US 10959649

Patent Summary: US 10,959,649 B2

Date of Analysis: May 1, 2026

A comprehensive review of US Patent 10,959,649, titled "Systems and methods for stride length calibration," has been conducted. The following summary provides key details of the patent and a plain-language explanation of its independent claims.

Key Patent Details:

• Title: Systems and methods for stride length calibration
• Assignee: Beijing Shunyuan Kaihua Technology Co Ltd
• Inventor: Ming Shun Fei
• Filing Date: January 29, 2015
• Issue Date: March 30, 2021
• Abstract: Described herein are systems, devices, and methods for determining a user's stride length and monitoring various aspects of the user's activities. An apparatus worn or carried by the user may determine and track when a user takes a step and, based at least in part on user-specific information, determine an estimated stride length of the user associated with a respective step rate or step rate range. The apparatus may further monitor the physical location, speed, or pace of the user during an activity and, in conjunction with step count information, determine a verified stride length for the user associated with a respective step rate or step rate range. The estimated and verified stride length determinations may be stored and used to determine one or more aspects of a user's subsequent activities, including but not limited to pace, speed, and calorie expenditure information, even when physical location information is unavailable.

Litigation Status:

A search of the Court of Appeals for the Federal Circuit (CAFC) 2026 dockets for litigation involving US Patent 10,959,649 did not yield any specific results. However, the patent's Google Patents page notes a case filed in the Court of Appeals for the Federal Circuit, docket number 25-1230. Further details on the status and specifics of this litigation are not available from the provided search results.

Plain-Language Overview of Independent Claims:

US Patent 10,959,649 contains two independent claims, which form the core of the invention. Below is a simplified explanation of each.

Independent Claim 1:

This claim describes a method for a wearable device to calculate a user's stride length. The process starts with the device gathering user-specific information like height, weight, and gender. It then measures the user's step rate (how many steps they take in a certain time). Using both the user's information and their step rate, the device calculates an initial "estimated" stride length.

Later, when the device can track the user's actual distance traveled (for example, using GPS), it measures the number of steps taken over that distance to calculate a "verified" stride length. This verified stride length is then saved. The key part of the invention is that this new, more accurate "verified" stride length is used to improve the initial "estimated" stride length for different step rates. For instance, if the verified stride length is longer than the estimate at a certain step rate, the device will adjust its estimates for other step rates to be longer as well. This allows the device to provide more accurate distance and speed data in the future, especially when GPS isn't available (like on a treadmill).

Independent Claim 13:

This claim focuses on the wearable device itself (the "apparatus") rather than the method. It outlines a system that includes at least one accelerometer to detect steps, a time-keeping component, a display, and a processor. The processor is the brain of the operation and is programmed to perform the method described in Claim 1.

Specifically, the processor is configured to:

• Receive user-specific data.
• Calculate the user's step rate from the accelerometer data.
• Determine an initial "estimated" stride length based on the user data and step rate.
• When GPS or other location data is available, calculate a "verified" stride length by dividing the distance traveled by the number of steps.
• Store this verified stride length and use it to adjust and improve the estimated stride lengths for other step rates.
• Use these refined stride length values to calculate the user's speed and pace, even when location data is unavailable.

Based on the previous analysis of US Patent 10,959,649, this section details the patent's prosecution history, outlining the interactions between the inventor's representative and the USPTO examiner that led to the patent's grant.

Prosecution History Analysis

The prosecution history, or "file wrapper," for US Patent 10,959,649 (filed under application number 14/608,571) reveals a negotiation process with the USPTO focused on distinguishing the invention from existing technologies. The key patentable feature that emerged was the specific method of using a "verified" stride length to iteratively calibrate "estimated" stride lengths across different step-rate ranges.

Initial Filing and Claims (January 29, 2015)

The application was filed with a set of claims broadly directed at a method and apparatus for determining stride length. The originally filed independent claims were broader than those ultimately granted. They focused on the general concept of:

1. Receiving user-specific information.
2. Determining a step rate.
3. Calculating an "estimated" stride length based on the user information and step rate.
4. Calculating a "verified" stride length when location data (e.g., GPS) was available.
5. Using the historical data (estimated or verified) for future calculations when GPS is unavailable.

Non-Final Rejection (September 1, 2017)

The USPTO examiner issued a Non-Final Rejection, primarily under 35 U.S.C. § 103 for obviousness. The examiner contended that the invention as claimed would have been obvious to a person of ordinary skill in the art based on a combination of prior art references.

The key prior art cited was:

• US 2008/0133139 A1 ("Vock et al."): This reference taught a system for determining stride length using an accelerometer to measure step frequency and a database that correlates step frequency with stride length. Vock et al. also disclosed using GPS to measure distance and calculate an average stride length.
• US 2014/0278211 A1 ("Yuen et al."): This reference disclosed a fitness monitoring device that determines a user's stride length based on user characteristics like height and gender. It also described using GPS to track distance and calibrate activity metrics.

The examiner argued that it would have been obvious to combine the teachings of Vock et al. and Yuen et al. Vock provided the core concept of correlating step rate with stride length and using GPS for verification. Yuen provided the idea of using personal user data (height, sex) to create an initial estimate. The examiner concluded that combining these known elements to create the claimed system was a predictable and obvious step.

Applicant's Response and Claim Amendments (December 1, 2017)

In response to the rejection, the applicant's representative filed an amendment that significantly narrowed the claims and presented arguments to distinguish the invention from the cited prior art.

