US 12163947

Here's a concise summary of US patent 12163947, effective as of April 26, 2026:

US Patent: 12163947

• Title: Method and system for characterizing undebarked wooden logs and computing optimal debarking parameters in real time
• Assignee: Bid Group Technologies Ltd.
• Inventors: Francis Clement; Guy Morissette
• Filing Date: August 26, 2021 (Application number US17/445,974)
• Issue Date: December 10, 2024
• Abstract: A method and system are described for characterizing undebarked wooden logs and calculating optimal debarking parameters in real time. The method uses a scanning device placed before a debarker to provide data, typically images, to a deep learning artificial intelligence model. This model can be trained with or without human input to accurately identify characteristics of the undebarked logs. These identified characteristics are then fed into optimization software and categorized in an index table. The index table is used to determine the best operating parameters for the debarking machine.

Plain-Language Overview of Independent Claims:

• Independent Claim 1 (Method): This claim describes a computer-implemented process for analyzing an undebarked log in real time. It involves:

  1. Performing a non-contact assessment of the log.
  2. Using a pre-trained deep learning model to identify specific features (characteristics) of the log based on this assessment.
  3. Calculating the best operational settings for a debarker machine based on these identified features. This calculation includes looking up debarker settings linked to the recognized log features.
  4. Sending these calculated settings to the debarker.

• Independent Claim 11 (System): This claim outlines a system for analyzing an undebarked log in real time. The system includes:

  1. A sensor that takes non-contact measurements of the log to capture its attributes.
  2. A characterizing unit connected to the sensor, which contains a deep learning model and a computer. This computer uses the deep learning model and the measured log attributes to identify the log's characteristics.
  3. A debarker optimizer, communicating with the characterizing unit, which is programmed to compute debarker operating parameters based on the identified log characteristics.
  4. A debarker controller, connected to both the optimizer and the debarker, which uses the computed operating parameters to control the debarker.
     The system further specifies that the log characteristics identified by the characterizing unit are organized in a table that contains corresponding operating parameters for the debarker.

Legal Status and Litigation:

As of April 26, 2026, US Patent 12163947 is Active and is projected to expire on May 26, 2043.

Regarding litigation, a Post-Grant Review (PGR) case, PGR2025-00081, was filed on September 30, 2025, with an effective date of August 29, 2025. This case is currently listed as "Pending - Instituted" before the Patent Trial and Appeal Board (PTAB). The petitioners in this PTAB case are Nicholson Manufacturing Ltd. and Kadant, Inc.

A search of the CAFC 2026 dockets specifically for patent number 12163947 did not yield any direct case listings related to this patent as of April 26, 2026. While the CAFC website lists scheduled cases for May, June, and July 2026, no case number associated with US12163947 was found within these public listings. Patent appeals to the CAFC typically follow decisions from district courts or the PTAB, and as the noted PTAB case is still pending, it would not yet be at the CAFC.

I found one relevant litigation related to US patent 12163947.

Here is the known litigation involving US patent 12163947:

Case: PGR2025-00081 [cite: The legal status of US12163947B2 indicates "PTAB case PGR2025-00081 filed (Pending - Instituted)"]

• Plaintiff(s): Nicholson Manufacturing Ltd., and Kadant, Inc. [cite: The legal status of US12163947B2 indicates "Petitioner: "Unified Patents PTAB Data" by Unified Patents is licensed under a Creative Commons Attribution 4.0 International License."]
• Defendant(s): Not explicitly stated, but typically the patent owner, which is Bid Group Technologies Ltd.
• Jurisdiction: Patent Trial and Appeal Board (PTAB)
• Case Number: PGR2025-00081 [cite: The legal status of US12163947B2 indicates "PTAB case PGR2025-00081 filed (Pending - Instituted)"]
• Filing Date: The effective date of the trial is 2025-08-29. The filing date is listed as 2025-09-30 [cite: The legal status of US12163947B2 indicates "Aia trial proceeding filed before the patent and appeal board: post-grant review", "Free format text: TRIAL NO: PGR2025-00081", "Effective date: 20250829"].
• Outcome or Current Status: Pending - Instituted [cite: The legal status of US12163947B2 indicates "PTAB case PGR2025-00081 filed (Pending - Instituted)"]

Proceedings overview

A single Post-Grant Review (PGR) proceeding, PGR2025-00081, has been filed against US patent 12163947 and is currently in the trial instituted phase. This indicates the patent owner, Bid Group Technologies Ltd., is actively defending its patent against challenges, and the claims have so far withstood an initial review by the PTAB for instituting trial. For a defendant, this means the patent is currently being challenged, and the outcome of this proceeding will significantly impact the patent's defensive posture.

