DCT

1:26-cv-01141

SecureNet Solutions Group LLC v. Flock Group Inc

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I. Executive Summary and Procedural Information

  • Parties & Counsel:
  • Case Identification: 1:26-cv-01141, D. Del., 09/10/2026
  • Venue Allegations: Venue is alleged in the District of Delaware based on Defendant's residence in the state and its commission of infringing acts within the district.
  • Core Dispute: Plaintiff alleges that Defendant’s automated license plate reader (ALPR) cameras and integrated video analytics platforms infringe seven U.S. patents related to the correlation, analysis, and management of data from computerized security systems.
  • Technical Context: The technology at issue pertains to advanced surveillance systems that process, correlate, weight, and manage data from heterogeneous sensors to identify security threats, a key function in modern public safety and law enforcement technology.
  • Key Procedural History: The asserted patents are all related and share a common specification. Plaintiff alleges it provided Defendant with actual notice of infringement of one or more of the asserted patents via a letter dated January 27, 2026, which is cited as a basis for willful infringement.

Case Timeline

Date Event
2007-10-04 Earliest Priority Date for all Asserted Patents
2016-05-17 U.S. Patent No. 9,344,616 Issues
2017-04-11 U.S. Patent No. 9,619,984 Issues
2020-03-10 U.S. Patent No. 10,587,460 Issues
2020-12-08 U.S. Patent No. 10,862,744 Issues
2022-05-03 U.S. Patent No. 11,323,314 Issues
2024-03-12 U.S. Patent No. 11,929,870 Issues
2025-07-29 U.S. Patent No. 12,375,342 Issues
2026-01-27 Alleged Date of Notice Letter to Defendant
2026-09-10 Complaint Filed

II. Technology and Patent(s)-in-Suit Analysis

U.S. Patent No. 12,375,342 - "Correlation engine for correlating sensory events"

  • Patent Identification: U.S. Patent No. 12,375,342, "Correlation engine for correlating sensory events," issued July 29, 2025.

The Invention Explained

  • Problem Addressed: The patent documentation, as described in the complaint, identifies that prior-art smart surveillance systems could not handle the large volume of data from heterogeneous sensors, leading to false alarms and an inability to distinguish routine activity from suspicious activity Compl. ¶11 These systems also failed to account for differences in data quality among various sensors, treating data from an old analog camera the same as data from a newer digital camera, which could thwart threat detection Compl. ¶50
  • The Patented Solution: The invention claims a system that processes data through a specific architecture involving event analysis, normalization, correlation, and, critically, "machine-executed probabilistic weighting" Compl. ¶¶12-14 The system ingests sensory data, stores it in an event database, and evaluates historical correlations across time and space Compl. ¶59 A core aspect of this solution is the weighting of input data based on "attribute data" of the sensors—such as their age, reliability, or maintenance history—to lower false alarm rates and improve detection accuracy (’616 patent at 2:9-13; Compl. ¶51). This allows the system to assign greater significance to data from more reliable sources Compl. ¶51
  • Technical Importance: This approach enabled automated systems to detect complex anomalies distributed across disparate sensors and time periods, which would not be revealed by any single observation, thereby improving the overall functionality of large-scale surveillance operations Compl. ¶12 Compl. ¶58

Key Claims at a Glance

  • The complaint asserts claims 12, 17, and 20 Compl. ¶1 Independent claims 1 and 20 are quoted in the complaint Compl. ¶120
  • Independent Claim 20: The essential elements include a non-transitory storage medium with code causing a processor to:
    • receive one or more sensory events (face, vehicle, or license plate detected) from an analytics module that processes data from at least one IP video camera;
    • store the sensory events for later retrieval;
    • evaluate one or more historical correlations among the stored sensory events across time and/or space, based on at least a weighting of the stored sensory events; and
    • store data from the sensors using a hierarchical storage manager adapted to manage and cascade data based at least on the sensory events.
  • The complaint also asserts dependent claims 12 (adding tip-data processing) and 17 (adding a hierarchical storage manager to claim 1) Compl. ¶120

U.S. Patent No. 11,323,314 - "Hierarchical data storage and correlation system for correlating and storing sensory events in a security and safety system"

  • Patent Identification: U.S. Patent No. 11,323,314, "Hierarchical data storage and correlation system for correlating and storing sensory events in a security and safety system," issued May 3, 2022.

