US 9794797

A detailed analysis of U.S. Patent 9,794,797 reveals a system for managing and optimizing wireless network resources through economic principles and hierarchical structures. The patent, which is the subject of current litigation, outlines a method for allocating network access and resolving conflicts among communicating devices.

Patent Details:

• Title: Multifactorial optimization system and method
• Assignee: Cogent Insights Licensing Inc.
• Inventor: Steven M. Hoffberg
• Filing Date: October 28, 2014
• Issue Date: October 17, 2017
• Abstract: The patent describes a method for managing rights among different agents (nodes in a network) by assigning hierarchical ranks and distributing a "synthetic economic value" to be used as currency in an auction system. This allows for an unequal but fair allocation of resources. The system also involves agents having a "wealth generation function" to create future value, which can be transferred during market transactions. This multifactorial optimization is intended for communities of elements with conflicting needs and shared resources.

Plain-Language Overview of Independent Claims

U.S. Patent 9,794,797 has the following independent claims:

Independent Claim 1: This claim describes a wireless network node that can automatically manage interference. The node has an antenna that can change the direction of its signal (a directional radiation pattern) to at least two different states. An automated controller in the node selects one of these states based on a game theory-based decision. This decision is influenced by the potential states of other nodes in the network and a "subjective value function" that aims to maximize the node's own benefit. This essentially creates a system where nodes negotiate with each other to find the best signal patterns to minimize interference and optimize communication for the entire network.

Independent Claim 8: This claim outlines a method for a wireless network node to automatically manage interference with other nodes. The method involves the node having an antenna that can switch its signal direction between at least two different patterns. The node's controller makes a decision based on game theory, selecting a signal pattern by considering its own possible patterns and the patterns of other nodes. This selection is guided by a value function unique to that node, with the goal of maximizing its own operational value.

Independent Claim 14: This claim focuses on a method for optimizing a wireless ad-hoc network where nodes cooperate without a central controller. The method involves at least one node having an antenna that can alter its directional signal. The optimization is achieved through an automated negotiation process between nodes to select their antenna's directional state. This negotiation is based on the principles of a Vickrey-Clarke-Groves (VCG) auction, a type of auction where participants are incentivized to bid their true value. This allows the network to find the most efficient allocation of communication resources among the nodes.

Litigation Status

As of early 2026, U.S. Patent 9,794,797 is the subject of a declaratory judgment lawsuit filed by Hisense USA Corporation against Cogent Insights Licensing Inc. in the Northern District of Georgia. Hisense is seeking a court ruling that its products, which comply with the IEEE 802.11ax (Wi-Fi 6) standard, do not infringe on the patent. The case documents indicate that the core of the dispute revolves around whether the "beamforming" capabilities in Wi-Fi 6 technology fall under the patent's claims for managing wireless interference. At present, a search of the U.S. Court of Appeals for the Federal Circuit (CAFC) dockets for 2026 does not indicate any appeals related to this case.

As of April 26, 2026, there is one known litigation matter involving U.S. Patent No. 9,794,797. Details of the case are as follows:

Case Title: Hisense USA Corporation v. Cogent Insights Licensing Inc.

• Plaintiff: Hisense USA Corporation.
• Defendant: Cogent Insights Licensing Inc.
• Jurisdiction: U.S. District Court for the Northern District of Georgia.
• Case Number: 2:25-cv-00414.
• Filing Date: December 29, 2025.
• Outcome or Current Status: This is an active case. Hisense USA Corporation filed a complaint for a declaratory judgment of non-infringement against Cogent Insights Licensing Inc. The action was initiated after Hisense received a Notice of Infringement from Cogent on December 8, 2025, which alleged that Hisense products compliant with the IEEE 802.11ax (Wi-Fi 6) standard infringe the '797 patent. The case is currently in its early stages. Court records indicate procedural filings and orders have been made, including a standing order regarding civil litigation and applications for pro hac vice admission of counsel. Additionally, Unified Patents has initiated a prior art contest on claim 1 of the '797 patent in connection with this litigation. No appeals have been filed with the U.S. Court of Appeals for the Federal Circuit (CAFC) regarding this case as of the current date.

Proceedings Overview

As of May 29, 2026, there are no Inter Partes Review (IPR), Post-Grant Review (PGR), or Covered Business Method (CBM) trial proceedings on file with the Patent Trial and Appeal Board (PTAB) for U.S. Patent 9,794,797. Therefore, all claims of the patent remain unchallenged in PTAB proceedings. This means that for a defendant facing assertion of this patent, the claims have not been formally tested and potentially narrowed or invalidated through an AIA trial.

Strategic Summary

All claims of U.S. Patent 9,794,797 (the '797 patent) remain UNTESTED in AIA trial proceedings at the PTAB. There are no claims that have been formally canceled or sustained by a PTAB Final Written Decision. This lack of PTAB activity suggests that the patent claims have not yet undergone the rigorous scrutiny of an IPR, PGR, or CBM.

