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 .
Amendment
This action is in response to the Amendment filed on 3/23/2026.
Claims 1, 2, 5-7 and 10 are pending.
Response to Arguments
Applicant's arguments with respect to claims 1, 2, 5-7 and 10 have been considered but are moot in view of the new grounds of rejection as necessitated by the amendments.
Claim Rejections - 35 U.S.C. § 103- Natarajan et al. (US 2017/0325748; hereinafter "Natarajan") in view of Xu et al. (US 2019/0097865; hereinafter "Xu") and Denison et al. (US 2014/0316230; hereinafter "Denison"), Hughes et al. (US 2019/0274574; hereinafter "Hughes").
The independent claims have been amended to now recite “wherein a time period of the partial biosignal is specified with respect to a time point at which an abnormal event is determined to have occurred according to the result of performing the primary analysis, wherein the time period of the partial biosignal is specified to include a time period temporally preceding the time point and a time period temporally following the time point.”
Applicant’s arguments with respect to the prior art applied in the office action mailed on 11/26/2025 have been considered and are persuasive and therefore the rejection has been withdrawn. However, upon further search and consideration, in view of the amendments, the claims are now rejected as discussed in the current office action below.
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.
Claims 1-2, 5-7, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Natarajan et al (U.S. Patent Application Publication Number: US 2017/0325748A1, hereinafter “Natarajan”- PREVIOUSLY CITED) in view of Xu et al (U.S. Patent Application Publication Number: US 2019/0097865A1, hereinafter “Xu”- PREVIOUSLY CITED) and Denison et al (U.S. Patent Application Publication Number: US 20140316230 A1, hereinafter “Denison”- PREVIOUSLY CITED) and further in view of Fischell et al (U.S. Patent Application Publication Number: US 2004/0215092 A1, hereinafter “Fischell”- PREVIOUSLY CITED).
Regarding claims 1, 6, and 7, Natarajan teaches a method for monitoring biosignals using a wearable device, the method comprising the steps of:
acquiring information on a result of performing a primary analysis of a biosignal measured by the device , using a primary analysis model trained to perform a primary analysis for detecting abnormal events from biosignals ([0019] orthonormal domain filters to classify the biosignals as normal or abnormal), and acquiring, from the biosignal, a partial biosignal associated with the result of performing the primary analysis ([0019] only transmit those biosignals classified as abnormal - partial biosignal is only the portion of the biosignal that is abnormal); and
performing a secondary analysis of the partial biosignal with reference to the information on the result of performing the primary analysis (computing device 106, [0021] receiving the transmitted biosignals from the biosignal sensors 104 and generating biofeedback data based on the received biosignals), using a secondary analysis model trained to perform a secondary analysis for detecting abnormal events from biosignals ([0031] further analyze the received abnormal biosignals), wherein the primary analysis model is a relatively light-weighted analysis model that requires relatively less computing resources compared to the secondary analysis model ([0018] size restriction inhibits the possibility of incorporating additional hardware to increase the compute ability),
wherein at least one filter removes low-frequency noise from the biosignal measured by the device ([0032] high-pass filters), and
wherein analog-to-digital conversion is performed on the biosignal ([0028] convert the analog signal to a digital signal) from which the low- frequency noise is removed (fig 4. transmission of signals (involving conversion from analog to digital) only occurs after the filtering steps), such that a number of bits of data extracted from an analog signal to generate a digital signal is determined within a range capable of covering signal values of the biosignal from which the low-frequency noise is removed. Determining the number of bits to be extracted to appropriately depict the data is well-known in the art and must be done in any analog to digital conversion,
wherein data outputted from the primary analysis model or the secondary analysis model is classified or grouped into a specific cardiac event according to a criterion ([0039] threshold to distinguish between normal and abnormal), and the criterion is dynamically updated while the primary analysis model or the secondary analysis model is trained ([0039] may be dynamically adjusted based on any other data corresponding).
Natarajan fails to teach that the primary and secondary analysis models are artificial neural networks. Xu teaches that primary analysis ([0367] pre-processing) and secondary analysis ([0337] similarity score derivation) can be done using an artificial neural network. It would have been obvious to a person having ordinary skill in the art before the effective filing date of this invention to modify Natarajan with Xu because it would constitute simple substitution of the generic analysis model for one that specifically uses an artificial neural network as is common in the art of biosignal analysis.