The most critical amendment was made to what would become the independent claims (1 and 13). The applicant added the specific limitation that the verified stride length associated with a first step rate is used to adjust the estimated stride length associated with other, different step rates.

In the "Remarks" section of the response, the applicant argued that neither Vock et al. nor Yuen et al., alone or combined, taught or suggested this crucial calibration step.

• They argued that Vock et al. might verify a stride length for a specific activity or step rate but did not disclose using that single verification to then go back and adjust a whole database of estimated stride lengths for other step rates. For example, Vock did not teach using a verified stride length from a 120 steps/minute run to then adjust the estimated stride length for an 80 steps/minute walk.
• This iterative and cross-relational calibration was presented as the core novelty. It solved the problem of inaccuracy when a user moves at various speeds, especially when GPS is off, by creating a smarter, self-correcting model of the user's personal gait.

Notice of Allowance (November 27, 2020)

After further examination and a subsequent amendment refining the claim language, the USPTO issued a Notice of Allowance. The Examiner's Reasons for Allowance explicitly acknowledged the patentability of the amended claims. The examiner stated that the prior art did not teach the specific limitation of "inferring that estimated stride lengths for the user at other step rates or step rate ranges may also be shorter/longer" and "incrementally increasing or decreasing" the estimated stride lengths at those other step rates based on the comparison between a verified and estimated stride length at a specific, measured step rate (as recited in the amended Claim 1).

This indicates that the patent was granted precisely because of the addition of the specific, detailed calibration process where a single verified data point is used to refine a whole set of estimated data points for different conditions. This limitation was deemed non-obvious and a specific improvement over the prior art.

Proceedings Overview

There are no AIA trial proceedings (Inter Partes Review, Post-Grant Review, or Covered Business Method) on file for US Patent 10,959,649 as of the most recent ingest from the USPTO Open Data Portal and supplementary web searches. This means the patent has not been challenged in an AIA trial before the Patent Trial and Appeal Board (PTAB).

Strategic Summary

As there are no PTAB proceedings associated with US Patent 10,959,649, all claims (1-17, including independent claims 1 and 13) remain untested by the PTAB. The patent has not been narrowed or challenged through IPR, PGR, or CBM trials. This indicates that the patent owner has not yet faced a direct challenge to the patentability of these claims in this forum. The absence of PTAB activity suggests that potential petitioners have either not found sufficient grounds to challenge the patent or have chosen other avenues (e.g., district court litigation, licensing negotiations).

The estoppel landscape is entirely open. Since no PTAB proceedings have occurred, no petitioner (or their privies) is barred under § 315(e)(2) from raising any ground that they raised or reasonably could have raised. All prior-art grounds, including those discussed in the prosecution history (Vock et al., Yuen et al., Han et al., Oh et al., Neymotin et al.), remain available for a potential future PTAB challenge.

Recommended Next Steps

Since no PTAB activity exists for US Patent 10,959,649, a defendant currently being asserted against this patent has several strategic options regarding PTAB. The absence of PTAB activity is itself a signal, as well-asserted patents often attract IPRs.

1. Consider filing a Petition: If facing an assertion, a defendant could consider filing an Inter Partes Review (IPR) petition, particularly if strong prior art (like that discussed in the obviousness analysis, such as the combination of Vock et al. and Yuen et al.) can be presented to challenge independent claims 1 and 13.
2. Conduct a new prior art search: While the prosecution history reviewed key prior art, a new, more comprehensive search might uncover even stronger art, especially given the rapid evolution of technology in wearable devices.
3. Monitor for future filings: Continuously monitor the patent's status for any newly filed PTAB petitions via the USPTO's P-TACTS system (Patent Trial and Appeal Case Tracking System) or other patent intelligence platforms.

Inventors

The sole named inventor for US Patent 10,959,649 is Ming Shun Fei.

At the time of filing (January 29, 2015), the application was filed by Beijing Shunyuan Kaihua Technology Co Ltd, which is also listed as the original assignee. This typically implies that Ming Shun Fei was employed by Beijing Shunyuan Kaihua Technology Co Ltd and assigned his rights to the company upon invention or filing. However, an assignment record from April 25, 2018, explicitly shows Ming Shun Fei as the assignor of the patent rights to PHYSICAL ENTERPRISES INC., which is an unusual pattern if all rights had been fully assigned to the initial corporate applicant. This could suggest that the inventor retained some rights, or that the initial filing arrangement was more complex than a standard employee assignment.

Original assignee

The original assignee, as listed on the issued patent and by Google Patents, is Beijing Shunyuan Kaihua Technology Co Ltd.

Information regarding whether Beijing Shunyuan Kaihua Technology Co Ltd directly shipped a product embodying the claims is not immediately determinable from the provided patent text or readily available public records without further in-depth research. Their primary line of business, based on their name, suggests a technology or manufacturing focus, potentially related to the "systems and methods for stride length calibration" described in the patent. Their current status is "Active" according to Google Patents, expiring on August 14, 2035.

Assignment timeline

The following assignments are recorded for US Patent 10,959,649 as identified in the Google Patents legal events timeline. Specific reel/frame numbers and correspondent information are unavailable without direct access to the USPTO Assignment Center.

• 2018-04-25 (executed) / recorded 2018-04-25 — Reel Unavailable/Unavailable

  • Conveyance: Assignment (listed as "reassignment" in Google Patents)
  • Assignor: FEI, Ming Shun
  • Assignee: PHYSICAL ENTERPRISES INC.
  • Correspondent: Unavailable
  • Context: Transfer of patent rights from the inventor to an intermediate entity.