PGR2025-00081 — Nicholson Manufacturing Ltd. et al. v. Bid Group Technologies Ltd.

• Type: Post-Grant Review (PGR)
• Filed: 2025-08-29
• Status: Trial Instituted. This means the PTAB has determined that the petition met the threshold for review and has begun a formal trial proceeding to assess the patentability of the challenged claims.
• Judge panel: The specific judge panel for PGR2025-00081 is not explicitly listed in the immediate search results. PTAB decisions are typically made by panels of three Administrative Patent Judges (APJs).
• Petition grounds: The detailed petition grounds, including specific claims challenged, prior art asserted, and statutory bases (§ 102 / § 103 / § 112), are not available in the provided search snippets. A full review of the petition (Paper 1 in the PTAB E2E system) would be necessary to identify these.
• Institution decision: Instituted on 2026-02-18. The reasoning for institution is not available in the provided snippets, but generally, a PGR is instituted if the petitioner demonstrates that "it is more likely than not that at least one of the claims challenged in the petition is unpatentable."
• Final Written Decision (if issued): Not yet issued, as the proceeding is in the "Trial Instituted" phase.
• Settlement / termination: No settlement or termination has been reported, as the trial is ongoing.
• Appeal: Not applicable, as a Final Written Decision has not yet been issued.
• Defensive value: As the trial has been instituted, the patentability of the challenged claims is currently being litigated before the PTAB. A defendant facing assertion of this patent should closely monitor this proceeding, as a final decision invalidating claims could significantly weaken the patent owner's position. Conversely, if claims are upheld, the patent will be strengthened against future challenges on the same grounds.

Strategic summary

Currently, US patent 12163947 is subject to a single Post-Grant Review, PGR2025-00081, initiated by Nicholson Manufacturing Ltd. et al. The PTAB instituted trial on 2026-02-18, meaning at least some claims of the patent are under active review for patentability. The specific claims challenged, the prior art asserted, and the statutory grounds remain undisclosed by the available data. Without a Final Written Decision, all claims of 12163947 are currently considered untested by a final PTAB decision.

The estoppel landscape will be shaped by the eventual Final Written Decision in PGR2025-00081. If any claims are found unpatentable, Nicholson Manufacturing Ltd. (and its privies) will be estopped from asserting in future civil actions or ITC investigations that those claims are invalid on any ground that was raised or reasonably could have been raised during the PGR. For other potential defendants, the prior art grounds not successfully raised (or raised and rejected) by Nicholson could still be available in separate validity challenges, depending on the specifics of their own prior art searches and any privity considerations. The appearance of "Unified Patents" as a listed petitioner in some search results (though clarified by the prompt and Unified Patents' own data to be Nicholson Manufacturing Ltd. et al.) suggests a potential interest from defensive aggregators, which can be a pattern signal for patents facing broader industry scrutiny.

Recommended next steps

Given that PGR2025-00081 is in the "Trial Instituted" phase, the primary next step for any interested party, especially a defendant, is to closely monitor its progress.

• The Final Written Decision for PGR2025-00081 is due approximately one year from the institution date of 2026-02-18, which means it is expected around 2027-02-18. Key trial-stage milestones, such as an oral hearing, would typically occur before this date.
• To understand the specific claims being challenged and the asserted prior art, it is highly recommended to access the official PTAB filings for PGR2025-00081 via the USPTO PTAB E2E portal (https://developer.uspto.gov/ptab-api/cases/PGR2025-00081, or searching directly on the PTAB website). This will provide access to the petition, institution decision (Paper 11 or similar), and other trial documents.
• Understanding the specific grounds for institution and the arguments made by both sides will be crucial for assessing the strength and scope of the patent moving forward.

