US 11698687

Here's a concise summary of US patent 11698687, based on the provided authoritative patent text and an updated search for its legal status:

US Patent 11698687

• Title: Electronic device for use in motion detection and method for obtaining resultant deviation thereof
• Assignee: CM HK Ltd
• Inventors: Shun-Nan Liou, Zhou Ye, Chin-Lung Li
• Filing Date: 2020-11-20
• Issue Date: 2023-07-11
• Abstract: The patent describes a method for motion detection in an electronic device. It involves generating measured angular velocities and axial accelerations. A "previous quaternion" (a mathematical representation of orientation) at a prior time (T-1) is established based on past acceleration and angular velocity measurements. This is used to convert current measured angular velocities into a "current quaternion" and "predicted axial accelerations." These predicted accelerations are then compared with the actual measured axial accelerations at the current time (T) to get a comparison result. This result, along with the current quaternion, is used to derive an "updated quaternion" for the current time T, which then becomes the "previous quaternion" for the next cycle. Finally, content is provided based on the resulting deviation derived from this updated quaternion in a spatial reference frame.

Plain-Language Overview of Independent Claims:

• Independent Claim 1: This claim describes a method for determining how an electronic device moves and rotates in a 3D space, especially in changing environments, while filtering out unwanted disturbances. The method involves:

  1. Starting with a known orientation (a "previous quaternion") from an earlier time.
  2. Measuring the device's current rotation speeds (angular velocities) and using these, along with the previous orientation, to predict its new orientation (a "current quaternion") and what its accelerations should be.
  3. Measuring the device's actual accelerations.
  4. Comparing the predicted accelerations with the actual measured accelerations to correct for errors.
  5. Using this correction to refine the predicted orientation, creating an "updated quaternion." This updated orientation then serves as the starting point for the next measurement cycle.
  6. Calculating the device's actual movement and rotation angles (like yaw, pitch, and roll) from this updated orientation, effectively removing disturbances.

• Independent Claim 11: This claim describes an electronic device designed to detect motion and calculate its deviation. The device includes:

  1. A nine-axis motion sensor module (combining a rotation sensor for angular velocities, an accelerometer for axial accelerations, and a magnetometer for magnetism).
  2. A processor connected to the sensor module. This processor is programmed to:
     • Obtain a "previous state" (orientation) from an earlier time.
     • Get current rotation speeds from the sensor.
     • Get current actual accelerations from the sensor.
     • Calculate predicted accelerations based on the current rotation speeds.
     • Compare these predicted and measured accelerations to generate an "updated state" (refined orientation) for the device.
     • Convert this updated state into the device's resultant movement and rotation angles in 3D space, excluding external disturbances.

• Independent Claim 15: This claim describes another method for determining the movement and rotation angles of a 3D pointing device, using a nine-axis motion sensor module in dynamic environments. This method is similar to Claim 1 but explicitly incorporates magnetometer data for a more robust compensation:

  1. Starting with a "previous state" (orientation) at an earlier time.
  2. Obtaining current measured rotation speeds from the sensor.
  3. Obtaining current measured axial accelerations from the sensor.
  4. Calculating predicted axial accelerations based on the current rotation speeds.
  5. Obtaining current measured magnetism from the sensor.
  6. Calculating predicted magnetism based on the current rotation speeds.
  7. Comparing the current state with both the measured accelerations/magnetism and the predicted accelerations/magnetism to obtain an "updated state." This process aims to filter out external interferences.
  8. Calculating the resultant movement and rotation angles in 3D space from this updated state.

USPTO and CAFC Status (as of April 26, 2026):

The patent 11698687 is listed as Active.

According to Google Patents, there is active litigation related to this patent:

• PTAB Case: IPR2025-01071 has been filed and is currently "Pending - Instituted." The petitioner is Unified Patents.
• US District Court Cases:
  • A case was filed in the Texas Eastern District Court.
  • Two cases were filed in the California Eastern District Court.

Due to the nature of the CAFC dockets search (requiring specific filters and not yielding direct results from a broad Google search), and the fact that the provided Google Patents data already cites specific litigation (including a PTAB case and District Court cases), this existing information is the most authoritative and up-to-date available within the given constraints. Directly searching public CAFC dockets for 2026 and this specific patent number through a general search often yields no direct links without more specific case identifiers (e.g., party names, full case numbers). The Google Patents entry provides the relevant litigation context, including the PTAB case number and the districts for the US cases.

Known litigation involving US patent 11698687 includes:

1. Case Name: IPR2025-01071

   • Plaintiff(s)/Petitioner: Unified Patents (as petitioner)
   • Defendant(s)/Patent Owner: CM HK Limited
   • Jurisdiction: Patent Trial and Appeal Board (PTAB)
   • Case Number: IPR2025-01071
   • Filing Date: Not explicitly stated in the provided snippets, but the case is active in 2025.
   • Outcome/Current Status: Pending - Instituted. CM HK Limited, the Patent Owner, has requested that the Board deny institution of the inter partes review. The projected final written decision due date is August 12, 2026. The petition to institute inter partes review was filed by [[Samsung Electronics Co.](/litigations/by-defendant/Samsung%20Electronics%20Co.), Ltd.](/litigations/by-plaintiff/Samsung%20Electronics%20Co.%2C%20Ltd.) ("Samsung").

2. Case in Texas Eastern District Court

   • Plaintiff(s): Likely CM HK Ltd (as the current assignee of the patent).
   • Defendant(s): Not explicitly stated in the provided information, but often involves the petitioner of the IPR, such as Samsung.
   • Jurisdiction: Texas Eastern District Court
   • Case Number: 2:24-cv-00880
   • Filing Date: Not explicitly stated in the provided information.
   • Outcome/Current Status: Active. This is mentioned as one of the parallel district court actions.

3. Case in California Eastern District Court

   • Plaintiff(s): Likely CM HK Ltd (as the current assignee of the patent).
   • Defendant(s): Not explicitly stated in the provided information, but often involves the petitioner of the IPR, such as Samsung.
   • Jurisdiction: California Eastern District Court
   • Case Number: 4:24-cv-06567
   • Filing Date: Not explicitly stated in the provided information.
   • Outcome/Current Status: Active. This is mentioned as one of the parallel district court actions.

4. Case in California Eastern District Court

   • Plaintiff(s): Likely CM HK Ltd (as the current assignee of the patent).
   • Defendant(s): Not explicitly stated in the provided information, but often involves the petitioner of the IPR, such as Samsung.
   • Jurisdiction: California Eastern District Court
   • Case Number: 3:24-cv-06567
   • Filing Date: Not explicitly stated in the provided information.
   • Outcome/Current Status: Active. This is mentioned as one of the parallel district court actions.

The plaintiff(s) for the district court cases (Texas and California Eastern District Courts) are inferred to be CM HK Ltd because they are the current assignee of US11698687, and patent owners typically initiate infringement litigation. The defendant(s) are inferred to be entities like Samsung, who are challenging the patent at the PTAB, but this is not explicitly stated for the district court cases in the provided snippets.

The search results for IPR2025-01071 provide a good amount of information, but some details are still missing, specifically the exact claims challenged and the precise prior art and statutory bases. The results indicate that the institution decision is "Trial Instituted," but also show that the Patent Owner (CM HK Limited) requested discretionary denial based on various arguments including the art being previously considered by the Examiner and parallel district court litigation with Samsung (the Petitioner). The date of institution is not explicitly stated, but the petition was filed on May 30, 2025, and the status is "Trial Instituted." The projected FWD due date is August 12, 2026. There is no mention of a judge panel in the search results, nor the specific claims challenged.