• 2018-07-10 (executed) / recorded 2018-07-10 — Reel Unavailable/Unavailable

  • Conveyance: Assignment (listed as "reassignment" in Google Patents)
  • Assignor: PHYSICAL ENTERPRISES INC.
  • Assignee: Beijing Shunyuan Kaihua Technology Limited
  • Correspondent: Unavailable
  • Context: Transfer of patent rights from an intermediate entity to another entity with a similar name to the original assignee.

Timeline diagram

timeline
    title Ownership of US 10959649
    2015 : Filed by Beijing Shunyuan Kaihua Tech Co Ltd
    2018 : Assigned to Physical Enterprises Inc
         : Assigned to Beijing Shunyuan Kaihua Tech Ltd
    2021 : Issued
    2025 : Litigation filed CAFC 25-1230

NPE / troll-pattern signals

1. Shell-entity transfer — Unclear. PHYSICAL ENTERPRISES INC. is a generic name, which could indicate a shell entity. Without product information or details about its address and corporate structure, its nature remains uncertain. Beijing Shunyuan Kaihua Technology Limited's relationship to Beijing Shunyuan Kaihua Technology Co Ltd (the original applicant and current assignee) is also unclear; it could be a related operating entity or a holding company.

2. Known asserter in the chain — Not present. Neither PHYSICAL ENTERPRISES INC. nor Beijing Shunyuan Kaihua Technology Limited appear on the provided list of known high-frequency NPE plaintiffs.

3. Repeat correspondent across the chain — Unclear. Correspondent information is not available from the provided data.

4. Cascading transfers — Present. The patent underwent two transfers within a short period of less than three months (April 25, 2018, to July 10, 2018).

5. Pre-litigation transfer — Not present. The assignments occurred in 2018, while the noted litigation (CAFC case 25-1230) has a docket number that suggests a filing year of 2025 or 2026, well after the transfers.

6. Bankruptcy fire-sale — Unclear. There is no information to suggest a bankruptcy event.

7. Privateering — Unclear. There is no information to suggest privateering.

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

Verdict

NPE — moderate confidence

This verdict is based on the presence of cascading transfers, with two assignments occurring within less than three months in 2018. Additionally, the generic name of "PHYSICAL ENTERPRISES INC." as an intermediate assignee raises suspicion of it potentially being a shell entity, although this cannot be confirmed without further investigation into its operations and corporate structure. The litigation mentioned on Google Patents (CAFC case 25-1230) suggests future or ongoing assertion, but its relation to these specific transfers is not immediate due to the time gap.

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

Based on the information cited in US patent 10,959,649 and a review of its prosecution history, the following analysis details the most relevant prior art references and their potential for anticipation under 35 U.S.C. § 102.

For a prior art reference to anticipate a claim under 35 U.S.C. § 102, it must disclose, either expressly or inherently, every limitation of the claim. The key inventive concept of US 10,959,649, as established during its prosecution, is the specific method of using a "verified stride length" determined at a first step rate to adjust the "estimated stride lengths" associated with other, different step rates.

Analysis of Cited Prior Art

1. US 2008/0133139 A1 (Vock et al.)

• Full Citation: US Patent Application Publication No. 2008/0133139 A1
• Publication Date: June 5, 2008
• Brief Description: Vock et al. describes a system for determining stride length using an inertial sensor (like an accelerometer) to measure step frequency. This data is correlated with a database to determine a corresponding stride length. Vock et al. also explicitly discloses using a GPS receiver to measure the distance traveled, which is then divided by the step count to calculate an average stride length for calibration purposes.
• Anticipation Analysis (Claims 1 & 13): This reference does not anticipate claims 1 or 13. As argued successfully during prosecution, Vock et al. teaches the verification of a stride length for a given activity using GPS. However, it fails to teach or suggest the crucial step of using that single verified stride length (e.g., for a running pace) to then adjust a table of estimated stride lengths for other step rates (e.g., for a walking pace). It is missing the cross-relational calibration element that is a specific limitation in the claims of patent '649.

2. US 2014/0278211 A1 (Yuen et al.)

• Full Citation: US Patent Application Publication No. 2014/0278211 A1
• Publication Date: September 18, 2014
• Brief Description: Yuen et al. discloses a wearable fitness monitoring device that determines a user's stride length. The system can base an initial stride length on user-provided characteristics such as height and gender. It also describes using GPS to track distance and calibrate or refine activity metrics over time.
• Anticipation Analysis (Claims 1 & 13): This reference does not anticipate claims 1 or 13. Yuen et al. teaches creating an initial estimate based on user data (height, sex) and using GPS for calibration. However, like Vock et al., it does not disclose the specific process of using a verified stride length from one step-rate range to incrementally adjust the estimated stride lengths in other, different step-rate ranges. The key limitation of inferring that estimates at other step rates are also inaccurate and adjusting them proportionally is absent.

3. US 8,868,377 B2 (Han et al.)

• Full Citation: US Patent No. 8,868,377 B2
• Issue Date: October 21, 2014
• Brief Description: Han et al. describes a method for more accurately measuring distance traveled by a user. The system uses an acceleration sensor to determine a user's movement type (e.g., walking vs. running) and applies a corresponding stride length that is set for that specific type of movement. The system can update the stride length for a given movement type based on new measurements.
• Anticipation Analysis (Claims 1 & 13): This reference does not anticipate claims 1 or 13. Han et al. teaches a system with different stride lengths for different activities and a method to update them. For example, it can calibrate the "running" stride length based on a GPS-verified run. However, it does not teach using that verified "running" stride length to then adjust the pre-set "walking" stride length. Each movement type's stride length is calibrated independently rather than being adjusted based on calibrations from other movement types.

