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 .
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 (i.e., changing from AIA to pre-AIA ) 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/17/26 has been entered.
Notice of Amendment
In response to the amendment(s) filed on 2/17/26, amended claim(s) 11-13 is/are acknowledged. The following new and/or reiterated ground(s) of rejection is/are set forth:
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.
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.
Claim(s) 11-12, 14 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2020/0170515 to Wen et al. (hereinafter “Wen”) in view of U.S. Patent Application Publication No. 2011/0112443 to Williams.
For claim 11, Wen discloses a mobile device (110/200) (para [0046] and [0056]), comprising:
a processor (220) (Fig. 2) (para [0056]) configured to:
collect or receive raw motion sensor data (as can be seen in Fig. 2) (also see para [0058]-[0059]);
record the raw motion sensor data in a three-dimensional format (see Figs. 6A-B) (also see para [0080], [0086], and [0090]);
generate an information structure representation of the collected raw motion sensor data (para [0061]);
determine a gait cycle based on the generated information structure representation (para [0061]);
identify gait events in the gait cycle (para [0063]);
extract gait biomarkers (Examiner’s Note: para [0036] of Applicant’s specification as originally filed identifying that “stance and swing phases” are examples of biomarkers) based on the identified gait events (para [0063] and [0065]) by:
extracting, using one or more signal processing operations performed on a graph, an analog signal from the graph (such as Figs. 5A-6B) (also see para [0061]-[0065]), wherein the one or more signal processing operations include noise reduction (Examiner’s Note: Applicant’s specification at para [0093] of the specification as originally filed identifies “low-pass filtering” as one example of a noise reduction technique) (para [0064]), and
conditioning the analog signal to extract the gait biomarkers (para [0064]) (also see para [0062]-[0063] and [0065]);
determine a diagnosis based on the extracted gait biomarkers (para [0066]); and
cause an electronic display of the mobile device to present the diagnosis (para [0054], [0070], [0094], and/or [0098]).
Wen does not expressly disclose wherein the motion signals and data from each sensor are sampled at frequencies ranging from 20 Hz to 2500 Hz.
However, Williams teaches wherein the motion signals and data from each sensor are sampled at frequencies ranging from 20 Hz to 2500 Hz (para [0052]).
It would have been obvious to a skilled artisan to modify Wen wherein the motion signals and data from each sensor are sampled at frequencies ranging from 20 Hz to 2500 Hz, in view of the teachings of Williams, because such a sampling rate is a suitable sampling rate to get an adequate amount of data so that processing on that data may be performed.
Wen does not expressly disclose wherein the processor is configured to generate the information structure representation of the collected raw motion sensor data by generating a biokinetographic waveform information structure based on the raw motion sensor data, wherein the BKG waveform information structure comprises a graph of scalar sums of acceleration over time.
However, Williams teaches generating information structure representation of the collected raw motion sensor data by generating a biokinetographic waveform information structure based on the raw motion sensor data (para [0056]) (also see para [0034], [0036], and [0038]), wherein the BKG waveform information structure comprises a graph of scalar sums of acceleration over time (para [0034], [0036], and [0038]).
It would have been obvious to a skilled artisan to modify Wen wherein the processor is configured to generate the information structure representation of the collected raw motion sensor data by generating a biokinetographic waveform information structure based on the raw motion sensor data, wherein the BKG waveform information structure comprises a graph of scalar sums of acceleration over time, in view of the teachings of Williams, because such a modification would be the simple substitution of one information structure for another information structure that would lead to the predictable result of visualizing the raw data.
For claim 12, Wen further discloses wherein the processor is configured to convert the conditioned analog signal to a digital output and render the digital output on the electronic display (para [0054], [0070], [0094], and/or [0098]).
For claim 14, Wen further discloses wherein the processor is configured to collect motion sensor data from the mobile device by passively collecting sensor data from a sensor in the mobile device (para [0059], [0069], and [0075]) during detected locomotion periods (para [006]-[0007], [0013]-[0014], [0022], [0025], and [0043]).
For claim 16, Wen further discloses wherein: the processor is further configured to use a neural network to identify gait patterns and classify gait abnormalities (para [0062]); and the processor is configured to determine the diagnosis based on the extracted gait biomarkers by determining the diagnosis based on the extracted gait biomarkers, identified gait patterns, and classified gait abnormalities (para [0062] and [0065]-[0066]).
For claim 17, Wen further discloses wherein: the processor is further configured to collect additional sensor data from one or more of a magnetometer, a pressure sensor, a light sensor, a microphone, or an infrared sensor (para [0079]); and the processor is configured to determine the diagnosis based on the extracted gait biomarkers and the additional sensor data (para [0066] and [0079]).
For claim 18, Wen further discloses wherein the mobile device is one of a smartphone or a wearable device (para [0046]).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wen in view of Williams, and further in view of U.S. Patent Application Publication No. 2023/0346253 to Inan et al. (hereinafter “Inan”) and U.S. Patent Application Publication No. 2010/0152623 to Williams (hereinafter “Williams 2”).