I will attempt another search to pinpoint the claims challenged and the specific prior art.

Inventors

• Shun-Nan Liou: Employer at time of filing not determinable from the provided patent text.
• Zhou Ye: Employer at time of filing not determinable from the provided patent text.
• Chin-Lung Li: Employer at time of filing not determinable from the provided patent text.

The patent text does not specify the employers of the inventors at the time of filing.

Original assignee

The original assignee named on the issued patent is CM HK Ltd.

CM HK Ltd, or China Mobile Hong Kong Company Limited, is a major telecommunications operator in Hong Kong and a wholly-owned subsidiary of China Mobile. They offer a wide range of products and services, including mobile communication, internet services, broadband, and various smart-home products and accessories such as mobile phones, Wi-Fi routers, keyboards, and mice. They were the first to launch Personal Communications Services (PCS) in Hong Kong in 1997 and are currently the largest telecommunications operator in the region.

Given their extensive product lines including smartphones and various electronic devices, it is highly probable that CM HK Ltd ships products embodying the claims of US 11698687, which pertains to an electronic device for motion detection (e.g., pointing devices, smartphones, navigation equipment).

CM HK Ltd is currently an operating company.

Assignment timeline

The provided patent text from Google Patents includes reassignment events, but does not specify reel/frame numbers, precise execution/recording dates (only a single event date), or correspondent information as would be found in a direct USPTO Patent Assignment Search. The following timeline is reconstructed based solely on the information available within the provided patent text.

• 2020-11-20 (Application filed) — No Reel/Frame
  • Conveyance: Application filing
  • Assignor: N/A (Filed by)
  • Assignee: CM HK Ltd
  • Correspondent: Not provided in the source text.
  • Context: Original assignment of patent rights upon application filing.
• 2024-09-25 (Reassignment event date) — No Reel/Frame
  • Conveyance: Reassignment
  • Assignor: CYWEEMOTION HK LIMITED
  • Assignee: CM HK LIMITED
  • Correspondent: Not provided in the source text.
  • Context: Internal reorganization or consolidation of patent ownership within the broader corporate family of CM HK.
• 2024-09-26 (Reassignment event date) — No Reel/Frame
  • Conveyance: Reassignment
  • Assignor: CYWEE GROUP LTD.
  • Assignee: CM HK LIMITED
  • Correspondent: Not provided in the source text.
  • Context: Internal reorganization or consolidation of patent ownership within the broader corporate family of CM HK.

Timeline diagram

timeline
    title Ownership of US 11698687
    2020 : Filed by CM HK Ltd
    2023 : Issued
    2024 : Reassigned to CM HK LIMITED from CYWEEMOTION HK LIMITED
         : Reassigned to CM HK LIMITED from CYWEE GROUP LTD

NPE / troll-pattern signals

1. Shell-entity transfer — unclear. The entities "CYWEEMOTION HK LIMITED" and "CYWEE GROUP LTD." transferred the patent to "CM HK LIMITED". While "LIMITED" in a company name is common, without information on their product lines, business activities, or registered agent addresses, it cannot be determined if these were shell entities. The patent was originally filed by CM HK Ltd, which is an operating company, and the transfers appear to be a consolidation back to "CM HK LIMITED" (likely the same or closely related entity).
2. Known asserter in the chain — not present. CM HK Ltd is a large operating telecommunications company. There is no indication from the provided information that CM HK Ltd, CYWEEMOTION HK LIMITED, or CYWEE GROUP LTD. are known NPEs.
3. Repeat correspondent across the chain — not present. Correspondent information (attorney name, firm, address) is not provided in the source text, so this signal cannot be evaluated.
4. Cascading transfers — present. There are two reassignments to CM HK LIMITED on consecutive days (2024-09-25 and 2024-09-26). This timing indicates a rapid sequence of transfers.
5. Pre-litigation transfer — unclear. The reassignments occurred in September 2024. District court cases (e.g., in Texas Eastern District Court, 2:24-cv-00880) were also filed in 2024. Without the exact filing dates of the lawsuits, it is unclear if these transfers occurred within 6 months prior to the litigation.
6. Bankruptcy fire-sale — not present. There is no mention of bankruptcy for any of the entities involved.
7. Privateering — not present. There is no evidence in the provided information to suggest privateering.
8. Defensive aggregator (anti-NPE) — not present. The chain does not terminate at a known defensive aggregator.

Verdict

Operating-company assertion

The patent was originally filed by CM HK Ltd, which is a large operating telecommunications company that ships products likely embodying the claims. Although there are two rapid reassignments in September 2024 (2024-09-25 and 2024-09-26), these appear to be internal reorganizations or consolidations to CM HK LIMITED (likely the same or a closely related operating entity) from other entities within the same corporate sphere, rather than transfers to a separate licensing entity. While litigation is active, the primary owner is an operating company.

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

To identify the most relevant prior art for US patent 11698687 and analyze its potential to anticipate claims under 35 U.S.C. § 102, I need to access the full citation list from the USPTO database for US11698687B2. However, the available tools do not directly provide a comprehensive list of prior art citations with detailed descriptions from a USPTO search for a given patent number. The Google Patents link provided earlier offers a "Prior art keywords" section but does not list specific cited patents.

To determine anticipation under 35 U.S.C. § 102, "each and every element as set forth in the claim" must be found, "either expressly or inherently described, in a single prior art reference."

Without direct access to the "Cited By" or "References Cited" section of US11698687B2 from a USPTO search, I cannot accurately identify the most relevant prior art references, their publication/filing dates, brief descriptions, or which claims they potentially anticipate. The initial Google Patents link for US11698687B2 does not provide a list of cited prior art.

Therefore, I cannot fulfill the request to provide specific prior art citations and analyze their potential anticipation of claims under 35 U.S.C. § 102 at this time with the available tools.

An obviousness analysis under 35 U.S.C. § 103 requires identifying prior art that teaches or suggests the claimed invention, and providing a motivation for a person having ordinary skill in the art (PHOSITA) to combine those references to arrive at the invention.

Level of Ordinary Skill in the Art

A PHOSITA in the field of the invention (motion detection for computers, navigation, or portable electronic devices) at the priority date of January 6, 2010, would possess knowledge of various motion sensor technologies (accelerometers, gyroscopes, magnetometers), fundamental principles of rigid body dynamics, coordinate transformations (including quaternions), and established techniques for sensor fusion and signal processing used in inertial navigation systems (INS) or attitude and heading reference systems (AHRS). They would also be aware of the inherent limitations of individual sensors and simpler compensation methods.

Primary Prior Art Reference

The patent itself references and discusses several prior art patents by Liberty (U.S. Pat. Nos. 7,158,118, 7,262,760, and 7,414,611). Any of these can serve as a primary reference. For this analysis, we will refer to them collectively as "Liberty's prior art."

Liberty's prior art describes a pointing device that uses a "5-axis motion sensor" comprising three accelerometers (Ax, Ay, Az) and two gyro-sensors (ωY, ωZ) to detect rotation about the Yp and Zp axes. This device aims to detect motions and translate them to a cursor on a 2D display, including a compensation mechanism for signals affected by gravity or "roll" related rotations.

