US 11775831

Here's a concise summary of US Patent 11775831:

US Patent 11775831: Cascaded Computing for Convolutional Neural Networks

• Title: Cascaded computing for convolutional neural networks
• Assignee: Skysong Innovations LLC
• Inventors: Jae-sun Seo, Minkyu Kim
• Filing Date: January 13, 2023
• Issue Date: October 3, 2023
• Abstract: The patent describes techniques for efficiently reducing the total computation in Convolutional Neural Networks (CNNs) without affecting output or classification accuracy. This is achieved by reducing computation redundancy, specifically in convolution and pooling operations. The method involves dividing input features into groups of precision values and cascading operations. A maximum value is identified (with high probability, e.g., 90%) using a small number of most significant bits (MSBs) from the input features. Full-precision convolution is then performed only on the input exhibiting this maximum, thereby reducing the total number of bits used for convolution while maintaining accuracy.

Plain-Language Overview of Independent Claim(s):

• Independent Claim 1: This claim covers a system (specifically, non-transitory computer-readable storage media storing instructions for a processor) that processes data within a Convolutional Neural Network (CNN). The system performs an initial, low-precision calculation for multiple data sets using only their most significant bits (MSBs). It then examines these initial results to determine if a clear maximum value is present among them. If a maximum is identified, the system performs a full, high-precision calculation only for the specific data set that showed the maximum value. Finally, this full-precision result is passed on to the next layer of the CNN.

Litigation Information:
The patent family is involved in litigation. As of the current date, the following cases are noted:

• A PTAB case, IPR2025-01399, was filed but not instituted procedurally.
• A US case was filed in the Texas Western District Court (case number 7:25-cv-00040).
• Another US case was filed in the Texas Eastern District Court (case number 2:25-cv-00098).
• The first worldwide family litigation has also been filed.

Known litigation involving US patent 11775831 is as follows:

1. PTAB Case

   • Case Number: IPR2025-01399
   • Filing Date: Not explicitly stated, but the case is listed as "filed" and the status is "Not Instituted - Procedural."
   • Plaintiff(s): Unified Patents PTAB Data indicates "Petitioner: 'Unified Patents PTAB Data'".
   • Defendant(s): Not explicitly stated in the provided text, but presumably the patent owner, Skysong Innovations LLC.
   • Jurisdiction: Patent Trial and Appeal Board (PTAB)
   • Outcome/Current Status: Not Instituted - Procedural.

2. US District Court Case (Texas Western District Court)

   • Case Number: 7:25-cv-00040
   • Filing Date: Not explicitly stated.
   • Plaintiff(s): Not explicitly stated in the provided text.
   • Defendant(s): Not explicitly stated in the provided text.
   • Jurisdiction: Texas Western District Court
   • Outcome/Current Status: Litigation, ongoing.

3. US District Court Case (Texas Eastern District Court)

   • Case Number: 2:25-cv-00098
   • Filing Date: Not explicitly stated.
   • Plaintiff(s): Not explicitly stated in the provided text.
   • Defendant(s): Not explicitly stated in the provided text.
   • Jurisdiction: Texas Eastern District Court
   • Outcome/Current Status: Litigation, ongoing.

4. First Worldwide Family Litigation

   • Case Information: This entry indicates a first worldwide family litigation filed, linking to Darts-ip.
   • Filing Date: Not explicitly stated, but the entry indicates "First worldwide family litigation filed".
   • Plaintiff(s): Not explicitly stated.
   • Defendant(s): Not explicitly stated.
   • Jurisdiction: Global, details available via Darts-ip link.
   • Outcome/Current Status: Litigation, ongoing.

Proceedings overview

One Inter Partes Review (IPR) proceeding has been filed against US patent 11775831, which was denied institution. This gives the patent owner a stronger defensive posture as the patent has survived a challenge without any claims being invalidated.

