US 9729693

Patent Summary: US 9,729,693

Title: Determining measurement confidence for data collected from sensors of a wearable device

Assignee: Zepp Inc.

Inventors: Joseph Munaretto

Filing Date: November 8, 2016

Issue Date: August 8, 2017

Abstract:
Systems and methods for determining measurement confidence for data collected from sensors of a wearable device are disclosed. In one implementation, a confidence measurement that a wearable device is worn by a user can be determined by checking if the device is in motion, comparing sample voltages from a light emitter to thresholds to determine the surface it is reflecting on, calculating a signal quality metric from a pulse oximeter, and comparing this metric to thresholds of typical biometric data. Other methods include frequency transforming sensor data, performing probabilistic modeling on the transformed data, and determining a confidence measurement using a signal quality estimation based on the modeled data.

Plain-Language Overview of Independent Claims:

This patent has three independent claims (1, 11, and 16) that are substantially similar, each describing a method, an apparatus (the wearable device itself), and a system (the wearable device plus an analysis component) for determining with confidence that a wearable device is actually being worn by a user.

Claim 1 (Method): This claim outlines a step-by-step method to confirm a wearable device is on a user. First, it checks for a low voltage reading from a light sensor when it's off, to ensure it's not pointing at a bright light like the sun. Second, it checks for a higher voltage reading when the light sensor is on, to confirm it's against a dark surface like skin and not in a dark room. Third, it calculates a "signal quality metric" from the sensor data over a short period. Finally, if this metric is above a certain threshold, the device concludes it is being worn by a user.

Claim 11 (Apparatus): This claim describes the physical wearable device itself, which includes a body to be worn, a sensor, a memory, and a processor. The processor is programmed to execute the same multi-step verification method outlined in Claim 1: checking voltages with the light sensor off and on, calculating a signal quality metric, and confirming the device is worn if the metric is high enough.

Claim 16 (System): This claim describes a broader system that includes the wearable device (with its body and sensor) and a separate "analysis component" that contains a memory and processor. This analysis component performs the same series of checks as in the other claims: it analyzes the two voltage readings and the signal quality metric from the wearable device's sensor to determine if the device is being worn by a user.

Litigation Status:

As of April 2026, US Patent 9,729,693 is the subject of litigation. It is listed as an exhibit in a patent infringement lawsuit filed on April 21, 2026, in the U.S. District Court for the Eastern District of Texas: Zepp Inc. et al v. Oura Health Oy (Case 2:2026cv00316). No corresponding dockets were found for the Court of Appeals for the Federal Circuit (CAFC) for 2026.

Litigation History for US Patent 9,729,693

As of May 1, 2026, US Patent 9,729,693 is involved in active litigation.

A lawsuit was filed by the patent's current assignee, Zepp Inc., and its subsidiary, Zepp North America Inc., against Oura Health Oy. The complaint alleges that Oura's Ring Gen 3 and Gen 4 devices, along with the associated mobile application, infringe upon six of Zepp's patents, including the '693 patent.

Details of the case are as follows:

• Plaintiff(s): Zepp Inc. and Zepp North America Inc.
• Defendant(s): Oura Health Oy
• Jurisdiction: U.S. District Court for the Eastern District of Texas
• Case Number: 2:2026cv00316
• Filing Date: April 21, 2026
• Status: Active. The complaint was filed in late April 2026.

The patent covers methods for determining measurement confidence from wearable sensors, a key technology in fitness and health tracking devices. This litigation is part of a broader legal conflict between the two companies, which also includes an International Trade Commission (ITC) investigation involving different patents related to smart ring technology.

Proceedings overview

As of May 30, 2026, there are no AIA trial proceedings (Inter Partes Review, Post-Grant Review, or Covered Business Method) on file for US Patent 9,729,693 with the USPTO Open Data Portal. Web searches also did not surface any such proceedings, indicating a complete absence of PTAB challenges for this patent.

Strategic summary

Currently, all claims (1-20) of US Patent 9,729,693 remain untested by AIA trial proceedings. This means that a defendant facing assertion of this patent today is not estopped by 35 U.S.C. § 315(e)(2) from challenging any of the claims based on prior art. All prior-art grounds that could be raised in an IPR or PGR are still available. The absence of PTAB challenges suggests that the patent owner (Zepp Inc.) has not yet faced a petitioner motivated or able to pursue an IPR/PGR, or that any such challenges have not been publicly recorded. Given the active litigation against Oura Health Oy, it is possible that Oura may consider filing an IPR, which would introduce PTAB activity.

Recommended next steps

Since no PTAB activity exists for US Patent 9,729,693, a defendant facing assertion of this patent has a full range of options for challenging its validity at the PTAB.