The Invention Explained

  • Problem Addressed: The patent documentation describes the problem arising from the "sheer volume of sensor data" in modern surveillance systems Compl. ¶72 Storing enormous amounts of data (e.g., 6 TB per day) on high-speed devices for fast retrieval is "beyond the ability of most organizations" '616 patent at 10:67-11:3 Compl. ¶72
  • The Patented Solution: The invention discloses a Hierarchical Storage Manager (HSM) that automates the movement of data between faster, higher-cost storage (e.g., cache) and slower, lower-cost storage (e.g., tape) '616 patent at 11:6-14 Compl. ¶72 This "cascading" of data is based on the data segment's "importance," a value calculated as a weighted average of various attributes, including the resolution of the data, the age of the camera, time since last maintenance, and the type of event detected '616 patent at 11:55-12:9 Compl. ¶73 This ensures that the most operationally critical data remains accessible at high speed while preventing system failure from data overload Compl. ¶77
  • Technical Importance: This technology provides a formal, event-driven framework for managing the massive data volumes produced by modern surveillance systems, addressing a key bottleneck in data storage and retrieval that limited the effectiveness of real-time threat detection Compl. ¶76 Compl. ¶77

Key Claims at a Glance

  • The complaint asserts claims 13 and 21 Compl. ¶133 Independent claims 1 (a method claim) and 21 (a system claim) are quoted in the complaint Compl. ¶133
  • Independent Claim 1: The essential elements include a non-transitory storage medium with code causing a processor to:
    • receive sensory events (e.g., face, vehicle, license plate, object size/speed) from an analytics module processing data from at least one IP video camera;
    • implement a hierarchical storage manager to manage and cascade data through a storage hierarchy based at least on the sensory events;
    • use an event queue and database to store the sensory events for later retrieval;
    • use a correlation module to evaluate historical correlations among stored events to identify critical events; and
    • use an alerting module to send alerts based on critical events, with all module communication occurring over an IP network.
  • The complaint reserves the right to assert dependent claim 13, which adds that sensory events are weighted based on sensor attribute data Compl. ¶133

Multi-Patent Capsule: U.S. Patent No. 9,344,616

  • Patent Identification: U.S. Patent No. 9,344,616, "Correlation engine for security, safety, and business productivity," issued May 17, 2016 Compl. ¶145
  • Technology Synopsis: The patent describes a system that receives sensory and IP data from sensors, normalizes the data, stores it, and evaluates historical correlations to monitor for critical events Compl. ¶147 The system also monitors the network status of the sensors to identify network failures and sends alerts based on critical events or network failures Compl. ¶147
  • Asserted Claims: Claim 48 Compl. ¶147
  • Accused Features: The complaint alleges that Flock’s Falcon® cameras, Vehicle Fingerprint® technology, and FlockOS® platform infringe by detecting events, processing IP and network-status information, normalizing and storing data, and evaluating historical correlations to generate alerts Compl. ¶¶148-155

Multi-Patent Capsule: U.S. Patent No. 11,929,870

  • Patent Identification: U.S. Patent No. 11,929,870, "Correlation engine for correlating sensory events," issued March 12, 2024 Compl. ¶159
  • Technology Synopsis: The patent describes a system for receiving and processing sensory events, storing them, and using a correlation engine to evaluate historical correlations based on weighting of the stored events with attribute data Compl. ¶161 The system is also adapted to process tip data and implement hierarchical storage management Compl. ¶161
  • Asserted Claims: Claims 1, 12, and 20 Compl. ¶161
  • Accused Features: The complaint alleges infringement by the Flock System’s features for receiving tip data, classifying sensory events, weighting them using confidence-based scoring, evaluating stored events, and implementing hierarchical storage Compl. ¶¶162-165

Multi-Patent Capsule: U.S. Patent No. 9,619,984

  • Patent Identification: U.S. Patent No. 9,619,984, "Systems and methods for correlating data from IP sensor networks for security, safety, and business productivity applications," issued April 11, 2017 Compl. ¶169
  • Technology Synopsis: The patent claims a monitoring system that receives and processes sensory and IP data, normalizes and stores it, and evaluates historical correlations to monitor for critical events and network failures in real-time Compl. ¶171 The asserted dependent claim adds weighting of events based on attribute data Compl. ¶171
  • Asserted Claims: Claim 10 Compl. ¶171
  • Accused Features: The complaint alleges infringement by the Flock System's functions for receiving sensory and IP data, storing events, normalizing information, evaluating historical correlations, and monitoring network status to generate alerts, allegedly using confidence scoring as a form of weighting Compl. ¶¶172-181