For claim 13, Wen further discloses wherein the processor is configured to collect motion sensor data from the mobile device by collecting sensor data from a sensor in the mobile device as the mobile device user walks (para [0011], [0075], and/or [0089]).
Wen and Williams do not expressly disclose a sensor that is positioned on or near a sternum of a mobile device user.
However, Inan teaches a sensor that is positioned on or near a sternum of a user (para [0050]).
It would have been obvious to a skilled artisan to modify Wen to include a sensor that is positioned on or near a sternum of a mobile device user, in view of the teachings of Inan, for the obvious advantage of providing an additional way or a substitute way to monitor respiratory parameters/health.
Wen, Williams, and Inan do not expressly disclose a closed course including a turnaround, and wherein the processor identifies steps before and after the turnaround.
However, Williams 2 teaches a closed course (para [0052] and [0156]) including a turnaround (claims 3 and 21), and wherein the processor identifies steps before and after the turnaround (claims 3 and 21).
It would have been obvious to a skilled artisan to modify Wen to include a closed course including a turnaround, and wherein the processor identifies steps before and after the turnaround, in view of the teachings of Williams 2, for the obvious advantage of having the user perform a predefined task so that measurements can be objectively compared between different trials.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wen in view of Williams, and further in view of U.S. Patent Application Publication No. 2015/0286279 to Lim et al. (hereinafter “Lim”) and U.S. Patent Application Publication No. 2022/0285022 to Ellis et al. (hereinafter “Ellis”).
For claim 15, Wen and Williams do not expressly disclose wherein the processor is further configured to reorient the raw motion sensor data based on an orientation of the mobile device during collection of the raw motion sensor data to ensure consistent alignment of an axis of three-dimensional sensor data.
However, Lim teaches wherein the processor is further configured to reorient the raw motion sensor data based on an orientation of the mobile device during collection of the raw motion sensor data to ensure consistent alignment of an axis of three-dimensional sensor data (para [0014]).
It would have been obvious to a skilled artisan to modify Wen wherein the processor is further configured to reorient the raw motion sensor data based on an orientation of the mobile device during collection of the raw motion sensor data to ensure consistent alignment of an axis of three-dimensional sensor data, in view of the teachings of Lim, for the obvious advantage of calibrating the mobile device.
Wen, Williams, and Lim do not expressly disclose format the raw motion sensor data to account for variations in data forms from different devices.
However, Ellis teaches format raw motion sensor data (para [0162]) (also see para [0030]-[0031]) to account for (Examiner’s Note: functional language/intended use, i.e., capable of) variations in data forms from different devices (para [0162]).
It would have been obvious to a skilled artisan to modify Wen to include format the raw motion sensor data to account for variations in data forms from different devices, in view of the teachings of Ellis, for the obvious advantage of making it available to vendor systems (see para [0162] of Ellis)
Response to Arguments
Applicant’s arguments filed 2/17/26 have been fully considered.
With respect to the 112 rejection, Applicant’s amendments and arguments are persuasive and thus the rejection is withdrawn.
With respect to the 101 rejections, Applicant’s amendments and arguments are persuasive and thus the rejections are withdrawn.
With respect to the 103 rejections, Applicant’s arguments will be treated in the order they were presented. With respect to the first argument, Wen is not relied upon for this feature of the claims, Williams is. With respect to the second argument, the claim language recites that the “one or more signal processing operations” are “performed on the graph” (see claim 11). The claimed “graph” is introduced into the claim earlier in that “the BKG waveform information structure comprises a graph…” (see claim 11). So the graph is an information structure of a waveform. Figs. 5A-6B of Wen are waveforms, they are graphs. When para [0062] of Wen mentions that “gait or balance feature generator 222” may generate features using methods such as “zero-crossing counting or peak detection,” that is done on an information structure (i.e., a graph) such as shown in Figs. 5A-6B. You can see the zero-crossings in Figs. 5A-6B. You can see the peaks in Figs. 5A-6B. You can count them. So the signal processing disclosed in Wen is performed on those graphs/information structures. Those graphs/information structures are representative of signals (that is what they depict/show). With respect to the third argument, the newly provided citations read on the newly amended claim language. With respect to the fourth argument, the previous Office action did not address the newly added limitations because they were not added yet. With respect to the fifth argument, the previous rationale does not apply to the newly added limitations because the newly added limitations were not present before the last Office action was issued. With respect to the sixth argument, this appears to be a repeat of what was previously argued and therefore the examiner repeats the same response.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LEE CERIONI whose telephone number is (313) 446-4818. The examiner can normally be reached M - F 8:00 AM - 5:00 PM PT.
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/DANIEL L CERIONI/Primary Examiner, Art Unit 3791
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