Internet: URL: http://citeseerx.ist.psu.edu/viewdoc/download sessionid=450713515DC7F19F8ED09AE961D4B60E. (Year: 2012).
Regulation (EU) 2016/679, “On the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation),” Official Journal of the European Union, May 4, 2016, pp. L 119/1-L 119/88 (Year: 2016).
Roesner et al, “Detecting and Defending Against Third-Party Tracking on the Web,” 9th USENIX Symposium on Networked Systems Design and Implementation, Apr. 11, 2013, pp. 1-14, ACM (Year: 2013).
Rozepz, “What is Google Privacy Checkup? Everything You Need to Know,” Tom's Guide web post, Apr. 26, 2018, pp. 1-11 (Year: 2018).
Sachinopoulou et al, “Ontology-Based Approach for Managing Personal Health and Wellness Information,” IEEE, pp. 1802-1805 (Year: 2007).
Dwork, Cynthia, Differential Privacy, Microsoft Research, p. 1-12.
Emerson, et al, “A Data Mining Driven Risk Profiling Method for Road Asset Management,” ACM, pp. 1267-1275 (Year: 2013).
Enck, William, et al, TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones, ACM Transactions on Computer Systems, vol. 32, No. 2, Article 5, Jun. 2014, p. 5:1-5:29.
Everypixel Team, “A New Age Recognition API Detects the Age of People on Photos,” May 20, 2019, pp. 1-5 (Year: 2019).
Falahrastegar, Marjan, et al., Tracking Personal Identifiers Across the Web, Medical Image Computing and Computer-Assisted Intervention—Miccai 2015, 18th International Conference, Oct. 5, 2015, Munich, Germany.
Fan et al, “Intrusion Investigations with Data-hiding for Computer Log-file Forensics,” IEEE, pp. 1-6 (Year: 2010).
Final Written Decision Regarding Post-Grant Review in Case PGR2018-00056 for U.S. Pat. No. 9,691,090 B1, Oct. 10, 2019.
Francis, Andre, Business Mathematics and Statistics, South-Western Cengage Learning, 2008, Sixth Edition.
Friedman et al, “Data Mining with Differential Privacy,” ACM, Jul. 2010, pp. 493-502 (Year: 2010).
Friedman et al, “Informed Consent in the Mozilla Browser: Implementing Value-Sensitive Design,” Proceedings of the 35th Annual Hawaii International Conference on System Sciences, 2002, IEEE, pp. 1-10 (Year: 2002).
Frikken, Keith B., et al, Yet Another Privacy Metric for Publishing Micro-data, Miami University, Oct. 27, 2008, p. 117-121.
Fung et al, “Discover Information and Knowledge from Websites using an Integrated Summarization and Visualization Framework”, IEEE, pp. 232-235 (Year: 2010).
Gajare et al, “Improved Automatic Feature Selection Approach for Health Risk Prediction,” Feb. 16, 2018, IEEE, pp 816-819 (Year: 2018).
Geko et al, “An Ontology Capturing the Interdependence of the General Data Protection Regulation (GDPR) and Information Security,” ACM, pp. 1-6, Nov. 15-16, 2018 (Year: 2018).
Ghiglieri, Marco et al; Personal DLP for Facebook, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (Percom Workshops); IEEE; Mar. 24, 2014; pp. 629-634.
Gilda, et al, “Blockchain for Student Data Privacy and Consent,” 2018 International Conference on Computer Communication and Informatics, Jan. 4-6, 2018, IEEE, pp. 1-5 (Year: 2018).
Golab, et al, “Issues in Data Stream Management,” ACM, SIGMOD Record, vol. 32, No. 2, Jun. 2003, pp. 5-14 (Year: 2003).
Golfarelli et al, “Beyond Data Warehousing: What's Next in Business Intelligence?,” ACM, pp. 1-6 (Year: 2004).
Gonçalves et al, “The XML Log Standard for Digital Libraries: Analysis, Evolution, and Deployment,” IEEE, pp. 312-314 (Year: 2003).
Goni, Kyriaki, “Deletion Process_Only you can see my history: Investigating Digital Privacy, Digital Oblivion, and Control on Personal Data Through an Interactive Art Installation,” ACM, 2016, retrieved online on Oct. 3, 2019, pp. 324-333. Retrieved from the Internet URL: http://delivery.acm.org/10.1145/2920000/291.
Gowadia et al, “RDF Metadata for XML Access Control,” ACM, pp. 31-48 (Year: 2003).
Grolinger, et al, “Data Management in Cloud Environments: NoSQL and NewSQL Data Stores,” Journal of Cloud Computing: Advances, Systems and Applications, pp. 1-24 (Year: 2013).
Guo, et al, “Opal: A Passe-partout for Web Forms,” ACM, pp. 353-356 (Year: 2012).
Gustarini, et al, “Evaluation of Challenges in Human Subject Studies “In-the-Wild” Using Subjects' Personal Smartphones,” ACM, pp. 1447-1456 (Year: 2013).
Hacigúmús, Hakan, et al, Executing SQL over Encrypted Data in the Database-Service-Provider Model, ACM, Jun. 1, 2002, pp. 216-227.
Halevy, et al, “Schema Mediation in Peer Data Management Systems,” IEEE, Proceedings of the 19th International Conference on Data Engineering, 2003, pp. 505-516 (Year: 2003).
Hauch, et al, “Information Intelligence: Metadata for Information Discovery, Access, and Integration,” ACM, pp. 793-798 (Year: 2005).
He et al, “A Crowdsourcing Framework for Detecting of Cross-Browser Issues in Web Application,” ACM, pp. 1-4, Nov. 6, 2015 (Year: 2015).
Hernandez, et al, “Data Exchange with Data-Metadata Translations,” ACM, pp. 260-273 (Year: 2008).
Hinde, “A Model to Assess Organisational Information Privacy Maturity Against the Protection of Personal Information Act” Dissertation University of Cape Town 2014, pp. 1-121 (Year: 2014).
Hodge, et al, “Managing Virtual Data Marts with Metapointer Tables,” pp. 1-7 (Year: 2002).
Horrall et al, “Evaluating Risk: IBM's Country Financial Risk and Treasury Risk Scorecards,” Jul. 21, 2014, IBM, vol. 58, issue 4, pp. 2:1-2:9 (Year: 2014).
Hu, et al, “Attribute Considerations for Access Control Systems,” NIST Special Publication 800-205, Jun. 2019, pp. 1-42 (Year: 2019).
Hu, et al, “Guide to Attribute Based Access Control (ABAC) Definition and Considerations (Draft),” NIST Special Publication 800-162, pp. 1-54 (Year: 2013).
Huang, et al, “A Study on Information Security Management with Personal Data Protection,” IEEE, Dec. 9, 2011, pp. 624-630 (Year: 2011).
Huner et al., “Towards a Maturity Model for Corporate Data Quality Management”, ACM, pp. 231-238, 2009 (Year 2009).
Hunton & Williams LLP, The Role of Risk Management in Data Protection, Privacy Risk Framework and the Risk-based Approach to Privacy, Centre for Information Policy Leadership, Workshop II, Nov. 23, 2014.
Huo et al, “A Cloud Storage Architecture Model for Data-lntensive Applications,” IEEE, pp. 1-4 (Year: 2011).
Iapp, Daily Dashboard, PIA Tool Stocked With New Templates for DPI, Infosec, International Association of Privacy Professionals, Apr. 22, 2014.
Iapp, ISO/IEC 27001 Information Security Management Template, Resource Center, International Association of Privacy Professionals.
Imran et al, “Searching in Cloud Object Storage by Using a Metadata Model”, IEEE, 2014, retrieved online on Apr. 1, 2020, pp. 121-128. Retrieved from the Internet: URL: https://ieeeexplore.ieee.org/stamp/stampjsp? (Year: 2014).
Iordanou et al, “Tracing Cross Border Web Tracking,” Oct. 31, 2018, pp. 329-342, ACM (Year: 2018).
Islam, et al, “Mixture Model Based Label Association Techniques for Web Accessibility,” ACM, pp. 67-76 (Year: 2010).
Jensen, et al, “Temporal Data Management,” IEEE Transactions on Knowledge and Data Engineering, vol. 11, No. 1, Jan./Feb. 1999, pp. 36-44 (Year: 1999).