Differences Between Liberty's Prior Art and US11698687

US patent 11698687 distinguishes itself from Liberty's prior art in several key aspects, as explicitly stated in the patent text:

1. Sensor Modality (Nine-Axis vs. Five-Axis): US11698687 utilizes a "nine-axis motion sensor module" which includes a full three-axis gyroscope (detecting angular velocities ωx, ωy, ωz), a three-axis accelerometer (Ax, Ay, Az), and a three-axis magnetometer (Mx, My, Mz). In contrast, Liberty's prior art is limited to a "5-axis motion sensor" (three accelerometers and two gyroscopes detecting rotations about Yp and Zp axes).
2. 3D Absolute Orientation Tracking: US11698687 emphasizes its capability to "accurately obtaining and calculating actual deviation angles in the spatial pointer frame" (3D) and to output these in an "absolute manner" reflecting actual movements, excluding undesirable interferences. Liberty's prior art, according to US11698687, "may not output deviation angles... in a 3D reference frame but rather a 2D reference frame only" and yields a "planar pattern in 2D reference frame only." It also generates "relative" movement patterns, which can lead to errors at display boundaries.
3. Enhanced Sensor Fusion Algorithm: US11698687 describes an "enhanced comparison method and/or model" that involves representing device orientation using quaternions (e.g., previous, current, updated states), calculating "predicted axial accelerations" and "predicted magnetism" based on measured angular velocities, and then comparing these predictions with actual "measured axial accelerations" and "measured magnetism" to obtain an "updated state" (quaternion). This sophisticated process aims to "eliminate the accumulated errors as well as noises over time" and to specifically exclude "undesirable external interferences" such as non-gravitational accelerations and magnetic field disturbances. Liberty's compensation is described as simpler, mainly addressing gravity effects and lacking the robustness against dynamic interferences.
4. Data Association for Interference Rejection: US11698687 further incorporates a "data association model" to intelligently process sensor signals, such as by determining if measured states (e.g., accelerations or magnetism) are "good enough" to be used for compensation based on a "predetermined value or range," thereby allowing for selective compensation and rejection of corrupted data, for instance, due to electromagnetic fields.

Obviousness Combination and Motivation

A PHOSITA would have been motivated to combine the teachings of Liberty's prior art with other known technologies and techniques available by the priority date (January 6, 2010) to arrive at the claimed invention.

1. Motivation to include a 9-axis sensor module:

   • Liberty's prior art explicitly failed to output 3D deviation angles, stating that the "5-axis motion sensor" could not detect or compensate for rotation about the Xp axis directly, requiring it to be "derived from the gravitational acceleration detected by the accelerometer." It also noted that accelerometers were unreliable when the device was not static, as they couldn't distinguish gravitational acceleration from other forces.
   • A PHOSITA, aware of these limitations, would have been motivated to incorporate a full three-axis gyroscope to directly measure rotation about all three axes (including Xp) and a three-axis magnetometer to provide an independent, global reference for heading (yaw) that is not subject to gravitational limitations or drift. By 2010, 9-axis inertial measurement units (IMUs) comprising 3-axis accelerometers, 3-axis gyroscopes, and 3-axis magnetometers were known components in the art for achieving robust 3D orientation (Attitude and Heading Reference Systems - AHRS) in applications like robotics, aerospace, and general navigation, where accurate, drift-free 3D orientation was critical.

2. Motivation to employ advanced sensor fusion with quaternions, prediction, and comparison:

   • The patent highlights that Liberty's prior art "cannot accurately or properly calculate or obtain movements, angles and directions of the pointing device while being subject to undesirable interferences... in the dynamic environment" and only provides "relative" movement patterns, leading to errors when a pointer exceeds display boundaries.
   • To overcome these deficiencies and achieve accurate, "absolute" 3D orientation tracking that is robust in dynamic environments and capable of filtering out interferences, a PHOSITA would have been motivated to implement well-known sensor fusion algorithms. Extended Kalman Filters (EKF) and complementary filters, which frequently use quaternions to represent 3D orientation (to avoid issues like gimbal lock), were standard techniques by 2010 for combining noisy data from accelerometers, gyroscopes, and magnetometers. These algorithms inherently involve:
     • Prediction: Estimating the current state (e.g., orientation, angular velocities, predicted accelerations, predicted magnetism) based on the previous state and gyroscope readings. Gyroscopes offer high-frequency, short-term accuracy.
     • Correction/Comparison: Comparing these predicted values with actual measurements from accelerometers (for pitch and roll relative to gravity) and magnetometers (for yaw relative to Earth's magnetic field). Accelerometers and magnetometers provide long-term stability but are susceptible to various disturbances.
     • Update: Fusing the predicted state with the measured discrepancies (innovation) to obtain a refined "updated state" (e.g., an updated quaternion), thereby "eliminating accumulated errors as well as noises over time" and actively "excluding undesirable external interferences."

3. Motivation to include data association:

   • The challenges of external interferences, such as "undesirable axial accelerations caused by undesirable external forces other than a force of gravity" or "undesirable magnetism caused by undesirable electromagnetic fields," were well-known in the field of sensor fusion.
   • A PHOSITA would have been motivated to incorporate known signal processing techniques, such as outlier rejection, adaptive weighting, or data association (as described in the patent), into the sensor fusion algorithm. These techniques are designed to detect and mitigate the impact of unreliable or erroneous sensor measurements, ensuring that the compensation or update steps only utilize trustworthy data. This approach directly addresses the patent's goal of preventing interferences from corrupting the orientation estimate.

Conclusion

It would have been obvious for a PHOSITA, at the priority date of US11698687, to combine the pointing device described in Liberty's prior art with the well-known and generally available 9-axis motion sensor technology and established sensor fusion algorithms (e.g., quaternion-based Kalman or complementary filters, possibly incorporating data association for outlier rejection). The strong motivation for such a combination would be to overcome the acknowledged limitations of Liberty's 5-axis system, particularly its inability to achieve accurate 3D orientation, its susceptibility to dynamic interferences, and its generation of only "relative" movement patterns. By integrating a full 9-axis sensor and implementing a sophisticated sensor fusion algorithm, a PHOSITA would logically expect to achieve a more robust, accurate, and "absolute" 3D motion detection and compensation system, thereby addressing the problems explicitly identified in the background of US11698687.

To accurately detail patent term adjustments (PTA), patent term extensions (PTE), continuation/divisional applications, related family members, and the projected expiration date for US patent 11698687, direct access to the USPTO's Patent Center or Public PAIR system would typically be required. The provided search results offer general information on how these factors are determined but do not contain the specific data for US11698687 itself. The USPTO does not calculate expiration dates for patents publicly, but provides tools and guidance for doing so.

However, based on the provided patent text and general rules of patent term:

• Standard Patent Term: For utility patents filed on or after June 8, 1995, the term generally begins on the issue date and ends 20 years from the earliest filing date of the application or any earlier application to which it claims priority under 35 U.S.C. §§ 120, 121, or 365(c).

  • US11698687 claims priority to:
    • U.S. application Ser. No. 16/953,771 (Filing Date: 2020-11-20)
    • U.S. application Ser. No. 15/611,970 (Filed: Jun. 2, 2017)
    • U.S. application Ser. No. 13/072,794 (Filed: Mar. 28, 2011, now U.S. Pat. No. 9,760,186)
    • U.S. application Ser. No. 12/943,934 (Filed: Nov. 11, 2010, now U.S. Pat. No. 8,441,438)
    • U.S. application Ser. No. 61/292,558 (Filed: Jan. 6, 2010 - this is a provisional application)

  The earliest non-provisional filing date to which US11698687 claims benefit is November 11, 2010 (U.S. application Ser. No. 12/943,934). Therefore, the base 20-year term would be 20 years from November 11, 2010, which is November 11, 2030.

