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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 25-29, 42-44, 64-66 and 70-76 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites the steps of 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; generate a set of coefficient estimates for weighting the time series of heart rate data using the machine learning model; weight the time series of heart rate data according to the set of coefficient estimates, thereby providing a weighted time series of heart rate data; 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; 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.
The limitation of generating a set of coefficient estimates, weighting the time series, sum the weighted time series and generate an intensity score, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more processors” and a “display” the claims are direct to concepts relating to organizing information in a way that can be performed mentally or analogous to human mental work and nothing in the claim element precludes the steps from practically being performed in the mind. For example, but for the processor, “generate”, “weight”, “sum” and “generate” in the context of this claim encompasses the user manually calculating the weights and summing them to get an intensity score. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of one or more sensors, a strap, a memory and a display. These sensors involve mere data gathering and amount to insignificant extra-solutional activity, specifically pre-solutional activity. The strap is a well understood component and is extra solutional as it merely holds the sensors in location to perform sensing. Additionally, the processor, memory and display are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly the dependent claims do not include additional elements that amount to significantly more. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept and well-understood, routine and conventional activity is not sufficient to amount to significantly more than the abstract idea itself. The claim is not patent eligible.
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.
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
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 determining 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).
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 ([¶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.
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 filed 11/26/25 have been fully considered but they are not persuasive.
Regarding Applicant’s arguments against the 101 rejection:
Applicant argues that claim 21 is not directed to a patent ineligible mental process, Examiner respectfully disagrees. The wearable device with sensors and the strap to couple the device are well-understood components that are used for extra solutional activity. Specifically, the sensors and the device housing and strap are used for pre-solutional data gathering. They are not considered the ineligible mental process and they do not add significantly more to the recited mental processing steps. The memory and processor are generic computer components and amount to no more than generic computer elements performing their standard function. The steps of using the model, determining coefficients, weighting the data, summing the data and determining an intensity score are the steps that are the mental process. All these steps can be performed by the human mind with the aid of pen and paper. There is nothing in the claim language that shows that any of these steps cannot be performed by hand.
Regarding Applicant’s argument in view of Thales Visionix, Examiner respectfully disagrees. Claim 21 does not recite a specific configuration of sensors, it recites one sensor. The Court also held that the device also determined the position and orientation more accurately. It is not clear that claim 21 provides any such technological improvement.
Regarding Applicant’s argument in view of McRo, Examiner respectfully disagrees. Part of the decision in McRo was that the claims took a previously subjective task and automated it with a rules based approach. That is not the case with the current claims. The prior art teaches the rules used so there is not a similar technological improvement.
Regarding Applicant’s argument in view of CardioNet, Examiner respectfully disagrees. The Court decided that the claims provided a clear technological improvement in the art and not just a generic process and machinery. It is not clear from the current claims that such a technological improvement is present. It is not clear that the current claims do not just automate a known process, or more accurately in this case just apply machine learning to a known process, for determining exercise intensity.
Regarding Applicant’s argument in view of SRI Int’l, Examiner respectfully disagrees. It is not clear in the current case that the scale or speed of calculation could not be performed by hand. There is no limitation directed toward the speed and heart rate data.
Regarding Applicant’s arguments in view of the PTAB decision, examiner respectfully disagrees. The PTAB decision is not precedential and the specific machine learning in question for that decision was adequately described and nonobvious neither of which apply to the current claims under examination.
Regarding Applicant’s arguments in view of the 101 examples, Examiner respectfully disagrees. Example 39 is related to facial detection and not similar to claim 21. Example 47 is related to anomaly detection which claim 21 is not and example 47 provides far more description relating to how the machine learning itself is novel. Examples 40 and 42 do not relate to the current claims as they are directed toward monitoring network traffic and data management for medical records. Similarly, as explained above and in the previous rejection the claims do not contain elements alone or when considered as a ordered combination that provide significantly more than the abstract mental processing.
Applicant’s arguments, see pgs. 15-17, filed 11/26/25, with respect to the rejection(s) of claim(s) 21-26, 28-31 and 41-50 under 35 USC 102 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 John et al.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Chen can be reached at 5712723672. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHAEL A CATINA/Examiner, Art Unit 3791 /TSE W CHEN/Supervisory Patent Examiner, Art Unit 3791