Joel Reardon et al., Secure Data Deletion from Persistent Media, ACM, Nov. 4, 2013, retrieved online on Jun. 13, 2019, pp. 271-283. Retrieved from the Internet: URL: http://delivery.acm.org/10.1145/2520000/2516699/p271-reardon.pdf? (Year: 2013).
Jones et al, “AI and the Ethics of Automating Consent,” IEEE, pp. 64-72, May 2018 (Year: 2018).
Joonbakhsh et al, “Mining and Extraction of Personal Software Process measures through IDE Interaction logs,” ACM/IEEE, 2018, retrieved online on Dec. 2, 2019, pp. 78-81. Retrieved from the Internet: URL: http://delivery.acm.org/10.1145/3200000/3196462/p78-joonbakhsh.pdf? (Year: 2018).
Jun et al, “Scalable Multi-Access Flash Store for Big Data Analytics,” ACM, pp. 55-64 (Year: 2014).
Kirkham, et al, “A Personal Data Store for an Internet of Subjects,” IEEE, pp. 92-97 (Year: 2011).
Korba, Larry et al; “Private Data Discovery for Privacy Compliance in Collaborative Environments”; Cooperative Design, Visualization, and Engineering; Springer Beriin Heidelberg; Sep. 21, 2008; pp. 142-150.
Office Action, dated Mar. 30, 2021, from corresponding U.S. Appl. No. 17/151,399.
Office Action, dated Mar. 4, 2019, from corresponding U.S. Appl. No. 16/237,083.
Office Action, dated May 14, 2020, from corresponding U.S. Appl. No. 16/808,497.
Office Action, dated May 14, 2020, from corresponding U.S. Appl. No. 16/808,503.
Office Action, dated May 15, 2020, from corresponding U.S. Appl. No. 16/808,493.
Office Action, dated May 16, 2018, from corresponding U.S. Appl. No. 15/882,989.
Office Action, dated May 17, 2019, from corresponding U.S. Appl. No. 16/277,539.
Office Action, dated May 18, 2021, from corresponding U.S. Appl. No. 17/196,570.
Office Action, dated May 2, 2018, from corresponding U.S. Appl. No. 15/894,809.
Office Action, dated May 2, 2019, from corresponding U.S. Appl. No. 16/104,628.
Office Action, dated May 29, 2020, from corresponding U.S. Appl. No. 16/862,944.
Office Action, dated May 29, 2020, from corresponding U.S. Appl. No. 16/862,948.
Office Action, dated May 29, 2020, from corresponding U.S. Appl. No. 16/863,226.
Office Action, dated May 5, 2020, from corresponding U.S. Appl. No. 16/410,336.
Office Action, dated Nov. 1, 2017, from corresponding U.S. Appl. No. 15/169,658.
Office Action, dated Nov. 10, 2021, from corresponding U.S. Appl. No. 17/380,485.
Office Action, dated Nov. 10, 2021, from corresponding U.S. Appl. No. 17/409,999.
Office Action, dated Nov. 12, 2020, from corresponding U.S. Appl. No. 17/034,355.
Office Action, dated Nov. 12, 2020, from corresponding U.S. Appl. No. 17/034,772.
Office Action, dated Nov. 12, 2021, from corresponding U.S. Appl. No. 17/346,586.
Office Action, dated Nov. 12, 2021, from corresponding U.S. Appl. No. 17/373,444.
Office Action, dated Nov. 15, 2018, from corresponding U.S. Appl. No. 16/059,911.
Office Action, dated Nov. 15, 2019, from corresponding U.S. Appl. No. 16/552,758.
Office Action, dated Nov. 16, 2021, from corresponding U.S. Appl. No. 17/370,650.
Office Action, dated Nov. 16, 2021, from corresponding U.S. Appl. No. 17/486,350.
Office Action, dated Nov. 18, 2019, from corresponding U.S. Appl. No. 16/560,885.
Office Action, dated Nov. 18, 2019, from corresponding U.S. Appl. No. 16/560,889.
Office Action, dated Nov. 18, 2019, from corresponding U.S. Appl. No. 16/572,347.
Office Action, dated Nov. 19, 2019, from corresponding U.S. Appl. No. 16/595,342.
Office Action, dated Nov. 20, 2019, from corresponding U.S. Appl. No. 16/595,327.
Office Action, dated Nov. 23, 2018, from corresponding U.S. Appl. No. 16/042,673.
Office Action, dated Nov. 23, 2021, from corresponding U.S. Appl. No. 17/013,756.
Office Action, dated Nov. 24, 2020, from corresponding U.S. Appl. No. 16/925,628.
Office Action, dated Nov. 26, 2021, from corresponding U.S. Appl. No. 16/925,550.
Office Action, dated Nov. 4, 2021, from corresponding U.S. Appl. No. 17/491,906.
Office Action, dated Nov. 8, 2021, from corresponding U.S. Appl. No. 16/872,130.
Office Action, dated Oct. 10, 2018, from corresponding U.S. Appl. No. 16/041,563.
Office Action, dated Oct. 10, 2018, from corresponding U.S. Appl. No. 16/055,083.
Office Action, dated Oct. 10, 2018, from corresponding U.S. Appl. No. 16/055,944.
Office Action, dated Oct. 12, 2021, from corresponding U.S. Appl. No. 17/346,509.
Office Action, dated Oct. 14, 2020, from corresponding U.S. Appl. No. 16/927,658.
Office Action, dated Oct. 15, 2018, from corresponding U.S. Appl. No. 16/054,780.
Office Action, dated Oct. 15, 2021, from corresponding U.S. Appl. No. 16/908,081.
Office Action, dated Oct. 16, 2019, from corresponding U.S. Appl. No. 16/557,392.
Office Action, dated Oct. 16, 2020, from corresponding U.S. Appl. No. 16/808,489.
Office Action, dated Oct. 23, 2018, from corresponding U.S. Appl. No. 16/055,961.
Office Action, dated Oct. 26, 2018, from corresponding U.S. Appl. No. 16/041,468.
Office Action, dated Oct. 8, 2019, from corresponding U.S. Appl. No. 16/552,765.
Office Action, dated Sep. 1, 2017, from corresponding U.S. Appl. No. 15/619,459.
Office Action, dated Sep. 11, 2017, from corresponding U.S. Appl. No. 15/619,375.
Final Office Action, dated Apr. 23, 2020, from corresponding U.S. Appl. No. 16/572,347.
Final Office Action, dated Apr. 27, 2021, from corresponding U.S. Appl. No. 17/068,454.
Final Office Action, dated Apr. 7, 2020, from corresponding U.S. Appl. No. 16/595,327.
Final Office Action, dated Aug. 10, 2020, from corresponding U.S. Appl. No. 16/791,589.
Final Office Action, dated Aug. 27, 2021, from corresponding U.S. Appl. No. 17/161,159.
Final Office Action, dated Aug. 28, 2020, from corresponding U.S. Appl. No. 16/410,336.
Final Office Action, dated Aug. 5, 2020, from corresponding U.S. Appl. No. 16/719,071.
Final Office Action, dated Aug. 9, 2021, from corresponding U.S. Appl. No. 17/119,080.
Final Office Action, dated Dec. 10, 2021, from corresponding U.S. Appl. No. 17/187,329.
Final Office Action, dated Dec. 7, 2020, from corresponding U.S. Appl. No. 16/862,956.
Final Office Action, dated Dec. 9, 2019, from corresponding U.S. Appl. No. 16/410,336.
Final Office Action, dated Feb. 19, 2020, from corresponding U.S. Appl. No. 16/404,491.
Final Office Action, dated Feb. 3, 2020, from corresponding U.S. Appl. No. 16/557,392.
Final Office Action, dated Feb. 8, 2021, from corresponding U.S. Appl. No. 16/927,658.
Final Office Action, dated Jan. 17, 2018, from corresponding U.S. Appl. No. 15/619,278.
Final Office Action, dated Jan. 21, 2020, from corresponding U.S. Appl. No. 16/410,762.
Final Office Action, dated Jan. 23, 2018, from corresponding U.S. Appl. No. 15/619,479.
Final Office Action, dated Jan. 23, 2020, from corresponding U.S. Appl. No. 16/505,430.
Final Office Action, dated Jul. 21, 2021, from corresponding U.S. Appl. No. 17/151,334.
Final Office Action, dated Jul. 7, 2021, from corresponding U.S. Appl. No. 17/149,421.
Final Office Action, dated Mar. 26, 2021, from corresponding U.S. Appl. No. 17/020,275.
Final Office Action, dated Mar. 5, 2019, from corresponding U.S. Appl. No. 16/055,961.
Final Office Action, dated Mar. 6, 2020, from corresponding U.S. Appl. No. 16/595,342.