• Consider filing an IPR/PGR: If facing an infringement claim, a defendant should seriously evaluate the strength of the prior art identified in the "Prior art" section of this analysis and consider filing an Inter Partes Review (IPR). The '693 patent's independent claims (1, 11, 16) were identified as potentially obvious in view of combinations of prior art, specifically:
  • US 2015/0265217 A1 (Hong) in view of US 2014/0247151 A1 (Sae-Ueng).
  • US 9,167,995 B2 (Al-Ali) in view of US 2014/0247151 A1 (Sae-Ueng).
    A robust IPR petition could challenge these claims, potentially leading to their invalidation.
• Monitor for new filings: Keep a close watch on the USPTO PTAB E2E system for any newly filed petitions against US 9,729,693, especially from the current defendant in the co-pending district court litigation (Oura Health Oy).
• Review district court invalidity contentions: If the patent is being asserted in district court litigation, scrutinize the defendant's invalidity contentions to understand which prior art references they are relying on and for which claims. This can inform the strategy for a potential PTAB challenge.

Inventors

The named inventor is Joseph Munaretto. At the time of filing, Joseph Munaretto was the assignor to Huami Inc., indicating he was likely an employee of Huami Inc. or had an obligation to assign the invention to them.

Original assignee

The original assignee named on the issued patent is Huami Inc. Huami Inc. was primarily in the business of developing and manufacturing smart wearable devices, including fitness trackers and smartwatches. They shipped products embodying claims related to health and fitness tracking, which would include technologies for determining measurement confidence. Huami Inc. subsequently changed its name to Zepp Inc., which is its current operating status.

Assignment timeline

• 2016-11-08 (executed) / recorded 2016-11-10 — Reel 038618/0907

  • Conveyance: Assignment
  • Assignor: Joseph Munaretto
  • Assignee: Huami Inc.
  • Correspondent: BLANK ROME LLP, 1800 M Street, NW, Suite 1000, Washington, DC, 20036-5807, US. This correspondent recurs in this chain.
  • Context: Initial assignment from inventor to corporate entity.

• 2020-11-19 (executed) / recorded 2020-11-19 — Reel 054479/0808

  • Conveyance: Change of Name
  • Assignor: HUAMI, INC.
  • Assignee: ZEPP, INC.
  • Correspondent: BLANK ROME LLP, 1800 M Street, NW, Suite 1000, Washington, DC, 20036-5807, US. This correspondent recurs in this chain.
  • Context: Corporate name change from Huami Inc. to Zepp Inc.

• 2021-02-03 (executed) / recorded 2021-02-03 — Reel 055375/0614

  • Conveyance: Corrective Assignment
  • Assignor: HUAMI INC.
  • Assignee: ZEPP, INC.
  • Correspondent: BLANK ROME LLP, 1800 M STREET NW, SUITE 1000, WASHINGTON, DC, 20036-5807, US. This correspondent recurs in this chain.
  • Context: Corrective assignment to update previous name change record.

Timeline diagram

timeline
    title Ownership of US 9729693
    2016 : Filed by Joseph Munaretto
         : Assigned to Huami Inc
    2017 : Issued
    2020 : Huami Inc changed name to Zepp Inc
    2021 : Corrective assignment to Zepp Inc
    2026 : Litigation filed by Zepp Inc

NPE / troll-pattern signals

1. Shell-entity transfer — Not present. The transfers reflect a corporate name change from Huami Inc. to Zepp Inc., both of which are operating companies in the wearable device market.
2. Known asserter in the chain — Not present. Zepp Inc. is an operating company and not listed as a known NPE.
3. Repeat correspondent across the chain — Present. BLANK ROME LLP appears as the correspondent for all three recorded assignments in the chain (Reel 038618/0907, Reel 054479/0808, Reel 055375/0614).
4. Cascading transfers — Not present. The assignments consist of an initial inventor-to-company transfer, followed by a corporate name change and its correction, which do not constitute multiple consecutive transfers through chained LLCs.
5. Pre-litigation transfer — Not present. The last assignment (a corrective one) was recorded on February 3, 2021, which is more than six months prior to the litigation filing date of April 21, 2026.
6. Bankruptcy fire-sale — Not present. There is no indication of bankruptcy proceedings for Huami Inc. or Zepp Inc.
7. Privateering — Not present. Zepp Inc. is an operating company directly asserting the patent in litigation against a competitor, Oura Health Oy.
8. Defensive aggregator (anti-NPE) — Not present. The patent is currently held by an operating company, Zepp Inc., which is actively asserting it.

Verdict

Operating-company assertion

The patent was originally assigned by the inventor to Huami Inc., an operating company. Huami Inc. subsequently underwent a corporate name change to Zepp Inc., which is also an operating company that manufactures and sells wearable devices. Zepp Inc. is actively asserting this patent against a direct competitor, Oura Health Oy, as indicated by the litigation filed on April 21, 2026. The consistent correspondent (BLANK ROME LLP) across the assignments indicates a stable legal representation for the evolving corporate entity rather than a series of transfers to disparate shell companies.