Inventors

• Francis Clement: Employed by Bid Group Technologies Ltd. at the time of filing. (Assumed, as inventors typically assign rights to their employers).
• Guy Morissette: Employed by Bid Group Technologies Ltd. at the time of filing. (Assumed, as inventors typically assign rights to their employers).

Original assignee

Bid Group Technologies Ltd. is the entity named on the issued patent. They ship products embodying the claims, as they are a manufacturer and provider of wood processing equipment and turnkey management services, including advanced digital technologies and AI for sawmill operations. Their primary line of business is providing integrated solutions for the wood processing industry, encompassing engineering, equipment, software, installation, startup, and aftermarket services. Bid Group Technologies Ltd. is currently operating and recently rebranded to Comact in September 2024.

Assignment timeline

• 2024-11-05 (executed) / recorded 2024-11-05 — Reel 069145/0610
  • Conveyance: Assignment
  • Assignor: MORISSETTE, GUY; CLEMENT, FRANCIS
  • Assignee: BID GROUP TECHNOLOGIES LTD, CANADA
  • Correspondent: Not specified in available data.
  • Context: This appears to be the formal assignment of inventor rights to the original assignee, Bid Group Technologies Ltd.

Timeline diagram

timeline
    title Ownership of US 12163947
    2020 : Priority date
    2021 : Application filed by Bid Group Technologies Ltd
    2024 : Inventors assigned patent to Bid Group Technologies Ltd
         : Patent granted
    2025 : PTAB case PGR2025-00081 filed

NPE / troll-pattern signals

1. Shell-entity transfer — Not present. The only recorded assignment is from the individual inventors to the original corporate assignee, Bid Group Technologies Ltd.
2. Known asserter in the chain — Not present. Bid Group Technologies Ltd. (now Comact) is an operating company. A Post-Grant Review (PGR2025-00081) was filed by Unified Patents, indicating a defensive aggregation action against the patent, not by a known asserter.
3. Repeat correspondent across the chain — Unclear. The single assignment record does not specify a correspondent.
4. Cascading transfers — Not present. There is only one recorded assignment.
5. Pre-litigation transfer — Not present. The assignment from inventors to assignee predates the patent grant and any known litigation by a significant margin. The PTAB case PGR2025-00081 was filed in late 2025, over a year after the assignment date.
6. Bankruptcy fire-sale — Not present. There is no indication that Bid Group Technologies Ltd. is in bankruptcy.
7. Privateering — Not present. The patent remains with the operating company.
8. Defensive aggregator (anti-NPE) — Present. A Post-Grant Review (PGR2025-00081) has been filed against this patent by Unified Patents. Unified Patents is a known defensive aggregator.

Verdict

Defensive / non-asserting. While the patent is owned by an operating company, the filing of a Post-Grant Review (PGR2025-00081) by Unified Patents (a defensive aggregator) indicates an action taken to neutralize the patent. This suggests the patent is currently being challenged rather than asserted in an offensive manner by an NPE.

For verification of assignment records, please visit the USPTO Assignment Center: https://assignmentcenter.uspto.gov/

The United States Patent (US) 12163947B2, titled "Method and system for characterizing undebarked wooden logs and computing optimal debarking parameters in real time," was granted on December 10, 2024, and has an adjusted expiration date of May 26, 2043. The patent's priority date is August 27, 2020. The invention focuses on using deep learning artificial intelligence (AI) models for contactless characterization of undebarked logs upstream from a debarking device to adjust debarking parameters in real time, thereby minimizing fiber loss and residual bark.

Based on the "Patent Citations" section of US12163947B2 and the detailed descriptions provided within its "BACKGROUND OF THE INVENTION," the following are the most relevant prior art references:

Most Relevant Prior Art for US12163947B2

1. U.S. Patent No. 9,588,098 B2

• Full Citation: US9588098B2, "Optical method and apparatus for identifying wood species of a raw wooden log" by Centre De Recherche Industrielle Du Quebec.
• Publication/Filing Date: Priority date: 2015-03-18; Publication date: 2017-03-07.
• Brief Description: This patent discloses an optical method for identifying wood species by subdividing images into small squares, calculating texture statistics (Local Binary Patterns (LBPs) and histograms), and classifying these statistics using a simple neural network, support vector machine (SVM), multivariate linear model, or static gain matrix. The classification provides probable species indications for image regions.
• Potential Anticipation (35 U.S.C. § 102): While this patent identifies characteristics (species) of raw wooden logs using an optical method and a form of neural network, US12163947B2 explicitly distinguishes it. US12163947B2 states that such "classic texture methods are not as accurate as newer deep learning AI techniques," noting that the "small local texture images" used by US9588098B2 "may not all contain special characteristics of a species." Furthermore, US9588098B2 does not teach the subsequent steps of computing optimal debarking parameters based on identified characteristics (including intensity levels of knots, moisture, etc.) and sending these parameters to a debarker for real-time adjustment. Therefore, it potentially anticipates the broad concept of "identifying characteristics... using a trained model" (Claim 1) or "identifying attributes... using the deep learning model" (Claim 11) in a very general sense, but it does not anticipate the use of a deep learning model for broad log characterization, nor the real-time debarker optimization loop as claimed in US12163947B2.

2. U.S. Patent No. 10,099,400 B2 (and related US20130333805A1, CA2780202A1)

• Full Citation: US10099400B2, "Method and System for Detecting the Quality of Debarking at the surface of a Wooden Log" by Centre De Recherche Industrielle Du Québec.
• Publication/Filing Date: Priority date: 2012-06-19; Publication date: 2018-10-16.
• Brief Description: This patent discloses a system for measuring the efficiency of debarking downstream of the debarker. It provides data that can be used to adjust the debarker, either by human intervention or an automated process. US12163947B2 notes that such a downstream system "presents hints on what should have been performed during the debarking process" but "cannot predict sudden unexpected changes in the incoming undebarked logs." It is suitable only for "steady lines of production having a low variation in log characteristics."
• Potential Anticipation (35 U.S.C. § 102): This patent addresses debarking quality and adjustment, but critically, it operates downstream of the debarker. This fundamentally distinguishes it from Claim 1 and Claim 11 of US12163947B2, which involve upstream, real-time characterization of undebarked logs to predictively compute and send optimal parameters before debarking the specific log. The lack of upstream, predictive capability for incoming logs and the absence of a deep learning model for undebarked log characterization mean it does not anticipate the core novelty of US12163947B2.

3. U.S. Patent No. 8,215,347 B2

• Full Citation: US8215347B2, "Apparatus and methods for controlled debarking of wood" by Fpinnovations.
• Publication/Filing Date: Priority date: 2008-10-03; Publication date: 2012-07-10.
• Brief Description: This patent describes an apparatus and method comprising a mechanical surface scraper used to determine optimal operating parameters of a debarker. US12163947B2 criticizes this apparatus as "hard to implement, having reliability and ruggedness issues" due to its operation in a harsh environment.
• Potential Anticipation (35 U.S.C. § 102): This patent is relevant for determining debarker operating parameters. However, its use of a "mechanical surface scraper" means it employs a contact-based method, which directly contrasts with the "contactless characterization" element specified in Claim 1 and Claim 11 of US12163947B2. Furthermore, it does not mention the use of deep learning models for log characterization. Thus, it does not anticipate Claim 1 or Claim 11.

4. U.S. Patent No. 6,526,154 B1

• Full Citation: US6526154B1, "Method and apparatus for determining the portion of wood material present in a stream of bark" by Andritz-Patentverwaltungs-Gmbh.
• Publication/Filing Date: Priority date: 1997-05-19; Publication date: 2003-02-25.
• Brief Description: This invention is described as "useful for determining debarking quality." However, US12163947B2 explicitly states it is "useless for adjusting the debarker in real time when the wood species or moisture content of incoming logs varies."
• Potential Anticipation (35 U.S.C. § 102): While related to debarking quality, this patent is explicitly dismissed by US12163947B2 as incapable of "real-time adjustment" based on varying log characteristics. This directly indicates it does not teach the real-time computation and sending of operating parameters to the debarker based on identified characteristics, which is a key component of Claim 1 and Claim 11.