Final Office Action, dated May 14, 2021, from corresponding U.S. Appl. No. 17/013,756.
Final Office Action, dated Nov. 29, 2017, from corresponding U.S. Appl. No. 15/619,237.
Final Office Action, dated Oct. 26, 2021, from corresponding U.S. Appl. No. 17/306,496.
Final Office Action, dated Oct. 28, 2021, from corresponding U.S. Appl. No. 17/234,205.
Final Office Action, dated Oct. 29, 2021, from corresponding U.S. Appl. No. 17/020,275.
Final Office Action, dated Sep. 17, 2021, from corresponding U.S. Appl. No. 17/200,698.
Final Office Action, dated Sep. 21, 2020, from corresponding U.S. Appl. No. 16/808,493.
Final Office Action, dated Sep. 21, 2020, from corresponding U.S. Appl. No. 16/862,944.
Final Office Action, dated Sep. 22, 2020, from corresponding U.S. Appl. No. 16/808,497.
Final Office Action, dated Sep. 23, 2020, from corresponding U.S. Appl. No. 16/862,948.
Final Office Action, dated Sep. 24, 2020, from corresponding U.S. Appl. No. 16/862,952.
Final Office Action, dated Sep. 25, 2019, from corresponding U.S. Appl. No. 16/278,119.
Final Office Action, dated Sep. 28, 2020, from corresponding U.S. Appl. No. 16/565,395.
Final Office Action, dated Sep. 8, 2020, from corresponding U.S. Appl. No. 16/410,866.
Office Action, dated Apr. 1, 2021, from corresponding U.S. Appl. No. 17/119,080.
Office Action, dated Apr. 15, 2021, from corresponding U.S. Appl. No. 17/161,159.
Office Action, dated Apr. 18, 2018, from corresponding U.S. Appl. No. 15/894,819.
Office Action, dated Apr. 2, 2021, from corresponding U.S. Appl. No. 17/151,334.
Office Action, dated Apr. 20, 2020, from corresponding U.S. Appl. No. 16/812,795.
Office Action, dated Apr. 22, 2019, from corresponding U.S. Appl. No. 16/241,710.
Office Action, dated Apr. 22, 2020, from corresponding U.S. Appl. No. 16/811,793.
Office Action, dated Apr. 28, 2020, from corresponding U.S. Appl. No. 16/798,818.
Office Action, dated Apr. 28, 2020, from corresponding U.S. Appl. No. 16/808,500.
Office Action, dated Apr. 28, 2021, from corresponding U.S. Appl. No. 16/808,497.
Office Action, dated Apr. 29, 2020, from corresponding U.S. Appl. No. 16/791,337.
Office Action, dated Apr. 5, 2019, from corresponding U.S. Appl. No. 16/278,119.
Office Action, dated Apr. 7, 2020, from corresponding U.S. Appl. No. 16/788,633.
Written Opinion of the International Searching Authority, dated Oct. 20, 2017, from corresponding International Application No. PCT/US2017/036917.
Written Opinion of the International Searching Authority, dated Oct. 3, 2017, from corresponding International Application No. PCT/US2017/036912.
Written Opinion of the International Searching Authority, dated Sep. 1, 2017, from corresponding International Application No. PCT/US2017/036896.
Written Opinion of the International Searching Authority, dated Sep. 12, 2018, from corresponding International Application No. PCT/US2018/037504.
Written Opinion of the International Searching Authority, dated Sep. 15, 2021, from corresponding International Application No. PCT/US2021/033631.
International Search Report, dated Aug. 15, 2017, from corresponding International Application No. PCT/US2017/036919.
International Search Report, dated Aug. 21, 2017, from corresponding International Application No. PCT/US2017/036914.
International Search Report, dated Aug. 29, 2017, from corresponding International Application No. PCT/US2017/036898.
International Search Report, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036889.
International Search Report, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036890.
International Search Report, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036893.
International Search Report, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036901.
International Search Report, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036913.
International Search Report, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036920.
International Search Report, dated Dec. 14, 2018, from corresponding International Application No. PCT/US2018/045296.
International Search Report, dated Dec. 22, 2021, from corresponding International Application No. PCT/US2021/051217.
International Search Report, dated Jan. 14, 2019, from corresponding International Application No. PCT/US2018/046949.
International Search Report, dated Jan. 5, 2022, from corresponding International Application No. PCT/US2021/050497.
International Search Report, dated Jan. 7, 2019, from corresponding International Application No. PCT/US2018/055772.
International Search Report, dated Jun. 21, 2017, from corresponding International Application No. PCT/US2017/025600.
International Search Report, dated Jun. 6, 2017, from corresponding International Application No. PCT/US2017/025605.
International Search Report, dated Jun. 6, 2017, from corresponding International Application No. PCT/US2017/025611.
International Search Report, dated Mar. 14, 2019, from corresponding International Application No. PCT/US2018/055736.
International Search Report, dated Mar. 4, 2019, from corresponding International Application No. PCT/US2018/055773.
International Search Report, dated Mar. 4, 2019, from corresponding International Application No. PCT/US2018/055774.
International Search Report, dated Nov. 12, 2021, from corresponding International Application No. PCT/US2021/043481.
International Search Report, dated Nov. 19, 2018, from corresponding International Application No. PCT/US2018/046939.
International Search Report, dated Nov. 3, 2021, from corresponding International Application No. PCT/US2021/040893.
International Search Report, dated Nov. 3, 2021, from corresponding International Application No. PCT/US2021/044910.
International Search Report, dated Oct. 11, 2018, from corresponding International Application No. PCT/US2018/043975.
International Search Report, dated Oct. 11, 2018, from corresponding International Application No. PCT/US2018/043976.
International Search Report, dated Oct. 11, 2018, from corresponding International Application No. PCT/US2018/043977.
International Search Report, dated Oct. 11, 2018, from corresponding International Application No. PCT/US2018/044026.
International Search Report, dated Oct. 11, 2018, from corresponding International Application No. PCT/US2018/045240.
International Search Report, dated Oct. 12, 2017, from corresponding International Application No. PCT/US2017/036888.
International Search Report, dated Oct. 12, 2018, from corresponding International Application No. PCT/US2018/044046.
International Search Report, dated Oct. 16, 2018, from corresponding International Application No. PCT/US2018/045243.
International Search Report, dated Oct. 18, 2018, from corresponding International Application No. PCT/US2018/045249.
International Search Report, dated Oct. 20, 2017, from corresponding International Application No. PCT/US2017/036917.
International Search Report, dated Oct. 3, 2017, from corresponding International Application No. PCT/US2017/036912.
International Search Report, dated Sep. 1, 2017, from corresponding International Application No. PCT/US2017/036896.