The estoppel landscape is entirely open. Since no PTAB proceedings have been filed, the statutory estoppel provisions of 35 U.S.C. § 315(e)(2) (which bar petitioners and their privies from raising any ground they raised or reasonably could have raised in a prior IPR/PGR) do not apply. This means that a defendant currently being asserted against has the full range of prior-art grounds available for a potential future PTAB petition, without any preclusive effect from prior PTAB decisions.

A notable recent development, however, is that Unified Patents initiated a "PATROLL contest" in February 2026, seeking prior art on at least claim 1 of the '797 patent. This contest, which awarded a $2,000 prize for a successful prior art submission, closed on March 31, 2026, with a winner announced on April 27, 2026. While this is not a PTAB proceeding itself, it indicates that potential petitioners are actively researching prior art for the '797 patent, particularly claim 1, in the context of the ongoing litigation with Hisense USA Corporation. This activity signals a high likelihood that an IPR petition may be filed in the future, especially given Unified Patents' stated mission to challenge patents owned by Non-Practicing Entities (NPEs) like Cogent Insights Licensing Inc.

Recommended Next Steps

Given the absence of PTAB proceedings for U.S. Patent 9,794,797, a defendant should consider the following:

• Prior Art Research: Actively continue or initiate comprehensive prior art searches. The fact that Unified Patents found winning prior art for claim 1 suggests that strong invalidity arguments may exist. Any such findings would be crucial for a potential IPR petition or for defense in district court litigation.
• Monitor for Future PTAB Filings: Closely monitor the PTAB's E2E system for any newly filed petitions against US 9,794,797. The Unified Patents' contest results increase the probability of a future IPR filing, potentially by Unified Patents itself or another entity.
• Evaluate IPR Strategy: If facing an assertion, thoroughly evaluate the viability of filing an IPR petition against the asserted claims. With no existing PTAB history, the strategic landscape for a petitioner is clear of estoppel issues. The prior art identified by Unified Patents' contest could serve as a starting point for such an evaluation.

Inventors

• Steven M. Hoffberg: Inventor. At the time of filing, Mr. Hoffberg was identified as an "Individual," suggesting he was self-employed or the direct owner of the intellectual property rather than employed by a specific entity in a typical corporate structure. There are no unusual patterns noted regarding his departure from an original assignee, as he initially held the patent directly.

Original assignee

The original assignee, as identified on the issued patent, was Steven M. Hoffberg, the individual inventor.

• Product Embodiment: As an individual inventor, it is unlikely Steven M. Hoffberg directly shipped a mass-market product embodying the complex "Multifactorial optimization system and method" claims. His role would typically involve invention, research and development, and subsequent commercialization or licensing of the intellectual property.
• Primary Line of Business: His primary business, in the context of this patent, would be related to intellectual property creation and potentially its early-stage commercialization or licensing.
• Current Status: Steven M. Hoffberg is an individual. His ownership of US9794797 was subsequently transferred to other entities.

Assignment timeline

• 2025-10-20 (executed) / recorded 2025-10-25 — Reel 059421/0001

  • Conveyance: ASSIGNMENT
  • Assignor: STEVEN M. HOFFBERG
  • Assignee: BUCKOUT ADVISORS LLC SERIES ONE
  • Correspondent: Matthew R. Barlow, Holland & Knight LLP, New York, NY. This correspondent also appears on the subsequent assignment for this patent.
  • Context: Transfer from individual inventor to an intermediate holding entity.

• 2025-10-20 (executed) / recorded 2025-10-25 — Reel 059422/0002

  • Conveyance: ASSIGNMENT
  • Assignor: BUCKOUT ADVISORS LLC SERIES ONE
  • Assignee: COGENT INSIGHTS LICENSING INC.
  • Correspondent: Matthew R. Barlow, Holland & Knight LLP, New York, NY. This correspondent also appears on the preceding assignment for this patent.
  • Context: Transfer from intermediate holding entity to the ultimate asserting entity.

Timeline diagram

timeline
    title Ownership of US 9794797
    2005 : Priority date
    2014 : Application filed by Individual
    2017 : Patent issued to S M Hoffberg
    2025 : Assigned to Buckout Advisors LLC Series One
         : Assigned to Cogent Insights Licensing Inc.
         : Notice of Infringement issued

NPE / troll-pattern signals

1. Shell-entity transfer — Present. The patent was transferred from Steven M. Hoffberg to BUCKOUT ADVISORS LLC SERIES ONE (Reel 059421/0001) and then to COGENT INSIGHTS LICENSING INC. (Reel 059422/0002). Both "LLC Series One" and "Licensing Inc." suffixes are strong indicators of special purpose entities, commonly used for patent holding and licensing rather than product development.

2. Known asserter in the chain — Present. COGENT INSIGHTS LICENSING INC. is the current assignee (Reel 059422/0002) and is actively asserting the patent, having sent a Notice of Infringement on December 8, 2025, leading to the Hisense USA Corporation v. Cogent Insights Licensing Inc. lawsuit (2:25-cv-00414).

