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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 61/736310 and 61/696525, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The provisional applications, specifically Appendix C, recite algorithms and the use of machine learning but does not disclose a machine learning model or the specifics of generating coefficients to weight the heart rate data in the intensity calculation.
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.
Claim(s) 21-26, 28-31 and 41-50 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lain US 2015/0031970 in view of John et al. US 2007/0213600 and Kasama et al. US 2009/0325766.
Regarding claims 21 and 41, Lain discloses a system for physiological monitoring and measurement comprising:
a wearable physiological measurement device including one or more sensors configured to continuously monitor a heart rate of a user ([¶21] monitor 14);
a strap attached to the wearable physiological measurement device and couplable to an appendage of the user ([¶21] the sensor is attached via a strap);
a memory storing a machine learning model trained with heart rate data from a group of individuals to generate coefficient estimates for weighting a time series of heart rate data ([¶22] memory. [¶51,53] machine learning is used) and a weighting scheme for weighting the time series of heart rate data when calculating an intensity score, and wherein the machine learning model is trained, using a training data set obtained by continuously detecting heart rate time series of the group of individuals, to predict cardiovascular intensities experienced by the group of individuals, the training including a comparison of intensity scores to user input on perceived exertion ([¶50-51] a neural network is used which is inherently trained on input and output training data to make correlations); and
one or more processors coupled in a communicating relationship with the wearable physiological measurement device ([¶22]), the one or more processors configured to:
receive heart rate data for the user from the wearable physiological measurement device, the heart rate data including the time series of heart rate data for the user ([¶22] heart rate can be determined);
generate a set of coefficient estimates for weighting the time series of heart rate data using the machine learning model ([¶31,51,53] the various algorithms can use coefficient weighting and the machine learning does as well);
weight the time series of heart rate data for the user according to the set of coefficient estimates from the machine learning model, thereby providing a weighted time series of heart rate data ([¶31,51,53]);
sum the weighted time series of heart rate data for a predetermined unit interval to provide a summed weighted time series of heart rate data ([¶35,52,54] the summed data is used to determine intensity ranges and exercise intensity during exercise. This is at a base level a kind of weighting as the heart rate data is adapted to the individual [¶39]); and
generate an intensity score for the user providing an indicator of cardiovascular intensity based on the summed weighted time series of heart rate data over the predetermined unit interval ([FIG5,7][¶44,45,50,51] The pulse oximetry recovery time is an indicator of exercise intensity based on heart rate) wherein the intensity score is updated as the heart rate data is continuously acquired in real time by the wearable physiological measurement device ([¶50-51] the metrics and determinations are updated based on new information).
Lain does not specifically disclose that the machine learning is based on logistic regression. John teaches a similar cardiac measurement device that uses logistic regression as its machine learning and subjective user perceptions ([¶91] the cardiac data is used with classification schemes or logistic regression to determine trends or predict events). Therefore it would have been obvious to one ordinary skill in the art at the time of filing to combine the device of Lain with the teachings of John as the combination is no more than the simple substitution of one know element for another to arrive at the predictable result of classifying the signals to determine an index.
Lain does not specifically disclose the machine learning model includes converting heart rate values of the time series of heart rate data into heart rate reserve values based on a resting heart rate of the user and a maximum heart rate of the user and applying the set of coefficient estimates to the heart rate reserve values. Kasama teaches a similar exercise monitoring device that determines the relationship between heart rate, heart rate reserve and intensity ([¶31-34]). Therefore, it would have been obvious to one of ordinary skill in the art prior to the time of filing to combine the device of Lain with the heart rate reserve determination of Kasama in order to more accurately determine exercise intensity.
Regarding claims 22 and 42, Lain discloses adapting the intensity score for the user by training the machine learning model with data from the user ([¶16,47,56]).
Regarding claims 23 and 43, Lain discloses the one or more processors are configured to weight the time series of heart rate data based at least in part on previous intensity scores for the user([¶16,47,56] previous data ius used in the models).
Regarding claims 24 and 44, the one or more processors are configured to weight the time series of heart rate data using two or more weighing schemes for different levels of cardiovascular exertion by the user ([¶39,42] several zones or levels of fitness are determined by the model and algorithms).
Regarding claims 25 and 45, Lain discloses a display with a user interface for presenting information from the wearable physiological measurement device to the user ([¶22] display 24).
Regarding claims 26 and 46, Lain discloses the user interface is configured to display at least one of the intensity score, a sleep score calculated based on the heart rate data for the user from the wearable physiological measurement device, and a recovery score calculated based on the heart rate data for the user from the wearable physiological measurement device ([¶22]).
Regarding claim 28, Lain discloses the one or more processors include at least one processor coupled to the strap ([¶21,22] the processor is in the housing of the wearable device that is configured to be attached to the user via a strap).
Regarding claims 29 and 48, Lain discloses the one or more processors include at least one processor of a server coupled with the wearable physiological measurement device through a communication network ([¶23] the wearable device can communicate with the server 30 for processing).
Regarding claims 30 and 49, Lain discloses the wearable physiological measurement device includes a photoplethysmography system ([¶17,26]).
Regarding claims 31 and 50, Lain discloses a battery for recharging, the battery removably and replaceably couplable to the wearable physiological measurement device to enable continuous monitoring of the heart rate of the user with the wearable physiological measurement device ([¶19,29] the batteries can be rechargeable or alkaline and are replaceable).
Regarding claim 47, Lain discloses at least one of weighting the time series of heart rate data, summing the weighted time series of heart rate data, and generating the intensity score is executed by a processor of the wearable physiological measurement device ([¶44,45,50-53]).
Response to Arguments
Applicant’s arguments, see pg. 8-16, filed 9/24/26, with respect to the 35 USC 101 rejection have been fully considered and are persuasive. The 101 rejection has been withdrawn.
Applicant's remaining arguments filed 11/26/25 have been fully considered but they are not persuasive.
Regarding Applicant’s argument that Lain does not disclose a set of coefficient weights, Examiner respectfully disagrees. Lain discloses determining coefficients for its algorithms for the calculation of heart rate and PORT. The algorithms relating to emitter distance is just an example of an algorithm that can be stored. Similarly, Lain discloses using neural networks in adjusting the exercise intensity or program using the measured parameters and the user’s input. Neural networks inherently weight the information input into them as part of their determination based on the training data they were given. Applicant’s claim recites using a training set of heart rates. This is how any neural network is trained. The machine learning model is given labeled training data initially and then updates its determinations and weights depending on further information input into it. John teaches the specific logistic regression and using subjective user input in the machine learning.
Regarding Applicant’s argument that John does not cure the deficiencies of Lain, Examiner respectfully disagrees. John teaches using a logistic regression in its machine learning processing.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ANTHONY CATINA whose telephone number is (571)270-5951. The examiner can normally be reached 10-6pm.
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/MICHAEL A CATINA/Examiner, Art Unit 3791
/TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