International Search Report, dated Sep. 12, 2018, from corresponding International Application No. PCT/US2018/037504.
International Search Report, dated Sep. 15, 2021, from corresponding International Application No. PCT/US2021/033631.
Invitation to Pay Additional Search Fees, dated Aug. 10, 2017, from corresponding International Application No. PCT/US2017/036912.
Invitation to Pay Additional Search Fees, dated Aug. 10, 2017, from corresponding International Application No. PCT/US2017/036917.
Invitation to Pay Additional Search Fees, dated Aug. 24, 2017, from corresponding International Application No. PCT/US2017/036888.
Invitation to Pay Additional Search Fees, dated Jan. 18, 2019, from corresponding International Application No. PCT/US2018/055736.
Invitation to Pay Additional Search Fees, dated Jan. 7, 2019, from corresponding International Application No. PCT/US2018/055773.
Invitation to Pay Additional Search Fees, dated Jan. 8, 2019, from corresponding International Application No. PCT/US2018/055774.
Invitation to Pay Additional Search Fees, dated Oct. 23, 2018, from corresponding International Application No. PCT/US2018/045296.
Office Action, dated Apr. 7, 2020, from corresponding U.S. Appl. No. 16/791,589.
Office Action, dated Aug. 13, 2019, from corresponding U.S. Appl. No. 16/505,430.
Office Action, dated Aug. 13, 2019, from corresponding U.S. Appl. No. 16/512,033.
Office Action, dated Aug. 15, 2019, from corresponding U.S. Appl. No. 16/505,461.
Office Action, dated Aug. 18, 2021, from corresponding U.S. Appl. No. 17/222,725.
Office Action, dated Aug. 19, 2019, from corresponding U.S. Appl. No. 16/278,122.
Office Action, dated Aug. 20, 2020, from corresponding U.S. Appl. No. 16/817,136.
Office Action, dated Aug. 23, 2017, from corresponding U.S. Appl. No. 15/626,052.
Office Action, dated Aug. 24, 2017, from corresponding U.S. Appl. No. 15/169,643.
Office Action, dated Aug. 24, 2017, from corresponding U.S. Appl. No. 15/619,451.
Office Action, dated Aug. 24, 2020, from corresponding U.S. Appl. No. 16/595,327.
Office Action, dated Aug. 27, 2019, from corresponding U.S. Appl. No. 16/410,296.
Office Action, dated Aug. 27, 2021, from corresponding U.S. Appl. No. 17/187,329.
Office Action, dated Aug. 27, 2021, from corresponding U.S. Appl. No. 17/334,948.
Office Action, dated Aug. 29, 2017, from corresponding U.S. Appl. No. 15/619,237.
Office Action, dated Aug. 30, 2017, from corresponding U.S. Appl. No. 15/619,212.
Office Action, dated Aug. 30, 2017, from corresponding U.S. Appl. No. 15/619,382.
Office Action, dated Aug. 30, 2021, from corresponding U.S. Appl. No. 16/938,520.
Office Action, dated Aug. 6, 2019, from corresponding U.S. Appl. No. 16/404,491.
Office Action, dated Aug. 6, 2020, from corresponding U.S. Appl. No. 16/862,956.
Office Action, dated Dec. 11, 2019, from corresponding U.S. Appl. No. 16/578,712.
Office Action, dated Dec. 13, 2021, from corresponding U.S. Appl. No. 17/476,209.
Office Action, dated Dec. 14, 2018, from corresponding U.S. Appl. No. 16/104,393.
Office Action, dated Dec. 15, 2016, from corresponding U.S. Appl. No. 15/256,419.
Office Action, dated Dec. 16, 2019, from corresponding U.S. Appl. No. 16/563,754.
Office Action, dated Dec. 16, 2019, from corresponding U.S. Appl. No. 16/565,265.
Office Action, dated Dec. 16, 2020, from corresponding U.S. Appl. No. 17/020,275.
Office Action, dated Dec. 17, 2021, from corresponding U.S. Appl. No. 17/395,759.
Office Action, dated Dec. 17, 2021, from corresponding U.S. Appl. No. 17/499,582.
Office Action, dated Dec. 18, 2020, from corresponding U.S. Appl. No. 17/030,714.
Office Action, dated Dec. 19, 2019, from corresponding U.S. Appl. No. 16/410,866.
Office Action, dated Dec. 2, 2019, from corresponding U.S. Appl. No. 16/560,963.
Office Action, dated Dec. 2, 2021, from corresponding U.S. Appl. No. 17/504,102.
Office Action, dated Dec. 23, 2019, from corresponding U.S. Appl. No. 16/593,639.
Office Action, dated Dec. 24, 2020, from corresponding U.S. Appl. No. 17/068,454.
Office Action, dated Dec. 27, 2021, from corresponding U.S. Appl. No. 17/493,332.
Office Action, dated Dec. 29, 2021, from corresponding U.S. Appl. No. 17/479,807.
Office Action, dated Dec. 3, 2018, from corresponding U.S. Appl. No. 16/055,998.
Office Action, dated Dec. 30, 2021, from corresponding U.S. Appl. No. 17/149,421.
Office Action, dated Dec. 31, 2018, from corresponding U.S. Appl. No. 16/160,577.
Office Action, dated Dec. 7, 2021, from corresponding U.S. Appl. No. 17/499,609.
Office Action, dated Dec. 8, 2020, from corresponding U.S. Appl. No. 17/013,758.
Office Action, dated Dec. 8, 2020, from corresponding U.S. Appl. No. 17/068,198.
Office Action, dated Feb. 10, 2021, from corresponding U.S. Appl. No. 16/862,944.
Office Action, dated Feb. 10, 2021, from corresponding U.S. Appl. No. 17/106,469.
Office Action, dated Feb. 15, 2019, from corresponding U.S. Appl. No. 16/220,899.
Office Action, dated Feb. 17, 2021, from corresponding U.S. Appl. No. 16/862,948.
Office Action, dated Feb. 18, 2021, from corresponding U.S. Appl. No. 16/862,952.
Office Action, dated Feb. 2, 2021, from corresponding U.S. Appl. No. 17/101,915.
Office Action, dated Feb. 26, 2019, from corresponding U.S. Appl. No. 16/228,250.
Notice of Allowance, dated Dec. 13, 2019, from corresponding U.S. Appl. No. 16/512,033.
Notice of Allowance, dated Dec. 13, 2021, from corresponding U.S. Appl. No. 16/908,081.
Notice of Allowance, dated Dec. 13, 2021, from corresponding U.S. Appl. No. 17/347,853.
Notice of Allowance, dated Dec. 15, 2020, from corresponding U.S. Appl. No. 16/989,086.
Notice of Allowance, dated Dec. 16, 2019, from corresponding U.S. Appl. No. 16/505,461.
Notice of Allowance, dated Dec. 17, 2020, from corresponding U.S. Appl. No. 17/034,772.
Notice of Allowance, dated Dec. 18, 2019, from corresponding U.S. Appl. No. 16/659,437.
Notice of Allowance, dated Dec. 2, 2021, from corresponding U.S. Appl. No. 16/901,654.
Notice of Allowance, dated Dec. 23, 2019, from corresponding U.S. Appl. No. 16/656,835.
Notice of Allowance, dated Dec. 23, 2020, from corresponding U.S. Appl. No. 17/068,557.
Notice of Allowance, dated Dec. 3, 2019, from corresponding U.S. Appl. No. 16/563,749.
Notice of Allowance, dated Dec. 30, 2021, from corresponding U.S. Appl. No. 16/938,520.
Notice of Allowance, dated Dec. 31, 2018, from corresponding U.S. Appl. No. 16/159,634.
Notice of Allowance, dated Dec. 31, 2019, from corresponding U.S. Appl. No. 16/404,399.
Notice of Allowance, dated Dec. 4, 2019, from corresponding U.S. Appl. No. 16/594,670.
Notice of Allowance, dated Dec. 5, 2017, from corresponding U.S. Appl. No. 15/633,703.
Notice of Allowance, dated Dec. 6, 2017, from corresponding U.S. Appl. No. 15/619,451.
Notice of Allowance, dated Dec. 6, 2017, from corresponding U.S. Appl. No. 15/619,459.
Notice of Allowance, dated Dec. 7, 2020, from corresponding U.S. Appl. No. 16/817,136.
Notice of Allowance, dated Dec. 8, 2021, from corresponding U.S. Appl. No. 17/397,472.
Notice of Allowance, dated Dec. 9, 2015, from corresponding U.S. Appl. No. 16/565,261.