Multi-Patent Capsule: U.S. Patent No. 10,587,460

  • Patent Identification: U.S. Patent No. 10,587,460, "Systems and Methods for Correlating Sensory Events and Legacy System Events Utilizing a Correlation Engine for Security, Safety, and Business Productivity," issued March 10, 2020 Compl. ¶185
  • Technology Synopsis: The patent describes a monitoring system that integrates data from primitive sensory events, legacy system events (e.g., CAD, RMS), and network events Compl. ¶187 It normalizes these disparate event types, stores them, and uses a correlation engine to evaluate historical correlations and send alerts, with asserted dependent claims adding weighting based on sensor age and maintenance history Compl. ¶187
  • Asserted Claims: Claims 11, 13, and 14 Compl. ¶187
  • Accused Features: The complaint alleges FlockOS® provides the accused legacy event analytics by integrating with systems like NCIC and CAD Compl. ¶189 It further alleges infringement through network analytics, normalization, correlation, alerting, and weighting based on sensor age and maintenance information Compl. ¶¶188-192

Multi-Patent Capsule: U.S. Patent No. 10,862,744

  • Patent Identification: U.S. Patent No. 10,862,744, "Correlation System for Correlating Sensory Events and Legacy System Events," issued December 8, 2020 Compl. ¶196
  • Technology Synopsis: The patent claims a monitoring system that processes and correlates data from both sensory event sources (e.g., IP cameras) and legacy systems (e.g., access control, personnel, law enforcement systems) Compl. ¶198 The system stores these events, analyzes historical correlations, and sends alerts, with asserted dependent claims adding event weighting and hierarchical storage management Compl. ¶198
  • Asserted Claims: Claims 16 and 26 Compl. ¶198
  • Accused Features: The complaint alleges FlockOS® provides the infringing legacy event analytics by integrating with systems like NCIC, CAD, and RMS Compl. ¶199 It further alleges infringement through hierarchical storage management across its three-tier architecture Compl. ¶200

III. The Accused Instrumentality

Product Identification

  • The accused instrumentality is the "Flock System," an integrated set of products and services that includes Flock Safety's Falcon® LPR cameras, Vehicle Fingerprint® technology, FlockOS® Real-Time Crime Center platform, Enhanced LPR software, Flock Safety Platform, and Raven® audio detection device Compl. ¶2

Functionality and Market Context

  • The Flock System is described as a network of solar-powered, LTE-connected cameras that capture images of vehicles and license plates from the physical environment Compl. ¶100 Cloud-based machine learning software processes these images to identify license plate characters and vehicle attributes (e.g., color, make, roof racks), generating a searchable "Vehicle Fingerprint" Compl. ¶101 A diagram in the complaint illustrates this as a "Detect / Decode / Deliver" workflow Compl. p. 36
  • The FlockOS® platform is alleged to unify data from various sources, including LPR, video, sensors, computer-aided dispatch (CAD), records management systems (RMS), 911, and gunshot detection, to analyze real-time data, link events, and deliver alerts Compl. ¶103 A depiction from Flock's materials shows FlockOS connecting these disparate devices, systems, and agencies in a common real-time view Compl. p. 38
  • The complaint alleges that the Flock System uses "confidence-based scoring" to process vehicle characteristics, with adjustable confidence levels for different attributes like vehicle body, color, and make Compl. ¶106 A screenshot of the "Vehicle Fingerprint" user interface is provided as evidence of these adjustable confidence levels Compl. p. 39 The system also allegedly self-monitors for connectivity and functionality, and low-confidence plate reads are not sent to the user Compl. ¶106