• Patent Term Adjustment (PTA): PTA compensates applicants for certain delays by the USPTO during patent prosecution. These delays include:

  • Failure to issue a first Office Action or notice of allowance within 14 months of filing.
  • Failure to issue an action within four months of an applicant's response.
  • Failure to issue a patent within four months of payment of the issue fee.
  • Failure to issue a patent within three years of the actual filing date (excluding applicant-caused delays).
    The PTA calculation is performed at the time of patent issuance and is included in the Issue Notification Letter. Without direct access to the USPTO's Patent Center or the Issue Notification Letter for US11698687, the specific PTA for this patent cannot be determined.

• Patent Term Extension (PTE): PTE is available for patents on certain human drugs, food or color additives, medical devices, animal drugs, and veterinary biological products to restore time lost due to regulatory review before commercial marketing or use. Since US11698687 relates to an "Electronic device for use in motion detection," it is highly unlikely to be eligible for PTE, as it does not fall into these categories.

• Continuation Applications: The patent states that "This application is a continuation application, which claims the benefit of and priority to U.S. application Ser. No. 15/611,970, filed on Jun. 2, 2017, which claims the benefit of and priority to U.S. application Ser. No. 13/072,794, filed on Mar. 28, 2011 (now U.S. Pat. No. 9,760,186, issued on Sep. 12, 2017), which is a continuation in part of and claims the priority benefit of U.S. application Ser. No. 12/943,934, filed on Nov. 11, 2010, now patented as U.S. Pat. No. 8,441,438, issued on May 14, 2013, which claims the priority benefit of U.S. application Ser. No. 61/292,558, filed on Jan. 6, 2010." This clearly outlines a chain of continuation and continuation-in-part applications.

• Divisional Applications: The patent text does not explicitly mention any divisional applications.

• Related Family Members:

  • US11698687B2 (This patent)
  • US20210089142A1 (Other version listed, likely a publication of the same application)
  • US16/953,771 (Application number for US11698687B2)
  • US15/611,970 (Parent application)
  • US13/072,794 (Grandparent application, now US Pat. No. 9,760,186)
  • US12/943,934 (Great-grandparent application, now US Pat. No. 8,441,438)
  • US61/292,558 (Provisional application)
  • US9760186B2 (Issued patent from 13/072,794)
  • US8441438B2 (Issued patent from 12/943,934)

• Projected Expiration Date: Based on the earliest non-provisional filing date of November 11, 2010, the anticipated expiration date is November 11, 2030. This is consistent with the "Anticipated expiration" date noted in the Google Patents information for US11698687B2. This date would be adjusted by any PTA. Without the specific PTA value, the exact expiration date cannot be definitively calculated, but the Google Patents information provides a strong indicator.

Defensive Disclosure: Enhancements and Alternative Implementations for US Patent 11698687

This defensive disclosure outlines several derivative variations and integration scenarios for the technology described in US Patent 11698687, pertaining to an electronic device for motion detection and method for obtaining resultant deviation. The aim is to establish prior art for potential future incremental improvements, rendering them obvious or non-novel.

Derivative Variations (Building on Independent Claim 11)

Derivative 1: High-Temperature, Radiation-Hardened Electronic Device for Extreme Environments

Axes: Material & Component Substitution; Operational Parameter Expansion

Enabling Description:
This derivative describes an electronic device designed for use in extreme environments, such as nuclear reactors, spacecraft, or industrial furnaces, where high temperatures and significant radiation exposure are prevalent. The core nine-axis motion sensor module and its processing logic, as described in US11698687 (specifically Claim 11), are adapted using specialized materials and components.

The rotation sensor (gyroscope) employs micro-electromechanical systems (MEMS) fabricated from Silicon Carbide (SiC), known for its wide bandgap and thermal stability, allowing operation up to 500° C. These SiC gyroscopes utilize piezoelectric transduction mechanisms (e.g., lead zirconate titanate (PZT) thin films on SiC) rather than capacitive, providing increased signal-to-noise ratio in high-temperature, high-radiation environments.

The accelerometer similarly utilizes SiC MEMS technology, potentially incorporating bulk acoustic wave (BAW) resonators for enhanced radiation hardness and temperature stability. These BAW accelerometers detect inertial forces via frequency shifts, which are less susceptible to drift from charge accumulation due to radiation.

The magnetometer consists of anisotropic magnetoresistive (AMR) thin-film elements constructed from high-Curie temperature ferromagnetic alloys, such as FeCoB, deposited on thermally conductive ceramic substrates (e.g., Aluminum Nitride (AlN)). The magnetic films are passivated with radiation-resistant ceramics like silicon nitride to prevent degradation.

The computing processor (348, 554, 648) and associated digital logic are implemented using radiation-hardened-by-design (RHBD) methodologies, typically employing Gallium Nitride (GaN) high-electron-mobility transistors (HEMTs) due to their superior radiation tolerance and high-temperature performance compared to silicon. The printed circuit board (PCB) (340, 540, 640) is fabricated from high-Tg ceramic-filled laminates (e.g., polyimide-based or ceramic-matrix composites) and interconnected using high-melting point gold-tin eutectic solders or direct wire bonding with radiation-resistant metals.

Firmware within the processor incorporates advanced temperature compensation routines, utilizing on-chip temperature sensors to dynamically adjust calibration parameters for each motion sensor element, accounting for thermal expansion and material property changes. Furthermore, the quaternion-based filtering algorithm includes adaptive weighting mechanisms that reduce reliance on sensor inputs exhibiting abnormal noise or drift characteristic of radiation-induced transient faults, ensuring robust attitude estimation. The update program (Claim 11) is configured to handle intermittent high-error rates from sensor signals, maintaining system stability and outputting the resultant deviation with a calculated confidence interval reflecting environmental severity.

graph TD
    A[Radiation-Hardened SiC Gyro (Piezoelectric)] --> B{High-Temp/Rad-Hard Signal Conditioning};
    C[Radiation-Hardened SiC Accelerometer (BAW)] --> B;
    D[High-Temp AMR Magnetometer] --> B;
    B --> E[GaN RHBD Computing Processor];
    E -- Dynamic Temp Compensation --> E;
    E -- Adaptive Quaternion Filter --> F[Updated Quaternion (T)];
    F --> G{Resultant Deviation (Yaw, Pitch, Roll)};
    G -- with Confidence Interval --> H[Output to System Control];

Derivative 2: Nanoscale Integrated Motion Unit with NEMS Sensors

Axes: Material & Component Substitution; Operational Parameter Expansion

Enabling Description:
This derivative discloses a miniature electronic device where the nine-axis motion sensor module and a highly integrated processing unit are realized at a nanoscale, targeting applications requiring extremely compact and low-mass motion tracking, such as medical implants, smart dust networks, or micro-robotics. The principles of US11698687, particularly the quaternion-based sensor fusion for robust orientation, are applied at this scale.

The rotation sensor comprises arrays of Nano-electromechanical Systems (NEMS) gyroscopes, specifically resonant carbon nanotube (CNT) gyroscopes. These CNTs, typically 1-10 nm in diameter and 1-10 µm in length, are suspended and capacitively actuated/detected, leveraging their high resonant frequencies (MHz-GHz) and extremely high Q-factors for exceptional angular velocity sensitivity in a sub-micron footprint.

Axial accelerations are detected by piezoresistive silicon nanowire (SiNW) accelerometers. Arrays of vertically aligned SiNWs (e.g., 50-200 nm diameter) are mechanically coupled to a proof mass. Deflection of the proof mass under acceleration strains the SiNWs, changing their electrical resistance proportionally, providing highly sensitive acceleration detection at nanoscale.