Notice of Allowance, dated Dec. 9, 2020, from corresponding U.S. Appl. No. 16/404,401.
Notice of Allowance, dated Feb. 10, 2020, from corresponding U.S. Appl. No. 16/552,765.
Notice of Allowance, dated Feb. 11, 2021, from corresponding U.S. Appl. No. 17/086,732.
Notice of Allowance, dated Feb. 12, 2020, from corresponding U.S. Appl. No. 16/572,182.
Notice of Allowance, dated Feb. 13, 2019, from corresponding U.S. Appl. No. 16/041,563.
Notice of Allowance, dated Feb. 14, 2019, from corresponding U.S. Appl. No. 16/226,272.
Notice of Allowance, dated Feb. 19, 2019, from corresponding U.S. Appl. No. 16/159,632.
Notice of Allowance, dated Feb. 19, 2021, from corresponding U.S. Appl. No. 16/832,451.
Notice of Allowance, dated Feb. 24, 2021, from corresponding U.S. Appl. No. 17/034,355.
Notice of Allowance, dated Feb. 24, 2021, from corresponding U.S. Appl. No. 17/068,198.
Notice of Allowance, dated Feb. 24, 2021, from corresponding U.S. Appl. No. 17/101,106.
Notice of Allowance, dated Feb. 24, 2021, from corresponding U.S. Appl. No. 17/101,253.
Notice of Allowance, dated Feb. 25, 2020, from corresponding U.S. Appl. No. 16/714,355.
Notice of Allowance, dated Feb. 25, 2021, from corresponding U.S. Appl. No. 17/106,469.
Notice of Allowance, dated Feb. 26, 2021, from corresponding U.S. Appl. No. 17/139,650.
Notice of Allowance, dated Feb. 27, 2019, from corresponding U.S. Appl. No. 16/041,468.
Notice of Allowance, dated Feb. 27, 2019, from corresponding U.S. Appl. No. 16/226,290.
Notice of Allowance, dated Feb. 3, 2021, from corresponding U.S. Appl. No. 16/827,039.
Notice of Allowance, dated Feb. 3, 2021, from corresponding U.S. Appl. No. 17/068,558.
Notice of Allowance, dated Jan. 1, 2021, from corresponding U.S. Appl. No. 17/026,727.
Notice of Allowance, dated Jan. 11, 2022, from corresponding U.S. Appl. No. 17/371,350.
Notice of Allowance, dated Jan. 12, 2022, from corresponding U.S. Appl. No. 17/334,948.
Notice of Allowance, dated Jan. 12, 2022, from corresponding U.S. Appl. No. 17/463,775.
Notice of Allowance, dated Jan. 14, 2020, from corresponding U.S. Appl. No. 16/277,715.
Notice of Allowance, dated Jan. 15, 2021, from corresponding U.S. Appl. No. 17/030,714.
Notice of Allowance, dated Jan. 18, 2018, from corresponding U.S. Appl. No. 15/619,478.
Notice of Allowance, dated Jan. 18, 2019 from corresponding U.S. Appl. No. 16/159,635.
Notice of Allowance, dated Jan. 2, 2020, from corresponding U.S. Appl. No. 16/410,296.
Notice of Allowance, dated Jan. 23, 2018, from corresponding U.S. Appl. No. 15/619,251.
Notice of Allowance, dated Mar. 1, 2019, from corresponding U.S. Appl. No. 16/059,911.
Notice of Allowance, dated Mar. 10, 2021, from corresponding U.S. Appl. No. 16/925,628.
Notice of Allowance, dated Mar. 10, 2021, from corresponding U.S. Appl. No. 17/128,666.
Notice of Allowance, dated Mar. 13, 2019, from corresponding U.S. Appl. No. 16/055,083.
Notice of Allowance, dated Mar. 14, 2019, from corresponding U.S. Appl. No. 16/055,944.
Notice of Allowance, dated Mar. 16, 2020, from corresponding U.S. Appl. No. 16/778,704.
Notice of Allowance, dated Mar. 16, 2021, from corresponding U.S. Appl. No. 17/149,380.
Notice of Allowance, dated Mar. 17, 2020, from corresponding U.S. Appl. No. 16/560,885.
Notice of Allowance, dated Mar. 18, 2020, from corresponding U.S. Appl. No. 16/560,963.
Notice of Allowance, dated Mar. 19, 2021, from corresponding U.S. Appl. No. 17/013,757.
Notice of Allowance, dated Mar. 2, 2018, from corresponding U.S. Appl. No. 15/858,802.
Notice of Allowance, dated Mar. 24, 2020, from corresponding U.S. Appl. No. 16/552,758.
Notice of Allowance, dated Mar. 25, 2019, from corresponding U.S. Appl. No. 16/054,780.
Notice of Allowance, dated Mar. 26, 2020, from corresponding U.S. Appl. No. 16/560,889.
Notice of Allowance, dated Mar. 26, 2020, from corresponding U.S. Appl. No. 16/578,712.
Notice of Allowance, dated Mar. 27, 2019, from corresponding U.S. Appl. No. 16/226,280.
Notice of Allowance, dated Mar. 29, 2019, from corresponding U.S. Appl. No. 16/055,998.
Notice of Allowance, dated Mar. 31, 2020, from corresponding U.S. Appl. No. 16/563,744.
Notice of Allowance, dated Mar. 31, 2021, from corresponding U.S. Appl. No. 17/013,758.
Notice of Allowance, dated Mar. 31, 2021, from corresponding U.S. Appl. No. 17/162,205.
Notice of Allowance, dated May 1, 2020, from corresponding U.S. Appl. No. 16/586,202.
Notice of Allowance, dated May 11, 2020, from corresponding U.S. Appl. No. 16/786,196.
Notice of Allowance, dated May 13, 2021, from corresponding U.S. Appl. No. 17/101,915.
Notice of Allowance, dated May 19, 2020, from corresponding U.S. Appl. No. 16/505,430.
Notice of Allowance, dated May 19, 2020, from corresponding U.S. Appl. No. 16/808,496.
Notice of Allowance, dated May 20, 2020, from corresponding U.S. Appl. No. 16/707,762.
Notice of Allowance, dated May 21, 2018, from corresponding U.S. Appl. No. 15/896,790.
Notice of Allowance, dated May 26, 2021, from corresponding U.S. Appl. No. 16/808,493.
Notice of Allowance, dated May 26, 2021, from corresponding U.S. Appl. No. 16/865,874.
Notice of Allowance, dated May 26, 2021, from corresponding U.S. Appl. No. 17/199,514.
Notice of Allowance, dated May 27, 2020, from corresponding U.S. Appl. No. 16/820,208.
Notice of Allowance, dated May 27, 2021, from corresponding U.S. Appl. No. 16/927,658.
Notice of Allowance, dated May 27, 2021, from corresponding U.S. Appl. No. 17/198,757.
Notice of Allowance, dated May 28, 2019, from corresponding U.S. Appl. No. 16/277,568.
Notice of Allowance, dated May 28, 2020, from corresponding U.S. Appl. No. 16/799,279.
Notice of Allowance, dated May 28, 2021, from corresponding U.S. Appl. No. 16/862,944.
Notice of Allowance, dated May 5, 2017, from corresponding U.S. Appl. No. 15/254,901.
Notice of Allowance, dated May 5, 2020, from corresponding U.S. Appl. No. 16/563,754.
Notice of Allowance, dated May 7, 2020, from corresponding U.S. Appl. No. 16/505,426.
Notice of Allowance, dated May 7, 2021, from corresponding U.S. Appl. No. 17/194,662.
Notice of Allowance, dated Nov. 14, 2019, from corresponding U.S. Appl. No. 16/436,616.
Notice of Allowance, dated Nov. 16, 2021, from corresponding U.S. Appl. No. 17/491,871.
Notice of Allowance, dated Nov. 2, 2018, from corresponding U.S. Appl. No. 16/054,762.
Notice of Allowance, dated Nov. 22, 2021, from corresponding U.S. Appl. No. 17/383,889.
Notice of Allowance, dated Nov. 23, 2020, from corresponding U.S. Appl. No. 16/791,589.
Notice of Allowance, dated Nov. 24, 2020, from corresponding U.S. Appl. No. 17/027,019.
Notice of Allowance, dated Nov. 25, 2020, from corresponding U.S. Appl. No. 17/019,771.
Notice of Allowance, dated Nov. 26, 2019, from corresponding U.S. Appl. No. 16/563,735.
Notice of Allowance, dated Nov. 27, 2019, from corresponding U.S. Appl. No. 16/570,712.
Notice of Allowance, dated Nov. 27, 2019, from corresponding U.S. Appl. No. 16/577,634.
Office Action, dated Sep. 11, 2017, from corresponding U.S. Appl. No. 15/619,478.