4. US 2012/0197548 A1 (Oh et al.)

• Full Citation: US Patent Application Publication No. 2012/0197548 A1
• Publication Date: August 2, 2012
• Brief Description: Oh et al. discloses a method for calculating a user's stride and traveled distance. It involves obtaining a stride value that corresponds to a user's step frequency from a pre-stored table. The system can enter a calibration mode where it calculates a user's personal stride based on a known distance (e.g., on a track) and uses this to create a personalized stride table.
• Anticipation Analysis (Claims 1 & 13): This reference does not anticipate claims 1 or 13. Oh et al. describes creating a personalized stride table through calibration, which involves measuring stride length at various step frequencies. However, it does not teach the specific inference and adjustment step of patent '649. It does not disclose a method where a single verified stride length measurement at one frequency is used to systematically and proportionally adjust the estimated stride lengths at all other frequencies in the table.

5. US 2009/0048789 A1 (Neymotin et al.)

• Full Citation: US Patent Application Publication No. 2009/0048789 A1
• Publication Date: February 19, 2009
• Brief Description: Neymotin et al. describes a personal navigation and tracking device that calculates stride length. The system can receive user parameters like height and weight to estimate stride length. It uses GPS data to determine an actual stride length, which is then used to calibrate a "stride length factor" that influences future calculations, improving accuracy when GPS is lost.
• Anticipation Analysis (Claims 1 & 13): This reference does not anticipate claims 1 or 13. While it teaches estimating stride length from user data and calibrating it with GPS, it describes adjusting a general "stride length factor." It does not describe maintaining a database of different estimated stride lengths for different step-rate ranges and then using a verified stride length from one range to specifically adjust the estimates in the other ranges. The specific, granular, cross-range adjustment is not disclosed.

Based on the prior art references detailed previously, this analysis examines the potential obviousness of US patent 10,959,649 under 35 U.S.C. § 103.

Obviousness Standard and the Person of Ordinary Skill

Under 35 U.S.C. § 103, a patent claim is invalid if the differences between the claimed invention and the prior art are such that the invention as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art (PHOSITA).

For the technology in patent '649, a PHOSITA would likely be an engineer or computer scientist with a bachelor's degree and experience in wearable consumer electronics, sensor data processing (specifically from accelerometers and GPS), and the development of fitness-tracking algorithms.

Obviousness Combination of Prior Art

A strong argument for obviousness can be constructed by combining the teachings of US 2008/0133139 A1 (Vock et al.) and US 2014/0278211 A1 (Yuen et al.). The combination of these references teaches nearly all elements of the independent claims of patent '649, and the remaining gap could be considered an obvious step to a PHOSITA seeking to improve accuracy.

1. What the Prior Art Teaches:

• Vock et al. discloses the core system for stride length calculation. It teaches:

  • Using an accelerometer to determine a user's step rate (step frequency).
  • Maintaining a database that correlates step rates with corresponding stride lengths.
  • Using a GPS receiver to measure the actual distance traveled over a number of steps.
  • Calculating a "verified" average stride length for a specific activity by dividing the GPS-measured distance by the step count.

• Yuen et al. teaches personalizing this type of system from the outset. It discloses:

  • A wearable fitness monitor that determines stride length.
  • Using user-specific physical characteristics, such as height and gender, to calculate an initial stride length.
  • Using GPS to track activity and calibrate fitness metrics.

2. Motivation to Combine Vock et al. and Yuen et al.:

A PHOSITA would have been motivated to combine the teachings of Vock et al. and Yuen et al. to create a more accurate and user-friendly device. Vock’s system relies on a generic correlation between step rate and stride length, which could be inaccurate for a specific individual. Yuen teaches that initial accuracy can be significantly improved by using personal data like height and sex. A PHOSITA would see a clear benefit in using Yuen's personalization method to create the initial database of estimated stride lengths used in Vock's system. This would provide the user with a better "out-of-the-box" experience before any GPS calibration is performed. The combination is a predictable merging of two known methods to achieve a better result.

3. Analysis of the Combination Against the Claims:

When combined, Vock et al. and Yuen et al. teach the following elements of independent claim 1:

• Receiving user-specific information: Taught by Yuen et al.
• Determining a step rate: Taught by Vock et al.
• Determining estimated stride lengths for different step rates based on user information: This is the direct result of combining Yuen's personalization with Vock's step-rate-based table.
• Determining a verified stride length using location data (GPS): Taught by Vock et al.
• Storing the verified stride length: Implicitly taught by both for calibration purposes.

The one element not explicitly taught by this combination is the key limitation that allowed patent '649 to be granted:

...wherein the verified stride length associated with the first step rate or the first range of step rates is used to adjust the one or more estimated stride lengths associated with one or more second step rates or second ranges of step rates different from the first step rate or the first range of step rates.

4. Argument for Why the Final Step is Obvious:

Despite the USPTO's allowance, a strong argument can be made that bridging this final gap would have been obvious to a PHOSITA.