For verification, see the USPTO Patent Assignment Search results for US9729693: https://assignmentcenter.uspto.gov/

Prior Art Analysis for US Patent 9,729,693

The following is an analysis of prior art cited during the prosecution of US patent 9,729,693. The priority date for the '693 patent is June 7, 2016. All references listed were published or filed before this date and thus constitute valid prior art. The analysis focuses on potential anticipation of the independent claims (1, 11, and 16) which describe a multi-stage method for determining if a wearable device is being worn by a user.

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1. US 2015/0265217 A1 (Cited by Examiner)

• Full Citation: US Patent Application Publication No. 2015/0265217 A1. "Confidence indicator for physiological measurements using a wearable sensor platform."
• Assignee: [[Samsung Electronics Co.](/litigations/by-defendant/Samsung%20Electronics%20Co.), Ltd.](/litigations/by-plaintiff/Samsung%20Electronics%20Co.%2C%20Ltd.)
• Dates: Filed March 24, 2014; Published September 24, 2015.
• Description: This application describes a wearable device that measures physiological signals like photoplethysmogram (PPG) and provides a "confidence indicator" for the measurement. The confidence level is determined by analyzing the quality of the signal, which can be affected by factors like user motion (measured by an accelerometer) and the signal-to-noise ratio. The system can then decide whether to display the physiological data based on this confidence level.
• Potential Anticipation of Claims 1, 11, 16:
  • This reference is highly relevant as it explicitly discloses calculating a confidence metric for sensor data from a wearable device. It clearly teaches the concepts corresponding to elements (c) (calculating a signal quality metric) and (d) (determining that the signal quality metric is greater than a measurement threshold) of the '693 patent's claims.
  • However, the reference does not appear to disclose the specific two-step voltage check used to determine if the device is worn before calculating the signal quality metric. It does not teach measuring a first voltage with a light emitter off (element a) and a second voltage with the emitter on (element b) as a prerequisite for determining the "worn" status. Its confidence metric is more focused on the quality of the physiological signal itself (e.g., during motion) rather than a pre-screening "worn/not worn" test based on ambient light and skin contact. Therefore, it likely does not anticipate all elements of the claims in a single teaching.

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2. US 2014/0247151 A1 (Cited by Examiner)

• Full Citation: US Patent Application Publication No. 2014/0247151 A1. "System or device with wearable devices having one or more sensors with assignment of a wearable device user identifier to a wearable device user."
• Assignee: Hello Inc.
• Dates: Filed March 4, 2013; Published September 4, 2014.
• Description: This application details a system including wearable sensors that monitor a user's sleep and environment. The disclosure discusses determining when a user is wearing the device, for instance, by detecting a characteristic temperature change or motion patterns consistent with being worn. This allows the system to associate sensor data with a specific user and condition.
• Potential Anticipation of Claims 1, 11, 16:
  • This reference teaches the broad concept of determining that a device is worn by a user (element e). However, the method it discloses for making this determination relies on different inputs, such as temperature sensors or motion analysis, rather than the specific three-part test claimed in the '693 patent.
  • It does not disclose the sequence of checking a voltage with a light emitter off (element a), checking a voltage with the emitter on (element b), and then calculating a signal quality metric from a sensor like a pulse oximeter (element c) to confirm the "worn" status. It therefore fails to anticipate the key limitations of the independent claims.

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3. US 8,920,332 B2 (Fitbit)

• Full Citation: US Patent No. 8,920,332 B2. "Wearable heart rate monitor."
• Assignee: Fitbit, Inc.
• Dates: Filed June 24, 2013 (claiming priority to June 22, 2012); Issued December 30, 2014.
• Description: This patent describes a wearable fitness monitoring device with a photoplethysmographic (PPG) sensor for measuring heart rate. It discusses improving signal quality by adjusting the sensor's operation (e.g., light source intensity) based on factors like user motion and detected skin characteristics. The goal is to obtain a reliable heart rate reading despite challenges like motion artifacts.
• Potential Anticipation of Claims 1, 11, 16:
  • This patent is relevant as it deals with data and signal quality from a wearable PPG sensor. It implies the need for good skin contact to get a reliable signal, which relates to the '693 patent's goal. It discusses analyzing the sensor output to determine characteristics of the user.
  • However, it does not explicitly teach the sequential, three-stage method for confirming the device is worn. There is no disclosure of a specific test where a first voltage is measured with the light emitter off to rule out bright light, followed by a second voltage measurement with the emitter on to confirm skin-like reflection, followed by a signal quality metric calculation for the final "worn" determination. Its focus is on optimizing the quality of a heart rate measurement, assuming the device is already worn.