IPR2025-01399 — CrowdStrike, Inc. et al. v. Skysong Innovations LLC

• Type: Inter Partes Review
• Filed: 2025-09-26
• Status: Discretionary Denial. The Patent Trial and Appeal Board (PTAB) declined to institute a trial.
• Judge panel: Information not publicly available in the provided snippets.
• Petition grounds: CrowdStrike, Inc. et al. challenged claims 1-11 of US11775831. The primary ground was obviousness over Ujiie in view of Moons, specifically arguing that claims 1, 10, and 11 were obvious. Ujiie disclosed a method of approximate convolution and exact full-precision convolution, while Moons taught precision scaling using fewer bits for weights and inputs. Dependent claims 3-6 and 8-9 were also challenged, with Kaul cited for resolving ties in low-precision results by iteratively increasing bit-width.
• Institution decision: Denied on 2026-01-16. The denial was discretionary, likely based on factors such as "settled expectations" as the patent has been in force for several years and the petitioner's awareness of the patent. The USPTO introduced a new Interim Process for PTAB Workload Management in March 2025, which bifurcates decisions on institution into discretionary and merits/statutory considerations, and "settled expectations" has become a new basis for discretionary denial.
• Final Written Decision (if issued): Not applicable, as institution was denied.
• Settlement / termination: Not applicable, as institution was denied.
• Appeal: No Federal Circuit appeal on the merits of patentability, as institution was denied.
• Defensive value: This proceeding demonstrates that the patent has survived a direct challenge at the PTAB, albeit on discretionary grounds rather than a full merits review. While the claims were not "hardened" on the merits, the discretionary denial makes it more difficult for CrowdStrike (and potentially their privies) to challenge the patent again on similar grounds, especially if "settled expectations" played a role. It also signals that Skysong Innovations LLC is actively enforcing its patents, having filed district court complaints against CrowdStrike and Fortinet.

Strategic summary

US patent 11775831 has been subjected to one IPR proceeding, IPR2025-01399, initiated by CrowdStrike, Inc. The PTAB issued a discretionary denial of institution for this IPR, meaning the Board declined to proceed with a full review of the patentability challenges. Consequently, all claims (1-11) of US11775831 remain UNTESTED on the merits at the PTAB, and none have been canceled. They are currently presumed valid as issued by the USPTO.

Regarding estoppel, since institution was discretionarily denied, statutory estoppel under 35 U.S.C. § 315(e)(2) generally does not apply to the merits of the grounds raised. However, the petitioner (CrowdStrike, Inc.) may face challenges in filing subsequent petitions against the same patent on similar grounds due to judicial estoppel or the PTAB's discretionary rules, particularly if the denial was based on factors such as the petitioner's prior knowledge of the patent or "settled expectations." The PTAB has recently expanded its bases for discretionary denials beyond the traditional Fintiv factors, including "settled expectations" and other workload management considerations. This suggests a more stringent environment for IPR institution, which could deter future petitioners.

The fact that Skysong Innovations LLC, the patent owner, has initiated district court litigation against CrowdStrike and Fortinet, asserting US11775831 among other patents, indicates an active enforcement strategy. The IPR filing by CrowdStrike was likely a defensive response to this assertion. The discretionary denial of the IPR petition benefits the patent owner by allowing them to continue their district court litigation without the immediate threat of claim cancellation at the PTAB.

Recommended next steps

• For a defendant facing assertion of US11775831, it is important to understand the specific reasoning behind the discretionary denial in IPR2025-01399 to assess its implications. This denial means the patent claims were not invalidated, and thus, infringement theories built upon them are still viable.
• Given the discretionary denial, a new IPR petition, especially by CrowdStrike or its privies on similar grounds, would face significant hurdles. However, other potential petitioners not impacted by the same discretionary factors might still consider filing an IPR, particularly if strong prior art exists that was not addressed or if different grounds are presented.
• The patent owner, Skysong Innovations LLC, is actively asserting this patent in district court. Defendants should monitor the ongoing litigation (e.g., Skysong Innovations, LLC v. CrowdStrike, Case No. 7:25-cv-00040 in the Western District of Texas and Skysong Innovations, LLC v. Fortinet, Case No. 2:25-cv-00098 in the Eastern District of Texas) for any developments that could affect the validity or enforceability of the patent, such as claim construction rulings or summary judgment decisions.
• Reviewing the full decision for IPR2025-01399 on the USPTO PTAB E2E system (by searching for "IPR2025-01399") would provide a more complete understanding of the grounds challenged, the arguments made, and the precise reasoning for the discretionary denial.