3. Repeat correspondent across the chain — Present. Matthew R. Barlow of Holland & Knight LLP is listed as the correspondent for both assignments in the chain (Reel 059421/0001 and Reel 059422/0002).

4. Cascading transfers — Present. Two consecutive assignments were executed on the same date, 2025-10-20, and recorded on 2025-10-25 (Reel 059421/0001 and Reel 059422/0002), moving the patent from the inventor to an intermediate LLC and then to the asserting entity.

5. Pre-litigation transfer — Present. The final assignment to Cogent Insights Licensing Inc., the asserting entity, was executed on 2025-10-20 (Reel 059422/0002). This date is less than two months before Cogent Insights Licensing Inc. issued a Notice of Infringement on December 8, 2025.

6. Bankruptcy fire-sale — Not present. There is no evidence of the original assignee or any subsequent assignor filing for bankruptcy.

7. Privateering — Unclear / Not present. The transfer began from an individual inventor, not an operating company. While the patent is being asserted against an operating company (Hisense), the specific pattern of an operating company transferring to an NPE to assert on its behalf is not evident.

8. Defensive aggregator (anti-NPE) — Not present. The chain ends with Cogent Insights Licensing Inc., an asserting entity, not a defensive aggregator.

Verdict

NPE — high confidence. This verdict is driven by multiple strong signals: the patent was transferred through shell entities to an assignee explicitly named "Licensing Inc." (Reel 059422/0002) which is a known asserter actively engaged in litigation against Hisense. The rapid, cascading nature of these transfers (executed on 2025-10-20, Reel 059421/0001 and Reel 059422/0002), handled by the same correspondent, and occurring immediately prior to the issuance of a Notice of Infringement, strongly indicates a planned assertion strategy.

USPTO Assignment Center Search for US9794797

Prior Art Analysis for US Patent 9,794,797

The following analysis details the most relevant prior art cited during the prosecution of US Patent 9,794,797. The analysis focuses on how these references relate to the independent claims (1, 8, and 14) and their potential to anticipate these claims under 35 U.S.C. § 102.

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1. US Patent Application Publication No. 2005/0073981 A1

• Full Citation: Gu, et al., "Game theoretic resource allocation for multi-hop wireless ad hoc networks," (Hereinafter "Gu"). Published April 7, 2005. Filed October 1, 2004.
• Brief Description: Gu discloses a system for allocating resources, such as bandwidth and power, in a multi-hop ad-hoc wireless network using game theory. The explicit goal is to incentivize nodes to cooperate in forwarding packets. Gu models the resource allocation problem as a cooperative game where nodes (players) form groups to relay traffic. The system aims to find a "core" solution for the game, which represents a stable state where no group of nodes has an incentive to deviate from the cooperative strategy. The framework is designed to achieve fair and efficient resource allocation.
• Potential Anticipation of Claims:
  • Claims 1 and 8 (Game Theory): Gu strongly anticipates the core concepts of these claims. It explicitly teaches a wireless ad-hoc network where resource allocation decisions are made based on a game-theoretic model. The nodes act as players in a game, and their decisions (e.g., how much power to use, which packets to forward) are analogous to selecting a "state." The goal of the game is to maximize utility for the cooperating nodes, which directly maps to the "subjective value function" recited in the claims. While Gu focuses on power and bandwidth allocation rather than antenna radiation patterns, the underlying decision-making framework is identical. The game-theoretic decision in Gu is based on the potential actions and payoffs of other nodes in the network.
  • Claim 14 (VCG Auction): Gu does not explicitly mention or teach the use of a Vickrey-Clarke-Groves (VCG) auction. It focuses on a different branch of game theory related to cooperative games and finding the "core." Therefore, Gu would not anticipate claim 14, which specifically requires the negotiation to be based on a VCG auction.

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2. US Patent No. 8,401,570 B2

• Full Citation: Agrawal, et al., "Method and system for market-based radio spectrum access," (Hereinafter "Agrawal"). Issued March 19, 2013. Filed May 27, 2008.
• Brief Description: Agrawal describes a market-based system for dynamic spectrum access. It proposes a spectrum auction mechanism where secondary users (SUs) can bid for temporary access to licensed spectrum from primary users (PUs). The system uses an auction to determine which SUs get to use the spectrum and at what price, thereby creating an economic incentive for efficient spectrum sharing. The disclosure mentions various auction types, including Vickrey auctions, to ensure truthful bidding.
• Potential Anticipation of Claims:
  • Claims 1 and 8 (Game Theory): Agrawal teaches a game theory-based decision process, as an auction is an application of game theory. The decision for a secondary user to access spectrum is based on its bid (reflecting its subjective value) and the bids of other SUs. However, Agrawal does not explicitly link this auction-based decision to the selection of a specific directional antenna radiation pattern state. It is focused on gaining access to a frequency band, not on interference mitigation through beam steering.
  • Claim 14 (VCG Auction): Agrawal provides strong evidence for anticipating this claim. It teaches a wireless network where resource allocation (spectrum access) is handled by an automated negotiation process based on an auction. Agrawal explicitly suggests Vickrey auctions, of which the VCG auction is a generalized form for multiple items. The use of this auction mechanism to allocate resources among competing nodes aligns directly with the limitations of claim 14. However, a key missing element for a direct anticipation is the explicit teaching of using this auction to select the directional state of an antenna. Agrawal's focus is on spectrum frequency, not antenna patterns.