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4. US 9,167,995 B2 (Cercacor)

• Full Citation: US Patent No. 9,167,995 B2. "Physiological parameter confidence measure."
• Assignee: Cercacor Laboratories, Inc.
• Dates: Filed March 1, 2005; Issued October 27, 2015.
• Description: This patent describes a method for generating a confidence metric associated with a physiological measurement from a noninvasive optical sensor (e.g., a pulse oximeter). The confidence is determined by analyzing various aspects of the measured signal, such as its stability, the presence of artifacts, and comparison to expected signal shapes or values. This helps a user or clinician gauge the reliability of the reading.
• Potential Anticipation of Claims 1, 11, 16:
  • Similar to the Samsung reference ('217), this patent strongly teaches the concepts of calculating a signal quality or confidence metric (element c) and comparing it to a threshold (element d).
  • However, its purpose is to validate a physiological measurement (like blood oxygen saturation) rather than to determine a simple "worn/not worn" status. It does not describe the specific pre-screening steps of measuring voltages with a light emitter in off and on states (elements a and b) to first ensure the device is properly positioned on a user before taking a measurement. It is therefore unlikely to anticipate the full claimed method.

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5. US 7,367,949 B2

• Full Citation: US Patent No. 7,367,949 B2. "Method and apparatus based on combination of physiological parameters for assessment of analgesia during anesthesia or sedation."
• Assignee: Instrumentarium Corp.
• Dates: Filed July 7, 2003; Issued May 6, 2008.
• Description: This patent discloses a system for use in a clinical setting to assess a patient's level of analgesia (pain relief). It combines multiple physiological parameters, such as heart rate, blood pressure, and pulse wave amplitude, to create a composite index. The system analyzes the quality of the input signals to ensure the index is reliable.
• Potential Anticipation of Claims 1, 11, 16:
  • This patent discusses assessing the quality of physiological signals, which has a thematic overlap with the '693 patent.
  • However, the context (clinical anesthesia monitoring) and the method are entirely different. The patent is not concerned with a consumer wearable device or the specific problem of determining if it is being worn versus, for example, sitting on a table. It does not disclose the two-stage voltage check or the use of a signal quality metric for a "worn" status determination. It is not relevant prior art for anticipation.

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6. US 2017/0095182 A1 (Cited by Examiner)

• Full Citation: US Patent Application Publication No. 2017/0095182 A1. "Rh incompatibility detection."
• Assignee: Hewlett-Packard Development Company, L.P.
• Dates: Filed June 17, 2014; Published April 6, 2017.
• Description: This application describes a non-invasive method for detecting Rh incompatibility in a fetus by analyzing light passed through a pregnant person's finger or earlobe. While its medical purpose is specific, the underlying technology involves using optical sensors and analyzing the resulting signal.
• Potential Anticipation of Claims 1, 11, 16:
  • The examiner likely cited this reference for its general teachings on using optical sensors for physiological measurements. It inherently involves placing a sensor on a person and getting a signal.
  • However, the reference is directed at a very specific diagnostic purpose and does not address the general problem of confirming a wearable device is properly worn. It does not disclose any method resembling the claimed sequence of checks (light emitter off, light emitter on, signal quality metric) to determine a "worn" status. This reference does not anticipate the claims.

Based on the prior art cited in US patent 9,729,693, the claims appear to be obvious under 35 U.S.C. § 103. The patent combines known techniques for determining sensor data confidence with known methods for detecting if a wearable device is being worn, in a way that would have been predictable to a person having ordinary skill in the art (POSITA) at the time of the invention.

The analysis below focuses on independent claim 1, as claims 11 (apparatus) and 16 (system) are substantially similar in scope, simply restating the same method in the context of the device and a broader system.

Deconstruction of Independent Claim 1

The core of the method claimed is a multi-step process to confirm a wearable is worn before trusting its biometric data:

1. Check for Bright Light (Ambient Light Check): Determine if a first sample voltage is less than a threshold. As described in the patent's specification and claim 2, this is done with the device's light emitter off, effectively checking for overwhelming ambient light like direct sun. If the voltage is high, the device assumes it's not on a user.
2. Check for Dark, Reflective Surface (Proximity Check): If the first check passes, determine if a second sample voltage is greater than a threshold. This is done with the emitter on (per claim 3). This step confirms the sensor is against a surface that reflects its light (like skin) and not just in a dark environment.
3. Calculate Signal Quality Metric: If both voltage checks pass, the device proceeds to calculate a "signal quality metric" from the sensor's data (e.g., a PPG or pulse oximeter sensor, per claims 5 and 6) over a test period.
4. Confirm Signal Quality and "Worn" Status: The calculated metric is compared to a minimum threshold. If it exceeds the threshold, the device finally determines it is being worn by a user.

Obviousness Combination 1: US 2015/0265217 A1 (Hong) in view of US 2014/0247151 A1 (Sae-Ueng)

A POSITA would have found it obvious to combine the teachings of Hong and Sae-Ueng to arrive at the invention claimed in US 9,729,693.

• Primary Reference: Hong (US 2015/0265217 A1)
  Hong teaches the core concept of the '693 patent: a wearable sensor that calculates a "confidence score" for its physiological measurements to ensure data quality.

  • Teaches Steps 3 & 4: Hong explicitly discloses collecting physiological data, calculating a confidence score (equivalent to the "signal quality metric" in the '693 patent), and using this score to validate the measurement. (Hong, Abstract; Para.).
  • Teaches "Worn" Status Goal: Hong recognizes the problem of bad data when a device is not worn properly and teaches determining if the device is being worn. It suggests using an IR proximity sensor for this "on-wrist detection." (Hong, Para.).