The magnetometer employs patterned Giant Magnetoresistance (GMR) spin-valve sensors. These multi-layered thin-film structures (e.g., FeNi/Cu/FeNi layers with thicknesses ~10 nm) are patterned using electron beam lithography into nanometer-scale elements, allowing for detection of localized magnetic fields with high spatial resolution and sensitivity down to nT levels.

The computing processor (348, 554, 648) is an ultra-low-power, highly parallelized embedded microcontroller, possibly an ARM Cortex-M0 derivative or a custom ASIC, fabricated in an advanced CMOS process node (e.g., 22 nm FD-SOI or beyond). It incorporates specialized hardware accelerators for quaternion mathematics and fixed-point arithmetic to maintain computational efficiency while operating at micro-watt power budgets. The data acquisition (DAQ) interfaces are designed for extremely low signal levels and high-frequency NEMS outputs, integrating custom analog front-ends (AFEs) directly adjacent to the NEMS elements. The data transmitting unit (346, 546, 646) utilizes near-field inductive coupling or optical waveguides for short-range, ultra-low-power data communication. The quaternion-based filtering algorithm is optimized for memory footprint and computational cycles, using reduced-order models or adaptive sampling to match the constrained resources while still effectively compensating for inherent NEMS noise sources and external micro-scale disturbances.

classDiagram
    class NanoMotionUnit {
        +NEMS_Gyroscope[3]
        +SiNW_Accelerometer[3]
        +GMR_Magnetometer[3]
        +UltraLowPower_Processor
        +Hardware_Quaternion_Accelerator
        +NFC/Optical_TxUnit
        +QuaternionFilter_Firmware()
    }
    class NEMS_Gyroscope {
        +Resonant_CNT_Array
        +Piezoelectric_Transduction
        +measureAngularVelocity()
    }
    class SiNW_Accelerometer {
        +Piezoresistive_SiNW_Array
        +ProofMass
        +measureAcceleration()
    }
    class GMR_Magnetometer {
        +SpinValve_ThinFilm
        +measureMagnetism()
    }
    class UltraLowPower_Processor {
        +executeQuaternionFilter()
        +managePowerStates()
    }
    NanoMotionUnit --* NEMS_Gyroscope : contains
    NanoMotionUnit --* SiNW_Accelerometer : contains
    NanoMotionUnit --* GMR_Magnetometer : contains
    NanoMotionUnit --* UltraLowPower_Processor : contains

Derivative 3: Electronic Device for Agricultural Drone Navigation

Axes: Cross-Domain Application

Enabling Description:
This derivative describes the application of the electronic device from US11698687 in an agricultural context, specifically for precision navigation and stability control of autonomous agricultural drones (UAVs). The device is integrated into the drone's flight control system to provide highly accurate and interference-compensated attitude (yaw, pitch, roll) data.

The nine-axis motion sensor module (rotation sensor, accelerometer, magnetometer) (302, 502, 602) is ruggedized for outdoor agricultural environments, featuring sealed enclosures to protect against dust, moisture, and chemical sprays. The accelerometer is designed with robust shock absorption for typical drone landings and minor collisions. The magnetometer includes additional shielding to mitigate electromagnetic interference from powerful drone motors, ESCs (Electronic Speed Controllers), and high-current power lines.

The computing processor (348, 554, 648) executes the quaternion-based comparison and update algorithm, specifically tailored to compensate for dynamic disturbances common in agricultural drone operations. These disturbances include rapid air currents (wind gusts, downdrafts from rotors), vibrations from propulsion systems, and varying payload mass (e.g., full spray tank vs. empty). The algorithm dynamically adjusts its filter gains based on detected flight modes (hover, forward flight, spraying maneuver) to optimize response and stability.

The resultant deviation, comprising the compensated yaw, pitch, and roll angles, is fed in real-time to the drone's flight controller via a high-speed serial interface (e.g., CAN bus or UART). This precise attitude information enables the drone to maintain stable flight paths over undulating terrain, execute precise turnarounds at field edges, and accurately position spray nozzles or multispectral cameras for targeted applications. The compensation for "undesirable external interferences" (Claim 11, 15) is critical here to distinguish actual drone motion from environmental noise, ensuring consistent navigation accuracy essential for precision agriculture.

sequenceDiagram
    Flight Controller ->> Electronic Device: Request Attitude Data
    Electronic Device ->> Gyroscope: Sense Angular Velocity
    Electronic Device ->> Accelerometer: Sense Axial Acceleration
    Electronic Device ->> Magnetometer: Sense Magnetic Field
    Gyroscope -->> Electronic Device: ωx, ωy, ωz
    Accelerometer -->> Electronic Device: Ax, Ay, Az
    Magnetometer -->> Electronic Device: Mx, My, Mz
    Electronic Device ->> Processor: Raw Sensor Data
    Processor ->> Processor: Perform Quaternion Filter & Compensation
    Processor -->> Electronic Device: Compensated Yaw, Pitch, Roll
    Electronic Device -->> Flight Controller: Real-time Attitude
    Flight Controller ->> Drone Motors/Actuators: Adjust Flight (Stable Navigation)

Derivative 4: Electronic Device for Industrial Robotic Arm Precision Control

Axes: Cross-Domain Application

Enabling Description:
This derivative describes the integration of the electronic device from US11698687 into an industrial robotic arm to enhance its precision, especially during fine manipulation, welding, or assembly tasks where maintaining an exact end-effector orientation is critical. The device is mounted directly on the robotic arm's end-effector.

The nine-axis motion sensor module (rotation sensor, accelerometer, magnetometer) (302, 502, 602) provides high-frequency, low-latency angular velocity, acceleration, and magnetic field data. The sensor array is robust against the harsh electromagnetic interference (EMI) found in industrial environments, often incorporating internal shielding and differential signaling for improved noise rejection.

The computing processor (348, 554, 648) runs the quaternion-based comparison and update algorithm, specifically optimized for the rapid, dynamic movements and high-frequency vibrations characteristic of robotic operations. The algorithm is tuned to compensate for various "undesirable external interferences" (Claim 11, 15) such as motor ripple, structural resonances within the robot arm links, payload-induced flex, and external forces during interaction with workpieces. The comparison method (Claim 1) is calibrated to rapidly distinguish between intentional end-effector motion and unwanted transient oscillations, providing a very clean and stable orientation output.

The resultant deviation (compensated yaw, pitch, roll angles) is transmitted via a high-speed, deterministic industrial Ethernet protocol (e.g., EtherCAT, Profinet IRT) to the robot's kinematics controller. This real-time feedback loop allows the controller to perform micro-adjustments in joint angles to precisely maintain the desired end-effector pose, significantly improving the accuracy and repeatability of the robotic arm for tasks requiring sub-millimeter precision, such as laser welding, intricate component placement, or grinding.

graph TD
    A[Robot Arm Controller] --> B(Kinematics Solver);
    B --> C[Joint Actuators];
    C --> D(Robotic Arm Motion);
    D --> E[Electronic Device (End-Effector)];
    E -- Raw Sensor Data --> F[Nine-Axis Sensor Module];
    F --> G[Computing Processor];
    G -- Compensated Yaw, Pitch, Roll --> H[Orientation Feedback];
    H --> B;

Derivative 5: Electronic Device for Underwater Autonomous Vehicle (UAV) Attitude Reference

Axes: Cross-Domain Application

Enabling Description:
This derivative describes the application of the electronic device from US11698667 for providing robust attitude reference in Autonomous Underwater Vehicles (UAVs), where traditional GPS is unavailable and magnetic fields can be highly distorted. The device is housed in a pressure-tolerant, corrosion-resistant enclosure suitable for deep-sea deployment.