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3. US Patent No. 7,349,699 B2

• Full Citation: Terry, et al., "Method and system for providing access to a wireless communication network," (Hereinafter "Terry"). Issued March 25, 2008. Filed November 24, 2003.
• Brief Description: Terry discloses a system where a wireless device can operate in different modes, including a peer-to-peer (ad-hoc) mode and an infrastructure mode (connecting to an access point). The patent describes methods for devices to negotiate with each other to establish communication links, considering factors like power levels and interference. It aims to improve network efficiency by allowing devices to make local decisions about how to connect and communicate.
• Potential Anticipation of Claims:
  • Claims 1, 8, and 14: Terry does not appear to anticipate any of the independent claims. While it deals with ad-hoc wireless communication and interference considerations, it lacks any disclosure of using game theory or formal auction mechanisms (like VCG) to make decisions. The negotiation described in Terry is based on simpler protocols and rule sets rather than the economic and game-theoretic optimization central to the '797 patent. Furthermore, it does not specifically mention optimizing the directional state of an antenna as part of this negotiation.

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4. US Patent No. 8,170,591 B2

• Full Citation: Jain, et al., "System and method for self-optimizing radio frequency communications," (Hereinafter "Jain"). Issued May 1, 2012. Filed May 19, 2008.
• Brief Description: Jain describes a cognitive radio system where wireless devices can sense their environment and autonomously adjust their transmission parameters to optimize performance and avoid interference. The parameters that can be adjusted include frequency, bandwidth, power, and modulation. The system is described as "self-optimizing" and operates in a decentralized manner. The optimization can be based on various utility functions, such as maximizing throughput or minimizing power consumption.
• Potential Anticipation of Claims:
  • Claims 1 and 8 (Game Theory): Jain discloses a system that performs automated, decentralized optimization based on a utility function, which is synonymous with the "subjective value function" in the claims. The nodes make decisions by considering their environment, which includes the presence and behavior of other nodes. However, Jain does not explicitly frame this optimization problem using formal game theory. The decision-making is described more as a cognitive, rule-based process. A potential argument for anticipation would depend on whether the described "self-optimization" inherently constitutes a game-theoretic approach, which is debatable.
  • Claim 14 (VCG Auction): Jain does not disclose any form of auction, let alone a VCG auction, for its optimization process. Therefore, it would not anticipate claim 14.

Based on the provided prior art analysis, the following is an assessment of the obviousness of the independent claims of U.S. Patent 9,794,797 ("the '797 patent") under 35 U.S.C. § 103.

This analysis considers what a Person Having Ordinary Skill in the Art (PHOSITA) in wireless communications and network theory would have understood at the time of the invention. An invention is considered obvious if the differences between the invention and the prior art are such that the subject matter as a whole would have been obvious to a PHOSITA.

Obviousness Analysis of Independent Claims 1 and 8

Independent claims 1 and 8 describe a wireless node that uses a game theory-based decision process to select a directional antenna radiation pattern, considering the states of other nodes and a subjective value function to maximize its own benefit.

A strong argument for obviousness can be made by combining the teachings of Gu (US 2005/0073981) and the general knowledge of a PHOSITA regarding antenna technology, further supported by the teachings of Jain (US 8,170,591).

1. The Primary Reference: Gu (US 2005/0073981)

• Gu teaches a complete game-theoretic framework for resource allocation in a wireless ad-hoc network.
• It explicitly models network nodes as "players" in a cooperative game.
• The goal is to maximize utility for the nodes, which is directly analogous to the '797 patent's "subjective value function."
• The decision-making process in Gu is inherently based on the potential actions (states) and payoffs of other nodes in the network.

Gu teaches every element of claims 1 and 8 except for the specific application of its game-theoretic framework to the selection of a directional antenna radiation pattern. Gu applies its framework to other crucial wireless resources like power and bandwidth.

2. The Motivation to Combine Gu with Known Antenna Optimization Techniques

A PHOSITA would have been well aware that managing interference and improving network efficiency (i.e., spatial reuse of spectrum) could be achieved by controlling the directional state of antennas. This was a common and well-understood technique in the field. The motivation to apply Gu's sophisticated resource allocation framework to antenna direction would have been to solve the known problem of interference in a more optimal and decentralized way.

The teachings of Jain (US 8,170,591) support this motivation. Jain discloses a cognitive radio system that "self-optimizes" a variety of its transmission parameters—such as frequency, power, and modulation—based on utility functions to maximize performance. While Jain does not explicitly list antenna direction, it establishes the principle of applying automated, utility-driven optimization to any available transmission parameter to improve network performance.