Office Action, dated Sep. 15, 2021, from corresponding U.S. Appl. No. 16/623,157.
Office Action, dated Sep. 16, 2019, from corresponding U.S. Appl. No. 16/277,715.
Office Action, dated Sep. 19, 2017, from corresponding U.S. Appl. No. 15/671,073.
Office Action, dated Sep. 22, 2017, from corresponding U.S. Appl. No. 15/619,278.
Office Action, dated Sep. 24, 2021, from corresponding U.S. Appl. No. 17/342,153.
Office Action, dated Sep. 4, 2020, from corresponding U.S. Appl. No. 16/989,086.
Office Action, dated Sep. 5, 2017, from corresponding U.S. Appl. No. 15/619,469.
Office Action, dated Sep. 6, 2017, from corresponding U.S. Appl. No. 15/619,479.
Office Action, dated Sep. 7, 2017, from corresponding U.S. Appl. No. 15/633,703.
Office Action, dated Sep. 8, 2017, from corresponding U.S. Appl. No. 15/619,251.
Notice of Allowance, dated Apr. 12, 2017, from corresponding U.S. Appl. No. 15/256,419.
Notice of Allowance, dated Apr. 17, 2020, from corresponding U.S. Appl. No. 16/593,639.
Notice of Allowance, dated Apr. 19, 2021, from corresponding U.S. Appl. No. 17/164,029.
Notice of Allowance, dated Apr. 2, 2019, from corresponding U.S. Appl. No. 16/160,577.
Notice of Allowance, dated Apr. 2, 2021, from corresponding U.S. Appl. No. 17/162,006.
Notice of Allowance, dated Apr. 22, 2021, from corresponding U.S. Appl. No. 17/163,701.
Notice of Allowance, dated Apr. 25, 2018, from corresponding U.S. Appl. No. 15/883,041.
Notice of Allowance, dated Apr. 28, 2021, from corresponding U.S. Appl. No. 17/135,445.
Notice of Allowance, dated Apr. 28, 2021, from corresponding U.S. Appl. No. 17/181,828.
Notice of Allowance, dated Apr. 29, 2020, from corresponding U.S. Appl. No. 16/700,049.
Notice of Allowance, dated Apr. 30, 2020, from corresponding U.S. Appl. No. 16/565,265.
Notice of Allowance, dated Apr. 30, 2020, from corresponding U.S. Appl. No. 16/820,346.
Notice of Allowance, dated Apr. 30, 2021, from corresponding U.S. Appl. No. 16/410,762.
Notice of Allowance, dated Apr. 8, 2019, from corresponding U.S. Appl. No. 16/228,250.
Notice of Allowance, dated Apr. 8, 2020, from corresponding U.S. Appl. No. 16/791,348.
Notice of Allowance, dated Apr. 9, 2020, from corresponding U.S. Appl. No. 16/791,075.
Notice of Allowance, dated Aug. 10, 2020, from corresponding U.S. Appl. No. 16/671,444.
Notice of Allowance, dated Aug. 10, 2020, from corresponding U.S. Appl. No. 16/788,633.
Notice of Allowance, dated Aug. 12, 2020, from corresponding U.S. Appl. No. 16/719,488.
Notice of Allowance, dated Aug. 12, 2021, from corresponding U.S. Appl. No. 16/881,832.
Notice of Allowance, dated Aug. 14, 2018, from corresponding U.S. Appl. No. 15/989,416.
Notice of Allowance, dated Aug. 18, 2017, from corresponding U.S. Appl. No. 15/619,455.
Notice of Allowance, dated Aug. 20, 2019, from corresponding U.S. Appl. No. 16/241,710.
Notice of Allowance, dated Aug. 24, 2018, from corresponding U.S. Appl. No. 15/619,479.
Notice of Allowance, dated Aug. 26, 2019, from corresponding U.S. Appl. No. 16/443,374.
Notice of Allowance, dated Aug. 26, 2020, from corresponding U.S. Appl. No. 16/808,503.
Notice of Allowance, dated Aug. 28, 2019, from corresponding U.S. Appl. No. 16/278,120.
Notice of Allowance, dated Aug. 30, 2018, from corresponding U.S. Appl. No. 15/996,208.
Notice of Allowance, dated Aug. 31, 2021, from corresponding U.S. Appl. No. 17/326,901.
Notice of Allowance, dated Aug. 4, 2021, from corresponding U.S. Appl. No. 16/895,278.
Notice of Allowance, dated Aug. 7, 2020, from corresponding U.S. Appl. No. 16/901,973.
Notice of Allowance, dated Aug. 9, 2018, from corresponding U.S. Appl. No. 15/882,989.
Notice of Allowance, dated Aug. 9, 2021, from corresponding U.S. Appl. No. 16/881,699.
Notice of Allowance, dated Dec. 10, 2018, from corresponding U.S. Appl. No. 16/105,602.
Notice of Allowance, dated Dec. 11, 2019, from corresponding U.S. Appl. No. 16/278,122.
Notice of Allowance, dated Dec. 11, 2019, from corresponding U.S. Appl. No. 16/593,634.
Notice of Allowance, dated Dec. 12, 2017, from corresponding U.S. Appl. No. 15/169,643.
Notice of Allowance, dated Dec. 12, 2017, from corresponding U.S. Appl. No. 15/619,212.
Notice of Allowance, dated Dec. 12, 2017, from corresponding U.S. Appl. No. 15/619,382.
Notice of Allowance, dated Nov. 3, 2020, from corresponding U.S. Appl. No. 16/719,071.
Notice of Allowance, dated Nov. 5, 2019, from corresponding U.S. Appl. No. 16/560,965.
Notice of Allowance, dated Nov. 7, 2017, from corresponding U.S. Appl. No. 15/671,073.
Notice of Allowance, dated Nov. 8, 2018, from corresponding U.S. Appl. No. 16/042,642.
Notice of Allowance, dated Nov. 9, 2020, from corresponding U.S. Appl. No. 16/595,342.
Notice of Allowance, dated Oct. 1, 2021, from corresponding U.S. Appl. No. 17/340,395.
Notice of Allowance, dated Oct. 10, 2019, from corresponding U.S. Appl. No. 16/277,539.
Notice of Allowance, dated Oct. 17, 2018, from corresponding U.S. Appl. No. 15/896,790.
Notice of Allowance, dated Oct. 17, 2018, from corresponding U.S. Appl. No. 16/054,672.
Notice of Allowance, dated Oct. 17, 2019, from corresponding U.S. Appl. No. 16/563,741.
Notice of Allowance, dated Oct. 21, 2019, from corresponding U.S. Appl. No. 16/404,405.
Notice of Allowance, dated Oct. 21, 2020, from corresponding U.S. Appl. No. 16/834,812.
Notice of Allowance, dated Oct. 22, 2021, from corresponding U.S. Appl. No. 17/346,847.
Notice of Allowance, dated Oct. 3, 2019, from corresponding U.S. Appl. No. 16/511,700.
Notice of Allowance, dated Sep. 1, 2021, from corresponding U.S. Appl. No. 17/196,570.
Notice of Allowance, dated Sep. 1, 2021, from corresponding U.S. Appl. No. 17/222,556.
Notice of Allowance, dated Sep. 12, 2019, from corresponding U.S. Appl. No. 16/512,011.
Notice of Allowance, dated Sep. 13, 2018, from corresponding U.S. Appl. No. 15/894,809.
Notice of Allowance, dated Sep. 13, 2018, from corresponding U.S. Appl. No. 15/894,890.
Notice of Allowance, dated Sep. 14, 2021, from corresponding U.S. Appl. No. 16/808,497.
Notice of Allowance, dated Sep. 16, 2020, from corresponding U.S. Appl. No. 16/915,097.
Notice of Allowance, dated Sep. 17, 2020, from corresponding U.S. Appl. No. 16/863,226.
Notice of Allowance, dated Sep. 18, 2018, from corresponding U.S. Appl. No. 15/894,819.
Notice of Allowance, dated Sep. 18, 2018, from corresponding U.S. Appl. No. 16/041,545.
Notice of Allowance, dated Sep. 18, 2020, from corresponding U.S. Appl. No. 16/812,795.
Notice of Allowance, dated Sep. 23, 2020, from corresponding U.S. Appl. No. 16/811,793.
Notice of Allowance, dated Sep. 23, 2021, from corresponding U.S. Appl. No. 17/068,454.
Notice of Allowance, dated Sep. 24, 2021, from corresponding U.S. Appl. No. 17/334,939.
Notice of Allowance, dated Sep. 25, 2020, from corresponding U.S. Appl. No. 16/983,536.
Notice of Allowance, dated Sep. 27, 2017, from corresponding U.S. Appl. No. 15/626,052.
Notice of Allowance, dated Sep. 27, 2021, from corresponding U.S. Appl. No. 17/222,523.
Notice of Allowance, dated Sep. 28, 2018, from corresponding U.S. Appl. No. 16/041,520.
Notice of Allowance, dated Sep. 29, 2021, from corresponding U.S. Appl. No. 17/316,179.