The nine-axis motion sensor module (302, 502, 602) components are specifically selected and adapted for the underwater environment. The rotation sensor (gyroscope) utilizes fluid-damped MEMS gyroscopes to minimize sensitivity to acoustic noise and hydrodynamic vibrations. The accelerometer is pressure-compensated to prevent housing deformation from affecting sensor readings and includes specialized filtering to account for buoyancy changes and drag forces. The magnetometer consists of low-noise fluxgate magnetometers, which offer higher sensitivity and stability than typical MEMS magnetometers in variable magnetic environments, crucial for detecting subtle changes in the Earth's magnetic field while navigating near metallic structures or geological anomalies.

The computing processor (348, 554, 648) implements the quaternion-based comparison and update algorithm with an Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) to optimally fuse the noisy sensor data. The "undesirable external interferences" (Claim 11, 15) explicitly include strong hydrodynamic forces from currents or thruster wash, acoustic noise from propulsion, and localized magnetic distortions (e.g., from the UAV's metallic hull, onboard electronics, or ferrous seabed deposits). The comparison method (Claim 15) is augmented to intelligently weight magnetometer data, potentially reducing its influence if detected magnetic field variations deviate significantly from predicted local geomagnetic models or known anomalies, thereby preventing corrupted yaw estimates.

The resultant deviation (compensated yaw, pitch, roll angles) provides critical attitude feedback to the UAV's thruster control system and navigation algorithms. This enables precise depth and heading control, stable sensor platform (e.g., sonar, camera, CTD) operation, and accurate waypoint navigation over extended missions in challenging subsea environments.

stateDiagram
    [*] --> Surface_Mode : Initial Power-Up
    Surface_Mode --> Descend_Mode : Command_Dive
    Descend_Mode --> Submerged_Mode : Reached_Depth
    Submerged_Mode --> Ascent_Mode : Command_Surface
    Ascent_Mode --> Surface_Mode : Reached_Surface
    
    state Surface_Mode {
        Surface_Mode : GPS & Comp. IMU
        Surface_Mode --> Attitude_Ref_Cal : GPS Lock
    }
    state Submerged_Mode {
        Submerged_Mode : IMU+Mag (Compensated)
        Submerged_Mode : (Adaptive Weighting for Mag)
        Submerged_Mode --> Thruster_Control : Attitude Output
        Submerged_Mode --> Sensor_Platform_Stab : Attitude Output
    }
    state Descend_Mode {
        Descend_Mode : IMU (Compensated)
    }
    state Ascent_Mode {
        Ascent_Mode : IMU (Compensated)
    }
    Attitude_Ref_Cal --> Submerged_Mode : Calibrated

Derivative 6: AI-Driven Sensor Fusion Optimization

Axes: Integration with Emerging Tech

Enabling Description:
This derivative enhances the electronic device of US11698687 by integrating Artificial Intelligence (AI) for dynamic optimization of the sensor fusion algorithm, specifically the quaternion-based comparison and update process. This allows the device to adaptively improve its accuracy and robustness in highly variable and unpredictable dynamic environments.

The electronic device includes a dedicated, low-power AI inference engine (e.g., a Neural Processing Unit (NPU) or specialized DSP core) alongside the main computing processor (348, 554, 648). This NPU hosts a pre-trained machine learning model, such as a Recurrent Neural Network (RNN) or a Deep Reinforcement Learning agent. The RNN continuously ingests the raw sensor data streams (angular velocities, axial accelerations, magnetism) from the nine-axis motion sensor module (302, 502, 602) and monitors internal diagnostics (e.g., sensor temperature, estimated biases, vibration frequencies detected from accelerometer spectrum).

The AI model dynamically adjusts the parameters of the quaternion-based Kalman filter or equivalent comparison model (Claim 11, 15). For example, it can modulate the process noise covariance (Q), measurement noise covariance (R), and the Kalman gain matrix (K) in real-time. If the AI detects a sustained high-frequency vibration signature (e.g., from machine operation), it may increase the measurement noise covariance for the accelerometer, thereby temporarily reducing its weight in the state update to prevent noise from corrupting the orientation estimate. Conversely, if a stable magnetic environment is detected, the AI might increase the magnetometer's weighting for more accurate yaw correction. If a known magnetic anomaly region is entered (e.g., via geo-fencing), the AI might temporarily reduce or completely ignore magnetometer input until the anomaly is cleared, relying solely on gyroscopes and accelerometers.

This AI-driven optimization enables the system to continuously self-tune, providing superior performance compared to statically tuned filters. The outputted resultant deviation (yaw, pitch, roll) is therefore more accurate and resilient against a wider range of "undesirable external interferences" (Claim 11, 15), as the system intelligently adapts its compensation strategy.

graph TD
    A[Nine-Axis Sensor Module] --> B{Raw Sensor Data};
    B --> C[AI Inference Engine (NPU)];
    B --> D[Computing Processor (Quaternion Filter)];
    C -- Dynamic Filter Parameters (Q, R, K) --> D;
    D --> E[Updated Quaternion];
    E --> F[Resultant Deviation Output];
    C -- Real-time Diagnostics/Context --> C;

Derivative 7: IoT Edge Computing with Real-time Anomaly Detection

Axes: Integration with Emerging Tech

Enabling Description:
This derivative transforms the electronic device of US11698687 into an intelligent Internet of Things (IoT) edge node, capable of performing local data processing and real-time anomaly detection before securely transmitting processed information to a central cloud or local server.

The electronic device (300, 500, 600) integrates the nine-axis motion sensor module (302, 502, 602) with the computing processor (348, 554, 648) and a secure data transmitting unit (346, 546, 646) equipped with wireless connectivity (e.g., Wi-Fi, LoRaWAN, 5G NR). The processor first executes the core quaternion-based comparison and update algorithm (Claim 11, 15) to derive the highly accurate, interference-compensated resultant deviation (yaw, pitch, roll angles).

In addition to outputting these orientation angles, the processor (or a co-processor at the edge) runs a lightweight, real-time anomaly detection algorithm on both the raw sensor data and the calculated deviation. This algorithm employs statistical process control (e.g., Shewhart charts, EWMA) or simple machine learning models (e.g., one-class SVM, isolation forest) to identify unusual patterns in angular velocity drift, acceleration magnitudes, or magnetic field fluctuations that could indicate sensor malfunction, mechanical stress, or an environmental event beyond normal operating parameters.