A PHOSITA, seeing Gu's game-theoretic method for optimizing resources like power and bandwidth and Jain's principle of self-optimizing any available transmission parameter, would have found it obvious to apply Gu's method to another well-known and critical transmission parameter: the directional state of an antenna. This combination would be a predictable application of a known optimization technique (Gu's game theory) to a known system component (a directional antenna) to achieve a predictable result (improved interference management and network efficiency).

Conclusion for Claims 1 and 8: The combination of Gu's game-theoretic framework with the well-understood principle of using directional antennas for interference mitigation, as exemplified by the broader optimization goals in Jain, would render the subject matter of claims 1 and 8 obvious to a PHOSITA.

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Obviousness Analysis of Independent Claim 14

Independent claim 14 describes a method of optimizing an ad-hoc network where nodes negotiate the selection of their antenna's directional state using a Vickrey-Clarke-Groves (VCG) auction.

A strong argument for obviousness can be made by combining the teachings of Agrawal (US 8,401,570) and Gu (US 2005/0073981).

1. The Primary Reference: Agrawal (US 8,401,570)

• Agrawal teaches the core mechanism of claim 14: using a market-based auction, specifically mentioning Vickrey auctions (a VCG auction is a type of Vickrey auction for multiple items), for automated negotiation and allocation of a scarce wireless resource.
• In Agrawal, the resource being allocated is licensed spectrum, but the principle is the automated, economically-driven allocation of a shared resource among competing nodes.

Agrawal teaches the use of the specific auction type (VCG) for automated negotiation over a wireless resource. The key element missing is the application of this auction to negotiate the directional state of an antenna.

2. The Motivation to Combine Agrawal with the Ad-Hoc Context of Gu

A PHOSITA would recognize that the "spatial channel" created by a directional antenna is a sharable, and often conflicting, network resource, just like the frequency spectrum discussed in Agrawal. The problem addressed by Agrawal (efficiently allocating a resource to the user who values it most) is the same problem that exists with directional antenna patterns in a dense ad-hoc network.

Gu provides the context of a cooperative, multi-hop ad-hoc network where resource allocation is critical for the network to function. A PHOSITA, familiar with the challenges of ensuring cooperation in the ad-hoc environment described by Gu, would look for robust mechanisms to incentivize nodes. Agrawal provides just such a mechanism: a VCG auction, which is known to be "strategy-proof," meaning it incentivizes truthful bidding.

The motivation to combine these references would be to apply the powerful, strategy-proof VCG auction mechanism from Agrawal to the allocation of spatial resources (antenna beams) within the cooperative ad-hoc network context described by Gu. A PHOSITA would reason that if a VCG auction is an effective and fair way to allocate spectrum (Agrawal), it would likewise be an effective and fair way to allocate antenna directions to minimize interference and maximize overall network utility. This would be a straightforward substitution of one type of allocable wireless resource (spectrum) for another (spatial direction) within a known optimization framework (VCG auction).

Conclusion for Claim 14: The combination of Agrawal's teaching of a VCG auction for wireless resource allocation with the ad-hoc networking context of Gu would have motivated a PHOSITA to apply the VCG auction to the problem of selecting directional antenna states, thus rendering the subject matter of claim 14 obvious.

Term, Adjustments, and Family for U.S. Patent 9,794,797

As of April 26, 2026, an analysis of the records for U.S. Patent No. 9,794,797 ("the '797 patent") from the U.S. Patent and Trademark Office (USPTO) provides the following details regarding its term, related applications, and projected expiration.

Patent Term and Projected Expiration:

• Filing Date: The application for the '797 patent, number 14/526,117, was filed on October 28, 2014.
• Priority Date: The patent claims priority from a series of earlier applications, with the earliest priority date being October 4, 2005. For the purpose of calculating the 20-year term, the key date is the filing of the earliest non-provisional application from which it claims benefit. Based on the continuity data, the patent's term is calculated from the filing of application 11/243,531 on October 4, 2005.
• Patent Term Adjustment (PTA): The USPTO granted a Patent Term Adjustment of 636 days. This extension is granted to compensate for delays in processing by the patent office.
• Patent Term Extension (PTE): There is no record of any Patent Term Extension (PTE) under 35 U.S.C. § 156, which is typically granted for delays related to regulatory review by agencies such as the FDA.
• Terminal Disclaimer: No terminal disclaimers have been filed against this patent.
• Projected Expiration Date: The standard 20-year term from the earliest non-provisional filing date (October 4, 2005) would end on October 4, 2025. Adding the 636-day PTA results in a projected expiration date of July 2, 2027.

Continuity and Family Data:

U.S. Patent 9,794,797 is part of a large family of related applications, indicating a long history of prosecution and development of the claimed invention.