Notice of Allowance, dated Sep. 4, 2018, from corresponding U.S. Appl. No. 15/883,041.
Notice of Allowance, dated Sep. 4, 2020, from corresponding U.S. Appl. No. 16/808,500.
Notice of Allowance, dated Sep. 4, 2020, from corresponding U.S. Appl. No. 16/901,662.
Notice of Allowance, dated Sep. 9, 2021, from corresponding U.S. Appl. No. 17/334,909.
Restriction Requirement, dated Apr. 10, 2019, from corresponding U.S. Appl. No. 16/277,715.
Restriction Requirement, dated Apr. 13, 2020, from corresponding U.S. Appl. No. 16/817,136.
Restriction Requirement, dated Apr. 24, 2019, from corresponding U.S. Appl. No. 16/278,122.
Restriction Requirement, dated Aug. 7, 2019, from corresponding U.S. Appl. No. 16/410,866.
Restriction Requirement, dated Aug. 9, 2019, from corresponding U.S. Appl. No. 16/404,399.
Restriction Requirement, dated Dec. 17, 2021, from corresponding U.S. Appl. No. 17/475,244.
Restriction Requirement, dated Dec. 31, 2018, from corresponding U.S. Appl. No. 15/169,668.
Restriction Requirement, dated Dec. 9, 2019, from corresponding U.S. Appl. No. 16/565,395.
Restriction Requirement, dated Jan. 18, 2017, from corresponding U.S. Appl. No. 15/256,430.
Restriction Requirement, dated Jul. 28, 2017, from corresponding U.S. Appl. No. 15/169,658.
Restriction Requirement, dated Jun. 15, 2021, from corresponding U.S. Appl. No. 17/187,329.
Restriction Requirement, dated Jun. 15, 2021, from corresponding U.S. Appl. No. 17/222,556.
Restriction Requirement, dated Jun. 9, 2021, from corresponding U.S. Appl. No. 17/222,725.
Notice of Allowance, dated Jan. 24, 2022, from corresponding U.S. Appl. No. 17/340,699.
Notice of Allowance, dated Jan. 25, 2021, from corresponding U.S. Appl. No. 16/410,336.
Notice of Allowance, dated Jan. 26, 2018, from corresponding U.S. Appl. No. 15/619,469.
Notice of Allowance, dated Jan. 26, 2022, from corresponding U.S. Appl. No. 17/491,906.
Notice of Allowance, dated Jan. 29, 2020, from corresponding U.S. Appl. No. 16/278,119.
Notice of Allowance, dated Jan. 5, 2022, from corresponding U.S. Appl. No. 17/475,241.
Notice of Allowance, dated Jan. 6, 2021, from corresponding U.S. Appl. No. 16/595,327.
Notice of Allowance, dated Jan. 6, 2022, from corresponding U.S. Appl. No. 17/407,765.
Notice of Allowance, dated Jan. 7, 2022, from corresponding U.S. Appl. No. 17/222,725.
Notice of Allowance, dated Jan. 8, 2020, from corresponding U.S. Appl. No. 16/600,879.
Notice of Allowance, dated Jul. 10, 2019, from corresponding U.S. Appl. No. 16/237,083.
Notice of Allowance, dated Jul. 10, 2019, from corresponding U.S. Appl. No. 16/403,358.
Notice of Allowance, dated Jul. 12, 2019, from corresponding U.S. Appl. No. 16/278,121.
Notice of Allowance, dated Jul. 14, 2020, from corresponding U.S. Appl. No. 16/701,043.
Notice of Allowance, dated Jul. 15, 2020, from corresponding U.S. Appl. No. 16/791,006.
Notice of Allowance, dated Jul. 16, 2020, from corresponding U.S. Appl. No. 16/901,979.
Notice of Allowance, dated Jul. 17, 2019, from corresponding U.S. Appl. No. 16/055,961.
Notice of Allowance, dated Jul. 17, 2020, from corresponding U.S. Appl. No. 16/778,709.
Notice of Allowance, dated Jul. 19, 2021, from corresponding U.S. Appl. No. 17/306,252.
Notice of Allowance, dated Jul. 21, 2020, from corresponding U.S. Appl. No. 16/557,392.
Notice of Allowance, dated Jul. 23, 2019, from corresponding U.S. Appl. No. 16/220,978.
Notice of Allowance, dated Jul. 26, 2019, from corresponding U.S. Appl. No. 16/409,673.
Notice of Allowance, dated Jul. 26, 2021, from corresponding U.S. Appl. No. 17/151,399.
Notice of Allowance, dated Jul. 26, 2021, from corresponding U.S. Appl. No. 17/207,316.
Notice of Allowance, dated Jul. 31, 2019, from corresponding U.S. Appl. No. 16/221,153.
Notice of Allowance, dated Jul. 8, 2021, from corresponding U.S. Appl. No. 17/201,040.
Notice of Allowance, dated Jun. 1, 2020, from corresponding U.S. Appl. No. 16/813,321.
Notice of Allowance, dated Jun. 11, 2021, from corresponding U.S. Appl. No. 16/862,948.
Notice of Allowance, dated Jun. 11, 2021, from corresponding U.S. Appl. No. 16/862,952.
Notice of Allowance, dated Jun. 11, 2021, from corresponding U.S. Appl. No. 17/216,436.
Notice of Allowance, dated Jun. 12, 2019, from corresponding U.S. Appl. No. 16/278,123.
Notice of Allowance, dated Jun. 12, 2019, from corresponding U.S. Appl. No. 16/363,454.
Notice of Allowance, dated Jun. 16, 2020, from corresponding U.S. Appl. No. 16/798,818.
Notice of Allowance, dated Jun. 17, 2020, from corresponding U.S. Appl. No. 16/656,895.
Notice of Allowance, dated Jun. 18, 2019, from corresponding U.S. Appl. No. 16/410,566.
Notice of Allowance, dated Jun. 19, 2018, from corresponding U.S. Appl. No. 15/894,890.
Notice of Allowance, dated Jun. 19, 2019, from corresponding U.S. Appl. No. 16/042,673.
Notice of Allowance, dated Jun. 19, 2019, from corresponding U.S. Appl. No. 16/055,984.
Notice of Allowance, dated Jun. 2, 2021, from corresponding U.S. Appl. No. 17/198,581.
Notice of Allowance, dated Jun. 21, 2019, from corresponding U.S. Appl. No. 16/404,439.
Notice of Allowance, dated Jun. 22, 2020, from corresponding U.S. Appl. No. 16/791,337.
Notice of Allowance, dated Jun. 27, 2018, from corresponding U.S. Appl. No. 15/882,989.
Notice of Allowance, dated Jun. 4, 2019, from corresponding U.S. Appl. No. 16/159,566.
Notice of Allowance, dated Jun. 5, 2019, from corresponding U.S. Appl. No. 16/220,899.
Notice of Allowance, dated Jun. 5, 2019, from corresponding U.S. Appl. No. 16/357,260.
Notice of Allowance, dated Jun. 6, 2018, from corresponding U.S. Appl. No. 15/875,570.
Notice of Allowance, dated Jun. 6, 2019, from corresponding U.S. Appl. No. 16/159,628.
Notice of Allowance, dated Jun. 7, 2021, from corresponding U.S. Appl. No. 17/099,270.
Notice of Allowance, dated Jun. 8, 2020, from corresponding U.S. Appl. No. 16/712,104.
Notice of Allowance, dated Mar. 1, 2018, from corresponding U.S. Appl. No. 15/853,674.
Restriction Requirement, dated May 5, 2020, from corresponding U.S. Appl. No. 16/808,489.
Restriction Requirement, dated Nov. 10, 2021, from corresponding U.S. Appl. No. 17/366,754.
Restriction Requirement, dated Nov. 15, 2019, from corresponding U.S. Appl. No. 16/586,202.
Restriction Requirement, dated Nov. 21, 2016, from corresponding U.S. Appl. No. 15/254,901.
Restriction Requirement, dated Nov. 5, 2019, from corresponding U.S. Appl. No. 16/563,744.
Restriction Requirement, dated Oct. 17, 2018, from corresponding U.S. Appl. No. 16/055,984.
Restriction Requirement, dated Oct. 6, 2021, from corresponding U.S. Appl. No. 17/340,699.
Restriction Requirement, dated Sep. 15, 2020, from corresponding U.S. Appl. No. 16/925,628.
Restriction Requirement, dated Sep. 9, 2019, from corresponding U.S. Appl. No. 16/505,426.
Advisory Action, dated Jan. 13, 2021, from corresponding U.S. Appl. No. 16/808,493.
Advisory Action, dated Jan. 13, 2021, from corresponding U.S. Appl. No. 16/862,944.
Advisory Action, dated Jan. 13, 2021, from corresponding U.S. Appl. No. 16/862,948.
Advisory Action, dated Jan. 13, 2021, from corresponding U.S. Appl. No. 16/862,952.