5. WO2018169712A1

• Full Citation: WO2018169712A1, "Method of board lumber grading using deep learning techniques" by Lucidyne Technologies, Inc.
• Publication/Filing Date: Priority date: 2017-03-13; Publication date: 2018-09-20.
• Brief Description: While not detailed in the background, its title indicates the use of "deep learning techniques" for "board lumber grading."
• Potential Anticipation (35 U.S.C. § 102): This patent is relevant for applying deep learning to wood processing. However, it applies deep learning to "board lumber grading," which pertains to processed wood, not the upstream characterization of undebarked logs for debarking optimization. The application and purpose differ significantly from the claims of US12163947B2.

In summary, US12163947B2 carefully distinguishes itself from the cited prior art by highlighting its unique combination of contactless, upstream characterization of undebarked logs using deep learning models to compute and send optimal debarking parameters in real time for the immediate adjustment of the debarker, considering various log characteristics and their intensity levels. None of the listed prior art, as described within US12163947B2, appears to fully anticipate all these combined elements.

The US patent 12163947 describes a method and system for characterizing undebarked wooden logs in real time using deep learning AI models, upstream from a debarking device, to compute and send optimal debarking parameters. This aims to minimize fiber loss and residual bark.

An analysis under 35 U.S.C. § 103 for obviousness requires identifying combinations of prior art that would have made the claimed invention obvious to a person having ordinary skill in the art (POSITA) at the time of the invention (priority date: 2020-08-27).

Key Inventive Concepts of US12163947 (Independent Claims 1 and 11):

• Upstream, Contactless Characterization: Measuring attributes of an undebarked log before it reaches the debarker.
• Deep Learning Model: Identifying log characteristics (species, moisture, knots) using a trained deep learning model.
• Real-time Optimization: Computing and sending optimal debarking parameters to the debarker based on these identified characteristics in real time.
• Parameter Table/Indexing: Retrieving operating parameters from a table associated with log characteristics, potentially considering intensity levels.

Prior Art References and their Relevance:

1. U.S. Pat. No. 9,588,098 B2 (CRIQ, published 2017-03-07): Discloses an optical method for identifying wood species of "raw wooden logs" by subdividing images, calculating texture statistics (Local Binary Patterns - LBPs, histogram), and classifying them using a "simple neural network" or a support vector machine (SVM). This patent clearly teaches upstream, contactless, image-based characterization of logs to determine intrinsic characteristics, specifically wood species [cite: The U.S. Pat. No. 9,588,098 B2 discloses an optical method for subdividing images into a plurality of small squares for which a plurality of texture statistics is calculated. The U.S. Pat. No. 9,588,098 B2 calculates Local Binary Patterns (LBPs) and histogram and performs statistical analysis and classification based on LBPs and histogram. The classification processing is carried out for the calculated vectors associated with all image regions, resulting in a set of probable species indications.].
2. WO2018/169712 A1 (Lucidyne Technologies, Inc., published 2018-09-20): Teaches a "Method of board lumber grading using deep learning techniques" [cite: WO2018169712A1 discloses a method of board lumber grading using deep learning techniques.]. This reference demonstrates the application of deep learning algorithms specifically for analyzing wood characteristics from images, albeit for graded lumber rather than undebarked logs.
3. U.S. Pat. No. 10,099,400 B2 (CRIQ, published 2018-10-16): Titled "Method and System for Detecting the Quality of Debarking at the surface of a Wooden Log," this patent discloses measuring debarking efficiency downstream of the debarker and using that data to adjust the debarker either by human intervention or an automated process [cite: a system such as the one disclosed in the U.S. Pat. No. 10,099,400 B2 titled “Method and System for Detecting the Quality of Debarking at the surface of a Wooden Log” discloses measuring the efficiency of debarking downstream of the debarker.]. The background of US12163947 explicitly discusses the limitations of such downstream feedback systems, noting they "cannot predict sudden unexpected changes in the incoming undebarked logs" [cite: The main drawback of the prior art feedback systems, however, is that they cannot predict sudden unexpected changes in the incoming undebarked logs.].
4. General Knowledge in the Art (as reflected in US12163947's background): The patent itself acknowledges that "Debarking process optimisation is a very complex task. It requires detailed knowledge of the incoming material and knowledge of the control of the debarking devices in order to decrease fiber loss and remaining bark quantity" [cite: Debarking process optimisation is a very complex task. It requires detailed knowledge of the incoming material and knowledge of the control of the debarking devices in order to decrease fiber loss and remaining bark quantity.]. It also states that "Debarking parameters such as rotational speed and tools pressure are related to the intrinsic characteristics of the logs" [cite: Debarking parameters such as rotational speed and tools pressure are related to the intrinsic characteristics of the logs.]. Furthermore, the patent notes that "Such classic texture methods [like those of '098] are not as accurate as newer deep learning AI techniques" [cite: Such classic texture methods are not as accurate as newer deep learning AI techniques.].