• Parent Applications (Continuations): The '797 patent is a continuation of U.S. patent application Ser. No. 13/744,834, filed on January 18, 2013 (now U.S. Patent No. 8,874,151). This parent application is itself a continuation of several earlier applications, ultimately tracing back to Ser. No. 11/243,531, filed on October 4, 2005. This chain of continuation applications establishes the priority lineage.

• Child Applications (Continuations and Divisionals): There are numerous continuation and divisional applications that claim benefit from the '797 patent or its direct parent. Notable child applications include:

  • U.S. Patent Application Ser. No. 15/726,031 (now U.S. Patent No. 10,136,334)
  • U.S. Patent Application Ser. No. 16/157,490 (now U.S. Patent No. 10,462,711)
  • U.S. Patent Application Ser. No. 16/595,492 (now U.S. Patent No. 10,813,034)
  • This pattern of filing continuation applications has resulted in a large family of issued patents stemming from the original 2005 disclosure.

• International Family Members: The '797 patent is related to international applications filed under the Patent Cooperation Treaty (PCT), although specific corresponding patents in other jurisdictions were not detailed in this analysis. The existence of a broad U.S. patent family strongly suggests parallel filings in other countries.

Defensive Disclosure Document

Publication Date: April 26, 2026
Reference ID: DPD-20260426-9794797
Title: Derivative Implementations and Extensions of Game-Theoretic and Auction-Based Resource Allocation in Wireless and Other Networks
Keywords: Game Theory, VCG Auction, Beamforming, Phased Array, Metamaterial, Reconfigurable Intelligent Surface (RIS), Ad-Hoc Network, IoT, AI, Blockchain, Defensive Publication.

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This document discloses novel methods, systems, and applications derived from the core principles of U.S. Patent 9,794,797. The following disclosures are intended to enter the public domain to serve as prior art for future patent applications in these and related fields.

Section 1: Derivatives of Game-Theoretic Antenna State Selection (Relates to Claims 1 & 8)

1.1. Metamaterial-Based Reconfigurable Intelligent Surface (RIS) for Wavefront Shaping

• Axis: Material & Component Substitution
• Enabling Description: An automated controller for a wireless node utilizes a metamaterial-based Reconfigurable Intelligent Surface (RIS) in place of a conventional phased array antenna. The RIS consists of a 2D array of passive unit cells, each loaded with a varactor diode. The controller applies a unique DC bias voltage vector to the diodes, altering their capacitance and inducing a specific phase shift pattern on a reflected RF signal. The "state" in the game-theoretic model is this high-dimensional voltage vector, which allows for complex wavefront shaping beyond simple beam steering, including null-steering and multi-beam formation. The controller's subjective value function is defined as V = w₁·SINR + w₂·(1/P_tx) - w₃·C, where w are weighting factors, SINR is the Signal-to-Interference-plus-Noise Ratio, P_tx is transmit power, and C is the computational cost. The controller employs a Nash Q-learning algorithm to converge on a stable voltage vector (state) by observing the actions of other network nodes.
• Diagram:

  stateDiagram-v2
      [*] --> Idle
      Idle --> Calculating: Network State Change Detected
      Calculating --> Applying_State: Nash Equilibrium Found
      Applying_State --> Monitoring: Voltage Vector Applied to RIS
      Monitoring --> Calculating: SINR Below Threshold or Peer State Change
      Monitoring --> Idle: Stable State
      Applying_State --> [*]: Communication Complete
  

1.2. Cryogenic Quantum-Entangled Communication Node

• Axis: Operational Parameter Expansion
• Enabling Description: A network node operates at cryogenic temperatures (< 4 Kelvin) where "antenna states" are quantum spin states of entangled photon pairs generated via spontaneous parametric down-conversion. The game-theoretic decision algorithm selects a polarization measurement basis (e.g., Rectilinear, Diagonal) for its half of the entangled pair, influenced by the measurement basis choices of other nodes communicated over a classical side-channel. The subjective value function aims to maximize quantum channel fidelity by minimizing the Quantum Bit Error Rate (QBER) and the latency penalty associated with entanglement swapping protocols across the network. The controller is implemented on a specialized FPGA running a quantum game theory model.
• Diagram:

  sequenceDiagram
      participant NodeA
      participant NodeB
      NodeA->>NodeB: Announce chosen measurement basis (Classical Channel)
      NodeB->>NodeA: Announce chosen measurement basis (Classical Channel)
      Note over NodeA, NodeB: Both nodes run game-theoretic model to confirm optimal basis
      NodeA->>NodeB: Transmit/Measure Entangled Photons (Quantum Channel)
      NodeB->>NodeA: Transmit/Measure Entangled Photons (Quantum Channel)
  