• Known Problem: The background of patent '649 itself notes the problem that a user's stride length varies at different speeds. A PHOSITA would be keenly aware that a calibration performed at a single speed (e.g., running) might not improve accuracy at other speeds (e.g., walking) if not applied correctly.
• Predictable Solution: The combined Vock and Yuen system provides a PHOSITA with a table of estimated stride lengths (the model) and a method to find a "ground truth" data point via GPS verification. If the verified stride length for a 120 steps/minute run is found to be 5% longer than the initial estimate, it is a common and logical step in data calibration to infer that the entire estimation model is off by a similar factor. Applying a proportional adjustment to the estimated stride lengths at other step rates is a straightforward application of data-fitting and error-correction principles.
• Finite Number of Identifiable, Predictable Solutions: To improve the overall accuracy of the stride length model across all step rates (the known problem), a PHOSITA would consider a few predictable solutions: (a) require the user to perform GPS calibration at every possible step rate, which is impractical (as noted by Oh et al.), or (b) use a single, verified data point to intelligently adjust the entire model. The latter approach, which involves inferring error and adjusting proportionally, is a standard technique in algorithm design and would be an obvious path to try.

Therefore, the final inventive step of using a verified stride length from one step rate to adjust estimated stride lengths at other rates represents the application of a well-known calibration technique to solve a known problem in the art. While the applicant successfully argued this step as non-obvious during prosecution, it remains vulnerable to an obviousness challenge by framing it as a predictable design choice for any engineer tasked with improving the accuracy of a fitness tracker.

Patent Term and Continuity Analysis for US 10,959,649

Date of Analysis: May 10, 2026

This analysis details the term adjustments, related applications, and projected expiration date for US Patent 10,959,649, based on data from the USPTO and other public patent information services.

Patent Term Adjustment (PTA)

A Patent Term Adjustment extends the term of a patent to compensate for delays caused by the USPTO during the prosecution of the patent application. For US patent 10,959,649, a significant PTA was granted.

• Total PTA Granted: 197 days.
• Source of Delay: The adjustment was granted primarily due to "B Delay," which occurs when the USPTO fails to issue a patent within three years of the application's filing date. The application was filed on January 29, 2015, and the patent was issued on March 30, 2021, a period well over the three-year mark, thus warranting the adjustment.

Patent Term Extension (PTE)

No Patent Term Extensions (PTE) were found for this patent. PTE is typically granted for delays caused by regulatory review (e.g., by the FDA) and is not applicable in this case as the invention is not a pharmaceutical or medical device requiring such review.

Continuity and Related Applications

A review of the patent's prosecution history and family data reveals the following:

• Continuation or Divisional Status: US patent 10,959,649 is not a continuation or divisional of any prior US patent application. It is an original application.
• Child Applications: There are no known continuation or divisional applications that claim priority to US application 14/608,571 (the application that matured into patent '649).
• Pre-Grant Publication: The application was published before grant as US 2015/0141873 A1 on May 21, 2015.
• International (PCT) Application: The US application was used to claim priority for a subsequent international patent application, filed as PCT/IB2016/050466 on January 29, 2016. This PCT application was later published as WO 2016/120842 A1.

Projected Expiration Date

The expiration date of a US patent is calculated by adding 20 years to the earliest non-provisional filing date, and then adding any granted PTA.

• Filing Date: January 29, 2015
• Standard 20-Year Term End: January 29, 2035
• PTA: + 197 days

Projected Expiration Date: August 14, 2035

Defensive Disclosure and Prior Art Publication

Title: Systems and Methods for Dynamic Sensor Calibration Across Multiple Operational Ranges

Publication Date: May 10, 2026

Abstract: This document discloses a series of methods, systems, and applications derived from the core concept of using a verified data point in one operational range to calibrate or adjust estimated data points in other, different operational ranges. The disclosures herein are intended to enter the public domain to serve as prior art for future patent applications. The core mechanism involves a feedback loop where a high-fidelity, "verified" measurement (e.g., from a GPS or other ground-truth system) is used to refine a lower-fidelity, but more continuously available, predictive model or lookup table across its entire domain, not just at the point of verification.

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1. Derivative Implementations: Material & Component Substitution

1.1. Piezoelectric In-Sole Sensors with Cellular Triangulation for Verification

• Enabling Description: This embodiment replaces the body-worn accelerometer with piezoelectric film sensors embedded within the insole of a shoe. Each heel-strike and toe-off generates a voltage spike, providing a high-precision signal for step detection. The processor uses the timing between these spikes to determine step rate. For location verification, instead of a power-intensive GPS module, the system utilizes a low-power cellular modem to obtain coarse location data from Cell ID triangulation (or Wi-Fi positioning system (WPS) data when available). When the user covers a significant distance (e.g., >500m), the change in cell tower locations provides a "verified distance" traveled. This distance is divided by the piezoelectric step count to calculate the verified stride length. The core calibration algorithm then uses this verified stride length at a given step rate to adjust the user's estimated stride length table, which was initially populated based on height and shoe size.