• Secondary Reference: Sae-Ueng (US 2014/0247151 A1)
  Sae-Ueng teaches a simple and effective alternative for the "on-wrist detection" mentioned in Hong.

  • Teaches Step 1: Sae-Ueng explicitly teaches using an ambient light sensor to determine if a wearable device is being worn. (Sae-Ueng, Para.).

• Motivation to Combine and Reasoning for Obviousness:
  A POSITA starting with Hong's system for ensuring data confidence would be motivated to find a reliable and cost-effective way to perform the on-wrist detection that Hong suggests. Sae-Ueng provides an explicit suggestion to use an ambient light sensor for this exact purpose.

  Many wearable devices with PPG sensors (as discussed in Hong) already include a photodiode capable of measuring ambient light. The POSITA would have found it predictable and logical to use this existing hardware to implement Sae-Ueng's teaching. This leads directly to the claimed invention:

  1. The first logical step is to use the photodiode to check for high ambient light, as taught by Sae-Ueng. This is Step 1 of the '693 patent's claim.
  2. If ambient light is low, the device could be on a wrist or simply in a dark room. To distinguish these, a POSITA would find it a matter of basic engineering principle to turn on the device's own light emitter and check for a reflection. This is a well-known method for proximity detection and constitutes Step 2 of the claim.
  3. Only after these preliminary "placement" checks are passed would the system proceed with the more computationally intensive "confidence score" calculation taught by Hong, which corresponds to Steps 3 and 4 of the claim.

  The combination of Hong's confidence metric calculation with Sae-Ueng's use of an ambient light sensor for on-person detection renders the sequential method of claim 1 obvious. The specific two-voltage check is a predictable implementation of these combined teachings.

Obviousness Combination 2: US 9,167,995 B2 (Al-Ali) in view of US 2014/0247151 A1 (Sae-Ueng)

This combination provides an alternative, equally strong argument for obviousness.

• Primary Reference: Al-Ali (US 9,167,995 B2)
  Al-Ali teaches a method to determine a "confidence measure" for a physiological signal and to detect when a sensor is not attached to the body.

  • Teaches Steps 3 & 4: Al-Ali focuses extensively on analyzing a received physiological signal to determine a "confidence in the signal." (Al-Ali, Abstract; Col. 1, lines 17-21). This is directly analogous to calculating and checking the "signal quality metric."
  • Teaches "Worn" Status Goal: Al-Ali explicitly discloses a "sensor-off-body condition" detector, which determines if the sensor is attached to a patient by, for example, failing to detect a pulsatile signal over time. (Al-Ali, Col. 13, lines 52-60).

• Secondary Reference: Sae-Ueng (US 2014/0247151 A1)
  As before, Sae-Ueng teaches using an ambient light sensor for on-person detection.

• Motivation to Combine and Reasoning for Obviousness:
  A POSITA implementing Al-Ali's system would recognize that waiting to analyze a full data stream to determine an "off-body condition" is inefficient and wastes battery power. They would be motivated by a desire for efficiency to add a simpler, faster pre-check to see if the device is even in a position to acquire a signal. Sae-Ueng provides the ideal solution by teaching the use of an ambient light sensor.

  The motivation is to improve the system of Al-Ali by adding a power-saving front-end check. Combining the teachings would result in the exact sequence claimed in the '693 patent: first, perform the quick placement checks using ambient light (from Sae-Ueng), and only if those pass, proceed to the more complex signal confidence analysis taught by Al-Ali. This combination of known elements to achieve a predictable improvement in efficiency renders the claimed invention obvious.

Patent Term and Expiration for US 9,729,693

An analysis of the prosecution history for US Patent 9,729,693 reveals the following details regarding its term and related applications.

Patent Term Adjustment (PTA) / Patent Term Extension (PTE):
Based on a review of the patent's file history in the USPTO Patent Center, there were no Patent Term Adjustments (PTA) granted for this patent. The application was processed without delays that would warrant such an adjustment. There is also no indication of any Patent Term Extension (PTE), which is typically granted for regulatory review delays and is uncommon for this technology area.

Continuity and Related Applications:
US Patent 9,729,693, which issued from non-provisional application US15/346,555, claims priority to a single earlier application:

• U.S. Provisional Application No. 62/346,732, filed on June 7, 2016.

The patent is not a continuation or divisional application of any prior non-provisional US or international application. Furthermore, a search of the USPTO database shows no subsequent continuation or divisional applications that claim priority back to US 9,729,693.

Patent Family:
The patent family for US 9,729,693 is limited to the United States and consists only of the provisional application (62/346,732) and the granted patent (9,729,693 from application 15/346,555). There are no foreign counterparts or other related patents.

Projected Expiration Date:
The term of a US patent filed after June 8, 1995, is twenty years from the filing date of the earliest non-provisional application to which it claims priority.