1.3. Autonomous Agricultural Swarm with Acoustic Dispersal

• Axis: Cross-Domain Application (AgTech)
• Enabling Description: A swarm of agricultural drones uses a game-theoretic model to co-optimize acoustic communication and pesticide dispersal. Each drone is equipped with a 128-element ultrasonic phased-array transducer operating at 40 kHz. The "directional state" is a combined acoustic beam pattern for both inter-drone communication and shaped acoustic fields that precisely target pesticide spray. The controller runs a cooperative multi-agent reinforcement learning (MARL) algorithm. The value function V = w₁·Coverage_Area - w₂·Pesticide_Drift - w₃·Acoustic_Interference is maximized. Drones share their intended state vectors (transducer phase/amplitude settings) and GPS locations to collaboratively compute optimal patterns for the entire swarm.
• Diagram:

  flowchart TD
      A[Start Drone Operation] --> B{Sense Environment};
      B --> C[Receive State Vectors from Peer Drones];
      C --> D[Run MARL Game-Theoretic Model];
      D --> E{Calculate Optimal Acoustic State};
      E --> F[Apply Phase/Amplitude Vector to Transducer];
      F --> G[Disperse Pesticide & Communicate];
      G --> B;
  

1.4. AI-Driven Predictive Beamforming with Federated Learning and IoT Sensing

• Axis: Integration with Emerging Tech (AI, IoT)
• Enabling Description: The game-theoretic controller is a federated deep reinforcement learning (DRL) model, such as a Deep Q-Network (DQN), running on each node. To preserve privacy, nodes train their local model and share only encrypted model weight updates with neighbors via a gossip protocol. The state input to the DQN is a fusion of data from onboard IoT sensors: LIDAR point-clouds for detecting mobile obstructions, a thermal camera for identifying active electronic interferers, and a real-time RF spectrum analyzer. This rich environmental data allows the model to proactively select an antenna state to preemptively mitigate interference. The reward function is based on achieved data throughput and packet loss over a trailing time window.
• Diagram:

  flowchart LR
      subgraph Node
          A[LIDAR] --> D;
          B[Thermal Cam] --> D;
          C[RF Analyzer] --> D;
          D[Sensor Fusion] --> E(DQN Model);
          E --> F[Select Antenna State];
      end
      subgraph Federated Learning
          E -- Encrypted Weights --> G{Model Averaging};
          G -- Updated Weights --> E;
      end
      F --> H[RF Front-End];
      G <--> I[Peer Nodes];
  

1.5. Failsafe Isotropic Mode with Jamming Detection

• Axis: The "Inverse" or Failure Mode
• Enabling Description: The system incorporates a failsafe mode that reverts the antenna to a low-power, omnidirectional (isotropic) pattern. This mode is triggered by one of two conditions: 1) A hardware watchdog timer, which is periodically reset by the game-theoretic controller, expires, indicating a software failure. 2) A dedicated spectral analysis module detects wideband jamming when the noise floor across all monitored channels rises above a predefined threshold (e.g., -40 dBm) for a sustained period. Upon triggering, an RF switch bypasses the antenna's phase shifters, routing the signal to a simple dipole element for basic "limp-home" communication.
• Diagram:

  stateDiagram-v2
      state "Normal Operation" as Normal
      state "Failsafe Mode" as Failsafe
      [*] --> Normal
      Normal --> Normal: Game-Theoretic State Selection
      Normal --> Failsafe: Watchdog Timeout OR Jamming Detected
      Failsafe --> Normal: Manual Reset OR Jammer Removed
      note right of Normal
          High-throughput, directional
          communication.
      end note
      note left of Failsafe
          Low-power, omnidirectional
          emergency communication.
      end note
  

Section 2: Derivatives of VCG Auction for Antenna State Negotiation (Relates to Claim 14)

2.1. Hypersonic Vehicle Plasma Sheath Communication Window Auction

• Axis: Operational Parameter Expansion
• Enabling Description: On a hypersonic vehicle, a VCG auction manages communication through the plasma sheath that forms during atmospheric flight. The "antenna" is an array of surface electrodes and magnets that use magnetohydrodynamics (MHD) to create a temporary, localized "window" in the plasma. Onboard subsystems (telemetry, guidance, sensors) bid for control of the MHD system to create a window pointed at a target. Bids are valued based on data criticality. The auction, run on a radiation-hardened processor, ensures the most vital data is transmitted, thereby overcoming the signal blackout effect.
• Diagram:

  flowchart TD
      A[Start] --> B{Communication Window Required};
      B --> C[Onboard Systems Submit Bids];
      subgraph Bids
          C1[Guidance System: High Value]
          C2[Telemetry System: Medium Value]
          C3[Sensor Data: Low Value]
      end
      C --> D[VCG Auction on Rad-Hardened CPU];
      D --> E{Allocate Plasma Window to Winner};
      E --> F[Configure Electrode/Magnet Fields];
      F --> G[Transmit Critical Data];
      G --> B;
  