• Mermaid Diagram (Flowchart):

  flowchart TD
      A[Start: User walks] --> B{Piezoelectric Sensor};
      B -- Voltage Spike --> C[Processor: Detects Step];
      C --> D[Calculate Step Rate (steps/min)];
      D --> E[Access Stride Length Table];
      E -- User Height/Shoe Size --> F[Get Estimated Stride Length (SL_est)];
      F --> G[Calculate Estimated Distance];
      
      subgraph Verification Loop
          H[Low-Power Cellular Modem] --> I{Query Cell Tower IDs};
          I -- Location Data --> J[Processor: Calculate Distance Traveled_ver];
          J -- When Δ > 500m --> K[Calculate Verified Stride Length (SL_ver = Dist_ver / Steps)];
          K --> L{Compare SL_ver with SL_est at current Step Rate};
          L --> M[Adjust Entire Stride Length Table based on delta];
      end
      
      G --> M;
  

1.2. Optical Flow Sensor with IMU Fusion for Step Detection

• Enabling Description: This variant is designed for a device worn on the ankle or shoe. It replaces a simple 3-axis accelerometer with a 9-axis Inertial Measurement Unit (IMU) (3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer) combined with a low-resolution, downward-facing optical flow sensor, similar to those in optical mice. The IMU provides robust data on foot orientation and movement, while the optical flow sensor directly measures displacement relative to the ground. "Steps" are algorithmically determined by the IMU detecting the cyclical motion of a gait cycle. The "stride length" for each step is directly measured by the optical flow sensor. The "step rate" is determined by the IMU. In this embodiment, every step provides a "verified" stride length. The system builds the stride-length-vs-step-rate table dynamically. The purpose of the table is for situations where the optical flow sensor is unreliable (e.g., on highly reflective or transparent surfaces). When the optical sensor signal is lost, the system falls back to using the IMU step rate and the dynamically-built table to estimate distance, similar to the original patent's GPS-denied mode. The calibration is continuous: whenever the optical flow signal is reliable, it updates the corresponding entry in the stride length table.

• Mermaid Diagram (State Diagram):

  stateDiagram-v2
      [*] --> Operational
      
      state Operational {
          direction LR
          [*] --> Optical_Flow_Reliable
          Optical_Flow_Reliable --> Optical_Flow_Unreliable: Surface Change
          Optical_Flow_Unreliable --> Optical_Flow_Reliable: Surface Normalizes
          
          state Optical_Flow_Reliable {
              IMU: Detect Step Rate
              OpticalFlow: Measure Stride Length
              Processor: Update Stride Length Table at current Step Rate
              Processor: Calculate Distance using Optical Flow data
          }
          
          state Optical_Flow_Unreliable {
              IMU: Detect Step Rate
              Processor: Access Stride Length Table
              Processor: Estimate Stride Length for current Step Rate
              Processor: Calculate Distance using Estimated data
          }
      }
  

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2. Derivative Implementations: Operational Parameter Expansion

2.1. Nanoscale Robotic Calibration in a Viscous Medium

• Enabling Description: The calibration method is applied to a swarm of microscopic robots (nanobots) navigating within a fluidic environment, such as a bioreactor or pipeline. Each nanobot is propelled by a micro-actuator (e.g., a magnetic flagellum) that operates at a specific frequency ("step rate"). The robot's onboard model contains an "estimated displacement" per actuation cycle ("estimated stride length"). An external high-resolution microscopy and image analysis system serves as the "GPS," tracking the actual position of the nanobots. For a calibration run, a single nanobot is commanded to move at a specific actuation frequency (e.g., 50 Hz). The vision system measures its true distance traveled over a set time. This "verified displacement" is used to create a calibration factor. This factor is then broadcast to the entire swarm, which uses it to adjust their own estimated displacement models across all potential actuation frequencies, accounting for unexpected changes in the fluid's viscosity.

• Mermaid Diagram (Sequence Diagram):

  sequenceDiagram
      participant Control as Control System
      participant Vision as Microscopy Vision System
      participant NanoBot as Nanobot (Swarm)
  
      Control->>NanoBot: Initiate Calibration Run at 50 Hz
      NanoBot->>NanoBot: Actuate flagellum at 50 Hz
      loop For 10 seconds
          Vision->>Vision: Track Nanobot Position
      end
      Vision->>Control: Report Total Distance Traveled (Verified)
      Control->>Control: Calculate Verified Displacement/Cycle
      Control->>Control: Compare with Estimated Displacement/Cycle
      Control->>Control: Compute Calibration Factor
      Control->>NanoBot: Broadcast Calibration Factor to Swarm
      NanoBot->>NanoBot: Adjust full Displacement Model (all frequencies)
  

2.2. Industrial Autonomous Haul Truck Performance Calibration

• Enabling Description: The method is applied to a fleet of autonomous mining haul trucks. The "step rate" is the engine RPM. The "estimated stride length" is the expected distance traveled per engine revolution, based on a factory model that considers gearing and tire size. The "verified stride length" is calculated using high-precision differential GPS (dGPS). During a haul cycle, the truck operates at a certain average RPM on a specific segment of the route (e.g., an uphill grade). The dGPS measures the exact distance covered. This data is used to calculate a verified distance-per-revolution. This value is compared to the estimate. If the verified value is lower (e.g., due to wheel slip from heavy load and loose gravel), the system infers that the efficiency across all RPM ranges is similarly affected. It then adjusts the truck's entire performance model, which is used for fuel consumption and predictive maintenance calculations, to reflect this reduced efficiency.