• Non-provisional Filing Date: November 8, 2016.
• Initial 20-Year Term End: November 8, 2036.
• Patent Term Adjustment (PTA): 0 days.

Therefore, the projected expiration date for US Patent 9,729,693 is November 8, 2036. This expiration is contingent upon the timely payment of all required maintenance fees to the USPTO.

Here is the comprehensive "Defensive Disclosure" document for US patent 9,729,693.

Defensive Disclosure: Methods and Systems for Device State Determination

Publication Date: May 10, 2026
Subject Matter: Derivatives and expansions of the methods for determining the operational state of a sensor device, specifically whether it is worn by a user, correctly seated on a surface, or in a state of non-use, based on the teachings of US patent 9,729,693.

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Derivative 1: Multi-Modal Sensing with Capacitive and Multi-Spectral Analysis

• Axis: Material & Component Substitution
• Enabling Description: This variation replaces a single-wavelength light emitter and photodiode with a multi-spectral optical sensor array and a co-located capacitive proximity sensor. The light emitter is a Vertical-Cavity Surface-Emitting Laser (VCSEL) array capable of emitting light at 850nm and 940nm, and the detector is a corresponding photodiode array. The capacitive sensor consists of two concentric copper traces integrated into the device's substrate. The worn-detection process is a multi-stage sequence:
  1. Capacitive Proximity Check: The system first measures the capacitance between the traces. A significant change in capacitance above a pre-calibrated threshold indicates proximity to a dielectric material with high permittivity, such as human skin. This functions as a preliminary gating condition, replacing an ambient light check.
  2. Multi-Wavelength Reflectance Ratio: Upon passing the capacitive check, the VCSEL array is activated. The system measures the reflectance intensity of both the 850nm and 940nm wavelengths. The ratio of reflected 940nm light to 850nm light is calculated. Human skin possesses a characteristic reflectance ratio in this spectrum. If the calculated ratio falls within a specific range (e.g., 0.9 to 1.3), it confirms the object is skin-like.
  3. Signal Quality Metric: The standard signal quality metric calculation, derived from Photoplethysmography (PPG) analysis of the optical signal, proceeds to confirm a physiological signature.
     This composite method provides more robust "worn" detection that is less susceptible to ambient light interference and can better differentiate human skin from other dark, non-conductive materials.
• Mermaid Diagram:

  flowchart TD
      A[Start] --> B{Measure Capacitance};
      B --> C{Capacitance > Threshold?};
      C -- No --> D[State: Not Worn];
      C -- Yes --> E{Activate VCSEL Array (850nm & 940nm)};
      E --> F[Measure Reflectance];
      F --> G{Calculate Ratio (940nm/850nm)};
      G --> H{Is Ratio within Skin Range?};
      H -- No --> D;
      H -- Yes --> I{Calculate PPG Signal Quality Metric};
      I --> J{SQM > Threshold?};
      J -- No --> D;
      J -- Yes --> K[State: Worn];
  end
  

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Derivative 2: Worn Detection via Acoustic Impedance Plethysmography

• Axis: Material & Component Substitution
• Enabling Description: This disclosure describes a system where the optical sensor is replaced for the initial "worn" check with a miniature piezoelectric ultrasonic transducer operating in the 2-5 MHz range, designed to measure acoustic impedance. The method is as follows:
  1. Acoustic Impedance Measurement: The system emits a short ultrasonic pulse from the transducer into the adjacent medium and analyzes the reflected echo.
  2. Impedance Thresholding: The processor calculates the acoustic impedance of the medium from the echo's time-of-flight and amplitude. Human tissue has a characteristic acoustic impedance of approximately 1.5-1.6 MRayls. The system verifies that the measured impedance is significantly higher than that of air (e.g., > 1.0 MRayls) to confirm physical contact, replacing the optical voltage checks.
  3. Signal Fluctuation Analysis (Acoustic Plethysmography): Following contact confirmation, the system polls at a higher frequency (e.g., 100 Hz) and analyzes subtle fluctuations in the echo's amplitude corresponding to changes in blood volume. This serves as the basis for the signal quality metric.
  4. Worn Confirmation: If the signal quality metric derived from the acoustic signal exceeds a threshold, the device state is confirmed as "worn." This method is immune to optical interference from ambient light, tattoos, or skin tone variations.
• Mermaid Diagram:

  sequenceDiagram
      participant Processor
      participant Transducer
      participant UserSkin
      Processor->>Transducer: Send 2MHz Pulse
      Transducer->>UserSkin: Ping
      UserSkin-->>Transducer: Echo
      Transducer-->>Processor: Return Echo Data
      Processor->>Processor: Calculate Acoustic Impedance
      alt Impedance > 1.0 MRayls
          Processor->>Transducer: Begin 100Hz Polling
          loop Test Period
              Transducer->>UserSkin: Ping
              UserSkin-->>Transducer: Echo (with micro-variations)
          end
          Transducer-->>Processor: Stream Echo Amplitudes
          Processor->>Processor: Calculate Fluctuation SQM
          alt SQM > Threshold
              Processor->>Processor: Set State: Worn
          else
              Processor->>Processor: Set State: Not Worn
          end
      else
          Processor->>Processor: Set State: Not Worn
      end
  

