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 .
Election/Restrictions
Applicant’s election without traverse of Group I in the reply filed on 05/04/2026 is acknowledged.
Claims 2-3, 8-15, 17-18, 20-21, and 23-24 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Groups II and III, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 05/04/2026.
Claim Objections
Claims 16 and 22 are objected to because of the following informalities:
In claims 16 and 22, “a subject” should read “the living body”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 4-5 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hussami et al (US 20220269346), hereinafter Hussami.
Regarding claim 1, Hussami teaches a prediction device (100) (Abstract; Fig. 1), comprising:
an interface (the interface of 101) configured to receive time series data (“a first time series from a first sensor on a wearable device” Abstract) including multiple observed values of an observed parameter that are acquired at different time points for obtaining physiological information of a subject (physiological information from the “EMG sensors” [0108]); and
a processor (101) configured to:
input the time series data into a prediction model (“the inferential model” [0126]; “the statistical model” [0408]) to perform prediction of one or more unobserved values (“to predict the body state” [0126]; “to predict such spatial information” [0408) of the observed parameter (“processor 101 may use one or more trained inferential models 104 configured to predict body state information based, at least in part, on signals recorded by sensors 102.” [0115]; “the statistical model may be trained to predict such spatial information when particular sensor signals are recorded during performance of a particular task” [0408]; Fig. 1);
cause an output device to visualize a range (a range of predicted values in Fig. 26D) within which the one or more unobserved values may fall (“An example of a distribution of outputs generated by a trained inference model across a dataset with data collected from different users is shown in FIG. 26D…the trained model produces a prediction for every 80 millisecond (ms) chunk of collected EMG data, however, other time intervals can be analogously used.” [1071]),
wherein the range is changed in accordance with a time interval (Δt ) between the different time points (“an inferential model may be selected for use in predicting body state based on a prediction accuracy criterion (e.g., correlation between measured and predicted joint angles) and the delay time interval Δt used to generate the training dataset for training the inferential model. For example, of the inferential models satisfying a prediction accuracy criterion (e.g., accuracy above a set threshold), the selected inferential model may be the inferential model trained using the training dataset generated using the largest time interval. For example, two inferential models may satisfy the accuracy criterion (e.g., both models having an accuracy above an acceptable threshold). The first model may have greater accuracy than the second model, but the time interval used to generate the training dataset for training the first model may be less than the time interval used to generate the training dataset for training the second model. In this example, the second inferential model may be selected to predict the body state, as this second inferential model may have acceptable prediction accuracy and lower latency than the first inferential model.” [0126] “FIG. 4 shows two charts depicting user dependence in the empirical relationship between time interval and prediction accuracy, in accordance with embodiments of the present disclosure.” [0128]. The predicted range will change with the prediction accuracy depending on time interval).
Regarding claim 4, Hussami teaches the prediction device according to claim 1,
wherein the processor is configured to cause the output device to visualize a representative value of the range (a horizontal line in the bar ranges in Fig. 26D) (“An example of a distribution of outputs generated by a trained inference model across a dataset with data collected from different users is shown in FIG. 26D…the trained model produces a prediction for every 80 millisecond (ms) chunk of collected EMG data, however, other time intervals can be analogously used.” [1071]).
Regarding claim 5, Hussami teaches the prediction device according to claim 1,
wherein the processor is configured to cause the output device to visualize distribution of the unobserved values as predicted
(predicted values vs “Rotational offset” in Fig. 26D) (“An example of a distribution of outputs generated by a trained inference model across a dataset with data collected from different users is shown in FIG. 26D…the trained model produces a prediction for every 80 millisecond (ms) chunk of collected EMG data, however, other time intervals can be analogously used.” [1071]).
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 6 is rejected under 35 U.S.C. 103 as being unpatentable over Hussami as applied to claim 1, and further in view of Mothilal et al (US20260000322), hereinafter, Mothilal.
Regarding claim 6, Hussami teaches the prediction device according to claim 1,
Hussami does not teach that the prediction model is configured to predict the range based on the time interval as a feature.
However, in the patient health monitoring systems field of endeavor, Mothilal discloses detection of changes in patient health based on glucose data, which is analogous art. Mothilal teaches that the prediction model is configured to predict the range based on the time interval as a feature (“processing circuitry 80 of external device 12 applies a machine learning model to feature values and produce data indicative of a risk of cardiovascular event (404)...The at least one dataset includes different time intervals of the continuous glucose sensor measurements. It should be noted that there are a number of other possible features that can be input for the machine learning mode” [0117]).
Therefore, based on Mothilal’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Hussami to employ the prediction model that is configured to predict the range based on the time interval as a feature, as taught by Mothilal, in order to facilitate evaluating a risk of adverse health events.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hussami as applied to claim 1, and further in view of Abir et al (US 20230245778), hereinafter, Abir.
Regarding claim 7, Hussami teaches the prediction device according to claim 1,
Hussami does not teach that the prediction model is configured to predict the range based on a range within which one or more values interpolated in a time period corresponding to the time interval between the multiple observed values may fall.
However, in the patient health monitoring systems field of endeavor, Abir discloses systems, devices, and methods utilizing bio-potential data obtained by a plurality of bio-potential sensors for prenatal tracking, which is analogous art. Abir teaches that the prediction model is configured to predict the range based on a range (“4 bpm” [0062]) within which one or more values interpolated in a time period (“a given interpolated data point”) corresponding to the time interval between the multiple observed values may fall (“the algorithmic confidence for each event is defined as the average of the square root of the distance between the upper and lower confidence intervals (e.g., 95% confidence interval) of any imputed (e.g., interpolated) samples within the event's continuous segments. To provide an illustrative example using numerical values selected for clarity of illustration, a given interpolated data point may have a predicted value of 144 bpm with an upper 95% confidence interval of 146 bpm and a lower 95% confidence interval of 142 bpm. In this case, the confidence interval is 4 bpm (e.g., 146 bpm−142 bpm). If a data point is not interpolated, its confidence interval is zero." [0062]).
Therefore, based on Abir’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Hussami to employ the prediction model that is configured to predict the range based on a range within which one or more values interpolated in a time period corresponding to the time interval between the multiple observed values may fall, as taught by Abir, in order to facilitate evaluating a risk of adverse health events.
Allowable Subject Matter
Claims 16, 19, and 22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXEI BYKHOVSKI whose telephone number is (571)270-1556. The examiner can normally be reached on Monday-Friday: 8:30am - 5:00pm.
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/ALEXEI BYKHOVSKI/
Primary Examiner, Art Unit 3798
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