IV. Analysis of Infringement Allegations

’342 Patent Infringement Allegations

Claim Element (from Independent Claim 20) Alleged Infringing Functionality Complaint Citation Patent Citation
receive one or more sensory events from a sensory event analytics module that receives sensory data about a physical environment from one or more sensors and processes the sensory data from the one or more sensors to detect the one or more sensory events, wherein the one or more sensors comprise at least an Internet Protocol (IP) video camera, and wherein the one or more sensory events are selected from the group consisting of a face detected, a vehicle detected, and a license plate detected; The Falcon® LPR cameras are alleged to be IP-connected cameras that capture sensory data (video) and use Vehicle Fingerprint® technology with on-device processing to detect vehicles and license plates. ¶122 col. 45:1-14
store the sensory events for later retrieval as stored sensory events; Flock allegedly stores detected sensory events in cloud storage for later retrieval via the Flock Safety Platform, maintaining timestamped event and classification metadata. ¶123 col. 45:15-17
evaluate one or more historical correlations among the stored sensory events, by evaluating the stored sensory events for the one or more historical correlations across at least one of time and space based on at least weighting of the stored sensory events; Enhanced LPR is alleged to evaluate stored events for historical correlations across time and space through features like Multi-Geo Search and Convoy Search, while Vehicle Fingerprint® allegedly applies confidence-based scoring and trait-specific thresholds as a form of weighting. ¶124; ¶125 col. 46:1-6
and store data from the one or more sensors utilizing a hierarchical storage manager having access to a hierarchy of two or more data storage devices, wherein the hierarchical storage manager is adapted to manage storage and cascade of data through the hierarchy of two or more data storage devices based at least on the sensory events. The Flock System allegedly implements hierarchical storage across on-device temporary storage and AWS cloud storage, using an event-driven cascade with automated retention and deletion rules. ¶127 col. 46:7-16

’314 Patent Infringement Allegations

Claim Element (from Independent Claim 1) Alleged Infringing Functionality Complaint Citation Patent Citation
receive one or more sensory events from a sensory event analytics module that receives sensory data about a physical environment from one or more sensors and processes the sensory data...to detect the one or more sensory events, wherein the one or more sensors comprises at least an Internet Protocol (IP) video camera, and wherein the one or more sensory events are selected from the group consisting of a face detected, a vehicle detected, a license plate detected, a size of an object, and a speed of an object; The Falcon® cameras are alleged to capture sensory data and process it with Vehicle Fingerprint® technology to detect vehicles and license plates. The Raven® audio detection device is alleged to detect gunshot events. ¶135; ¶136 col. 49:2-15
a hierarchical storage manager...adapted to manage storage and cascade of data through the hierarchy of two or more data storage devices based at least on the sensory events; The Flock System is alleged to implement hierarchical storage management across on-device temporary storage, AWS cloud storage, and AWS GovCloud, using an event-driven cascade with automated rules. ¶141 col. 49:16-27
an event queue having access to an event database to store the sensory events for later retrieval as stored sensory events; The Flock System allegedly stores detected events for later retrieval and maintains timestamped event and classification metadata. ¶137 col. 49:25-29
a correlation module to evaluate one or more historical correlations among the stored sensory events...to identify one or more critical events...based at least on the one or more historical correlations; and Enhanced LPR is alleged to evaluate stored event records for historical correlations across time and space using features like Multi-Geo Search and Convoy Search. ¶138 col. 49:30-39
an alerting module to send one or more alerts based on the one or more critical events, wherein communication between the...modules occurs over an IP network. FlockOS® is alleged to provide real-time alerts, with communication among cameras, the FlockOS® platform, and the cloud occurring over IP networks, including via LTE-connected cameras. ¶139; ¶140 col. 49:40-47

Identified Points of Contention

  • Scope Questions: A central dispute may arise over whether the term "weighting...based at least on...attribute data," as described in the patents, reads on the "confidence-based scoring" and "trait-specific thresholds" allegedly used by Defendant's Vehicle Fingerprint® system (Compl. ¶124). The complaint alleges this functionality infringes, but the analysis may turn on whether a confidence score, which might be used for filtering or ranking, is legally and technically equivalent to the patents' described process of applying probabilistic weights derived from sensor attributes to lower false alarm rates Compl. ¶51
  • Technical Questions: A key question will be whether Flock's storage architecture, which allegedly uses on-device temporary storage, AWS cloud storage, and automated deletion rules (Compl. ¶127; Compl. ¶141), meets the "hierarchical storage manager" limitation. The analysis will likely focus on whether a time-based retention policy is equivalent to the patents' described "cascade" of data based on a calculated, multi-factor "importance" value derived from sensory events and their attributes ('616 patent at 12:10-32).