Inventors

• Jae-sun Seo (Arizona State University Downtown Phoenix campus)
• Minkyu Kim (Arizona State University Downtown Phoenix campus)

No unusual patterns, such as all inventors departing within 12 months of filing, are immediately determinable from the provided patent text.

Original assignee

Arizona State University Downtown Phoenix campus.

It is unclear from the provided text whether Arizona State University Downtown Phoenix campus shipped a product embodying the claims. Their primary line of business is education and research. They are currently operating.

Assignment timeline

• 2020-06-02 (executed) / recorded 2023-03-30 — Reel 063171/0487

  • Conveyance: Assignment
  • Assignor: KIM, MINKYU; SEO, JAE-SUN
  • Assignee: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY
  • Correspondent: MICHAEL BEST & FRIEDRICH LLP, 790 N WATER ST, STE 2500, MILWAUKEE, WISCONSIN, 53202-3580.
  • Context: Internal reorg (assignment from inventors to the Board of Regents for the university)

• 2025-01-30 (executed) / recorded 2025-01-30 — Reel unknown/unknown (Note: Reel/Frame information not available from Google Patents Legal Events. USPTO Assignment Center search would be needed for verification.)

  • Conveyance: Assignment
  • Assignor: ARIZONA BOARD OF REGENTS FOR AND ON BEHALF OF ARIZONA STATE UNIVERSITY
  • Assignee: Skysong Innovations, LLC
  • Correspondent: Not specified in provided text.
  • Context: Transfer from university to its technology transfer arm.

Timeline diagram

timeline
    title Ownership of US 11775831
    2016 : Priority date
    2023 : Application filed
         : Assigned to Arizona Board of Regents
         : Granted
    2025 : Assigned to Skysong Innovations LLC

NPE / troll-pattern signals

1. Shell-entity transfer — unclear. While Skysong Innovations, LLC is a technology transfer arm of Arizona State University, it's not explicitly stated if they engage solely in licensing or if the patent relates to products in commerce. Their name does not explicitly contain "IP / Patents / Licensing / Holdings / Ventures".

2. Known asserter in the chain — not present. None of the named assignees (Arizona State University Downtown Phoenix campus, Arizona Board of Regents on Behalf of Arizona State University, Skysong Innovations, LLC) are listed as known NPEs in common directories or the provided text.

3. Repeat correspondent across the chain — not present. Only one correspondent is listed in the provided assignment records, MICHAEL BEST & FRIEDRICH LLP, for the initial assignment from inventors to the Board of Regents. No recurrence is evident.

4. Cascading transfers — not present. There are two assignments recorded, with a gap between them, not meeting the criteria of multiple consecutive assignments in <24 months.

5. Pre-litigation transfer — unclear. The patent has litigation associated with it (IPR2025-01399, US cases in Texas Western and Eastern District Courts), but the exact filing dates of these suits relative to the assignment to Skysong Innovations, LLC (2025-01-30) are not precisely enough specified in the provided text to definitively make this call.

6. Bankruptcy fire-sale — not present. No indication of bankruptcy for any entity in the chain.

7. Privateering — not present. No evidence provided to suggest an operating company transferred to an NPE for assertion against competitors.

8. Defensive aggregator (anti-NPE) — not present. The chain does not terminate at a known defensive aggregator.

Verdict

Insufficient data.
While the patent is actively involved in litigation, the provided information, especially the lack of complete USPTO assignment records (reel/frame for the most recent assignment) and definitive information on Skysong Innovations, LLC's productization or assertion model, prevents a confident classification as an NPE. The initial assignments are consistent with standard university technology transfer. To verify for certain, a direct search on the USPTO Assignment Center would be necessary for complete reel/frame and correspondent information for the Skysong Innovations, LLC assignment and further investigation into Skysong Innovations, LLC's business practices.

