DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
1. Applicant’s arguments, see Pre-Appeal Brief Conference Request, filed 03/10/2026 with respect to the rejection(s) of claim(s) 1-5, 7, and 10-21 under U.S.C. 103, (specifically the rejections related to the trained machine learning model) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Lumme (US 20180192954 A1).
Applicant's arguments filed 03/10/2026, with respect to Baranski not teaching the temperature-based adjustments, have been fully considered but they are not persuasive. The Applicant argues that the art of Baranski does not teach how or where to adjust the device based on temperature. The Examiner respectfully disagrees. The claims currently disclose taking in sensor data, including external temperature values, and then adjusting the fit of the wearable device based on this. Baranski teaches that it is known that “the fit [of the wearable device] may be different and/or may be perceived to be different given certain environmental (e.g. temperature, humidity) or biological conditions (e.g., sweat, inflammation)”. They then go on to teach in Par. 0124-0127 that the device comprises a number of sensors, such as temperature sensors, and that the fit of the device can be adjusted based on sensor data. Based on this, Baranski would cover the breadth of the claims. The rejection still stands.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
2. Claims 1, 4-5, 10-11, 13-15, and 17-20 are rejected under pre-AIA 35 U.S.C. 103 as being obvious over Sun (US 20200146629 A1) in view of Baranski (US 20180027931 A1) and in further view of Lumme (US 20180192954 A1).
In regards to claims 1, 14, and 20, Sun discloses a method for providing information related to a fit of a wearable device (Abstract), the method comprising:
receiving, by one or more processors data from one or more sensors of the wearable device;
wherein the one or more sensors comprises one or more of a proximity sensor, accelerometer, an infrared sensor, a pressure sensor, a light sensor, a touch sensor, a humidity detection sensor, a sweat detection sensor, a gyroscope, a magnetic sensor, a microphone, a tilt sensor, a photoplethysmography (PPG) sensor, a temperature sensor, or a photodiode (Par. 0070 teaches using a PPG sensor);
receiving, by the one or more processors from the one or more sensors, health data (Par. 0070 teaches a PPG sensor, i.e. health data sensor);
providing as input, by the one or more processors into a trained machine learning model, wherein the trained machine learning model is trained to predict the fit of a wearable device based on data sets, each set having a known respective tightness and corresponding health sensor data (Par. 0136-0137 and 0155 teach that a software program, i.e. model, can are employed to take the sensor data and analyze it in order to determine the fit of the wearable device) ;
determining, by the one or more processors the fit of the wearable device is too loose or too tight; determining, by the one or more processors based on the fit, a suggestion to adjust the fit of the wearable device (Par. 0145 teaches employing processors to determine if the wearing position of the watch is too loose or tight); and
providing for output on a graphical user interface, by the one or more processors, a notification based on the suggestion to adjust the fit of the wearable device (Par. 0147 teaches using an output device to tell the user if the wearable device is in the correct wearing position; see also Fig 10).
While Sun teaches there being a temperature sensor, they do not disclose using this temperature sensor data to make dynamic, real-time predictions/suggestions about the fit of the watch. However, in the same field of endeavor Baranski teaches a dynamic, continuous fit adjustment system of a wearable device (Abstract and Par. 0278) wherein the system can take in data from a temperature sensor and send signals to the user to adjust the fit of the wearable device (Par. 0170-0178) in order to accurately account for environmental elements that can affect the user when making the fit suggestions (Par. 0005).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have taken the teachings of Sun and modified them by having the device account for temperature data when making fit predictions, as taught and suggested by Baranski, in order to accurately account for environmental elements that can affect the user when making the fit suggestions (Par. 0005 of Baranski).
While Sun and Baranski teach adjusting the fit of the wearable device based on sensor data, they do not explicitly teach this being done using machine learning methods. However, in the same field of endeavor, Lumme teaches a wearable device (Abstract) wherein the system uses a number of processors and algorithms to take in sensor data and make adjustments to the fit of the band of the wearable device (Par. 0050 and 0061) in order to quickly compute and apply measurement data provided by the sensors (Par. 0050).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have taken the teachings of Sun and Baranski and modified them by having the device use algorithms/machine learning models to make adjustments to the band fit, as taught and suggested by Lumme, in order to quickly compute and apply measurement data provided by the sensors (Par. 0050 of Lumme).