Upon detecting an anomaly, the device can trigger immediate local alerts (e.g., LED, buzzer), store high-fidelity raw data logs locally for post-event analysis, and prioritize transmission of anomaly alerts and relevant data snippets to the cloud platform. Regular, non-anomalous resultant deviation data is aggregated and transmitted at configurable intervals to conserve bandwidth and power. The "undesirable external interferences" (Claim 11, 15) filtered by the core algorithm are themselves monitored for patterns, allowing the system to distinguish between expected environmental noise and truly anomalous conditions potentially indicative of a fault in the monitored system or the sensor itself.

sequenceDiagram
    participant S as Nine-Axis Sensor Module
    participant P as Computing Processor
    participant A as Anomaly Detection Module
    participant T as Data Transmitting Unit (IoT)
    participant C as Cloud/Server Platform

    S->>P: Raw Sensor Data (ω, A, M)
    P->>P: Quaternion Filter (US11698687)
    P->>P: Calculate Yaw, Pitch, Roll (Compensated)
    P->>A: Raw Data & Compensated Deviation
    A->>A: Real-time Anomaly Detection
    alt Anomaly Detected
        A->>T: High-Priority Alert & Raw Snippets
        T->>C: Anomaly Alert
        A->>P: Adjust Filter Parameters (Optional)
    else No Anomaly
        A->>T: Aggregated Compensated Deviation
        T->>C: Regular Data Stream
    end
    P->>P: Update Previous State

Derivative 8: Low-Power Diagnostic Mode with Limited Functionality

Axes: The "Inverse" or Failure Mode

Enabling Description:
This derivative describes a low-power, limited-functionality operating mode for the electronic device of US11698687, designed to conserve energy while still providing essential diagnostic information or a rudimentary level of motion detection. This mode is particularly useful for battery-powered devices experiencing critically low power levels, or for applications requiring only intermittent, coarse motion sensing.

The electronic device (300, 500, 600) includes a configurable "Low-Power Diagnostic Mode" activated automatically upon reaching a predefined low battery threshold (e.g., <10% charge), prolonged inactivity, or an explicit software command. In this mode, the nine-axis motion sensor module (302, 502, 602) components are selectively powered down or operated at significantly reduced sampling rates. For instance, only the accelerometer might be active at a 1 Hz rate to detect gross movements or freefall, while the gyroscope and magnetometer are entirely disabled or sampled at a very low duty cycle.

The computing processor (348, 554, 648) executes a simplified version of the quaternion-based comparison and update algorithm (Claim 11, 15). This simplified algorithm may use single-precision floating-point arithmetic or even fixed-point arithmetic instead of full precision, and may reduce the order of the Kalman filter, or completely bypass certain compensation steps if less accurate data is acceptable for the current mode. For instance, yaw estimation might be entirely omitted, or solely derived from an initial magnetometer reading, with subsequent updates relying only on accelerometer pitch/roll relative to gravity if gyroscope data is unavailable.

The data transmitting unit (346, 546, 646) switches to a lower-bandwidth, intermittent communication protocol (e.g., Bluetooth Low Energy beacons with extended advertising intervals, or a pulsed radio burst every few seconds) and reduces its transmit power. The outputted resultant deviation is a reduced set of parameters (e.g., only pitch and roll relative to gravity) or reported at a lower frequency, providing minimal but critical information for system health monitoring or basic contextual awareness without draining the battery. An additional "Safe Mode" could be defined where only a critical event (e.g., catastrophic acceleration) triggers a system wake-up and alert, bypassing all complex quaternion processing for maximum power efficiency.

stateDiagram
    state Normal_Operation {
        Normal_Operation : Full 9-axis sensor fusion
        Normal_Operation : High sampling rate
        Normal_Operation : Full precision processing
        Normal_Operation : High bandwidth comms
    }
    state Low_Power_Diagnostic_Mode {
        Low_Power_Diagnostic_Mode : Reduced sensor activity (e.g., Accel only)
        Low_Power_Diagnostic_Mode : Low sampling rate (e.g., 1Hz)
        Low_Power_Diagnostic_Mode : Simplified quaternion filter
        Low_Power_Diagnostic_Mode : Low bandwidth/intermittent comms
    }
    state Safe_Mode {
        Safe_Mode : Minimal sensor polling (e.g., high-g detect)
        Safe_Mode : Event-driven wake-up
        Safe_Mode : Alert-only communication
    }

    [*] --> Normal_Operation
    Normal_Operation --> Low_Power_Diagnostic_Mode : Battery < Threshold
    Normal_Operation --> Low_Power_Diagnostic_Mode : User Command / Inactivity
    Low_Power_Diagnostic_Mode --> Normal_Operation : Battery Charged / User Command
    Low_Power_Diagnostic_Mode --> Safe_Mode : Critical Event / Very Low Battery
    Safe_Mode --> Normal_Operation : Reset / Power Restored

Derivative 9: Optoelectronic/Quantum Sensor Fusion for Navigation-Grade Performance

Axes: Material & Component Substitution

Enabling Description:
This derivative represents a high-end implementation of the electronic device from US11698687, leveraging cutting-edge optoelectronic and quantum sensors to achieve navigation-grade inertial measurement performance, far exceeding typical MEMS capabilities. This is suitable for applications requiring extreme accuracy and long-term stability without external references, such as strategic navigation systems, deep-space probes, or ultra-precision industrial metrology.

The nine-axis motion sensor module is entirely reimagined. The rotation sensor employs a compact atomic interferometer gyroscope, utilizing cold atoms (e.g., Rubidium or Cesium) trapped in a magneto-optical trap. This quantum-mechanical sensor measures rotation by detecting phase shifts in matter-wave interferometers, offering drift rates orders of magnitude (e.g., µdeg/hr) lower than conventional gyroscopes.

Axial accelerations are measured by optical accelerometers based on cavity optomechanics. These sensors use a micromirror suspended within an optical cavity. Acceleration causes minute displacement of the mirror, which is precisely measured by monitoring changes in the cavity's resonant frequency using a stabilized laser interferometer, providing femto-g level sensitivity.

The magnetometer utilizes nitrogen-vacancy (NV) center quantum diamond magnetometers. NV centers in synthetic diamonds exhibit spin states that are sensitive to magnetic fields and can be read out optically. This allows for vectorial magnetic field sensing with sub-nT sensitivity and high spatial resolution, offering superior performance to even fluxgate magnetometers.

The computing processor (348, 554, 648) is a powerful, multi-core system-on-chip (SoC) with specialized hardware accelerators for processing the complex, high-bandwidth signals from these quantum sensors. This includes high-speed, ultra-low-noise analog-to-digital converters (ADCs), dedicated digital signal processing (DSP) blocks for optical and quantum signal conditioning, and extended precision floating-point units (e.g., IEEE 754 quadruple precision) for the quaternion-based filtering algorithm. The quaternion filter and comparison method (Claim 11, 15) are refined to exploit the exceptional signal quality and low drift of these sensors, potentially operating with much longer integration times or more aggressive filter gains due to the reduced inherent noise. The "undesirable external interferences" filtered are pushed to even lower levels, allowing for unprecedented accuracy in resultant deviation (yaw, pitch, roll) determination.

classDiagram
    class OptoQuantumNavUnit {
        +Atomic_Interferometer_Gyro[3]
        +OptoMech_Accelerometer[3]
        +NV_Diamond_Magnetometer[3]
        +MultiCore_SoC_Processor
        +Quantum_Signal_DSP
        +QuadPrecision_Quaternion_Engine
        +Data_Transmitting_Unit
        +NavigationGrade_QuaternionFilter()
    }
    class Atomic_Interferometer_Gyro {
        +Cold_Atom_Source
        +Laser_Interferometry
        +measureAngularVelocity()
    }
    class OptoMech_Accelerometer {
        +Optical_Cavity
        +Micromirror_Suspension
        +measureAcceleration()
    }
    class NV_Diamond_Magnetometer {
        +NV_Center_Diamond
        +Optical_Readout
        +measureMagnetism()
    }
    class MultiCore_SoC_Processor {
        +executeNavigationFilter()
        +manageQuantumSensorData()
    }
    OptoQuantumNavUnit --* Atomic_Interferometer_Gyro : contains
    OptoQuantumNavUnit --* OptoMech_Accelerometer : contains
    OptoQuantumNavUnit --* NV_Diamond_Magnetometer : contains
    OptoQuantumNavUnit --* MultiCore_SoC_Processor : contains

Derivative 10: High-Frequency Vibration Analysis with Attitude Tracking

Axes: Operational Parameter Expansion

Enabling Description:
This derivative extends the functionality of the electronic device from US11698687 beyond simple attitude tracking to concurrently perform high-frequency vibration analysis, enabling its use in applications such as machinery diagnostics, structural health monitoring, or human-machine interface with haptic feedback.