USPTO Assignment Center search: https://assignmentcenter.uspto.gov/

To identify the most relevant prior art for US patent 11775831, I have examined the patent citations listed within the provided authoritative patent text. Only citations with a publication date prior to the priority date of US11775831 (September 26, 2016) are considered for potential anticipation under 35 U.S.C. § 102.

Due to limitations in retrieving the full abstracts of the cited patents, the "brief description" for each reference below is derived from its title as presented in the US11775831 patent document. A definitive assessment of anticipation would typically require a review of the full patent text, including the claims and detailed description of each cited prior art reference.

Here are the patent citations from US11775831 that qualify as prior art based on their publication date:

Relevant Prior Art for US11775831

1. US5822452A

   • Full Citation: US5822452A - System and method for narrow channel compression
   • Publication Date: 1998-10-13
   • Assignee: 3Dfx Interactive, Inc.
   • Brief Description: This patent describes a system and method for narrow channel compression.
   • Potential Anticipation (35 U.S.C. § 102): Based on the title, this patent appears to relate to general data compression techniques. The core innovation of US11775831 lies in its specific application of cascaded computing within Convolutional Neural Networks (CNNs) by initially using Most Significant Bits (MSBs) to identify a maximum value and then performing full-precision computation only on the data set exhibiting that maximum, particularly within convolution and pooling operations. A general compression method is unlikely to disclose these specific steps in the context of CNNs and cascaded precision. Therefore, it is unlikely to anticipate Claim 1 or its dependent claims.

2. US20150032449A1

   • Full Citation: US20150032449A1 - Method and Apparatus for Using Convolutional Neural Networks in Speech Recognition
   • Publication Date: 2015-01-29
   • Assignee: Nuance Communications, Inc.
   • Brief Description: This patent discloses a method and apparatus for using Convolutional Neural Networks in speech recognition.
   • Potential Anticipation (35 U.S.C. § 102): While this patent involves CNNs, its focus is on their application in speech recognition. The title does not indicate any disclosure of the cascaded computing methodology of US11775831, which involves initial low-precision computation using MSBs, determination of a maximum, and subsequent selective full-precision computation for efficiency in CNN layers. Thus, it is unlikely to anticipate Claim 1.

3. US20150255062A1

   • Full Citation: US20150255062A1 - System and method for applying a convolutional neural network to speech recognition
   • Publication Date: 2015-09-10
   • Assignee: Gerald Bradley PENN
   • Brief Description: This patent describes a system and method for applying a convolutional neural network to speech recognition.
   • Potential Anticipation (35 U.S.C. § 102): Similar to US20150032449A1, this reference details the use of CNNs in speech recognition. The title does not suggest the specific computational efficiency techniques of US11775831, which are centered around cascaded precision and selective computation based on maximum values in pooling layers. Therefore, it is unlikely to anticipate Claim 1.

4. US20150339571A1

   • Full Citation: US20150339571A1 - System and method for parallelizing convolutional neural networks
   • Publication Date: 2015-11-26
   • Assignee: Google Inc.
   • Brief Description: This patent describes a system and method for parallelizing convolutional neural networks.
   • Potential Anticipation (35 U.S.C. § 102): This patent addresses computational efficiency by parallelizing CNN operations. While both patents aim for efficiency, the method in US11775831 specifically reduces the total amount of computation by avoiding redundant calculations through a cascaded precision approach using MSBs and selective full-precision. Parallelization, while beneficial, does not inherently teach the specific steps of performing initial low-precision calculations, identifying a maximum, and then selectively applying full-precision computation. Thus, it is unlikely to anticipate Claim 1.