In regards to claims 4-5 and 17-18, the combined teachings of Sun, Baranski, and Lumme disclose the method of claim 1 further comprising detecting, by the one or more processors, a change in the fit upon a user taking an action on the wearable device (Figures 7-12 of Sun).
In regards to claim 13, the combined teachings of Sun, Baranski, and Lumme disclose the method of claim 1 wherein the health sensor is a PPG sensor (Par. 0070 of Sun).
In regards to claim 10-11 and 19, the combined teachings of Sun, Baranski, and Lumme disclose the method of claim 1, further comprising receiving, by the one or more processors, motion sensor data from the one or more motion sensors of the wearable device and wherein determining the fit of the wearable device is further based on the motion sensor data (Par. 0088 of Sun).
3. Claims 2, 15 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Sun, Baranski, and Lumme in view of Fang (CN105676677A).
In regards to claims 2, 15, and 21, the combined teachings of Sun, Baranski, and Lumme disclose the method of claim 1, except for the device comprising receiving, by the one or more processors from the one or more sensors, internal temperature data corresponding to an internal temperature of the wearable device.
However, in the same field of endeavor, Fang teaches receiving, by the one or more processors from the one or more sensors, internal temperature data corresponding to an internal temperature of the wearable device (temperature sensor in wearable device monitors internal temperature of the wearable device and sends to processor, p 4 lines 6-7) in order to determine when the device is hot and it will impact the obtained physiological and won't be able to collect accurate physiological data.
Therefore, it would have been obvious to one having ordinary skill in the art to include internal temperature sensor inside the wearable device, as taught and suggested by Fang, in order to determine when the device is hot and it will impact the obtained physiological and won't be able to collect accurate physiological data.
4. Claims 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sun, Baranski, and Lumme in view of Fang, and further in view of White (EP 3175226B1).
In regards to claims 3 and 16, Sun in view of Baranski, Lumme, and Fang disclose the method of claim 2, except for further comprising: determining, by the one or more processors based on the internal temperature data, a temperature offset; and adjusting, by the one or more processors based on the determined temperature offset, the external temperature.
While Fang teaches determining, by the one or more processors based on the internal temperature data, a temperature offset (p. 4, lines 6-7), they do not teach adjusting, by the one or more processors based on the determined temperature offset, the external temperature
However, in the same field of endeavor, White discloses a health wearable device and thus exists in the applicant’s field of endeavor, teaches and adjusting, by the one or more processors based on the determined temperature offset, the external temperature (see claim 8) in order to increase the accuracy of the calibration of the wearable device.
It would have been obvious to one having ordinary skill in the art to offset the temperature and applied the offset temperature to the external temperature sensor data, as taught and suggested by White, in order to increase the accuracy of the calibration of the wearable device.
5. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Sun, Baranski, and Lumme in view of Yoo (US 20170071524 A1).
In regards to claim 7, Sun, Baranski, and Lumme discloses the method of claim 1, except for wherein the trained machine learning model is a clustering model.
However, in the same field of endeavor, Yoo teaches wherein the trained machine learning model is a clustering model (clustering is the machine learning technique [0062)).
It would be obvious to one of ordinary skill in the art that, Yoo’s disclosure of a clustering model could readily be selected as the model subtype if it were the model best suited to parameter estimate optimization.
6. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Sun, Baranski, and Lumme in view of Lee et al. (KR 2022152423 A)
In regards to claim 12, Sun, Baranski, and Lumme teach the method of claim 1 except for wherein additional external temperature data is obtained from a second wearable dev ice. However, Lee teaches additional external temperature data obtained from a second wearable device (claim 1 — earbud situated in ear transmits additional external temperature data).
It would have been obvious to one having ordinary skill in the art to obtain external temperature data from the second device in order to analyze the accurate physiological data.
Conclusion
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/S.L.C./Examiner, Art Unit 3792
/LYNSEY C Eiseman/Primary Examiner, Art Unit 3796
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