Advisory Action, dated Jan. 6, 2021, from corresponding U.S. Appl. No. 16/808,497.
Advisory Action, dated Jun. 19, 2020, from corresponding U.S. Appl. No. 16/595,342.
Advisory Action, dated Jun. 2, 2020, from corresponding U.S. Appl. No. 16/404,491.
Advisory Action, dated May 21, 2020, from corresponding U.S. Appl. No. 16/557,392.
Written Opinion of the International Searching Authority, dated Jun. 6, 2017, from corresponding International Application No. PCT/US2017/025611.
Written Opinion of the International Searching Authority, dated Aug. 15, 2017, from corresponding International Application No. PCT/US2017/036919.
Written Opinion of the International Searching Authority, dated Aug. 21, 2017, from corresponding International Application No. PCT/US2017/036914.
Written Opinion of the International Searching Authority, dated Aug. 29, 2017, from corresponding International Application No. PCT/US2017/036898.
Written Opinion of the International Searching Authority, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036889.
Written Opinion of the International Searching Authority, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036890.
Written Opinion of the International Searching Authority, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036893.
Written Opinion of the International Searching Authority, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036901.
Written Opinion of the International Searching Authority, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036913.
Written Opinion of the International Searching Authority, dated Aug. 8, 2017, from corresponding International Application No. PCT/US2017/036920.
Written Opinion of the International Searching Authority, dated Dec. 14, 2018, from corresponding International Application No. PCT/US2018/045296.
Written Opinion of the International Searching Authority, dated Dec. 22, 2021, from corresponding International Application No. PCT/US2021/051217.
Written Opinion of the International Searching Authority, dated Jan. 14, 2019, from corresponding International Application No. PCT/US2018/046949.
Written Opinion of the International Searching Authority, dated Jan. 5, 2022, from corresponding International Application No. PCT/US2021/050497.
Written Opinion of the International Searching Authority, dated Jan. 7, 2019, from corresponding International Application No. PCT/US2018/055772.
Written Opinion of the International Searching Authority, dated Jun. 21, 2017, from corresponding International Application No. PCT/US2017/025600.
Written Opinion of the International Searching Authority, dated Jun. 6, 2017, from corresponding International Application No. PCT/US2017/025605.
Written Opinion of the International Searching Authority, dated Mar. 14, 2019, from corresponding International Application No. PCT/US2018/055736.
Written Opinion of the International Searching Authority, dated Mar. 4, 2019, from corresponding International Application No. PCT/US2018/055773.
Written Opinion of the International Searching Authority, dated Mar. 4, 2019, from corresponding International Application No. PCT/US2018/055774.
Written Opinion of the International Searching Authority, dated Nov. 12, 2021, from corresponding International Application No. PCT/US2021/043481.
Written Opinion of the International Searching Authority, dated Nov. 19, 2018, from corresponding International Application No. PCT/US2018/046939.
Written Opinion of the International Searching Authority, dated Nov. 3, 2021, from corresponding International Application No. PCT/US2021/040893.
Written Opinion of the International Searching Authority, dated Nov. 3, 2021, from corresponding International Application No. PCT/US2021/044910.
Written Opinion of the International Searching Authority, dated Oct. 11, 2018, from corresponding International Application No. PCT/US2018/043975.
Written Opinion of the International Searching Authority, dated Oct. 11, 2018, from corresponding International Application No. PCT/US2018/043976.
Written Opinion of the International Searching Authority, dated Oct. 11, 2018, from corresponding International Application No. PCT/US2018/043977.
Written Opinion of the International Searching Authority, dated Oct. 11, 2018, from corresponding International Application No. PCT/US2018/044026.
Written Opinion of the International Searching Authority, dated Oct. 11, 2018, from corresponding International Application No. PCT/US2018/045240.
Written Opinion of the International Searching Authority, dated Oct. 12, 2017, from corresponding International Application No. PCT/US2017/036888.
Written Opinion of the International Searching Authority, dated Oct. 12, 2018, from corresponding International Application No. PCT/US2018/044046.
Written Opinion of the International Searching Authority, dated Oct. 16, 2018, from corresponding International Application No. PCT/US2018/045243.
Written Opinion of the International Searching Authority, dated Oct. 18, 2018, from corresponding International Application No. PCT/US2018/045249.
Final Office Action, dated Oct. 27, 2022, from corresponding U.S. Appl. No. 17/346,586.
Notice of Allowance, dated Sep. 28, 2022, from corresponding U.S. Appl. No. 17/509,974.
Notice of Allowance, dated Sep. 28, 2022, from corresponding U.S. Appl. No. 17/689,683.
Office Action, dated Aug. 4, 2022, from corresponding U.S. Appl. No. 17/828,953.
Office Action, dated Sep. 16, 2022, from corresponding U.S. Appl. No. 17/306,438.
Notice of Allowance, dated Nov. 22, 2022, from corresponding U.S. Appl. No. 17/828,953.
Office Action, dated Dec. 21, 2022, from corresponding U.S. Appl. No. 17/013,756.
Office Action, dated Apr. 13, 2023, from corresponding U.S. Appl. No. 16/925,550.
Final Office Action, dated Feb. 23, 2023, from corresponding U.S. Appl. No. 17/370,650.
Final Office Action, dated Mar. 3, 2023, from corresponding U.S. Appl. No. 17/306,438.
Notice of Allowance, dated Feb. 8, 2023, from corresponding U.S. Appl. No. 17/831,700.
Notice of Allowance, dated Mar. 8, 2023, from corresponding U.S. Appl. No. 17/530,201.
Office Action, dated Feb. 15, 2023, from corresponding U.S. Appl. No. 17/499,852.
Office Action, dated Mar. 16, 2023, from corresponding U.S. Appl. No. 17/670,341.
Office Action, dated Mar. 16, 2023, from corresponding U.S. Appl. No. 17/494,220.
Office Action, dated Mar. 9, 2023, from corresponding U.S. Appl. No. 17/306,496.
Final Office Action, dated Mar. 9, 2023, from corresponding U.S. Appl. No. 17/679,734.
Office Action, dated Apr. 4, 2023, from corresponding U.S. Appl. No. 17/670,354.
Office Action, dated Apr. 4, 2023, from corresponding U.S. Appl. No. 17/346,586.

Related Publications (1)

	Number	Date	Country
	20220237190 A1	Jul 2022	US

Provisional Applications (1)

	Number	Date	Country
	63141216	Jan 2021	US

Systems and methods for discovery, classification, and indexing of data in a native computing system

Information

Patent Number

Date Filed

Date Issued

Inventors

Original Assignees

Examiners

Agents

CPC

Field of Search

CPC

International Classifications

Abstract