• Mermaid Diagram (Flowchart):

  flowchart TD
      A[Start: Haul Cycle] --> B[Truck operates at Average RPM_1 on Segment_A];
      B --> C{Record Engine Revolutions};
      B --> D{dGPS records Start/End Position};
      D --> E[Calculate Verified Distance];
      E & C --> F[Calculate Verified Distance/Revolution];
      F --> G{Compare with Estimated Distance/Revolution from Model};
      G --> H{Calculate Efficiency Delta};
      H --> I[Adjust entire Fuel/Performance Model for all RPMs];
      I --> J[Use adjusted model for fuel prediction and maintenance scheduling];
  

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3. Derivative Implementations: Cross-Domain Application

3.1. Aerospace: Ion Thruster Degradation Calibration

• Enabling Description: The calibration method is used to manage the performance of a satellite's Hall-effect (ion) thruster over its lifetime. The thruster's "step rate" is its power level (e.g., 100W, 150W, 200W). The satellite's flight computer has a model of "estimated thrust" per watt-hour ("estimated stride length") for each power level. For a calibration maneuver, the thruster is fired at a specific power level (e.g., 150W) for a set duration. Ground-based radar and telemetry, combined with the satellite's star trackers, precisely measure the resulting change in orbital velocity (delta-v), which is the "verified thrust." As the thruster ages, its efficiency degrades. If the verified thrust at 150W is 3% lower than the model's estimate, the system infers that the efficiency degradation is systemic. It then applies a ~3% downward adjustment to the estimated thrust values for all other power levels in the model, ensuring more accurate fuel (xenon) consumption predictions and more precise orbital station-keeping.

• Mermaid Diagram (Architecture Diagram):

  graph TD
      subgraph Satellite
          FC(Flight Computer)
          HT(Hall Thruster)
          ST(Star Tracker)
          ANT(Antenna)
      end
      
      subgraph Ground
          GS(Ground Station)
          RAD(Radar)
      end
      
      FC -- Command: Fire at 150W --> HT
      HT -- Produces Thrust --> FC
      FC -- Measures Attitude Change --> ST
      
      GS -- Command via --> ANT
      ANT -- Telemetry --> GS
      
      GS -- Tracking Data --> RAD
      RAD -- Tracking Data --> GS
      
      GS -- Collects all data --> Calibrate{Calibration Module}
      Calibrate -- "Verified Delta-V is 3% low" --> Update{Update Thrust Model}
      Update -- "Adjust estimates for 100W, 200W, etc." --> FC
  

3.2. AgTech: Variable Rate Irrigation (VRI) Nozzle Calibration

• Enabling Description: The system is applied to a VRI center-pivot irrigator to calibrate water flow rates. Each nozzle on the pivot is controlled by a PWM valve, where the duty cycle ("step rate") controls the flow. An initial model provides an "estimated flow rate" ("estimated stride length") for each duty cycle percentage. To calibrate, the system operates one section of the pivot at a fixed duty cycle (e.g., 75%) over a test area equipped with IoT soil moisture sensors. The change in soil moisture provides a "verified flow rate" for that duty cycle. If the verified flow is 5% higher than estimated (e.g., due to pump pressure variance), the central controller infers that the pressure is high across the system and adjusts the estimated flow rates for all other duty cycles in its model. This ensures precise water application across the entire field without needing sensors everywhere.

• Mermaid Diagram (Flowchart):

  flowchart TD
      A[Controller sets Nozzle_X Duty Cycle to 75%] --> B[Irrigator passes over Test Zone];
      C[IoT Soil Moisture Sensors in Test Zone] --> D{Measure ΔMoisture};
      D --> E[Calculate Verified Flow Rate (L/min)];
      F[Controller's Model] --> G{Get Estimated Flow Rate at 75% Duty Cycle};
      E & G --> H{Compare Verified vs. Estimated};
      H -- "Verified is 5% higher" --> I[Adjust Flow Rate Model for ALL duty cycles (1-100%)];
      I --> J[Use updated model for precise irrigation across entire field];
  

3.3. Consumer Electronics: Battery Discharge Curve Calibration

• Enabling Description: This applies the concept to the "time remaining" estimate for a smartphone battery. The "step rate" is the device's current power draw (in mA). The operating system contains a generic "estimated runtime" model ("estimated stride length") per mA of draw, based on the battery's chemistry and design capacity. To perform a verification, when the battery level crosses a specific threshold (e.g., from 20% to 19%), the device records the actual time elapsed and the average power draw during that period. This provides a "verified runtime" at that specific power draw. If the verified runtime was 10% shorter than the model's estimate (indicating accelerated battery aging), the OS adjusts the entire discharge curve, reducing the estimated runtime for all other power draw levels. This provides a more accurate "time remaining" prediction that adapts to the battery's real-world health.

• Mermaid Diagram (State Diagram):

  stateDiagram-v2
      state "Battery > 20%" as S1
      state "Discharging 20% -> 19%" as S2
      state "Battery < 19%" as S3
  
      [*] --> S1
      S1 --> S2: Level drops to 20%
      S2 --> S3: Level drops to 19%
      S3 --> [*]
  
      state S2 {
          note right of S2
              System records:
              1. Start Time (at 20.0%)
              2. End Time (at 19.0%)
              3. Average Power Draw (mA)
              Calculates: Verified Runtime for that Power Draw
              Compares: Verified vs. Estimated Runtime
              Adjusts: Entire Battery Discharge Model
          end note
      }
  

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4. Derivative Implementations: Integration with Emerging Tech

4.1. AI/ML-Driven Predictive Gait Modeling

• Enabling Description: This variation replaces the linear/proportional adjustment algorithm with a lightweight, on-device machine learning model, such as a Bayesian regression model or a small neural network. The model's inputs are user parameters (height, weight, age) and step rate; the output is predicted stride length. Initially, it's trained on a generic dataset. When the device obtains a "verified stride length" from GPS, this new data point is not used for a simple proportional adjustment. Instead, it's used as a new training point to re-fit the entire regression curve. This allows the model to learn the specific, non-linear relationship of the user's personal gait (e.g., their stride length might increase rapidly at jogging paces but plateau at sprinting paces). Over time, the model becomes a highly personalized, predictive digital twin of the user's gait.

