US 11328206

Patent Analyst Summary: US 11,328,206 B2

Date of Analysis: April 26, 2026

This report provides a concise summary of United States Patent 11,328,206 B2, including key bibliographic details, a summary of the invention, and a plain-language explanation of its independent claims.

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I. Bibliographic Information

• Title: Systems and methods for optimizing operations of computing devices using deep neural networks
• Assignee: SRI International
• Inventors: Sek M. Chai, David C. Zhang, Mohamed R. Amer, Timothy J. Shields, Aswin Nadamuni Raghavan, Bhaskar Ramamurthy
• Filing Date: June 16, 2017
• Issue Date: May 10, 2022
• Abstract: The patent describes a system where the operations of computing devices are managed by one or more deep neural networks (DNNs). These DNNs take in data from various sources like sensors, processor instructions, and outputs from other DNNs. The networks, which can be generative, process this information to produce outputs that can control the computing devices, predict future states, or issue warnings. The goal is to improve the performance, efficiency, and security of the computing devices. The system also allows for the DNNs to be dynamically trained to personalize operations by updating their parameters.

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II. Plain-Language Overview of Independent Claims

US Patent 11,328,206 B2 contains three independent claims: 1, 14, and 20. Below is a simplified explanation of each.

• Independent Claim 1: This claim describes a method for a computer to manage its own operations or those of other devices. The core of this method is a "deep neural network" (DNN), a type of artificial intelligence. This DNN receives various types of data as input:

  • Sensor data: Information about the physical environment or state of the device, like its temperature.
  • Processing data: Information about the tasks the device is performing, such as the instructions a processor is executing.
  • DNN data: Feedback from itself or other DNNs.

  The DNN then uses this input to generate signals that can control the device, predict its future behavior (like a potential crash or security threat), or provide warnings. A key feature is that this method can be personalized by updating the DNN's parameters, allowing it to adapt to specific workloads or user behaviors.

• Independent Claim 14: This claim focuses on the physical system itself, rather than the method. It outlines a system that includes:

  • A computing device with a processor.
  • A deep neural network (DNN) that can be on the same device or a connected one.

  This DNN is set up to receive the same types of data as described in Claim 1 (sensor, processing, and other DNN data). Based on this data, the DNN produces outputs that optimize the operations of the processor(s) in the system, aiming to enhance performance, efficiency, or security. The claim also specifies that the system can provide a warning if it predicts unexpected behavior, such as a system fault or a security breach.

• Independent Claim 20: This claim describes a processor with one or more "cores" (the part of the processor that does the actual computing). This processor is designed with an integrated control system that uses a deep neural network (DNN). The key elements are:

  • A processing datapath, which is the part of the processor that executes instructions.
  • A control unit that manages the datapath. This control unit includes a DNN.

  The DNN is trained to understand the processor's behavior under different workloads. It takes in data related to the processor's current operations and, based on its training, sends out control signals to the datapath. Essentially, the processor uses this built-in AI to learn from its tasks and optimize how it performs them. The claim also notes that this DNN can be a "generative" type, meaning it can create predictions about the processor's future states.

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III. Litigation and Legal Status

As of the date of this analysis, a search of the United States Patent and Trademark Office (USPTO) database and the 2026 dockets of the Court of Appeals for the Federal Circuit (CAFC) did not reveal any public records of litigation specifically involving US Patent 11,328,206. However, information provided with the patent text indicates a US case was filed in the Texas Western District Court (7:26-cv-00093). The current legal status of the patent is listed as "Active," with an adjusted expiration date of February 27, 2040.

Disclaimer: This summary is for informational purposes only and does not constitute legal advice. The interpretation of patent claims can be complex and may vary based on legal precedent and specific circumstances.