V. Key Claim Terms for Construction

  • The Term: "weighting of the stored sensory events... based at least on one or more attribute data associated with the sensory data" (’342 Patent, cl. 20)

  • Context and Importance: This term is critical because infringement hinges on whether the accused Flock System's "confidence-based scoring" Compl. ¶106 constitutes the claimed "weighting." Practitioners may focus on this term as it represents a core technical distinction the patentee asserts over the prior art—moving beyond simple data collection to machine-evaluated data significance.

  • Intrinsic Evidence for Interpretation:

    • Evidence for a Broader Interpretation: The specification describes the need for a method that "weights input data from disparate systems to lower false alarm rates" ('616 patent at 2:10-13), suggesting a functional goal. It also provides an example formula for a "weighted average" ('616 patent at 34:36-43), which could support interpreting the term to cover any numerical scoring system that influences how data is processed or prioritized.
    • Evidence for a Narrower Interpretation: The specification also describes weights as "probabilistic weights" and part of "multi-dimensional" weight vectors '616 patent at 31:24-31 This language, along with detailed diagrams of how different data sets (video, metadata, weights) interrelate '616 patent, Fig. 10, may support an argument that the term requires a more complex mathematical framework than a simple confidence score for filtering results.

  • The Term: "hierarchical storage manager... adapted to manage storage and cascade of data... based at least on the sensory events" (’314 Patent, cl. 1)

  • Context and Importance: This term's construction is central to whether Flock's use of on-device and cloud storage infringes. The dispute may focus on whether a standard data lifecycle policy (e.g., store locally, upload to cloud, delete after 30 days) is equivalent to the patent's specific, event-driven management system.

  • Intrinsic Evidence for Interpretation:

    • Evidence for a Broader Interpretation: The patent describes the benefit of HSM as moving "unused data... to lower level storage devices and frees up higher level (faster) storage devices" '616 patent at 11:25-28 This functional description could support a broad reading that covers any system moving data from temporary, fast storage to archival, slower storage based on rules.
    • Evidence for a Narrower Interpretation: The specification details a specific method where data is "cascaded" based on a calculated "importance ('Y')" value, which is itself a weighted average of numerous attributes like data resolution, camera age, time since last access, and associated events '616 patent at 11:55-12:32 This detailed embodiment may support a narrower construction requiring the storage management to be explicitly driven by a multi-factor, event-based importance score, not just a simple time-based retention policy.

VI. Other Allegations

  • Indirect Infringement: The complaint alleges induced infringement, stating that Defendant provides product documentation, deployment guidance, and technical support that instruct and encourage customers and partners to configure and use the Accused Products in ways that practice the asserted claims Compl. ¶206 It specifically notes that Defendant encourages integration with third-party systems like CAD and RMS, which allegedly leads to infringement of claims covering legacy system integration Compl. ¶207
  • Willful Infringement: The complaint alleges willful infringement based on pre-suit knowledge. It states that Defendant had actual notice of the asserted patents and their alleged infringement "at least as of January 27, 2026, when a letter was sent to Dan Haley, Flock's Chief Legal Officer" Compl. ¶129 The complaint alleges that Defendant continued its infringing activities despite this knowledge Compl. ¶129

VII. Analyst’s Conclusion: Key Questions for the Case

  • A core issue will be one of functional equivalence: does the accused "confidence-based scoring" system, which uses numerical thresholds to rank or filter vehicle recognition results, perform substantially the same function, in substantially the same way, to achieve substantially the same result as the claimed "weighting... based on... attribute data"? The case may turn on whether Flock's system is merely a filtering mechanism or if it embodies the patent's described probabilistic process for improving the computer's own analytical accuracy.

  • A second central question will be one of definitional scope: can the term "hierarchical storage manager," described in the patent as a system that "cascades" data based on a multi-factor, event-driven "importance" score, be construed to cover Flock's alleged use of temporary on-device storage followed by cloud archival with a time-based retention policy? The court will likely have to determine if a standard data lifecycle management policy qualifies as the specific, event-centric storage architecture claimed in the patents.