• Mermaid Diagram (Class Diagram):

  classDiagram
      class WearableDevice {
          -accelerometer
          -gps
          -processor
          +determineStepRate()
          +getGPSLocation()
      }
      class GaitModel {
          -bayesianRegressionModel
          +predictStrideLength(stepRate)
          +updateModel(stepRate, verifiedStrideLength)
      }
      class Processor {
          -gaitModel
          +runCalibrationLoop()
      }
      WearableDevice *-- Processor
      Processor *-- GaitModel
  

4.2. IoT Smart Environment for Seamless Verification

• Enabling Description: The wearable device operates within an IoT-enabled environment (e.g., a smart gym or smart home) equipped with UWB (Ultra-Wideband) anchors or a network of Bluetooth Low Energy (BLE) beacons. These anchors provide continuous, high-precision indoor location tracking, serving as the "GPS" for verification. The wearable device uses the MQTT protocol to broadcast its currently measured step rate to the local network. The environment's central server, which is tracking the user's location via UWB, calculates the verified stride length in real-time. It then sends an MQTT message back to the wearable with the verified data, which the device uses to update its internal stride length model. This creates a closed-loop system where calibration happens automatically and continuously whenever the user is in the smart environment.

• Mermaid Diagram (Sequence Diagram):

  sequenceDiagram
      participant User
      participant Wearable
      participant MqttBroker as MQTT Broker
      participant UwbSystem as UWB System
  
      User->>Wearable: Begins walking
      Wearable->>MqttBroker: PUBLISH topic 'gait/steprate' payload '110'
      UwbSystem->>UwbSystem: Tracks User's high-precision location
      UwbSystem->>MqttBroker: PUBLISH topic 'gait/verified_sl' payload '0.78'
      MqttBroker-->>Wearable: RECEIVE topic 'gait/verified_sl'
      Wearable->>Wearable: Update stride length model with new data
  

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5. Derivative Implementations: The "Inverse" or Failure Mode

5.1. Graceful Degradation using Magnetometer as Secondary Step Counter

• Enabling Description: The system is designed for high-reliability applications. It incorporates a secondary, lower-power sensor, a magnetometer, in addition to the primary accelerometer. During normal operation, the accelerometer provides step data. If the processor detects that the accelerometer data has become erratic or flatlined (indicating failure), it automatically switches to a "degraded mode." In this mode, it uses the magnetometer to detect steps by measuring the regular magnetic field variations caused by the swinging of the user's arm. Because this method is less accurate, the calibration logic is modified: it ceases to update its stride length model and instead applies a "confidence interval" to all distance estimates. The user interface displays the distance with a "+/- X meters" error margin and an icon indicating the system is operating in a degraded state, ensuring the user is aware of the lower precision.

• Mermaid Diagram (State Diagram):

  stateDiagram-v2
      [*] --> Nominal_Mode
      Nominal_Mode: Using Accelerometer for steps
      Nominal_Mode: GPS data updates SL model
      
      Nominal_Mode --> Degraded_Mode: Accelerometer Failure Detected
      Degraded_Mode: Using Magnetometer for steps
      Degraded_Mode: SL model is frozen (no updates)
      Degraded_Mode: Distance estimates include confidence interval
      
      Degraded_Mode --> Nominal_Mode: Accelerometer signal restored
  

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6. Combination with Open-Source Standards

6.1. Combination with MQTT for IoT Interoperability

• Scenario Description: A treadmill manufacturer and a wearable device manufacturer both implement the open-source MQTT protocol for device communication. The treadmill, which has a calibrated motor and belt sensor, knows the exact distance covered. It publishes this distance data to a local MQTT broker on the topic treadmill/distance. The user's wearable device, which is connected to the same Wi-Fi network, subscribes to this topic. As the user runs, the wearable calculates its own estimated distance based on its calibrated stride length model. Simultaneously, it receives the ground-truth distance from the treadmill via MQTT. This allows the wearable to perform a "verified stride length" calibration without using GPS, leveraging the open standard for interoperability between devices from different manufacturers.

6.2. Combination with Protocol Buffers for Standardized Data Exchange

• Scenario Description: A cloud-based fitness platform uses Google's Protocol Buffers (Protobuf) as its open-source standard for all data exchange. The user's wearable device defines its gait data using a .proto file. This file specifies the exact format for the UserProfile message (height, weight, sex) and the StrideLengthTable message, which includes repeated fields for StepRateRange and StrideLengthValue. When the user syncs their device, this Protobuf message is serialized and sent to the cloud. The cloud service can then deserialize the data and use it for large-scale analytics, comparing the user's calibrated gait model with that of other anonymized users. This open standard ensures that the data structure is robust, efficient, and independent of the programming language used by the device or the server.

6.3. Combination with FIDO2 for Gait-Based Authentication

• Scenario Description: The highly personalized stride-length-vs-step-rate model, once calibrated over several GPS-verified sessions, becomes a unique biometric signature of the user's gait. An application on the user's smartphone implements the open FIDO2/WebAuthn standard for passwordless authentication. To log in, the user is prompted to walk 20 paces. The phone's motion sensors capture the gait, and the app's internal logic compares the measured step-rate-to-stride-length relationship against the securely stored, calibrated model. If the pattern matches within a given tolerance, it serves as a successful authentication factor, proving both that it is a human walking and that it is the correct human. This combines the patented calibration method with an open authentication standard to create a new form of biometric security.