5. WO2016033506A1

   • Full Citation: WO2016033506A1 - Processing images using deep neural networks
   • Publication Date: 2016-03-03
   • Assignee: Google Inc.
   • Brief Description: This patent broadly relates to processing images using deep neural networks.
   • Potential Anticipation (35 U.S.C. § 102): This broad title indicates a patent related to a general application of deep neural networks, which can include CNNs, for image processing. However, it does not explicitly suggest the specific cascaded computing approach described in US11775831, which focuses on iteratively increasing precision (starting with MSBs) and performing full-precision computation only on data sets that exhibit maximum values in pooling operations to reduce overall computation. Therefore, it is unlikely to anticipate Claim 1.

6. US9436895B1

   • Full Citation: US9436895B1 - Method for determining similarity of objects represented in images
   • Publication Date: 2016-09-06
   • Assignee: Mitsubishi Electric Research Laboratories, Inc.
   • Brief Description: This patent describes a method for determining the similarity of objects represented in images.
   • Potential Anticipation (35 U.S.C. § 102): This patent's subject matter is image object similarity. While CNNs could be used for this purpose, the title does not suggest the specific computational efficiency methodology employed by US11775831, which involves cascaded precision based on MSBs and selective full-precision computation to optimize pooling operations. Hence, it is unlikely to anticipate Claim 1.

Based on the available information (titles of cited patents), none of the identified prior art references explicitly disclose all the elements of independent Claim 1 of US11775831, particularly the specific combination of performing an initial computation based on a first set of MSBs, examining for a maximum, and then conditionally performing a full-precision computation only for the data set exhibiting that maximum, and propagating that full precision value to a subsequent layer within a CNN. The cited references generally address broader aspects of CNNs, their applications, or different efficiency mechanisms.

Obviousness Analysis of US Patent 11775831 under 35 U.S.C. § 103

This analysis identifies combinations of prior art references that would render the claims of US Patent 11775831 obvious to a person having ordinary skill in the art (PHOSITA) as of the priority date of September 26, 2016. The core of the invention lies in using cascaded, low-precision computations (starting with most significant bits, MSBs) in Convolutional Neural Networks (CNNs) to efficiently identify a maximum value in a pooling operation, and then performing full-precision computation only on the data set that exhibited this maximum, thereby reducing overall computational load without affecting accuracy.

Independent Claim 1 of US11775831 Recites:

1. One or more non-transitory computer-readable storage media storing instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising:
   • in one or more layers of a convolutional neural network (CNN), performing a first iteration that includes computing a value based on a first set of most significant bits (MSBs) for each of a plurality of data sets;
   • examining a first set of values computed for the plurality of data sets in the first iteration to determine whether a maximum value is present among the first set of values;
   • responsive to identifying the maximum value, performing a full precision computation of the value for a data set, of the plurality of data sets, that exhibited the maximum value; and
   • propagating the full precision computation of the value to a subsequent layer of the CNN.

Identified Prior Art References and Their Relevance (Pre-September 26, 2016):

The following prior art references, listed in the "Citations" section of US11775831, are relevant due to their publication or priority dates being before September 26, 2016:

• [A] WO2016033506A1 (Google Inc., published March 3, 2016): Titled "Processing images using deep neural networks," this reference discloses the use of deep neural networks, which a PHOSITA would understand to encompass Convolutional Neural Networks (CNNs) with their characteristic layers, including convolution and pooling operations, for tasks like image processing. In CNNs, data is processed through layers, and the output of one layer is propagated to the next. Pooling layers, particularly max-pooling, are standard for identifying maximum values among a plurality of data sets to reduce spatial dimensions.
• [B] US20170300815A1 (Arizona Board of Regents On Behalf Of Arizona State University, priority date April 13, 2016): Titled "Static and dynamic precision adaptation for hardware learning and classification," this reference explicitly teaches methods for reducing computational cost and improving efficiency in neural networks through "precision adaptation." It describes dividing input features into "groups of precision values" and performing "cascaded operations." A PHOSITA would infer that "groups of precision values" for initial, coarse computations would logically refer to most significant bits (MSBs), and "cascaded operations" imply an iterative refinement process.