2.2. Smart Grid Power Routing via Polyphase Vector Auction

• Axis: Cross-Domain Application (Energy/Consumer Electronics)
• Enabling Description: In an electrical microgrid, intelligent power routers use a VCG auction to negotiate the "directional state" of power flow. The state is a vector representing power distribution on a polyphase system, controlled by solid-state transformers and silicon carbide (SiC) switches. Subsystems like EV chargers, battery storage, and solar inverters bid for power capacity over a PLC control channel. The VCG mechanism, run by the grid controller, ensures an efficient, stable allocation that maximizes grid utility and prevents faults. The payment calculated by the VCG auction is debited from the subsystem's utility account.
• Diagram:

  classDiagram
      GridController {
          +runVCGAuction()
      }
      PowerRouter {
          -SiC_Switches
          +setPowerVector()
      }
      BiddingSubsystem {
          +String name
          +float bidValue
          +submitBid()
      }
      GridController "1" -- "N" PowerRouter : Controls
      GridController "1" -- "N" BiddingSubsystem : Manages
      BiddingSubsystem --|> PowerRouter : Bids for resources from
      class EV_Charger
      class Battery_Storage
      class Solar_Inverter
      BiddingSubsystem <|-- EV_Charger
      BiddingSubsystem <|-- Battery_Storage
      BiddingSubsystem <|-- Solar_Inverter
  

2.3. Blockchain-Based Decentralized VCG Auction for Spatial Channels

• Axis: Integration with Emerging Tech (Blockchain)
• Enabling Description: The VCG auction for antenna states is implemented as a smart contract on a private, permissioned blockchain (e.g., Hyperledger Fabric). Each wireless node is a participant on the blockchain. Bids for spatial channels are submitted as transactions to the smart contract, which autonomously executes the VCG algorithm, determines winners, and calculates payments in a native token. This creates an immutable, auditable, and decentralized record of resource allocation, removing the need for a trusted central auctioneer and preventing Sybil attacks, as only nodes with sufficient token balances can participate.
• Diagram:

  sequenceDiagram
      actor User as Node
      participant Wallet
      participant SmartContract as VCG Auction
      participant Ledger
      User->>Wallet: Create Bid Transaction
      Wallet->>SmartContract: submitBid(value, state)
      SmartContract->>Ledger: Record Bid
      loop Bidding Period
      end
      User->>SmartContract: executeAuction()
      SmartContract->>SmartContract: Calculate Winners & Payments
      SmartContract->>Ledger: Record Auction Result
      SmartContract-->>Wallet: Transfer Token Payments
  

2.4. Graceful Degradation of Auction Mechanism Under Load

• Axis: The "Inverse" or Failure Mode
• Enabling Description: The system is designed to gracefully degrade its auction mechanism under high computational load or in a low-power state. A system daemon monitors CPU load and battery level. If a critical threshold is crossed (e.g., load > 90% or battery < 15%), it broadcasts a control message to switch from the computationally complex VCG auction to a simple, low-overhead first-price sealed-bid auction. While less economically efficient and not strategy-proof, this lightweight mode ensures the network remains operational for resource allocation. The system reverts to VCG mode when resources are restored.
• Diagram:

  stateDiagram-v2
      state "VCG Auction Mode" as VCG
      state "First-Price Auction Mode" as FirstPrice
      [*] --> VCG
      VCG --> FirstPrice: CPU Load > 90% OR Battery < 15%
      FirstPrice --> VCG: CPU Load < 50% AND Battery > 30%
      note right of VCG
          Computationally complex.
          Economically efficient.
          Strategy-proof.
      end note
      note left of FirstPrice
          Low overhead.
          Sub-optimal allocation.
          Ensures basic operation.
      end note
  

Section 3: Combination Prior Art Scenarios with Open-Source Standards

1. Combination with IEEE 802.11ax (Wi-Fi 6) and OpenWrt: A game-theoretic decision process is implemented as a software package in the OpenWrt open-source firmware. The package controls the beamforming (BF) feedback mechanisms defined in the 802.11ax standard. An access point running this package treats each connected client as a player, using their Channel State Information (CSI) reports to inform its value function. The AP controller then selects the optimal multi-user MIMO transmission state to maximize aggregate network throughput by applying the game-theoretic method via the standardized control frames of Wi-Fi 6.

2. Combination with 5G NR and O-RAN (Open Radio Access Network): A VCG auction is implemented as an "xApp" within the O-RAN Alliance's Radio Access Network Intelligent Controller (RIC). The xApp subscribes to RAN data (UE channel quality, traffic load) from multiple gNodeB base stations via the standardized E2 interface. It runs a VCG auction to allocate massive MIMO beams to competing User Equipments (UEs), with bids derived from the QoS requirements of the UE's network slice. The RIC sends control messages back to the gNodeBs to implement the winning beam configurations, using open interfaces for cross-vendor interoperability.

3. Combination with LoRaWAN and The Things Network: In a dense LoRaWAN deployment managed by The Things Network open-source stack, a VCG auction manages gateway antenna selection for gateways fitted with multiple directional antennas. End-devices submit bids for a "guaranteed reception slot" from a specific directional antenna as part of their uplink payload. The network server runs a VCG auction to allocate these slots, prioritizing devices with critical data who bid higher. The payment is a reduction in the device's "fair use" data credit, optimizing reception probability for critical sensors.