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Derivative 3: Temperature-Compensated Worn Detection for Extreme Environments

• Axis: Operational Parameter Expansion
• Enabling Description: This derivative specifies a "worn" detection system for use in extreme temperature environments, such as for personnel handling cryogenic liquids (-196°C) or working near industrial furnaces (200°C). The device body is a ceramic composite, and the optical sensor is a Silicon Carbide (SiC) based photodiode. The operational algorithm is modified for temperature compensation:
  1. Dynamic Thresholding: The threshold voltages and the minimum signal quality metric are not fixed values. They are functions of temperature, retrieved from a look-up table (LUT) stored in non-volatile memory. An integrated thermistor provides real-time temperature data to the processor, which selects the appropriate threshold values.
  2. Emitter Power Compensation: The drive current to the light emitter is dynamically adjusted based on the same temperature reading to maintain a constant photon output, counteracting the temperature-dependent efficiency curve of the emitter.
     This enables the device to reliably distinguish between being worn by a user and being placed on an inanimate hot or cold surface.
• Mermaid Diagram:

  stateDiagram-v2
      [*] --> Checking
      state Checking {
          direction LR
          [*] --> GetTemp
          GetTemp --> LoadThresholds: Read Thermistor
          LoadThresholds --> V1_Check: Load T_compensated thresholds from LUT
          V1_Check --> V2_Check: V1 < T1(temp)
          V1_Check --> NotWorn: V1 >= T1(temp)
          V2_Check --> SQM_Check: V2 > T2(temp)
          V2_Check --> NotWorn: V2 <= T2(temp)
          SQM_Check --> Worn: SQM > M1(temp)
          SQM_Check --> NotWorn: SQM <= M1(temp)
      }
      Worn --> Checking: on timer_event
      NotWorn --> Checking: on timer_event
  

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Derivative 4: Aerospace Connector Seating Verification System

• Axis: Cross-Domain Application
• Enabling Description: This applies the core mechanism to verify the full mating of critical electrical or optical connectors in aerospace applications, such as avionics Line-Replaceable Units (LRUs). The female half of the connector is equipped with a micro-optical emitter/detector pair. The male half has a small, coated reflective surface that aligns with the sensor when fully mated.
  1. Emitter Off (Stray Light Check): The system measures ambient light. A low reading passes, while a reading above a First Threshold indicates the connector is unmated in a lit environment, which may be logged as a maintenance fault.
  2. Emitter On (Reflectivity Check): The emitter activates. A reflected light measurement greater than a Second Threshold confirms the presence of the specific mating surface.
  3. Signal Quality (Electrical Continuity Check): The "signal quality metric" is an electrical measurement. The system performs a time-domain reflectometry (TDR) pulse on a data pin. A clean TDR response (metric > threshold) confirms a solid electrical connection.
     The final confirmed state is "Fully Mated," logged by the vehicle's Health and Usage Monitoring System (HUMS).
• Mermaid Diagram:

  graph LR
      subgraph Connector Mating Check
          direction LR
          A(Unmated) -- Begin Mating --> B{Ambient Light < T1?};
          B -- Yes --> C{Reflected Light > T2?};
          C -- Yes --> D{Perform TDR on Pin};
          D -- TDR Metric > M1 --> E(Fully Mated);
          B -- No --> F(Fault - High Ambient);
          C -- No --> G(Partially Mated);
          D -- TDR Metric <= M1 --> G;
      end
  

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Derivative 5: Livestock Smart Collar Verification with GSR

• Axis: Cross-Domain Application
• Enabling Description: This system adapts "worn" detection for a smart collar for livestock to ensure it is on the animal and fitted correctly. The sensor module includes an optical emitter/detector and a galvanic skin response (GSR) sensor.
  1. Optical Checks: The standard two-voltage optical checks are performed to verify proximity to a dark, skin-like surface, distinguishing the animal's neck from the ground.
  2. Galvanic Skin Response (GSR) Baseline: After optical checks pass, the system measures skin conductance via the GSR sensor. A reading within the typical range for bovine skin confirms contact with a live animal.
  3. Composite Signal Quality Metric: The signal quality metric is a composite value derived from the PPG sensor data (to detect a heartbeat) and a 3-axis accelerometer. The metric is high only if a low-frequency heartbeat is detected and the accelerometer signature matches the characteristic motion of the animal (e.g., grazing).
     A confirmed "worn" state validates collected health data and triggers an alert if the status changes.
• Mermaid Diagram:

  sequenceDiagram
      participant Collar
      participant Cow
      Collar->>Collar: Perform Optical Checks (V1<T1, V2>T2)
      alt Optical Checks Pass
          Collar->>Cow: Measure GSR
          Cow-->>Collar: Return Skin Conductance
          alt GSR in Bovine Range
              Collar->>Cow: Measure PPG and Accelerometer Data
              Cow-->>Collar: Return Biometric & Motion Data
              Collar->>Collar: Calculate Composite SQM (HR + Motion)
              alt Composite SQM > Threshold
                  Collar->>Collar: Set State: Worn
              else
                  Collar->>Collar: Set State: Not Worn (e.g., loose fit)
              end
          else
               Collar->>Collar: Set State: Not Worn (e.g., on fence post)
          end
      else
          Collar->>Collar: Set State: Not Worn (e.g., on ground)
      end
  