The nine-axis motion sensor module (302, 502, 602) is upgraded to include high-bandwidth MEMS gyroscopes and accelerometers, capable of sampling rates exceeding 10 kHz (e.g., Bosch BMI323 or analogous with extended bandwidth). The magnetometer is also chosen for rapid response time.

The computing processor (348, 554, 648) utilizes a parallel processing architecture, such as a field-programmable gate array (FPGA) or a multi-core digital signal processor (DSP). It concurrently executes two primary computational paths:

1. Attitude Tracking Path: This path performs the quaternion-based comparison and update algorithm (Claim 11, 15) as described in the patent, integrating angular velocities, accelerations, and magnetism to produce the resultant deviation (yaw, pitch, roll) for stable attitude determination. This path often includes low-pass filtering to remove high-frequency noise from the attitude estimate.
2. Vibration Analysis Path: This path takes the raw, high-frequency axial acceleration data (and potentially angular velocity data) and performs digital signal processing. This includes high-pass filtering (to remove DC and low-frequency motion components), Fast Fourier Transforms (FFTs) to obtain spectral content, and root mean square (RMS) amplitude calculations. This path specifically analyzes the "undesirable external interferences" (Claim 11, 15) that are typically filtered out for attitude estimation, turning them into the primary signals of interest.

The data transmitting unit (346, 546, 646) is configured to output two distinct data streams: one for the compensated resultant deviation (attitude) and another for real-time vibration metrics (e.g., peak frequencies, amplitudes, overall RMS levels) or full vibration spectra. This allows for simultaneous precise attitude tracking and continuous, detailed structural or machinery health monitoring from a single compact device.

graph TD
    A[Nine-Axis Sensor Module (High-Bandwidth)] --> B{Raw Sensor Data};
    B --> C[Computing Processor (FPGA/Multi-core DSP)];
    
    subgraph Processor Pipelines
        C -- High-Pass Filter --> D[Vibration Analysis Path];
        D -- FFT / RMS Calc --> E[Vibration Metrics Output];
        
        C -- Low-Pass Filter --> F[Attitude Tracking Path];
        F -- Quaternion Filter / Compensation --> G[Resultant Deviation Output (Yaw, Pitch, Roll)];
    end
    
    E --> H[Data Transmitting Unit];
    G --> H;
    H --> I[External System (Monitoring/Control)];

---

Combination Prior Art Scenarios

These scenarios illustrate how the core concepts of US11698687, particularly the robust, interference-compensated 3D motion detection, can be combined with existing open-source standards to create obvious or non-novel systems.

1. Integration with Robot Operating System (ROS) Standard

Open-Source Standard: Robot Operating System (ROS) provides a flexible framework for writing robot software, including standard message types (sensor_msgs/Imu, sensor_msgs/MagneticField) and the tf (transform frame) library for managing coordinate transformations.

Scenario: An electronic device embodying the nine-axis motion detection and compensation methods of US11698687 is implemented as a dedicated ROS node (software component) within a robotic system. The device's computing processor (348, 554, 648), after performing its proprietary quaternion-based filtering (Claim 11, 15) to eliminate "undesirable external interferences" and calculate the resultant deviation (yaw, pitch, roll), publishes this accurate 3D orientation data.

Specifically, the device would:

• Publish raw angular velocities (ωx, ωy, ωz) and linear accelerations (Ax, Ay, Az) as sensor_msgs/Imu messages on a /imu/data_raw topic.
• Publish raw magnetism (Mx, My, Mz) as sensor_msgs/MagneticField messages on a /imu/mag_raw topic.
• Crucially, the compensated resultant deviation (the updated quaternion or equivalent rotation matrix) from the processor's algorithm is converted into a geometry_msgs/TransformStamped message and published as a tf transform from the robot's base_link frame to the sensor's imu_link frame. This makes the device's accurate, absolute 3D orientation available throughout the entire ROS ecosystem, accessible to any other node (e.g., navigation stack, manipulator controller, perception systems) that needs precise attitude information for localization, mapping, or control. The tf messages would provide the real-time, drift-compensated orientation, making the sophisticated filtering and compensation directly consumable by standard robotics components.

2. Data Transmission via Bluetooth Low Energy (BLE) Generic Attribute Profile (GATT)

Open-Source Standard: Bluetooth Low Energy (BLE) is a wireless personal area network technology designed for low-power operation, and its Generic Attribute Profile (GATT) defines how two BLE devices transfer standardized or custom data.

Scenario: A portable electronic device (e.g., a 3D pointing device, wearable sensor) incorporating the nine-axis motion sensor module and processor of US11698687 is configured as a BLE peripheral. The device performs the compensation and calculation of resultant deviation (yaw, pitch, roll angles) as described in the patent (Claim 11). Instead of wired transmission, this highly accurate and interference-compensated 3D orientation data is wirelessly streamed to a connected BLE central device (e.g., smartphone, tablet, computer).

This is achieved by:

• Defining a custom BLE GATT service UUID for "Motion Device Orientation Service."
• Within this service, defining three distinct GATT characteristics, one for each of Yaw, Pitch, and Roll angles. These characteristics are configured with READ and NOTIFY properties.
• The device's data transmitting unit (346, 546, 646) would update these characteristics with the latest compensated angle values from the processor's output.
• A connected BLE central device can then subscribe to notifications for these characteristics, receiving real-time, low-latency updates of the device's 3D orientation. This combination makes the output of the patent's core innovation immediately available to a vast ecosystem of mobile and computing devices using a ubiquitous, low-power wireless standard.

3. Real-time Data Streaming with Apache Kafka

Open-Source Standard: Apache Kafka is a distributed streaming platform designed for building real-time data pipelines and streaming applications, capable of handling high-throughput, fault-tolerant data ingestion.

Scenario: In an industrial or large-scale IoT deployment, multiple electronic devices implementing the nine-axis motion detection and compensation methods of US11698687 are used to monitor structural integrity, machine health, or logistical movements across a wide area. Each device's computing processor (348, 554, 648) continuously calculates the resultant deviation (yaw, pitch, roll) with active compensation for "undesirable external interferences" (Claim 11, 15).

Instead of direct point-to-point communication, the data transmitting unit (346, 546, 646) in each device (or an aggregation gateway receiving data from multiple devices) acts as an Apache Kafka producer. It formats the timestamped resultant deviation data (e.g., as JSON or Avro messages, including yaw, pitch, roll, and potentially confidence metrics or raw sensor data) and publishes these messages to a designated Kafka topic (e.g., device_motion_stream).

Downstream, various Kafka consumers subscribe to this topic. These could include:

• A real-time analytics engine for immediate anomaly detection.
• A time-series database for long-term storage and historical trend analysis.
• A visualization dashboard for operational monitoring.
• Predictive maintenance algorithms that analyze motion signatures for equipment wear.

This combination leverages Kafka's scalability and fault-tolerance to ingest and process massive volumes of highly accurate motion data from numerous devices in a distributed, real-time manner, making the deployment of the patent's core technology in large-scale data streaming architectures an obvious extension.