Obviousness Combination: [A] WO2016033506A1 in view of [B] US20170300815A1

A PHOSITA would have found Independent Claim 1 of US11775831 obvious by combining the teachings of WO2016033506A1 and US20170300815A1.

Motivation for Combination:

The problem addressed by US11775831—that CNNs are "computationally and memory intensive" and "may involve many redundant operations" (US11775831, Detailed Description)—was a well-known challenge in the field of neural networks at the priority date of this patent. The patent explicitly states that a "largest redundancies in conventional CNNs is that a large amount of data is thrown away at each pooling layer, because only the maximum value is conveyed to the next layer" (US11775831, Detailed Description).

WO2016033506A1 teaches the use of CNNs, which involve computationally intensive convolution and pooling operations. US20170300815A1, from the same original assignee as US11775831, directly addresses the need to reduce computational cost and improve efficiency in neural networks by employing "precision adaptation" and "cascaded operations" with varying "groups of precision values."

Given the known computational burden of CNNs (as taught by [A]) and the explicit teaching of reducing computational cost through precision adaptation and cascaded operations (as taught by [B]), a PHOSITA would have been highly motivated to combine these teachings. The specific motivation would be to apply the precision adaptation techniques of [B] to the convolution and pooling operations within the CNNs of [A] to overcome the known redundancy issue where most computed values are discarded after pooling. It would be an obvious design choice to perform initial computations with lower precision (e.g., MSBs) to quickly identify the likely maximum before investing resources in full-precision computation.

Mapping Claim 1 Elements to the Combination:

• "in one or more layers of a convolutional neural network (CNN)": WO2016033506A1 explicitly discloses "deep neural networks" for "processing images," which are understood by a PHOSITA to include CNNs with multiple layers.
• "performing a first iteration that includes computing a value based on a first set of most significant bits (MSBs) for each of a plurality of data sets": US20170300815A1 teaches "precision adaptation" in neural networks by dividing "input features... into a group of precision values" and performing "cascaded operations" to reduce computational cost. A PHOSITA, seeking to perform an initial, coarse computation for efficiency, would naturally select the most significant bits (MSBs) as the "first set of bits" for this initial approximate calculation of values for a plurality of data sets within a CNN layer as taught by [A].
• "examining a first set of values computed for the plurality of data sets in the first iteration to determine whether a maximum value is present among the first set of values": This step involves a pooling operation, particularly max-pooling, which is a standard component of CNNs as taught by WO2016033506A1. A PHOSITA would apply this standard pooling operation to the results of the MSB-based computations performed as described above.
• "responsive to identifying the maximum value, performing a full precision computation of the value for a data set, of the plurality of data sets, that exhibited the maximum value": US20170300815A1's goal of "precision adaptation" to "reduce computational cost" inherently teaches performing higher-precision computation only when necessary. If the initial low-precision (MSB-based) calculation clearly identifies a maximum (as occurs in pooling), it would be an obvious implementation of efficiency to perform the more resource-intensive "full precision computation" exclusively on that single data set that exhibited the maximum, and not on the other data sets that are destined to be discarded by the pooling layer. This directly addresses the redundancy described in US11775831 where 75-89% of data is typically thrown away.
• "propagating the full precision computation of the value to a subsequent layer of the CNN.": This is a fundamental operation in any multi-layer neural network, including the CNNs disclosed by WO2016033506A1.

Addressing Dependent Claims 2, 3, and 11:

• Claims 2 and 3 (Second iteration with larger set of MSBs): US20170300815A1's teaching of "cascaded operations" and "precision adaptation" using "groups of precision values" directly anticipates a scenario where an initial low-precision computation is insufficient (e.g., if multiple values are too similar to confidently determine a maximum). In such a case, a PHOSITA would find it obvious to proceed with a subsequent iteration using a larger "group of precision values" (i.e., more MSBs) to refine the computation until a clear maximum is identified, consistent with the goal of "precision adaptation." This iterative refinement is a known technique for approximate computing.
• Claim 11 (Full precision computation on less data): This is inherently covered by the motivation described above. The explicit goal of US20170300815A1 to reduce computational cost through precision adaptation, when applied to a CNN with pooling (WO2016033506A1), would lead a PHOSITA to perform full precision computation only on the data selected by the pooling layer (the maximum), which by definition is a subset ("less data") of the initial plurality of data sets.