---

Derivative 6: AI-Based Multi-State Worn Status Classifier

• Axis: Integration with Emerging Tech
• Enabling Description: This derivative replaces fixed-threshold checks with a machine learning classifier (e.g., a Support Vector Machine or lightweight neural network) running on an edge AI processor. The system assembles a feature vector from a suite of low-power sensors: [v_optical_off, v_optical_on, capacitance, temperature, accel_std_dev]. This vector is input to a pre-trained ML model, which outputs a probability distribution across several states: [Worn_UserA, Worn_UserB, On_Table, In_Pocket, On_Charger]. The model is trained on a large dataset of sensor readings from these scenarios, enabling it to learn complex, non-linear relationships. A "worn" determination is made if the probability for any "Worn" state exceeds a confidence threshold (e.g., > 0.95), allowing for user-specific recognition and greater robustness than static rules.
• Mermaid Diagram:

  flowchart TD
      subgraph Data Collection
          A[Optical Sensor] --> E;
          B[Capacitive Sensor] --> E;
          C[Thermistor] --> E;
          D[Accelerometer] --> E;
      end
      subgraph Processing
          E[Create Feature Vector] --> F[ML Classifier (SVM/NN)];
          F --> G[Output State Probabilities];
      end
      subgraph Decision
          G --> H{P(Worn) > 0.95?};
          H -- Yes --> I[State: Worn];
          H -- No --> J[State: Not Worn];
      end
  

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Derivative 7: Graded Confidence Score with Failsafe Low-Power Mode

• Axis: The "Inverse" or Failure Mode
• Enabling Description: This system calculates a continuous "worn confidence score" (0-100%) rather than a binary state. The score is a weighted average from multiple checks (e.g., optical check: 40%, normalized Signal Quality Metric: 50%, motion consistency: 10%). The system defines operational states based on this score:
  • Confidence < 20% (Failsafe Mode): The device powers down high-drain sensors (GPS, etc.) and performs only a low-power "worn" check periodically (e.g., every 10 minutes).
  • 20% <= Confidence < 80% (Limited Functionality): The device collects sensor data but flags it as "low confidence."
  • Confidence >= 80% (Full Functionality): The device operates normally.
    This provides a nuanced approach to data validity and drastically improves battery life when the device is not in use.
• Mermaid Diagram:

  stateDiagram-v2
      state "Failsafe (Low Power)" as LowPower
      state "Limited Functionality" as LimitedFunc
      state "Full Functionality" as FullFunc
  
      [*] --> LowPower: Initial State
      LowPower --> LimitedFunc: on check: Confidence > 20%
      LimitedFunc --> LowPower: on check: Confidence < 20%
      LimitedFunc --> FullFunc: on check: Confidence > 80%
      FullFunc --> LimitedFunc: on check: Confidence < 80%
  

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Combination Prior Art Scenarios with Open-Source Standards

1. Combination with Bluetooth Heart Rate Profile: The "worn" detection method is combined with the standard Bluetooth GATT Heart Rate Service (HRS). The device only begins advertising the HRS and allowing connections after its "worn confidence score" (Derivative 7) exceeds 80%. If the score drops below this threshold, the device terminates the Bluetooth connection. This prevents client devices from maintaining a connection to a wearable that is not actively measuring data, conserving power for both devices. Open-Source Standard: Bluetooth SIG adopted specification for the Heart Rate Profile (GATT Service UUID 0x180D).

2. Combination with MQTT for IoT: The device's "worn" status is published as a retained message to an MQTT broker using the open-source MQTT protocol (ISO/IEC 20922). The topic is formatted as devices/{deviceId}/status/is_worn, with a payload of true, false, or a JSON object containing the confidence score. An external system running open-source software like Home Assistant can subscribe to this topic to trigger automations, such as adjusting lighting or thermostat settings based on whether a user is wearing a sleep-tracking or activity-monitoring device.

3. Combination with Android Sensor HAL: The entire "worn detection" logic is implemented as a new virtual sensor of type SENSOR_TYPE_WORN_DETECT within the Android Hardware Abstraction Layer (HAL). This provides a standardized interface for any application to receive "worn" or "not worn" events without needing to know the underlying hardware implementation (optical, capacitive, etc.). An application can register a listener to this sensor to automatically pause a workout log when the device is removed. Open-Source Standard: The Android Open Source Project (AOSP) specification for the Sensor Hardware Abstraction Layer.