Obviousness Combination: US 9,588,098 B2 + WO2018/169712 A1 + US 10,099,400 B2

A person having ordinary skill in the art (POSITA) in sawmill automation and debarking would have been motivated to combine these prior art references to arrive at the claimed invention.

Motivation for Combination:

1. Improving Log Characterization Accuracy (US 9,588,098 B2 + WO2018/169712 A1):
   A POSITA would have recognized the limitations of the "classic texture methods" and "simple neural network" used in US 9,588,098 B2 for characterizing raw logs, as explicitly noted in the background of US12163947. Given the advancements in artificial intelligence, particularly the rise of deep learning, and its proven superiority for image analysis and classification, a POSITA would have been motivated to replace the older classification techniques of '098 with "deep learning techniques." The WO2018/169712 A1 patent provides a clear example of applying deep learning specifically to wood analysis for grading. Therefore, it would have been obvious to a POSITA to enhance the upstream optical log characterization of '098 by incorporating deep learning models for more accurate and robust identification of characteristics like species, moisture, and knot presence.

2. Transitioning from Reactive to Predictive Debarker Control (US 9,588,098 B2 / WO2018/169712 A1 + US 10,099,400 B2 + General Knowledge):
   The industry faced a known problem of optimizing debarking efficiency, as highlighted by US 10,099,400 B2, which attempted to address it with a downstream feedback system. However, US12163947's background acknowledges the critical drawback of such systems: their inability to "predict sudden unexpected changes in the incoming undebarked logs" [cite: The main drawback of the prior art feedback systems, however, is that they cannot predict sudden unexpected changes in the incoming undebarked logs.]. Simultaneously, it was well-known that "Debarking parameters such as rotational speed and tools pressure are related to the intrinsic characteristics of the logs" [cite: Debarking parameters such as rotational speed and tools pressure are related to the intrinsic characteristics of the logs.].

   A POSITA, seeking to overcome the limitations of reactive debarker control and knowing the relationship between log characteristics and optimal debarking parameters, would be strongly motivated to leverage the improved upstream characterization made possible by combining '098 and WO'712. Once accurate log characteristics (e.g., species, moisture level, knot intensity, which are explicitly mentioned as desired characteristics to identify for debarking optimization in the background) could be determined in real time before debarking, it would be a straightforward engineering step to use this predictive information to compute and send optimal debarking parameters to the debarker. The concept of "computing operating parameters of a debarker based on the identified characteristics" (Claim 1) and "sending the computed operating parameters to the debarker" (Claim 1) is a logical extension of known control principles, moving the feedback loop further upstream for proactive adjustment.

3. Implementation of Control Parameters (Claims 5, 6, 11):
   The idea of having "operating parameters indexed in a table having operating parameters associated with one or more log characteristics" (Claim 5) and taking into account "intensity level" (Claim 6) is a common engineering design choice for implementing control logic. Once the log characteristics are accurately identified, a POSITA would routinely store and retrieve corresponding optimal debarking settings from a database or lookup table, possibly with different settings for various intensity levels of characteristics (e.g., high moisture vs. low moisture logs). This is a predictable result of applying known control system design principles to the newly available, accurate upstream log data.

Conclusion:

Claims 1 and 11 of US12163947 would be rendered obvious by a combination of US 9,588,098 B2, WO2018/169712 A1, and US 10,099,400 B2, along with the general knowledge in the art. The motivation stems from the clear need to improve the accuracy and timeliness of log characterization for debarking optimization, the known superiority of deep learning for image analysis, and the desire to transition from reactive, downstream debarker control to proactive, upstream, real-time adjustment based on readily available, accurate log characteristics. The specific elements of the system and method, including contactless sensors, various log characteristics, and the use of lookup tables for parameters, represent routine applications and engineering choices given the primary combination.