Conclusion:

The combination of WO2016033506A1, which discloses Convolutional Neural Networks with pooling operations, and US20170300815A1, which teaches precision adaptation and cascaded operations in neural networks to reduce computational cost, would render the subject matter of Independent Claim 1 (and its dependent claims) of US11775831 obvious to a PHOSITA. The pervasive problem of computational inefficiency and redundancy in CNNs, particularly in pooling layers, would provide ample motivation to combine these references to achieve a more efficient CNN architecture by selectively applying full-precision computation only to the data that truly matters for subsequent layers.

To provide the most accurate details regarding Patent Term Adjustments (PTA), Patent Term Extensions (PTE), continuation applications, divisional applications, related family members, and the projected expiration date for US Patent 11775831, a direct search of the USPTO's Patent Center or Assignment Center is required. General search results indicate how these are calculated but do not provide specific values for this particular patent.

Based on the information available and general patent law:

• Patent Term Adjustments (PTA): PTA compensates for delays caused by the USPTO during patent prosecution. The USPTO automatically determines and transmits a notice of PTA no later than the patent's issuance date. An applicant can request reconsideration of this determination within two months of issuance.

  • Specific PTA for US11775831: Not explicitly stated in the provided text or search snippets. To find the specific PTA, one would need to access the official file wrapper for US11775831 through USPTO Patent Center.

• Patent Term Extensions (PTE): PTEs are available for patents covering certain products, primarily pharmaceuticals and medical devices, to restore patent term lost due to regulatory review delays (e.g., FDA approval). The maximum extension is generally five years, and the total patent term with PTE cannot exceed 14 years from the date of FDA approval.

  • Specific PTE for US11775831: Given that US11775831 relates to "Cascaded computing for convolutional neural networks," which is a computing/AI technology, it is highly unlikely to be eligible for a Patent Term Extension under 35 U.S.C. § 156, as this typically applies to products requiring regulatory approval like drugs or medical devices.

• Continuation Applications:

  • US11775831 is a continuation of U.S. patent application Ser. No. 16/335,775, filed on March 22, 2019.
  • This application (16/335,775) itself is a U.S. National Stage Application under 35 USC § 371 of International Patent Application No. PCT/US2017/052736, filed on September 21, 2017.

• Divisional Applications:

  • The provided patent text does not explicitly mention any divisional applications related to US11775831.

• Related Family Members:

  • Priority Applications:
    • U.S. Provisional Patent Application Ser. No. 62/399,753, filed on September 26, 2016.
    • International Patent Application No. PCT/US2017/052736, filed on September 21, 2017.
    • U.S. patent application Ser. No. 16/335,775, filed on March 22, 2019 (which led to US11556779B2).
  • Other Published Applications/Granted Patents in the Family:
    • US20230222339A1 (Publication of a related application).
    • WO2018057749A1 (International publication from PCT/US2017/052736).
    • US11556779B2 (Granted patent from US16/335,775, titled "Cascaded computing for convolutional neural networks," shares the same priority date).

• Projected Expiration Date:

  • The patent states an "Anticipated expiration" date of 2037-09-21.
  • For U.S. utility patents filed on or after June 8, 1995, the general term is 20 years from the earliest filing date of the patent, or the earliest filing date of a parent application to which priority is claimed. The priority date for US11775831 is September 26, 2016. A 20-year term from this priority date would be September 26, 2036. The slightly later anticipated expiration date of 2037-09-21 suggests there might be a patent term adjustment (PTA) that added approximately one year to the patent's life. This is a common occurrence due to USPTO delays during prosecution.