Natarajan further teaches a multi-step process for analysis with a lightweight filtering module in the device and a higher compute ability processor externally for more complex analysis ([0019]). However, for the sake of completeness, a different prior art reference will be applied that explicitly claims a multi-step analysis process that performs the first, lighter weight analysis on the sensor device. Denison teaches wherein the server is configured to generate and distribute the primary analysis model to the device such that the primary analysis model is light-weighted to a level that enables real-time operation in the device ([0083] simple features, can be extracted from EEG data which have predictive value …particularly when used with other more complex feature analysis. Accordingly, through a combination of local algorithmic features, i.e. on the user's PED, and remote algorithmic features, i.e. those processing statistically or algorithmically extracted EEG data with machine learning classifiers running on remote servers, e.g. a cloud sourced backend, then these simpler features will essentially augment the more complex ones). It would have been obvious to a person having ordinary skill in the art before the effective filing date of this invention to modify the combination of Natarajan and Xu with Denison because there is some teaching, suggestion, or motivation to do so. Denison teaches that the local processing allows the remote processing to produce an overall greater classification accuracy.
Natarajan also does not specifically teach wherein a time period of the partial biosignal is specified with respect to a time point at which an abnormal event is determined to have occurred according to the result of performing the primary analysis, wherein the time period of the partial biosignal is specified to include a time period temporally preceding the time point and a time period temporally following the time point.
Fischell teaches a system and method for monitoring biosignals, detecting abnormal cardiac events and storing partial biosignal segments captured before, during and after an abnormal cardiac event for telemetering to another device, the stored data related to the event including partial biosignals specified with respect to a time point at which an abnormal event is determined to have occurred according to the result of performing the primary analysis, wherein the time period of the partial biosignal is specified to include a time period temporally preceding the time point and a time period temporally following the time point (e.g. [0042], [0234]), Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the partial (i.e. abnormal) biosignals in teachings of Natarajan and Xu with Denison with data collected before and after the abnormal event as taught by Fischell in order to provide the predictable results of providing a more accurate analysis of the collected data for a more accurate detection.
Regarding claim 2, Natarajan teaches a method for monitoring biosignals using a wearable device, the method comprising the steps of:
performing a primary analysis of a biosignal measured by the device, using a primary analysis model trained to perform a primary analysis for detecting abnormal events from biosignals ([0019] orthonormal domain filters to classify the biosignals as normal or abnormal);
extracting, from the biosignal, a partial biosignal associated with a result of performing the primary analysis ([0019] only transmit those biosignals classified as abnormal - partial biosignal is only the portion of the biosignal that is abnormal); and
transmitting information on the result of performing the primary analysis and the partial biosignal to a server (computing device 106, [0021] receiving the transmitted biosignals from the biosignal sensors 104 and generating biofeedback data based on the received biosignals), wherein the server includes a secondary analysis model trained to perform a secondary analysis for detecting abnormal events from biosignals ([0031] further analyze the received abnormal biosignals), and
wherein the primary analysis model is a relatively light-weighted model compared to the secondary analysis model that requires relatively less computing resources ([0018] size restriction inhibits the possibility of incorporating additional hardware to increase the compute ability).
wherein at least one filter removes low-frequency noise from the biosignal measured by the device ([0032] high-pass filters), and
wherein analog-to-digital conversion is performed on the biosignal ([0028] convert the analog signal to a digital signal) from which the low- frequency noise is removed (fig 4. transmission of signals (involving conversion from analog to digital) only occurs after the filtering steps), such that a number of bits of data extracted from an analog signal to generate a digital signal is determined within a range capable of covering signal values of the biosignal from which the low-frequency noise is removed. Determining the number of bits to be extracted to appropriately depict the data is well-known in the art and must be done in any analog to digital conversion.
Wherein data outputted from the primary analysis model or the secondary analysis model is classified or grouped into a specific cardiac event according to a criterion ([0039] threshold to distinguish between normal and abnormal), and the criterion is dynamically updated while the primary analysis model or the secondary analysis model is trained ([0039] may be dynamically adjusted based on any other data corresponding).
Natarajan fails to teach that the primary and secondary analysis models are artificial neural networks. Xu teaches that primary analysis ([0367] pre-processing) and secondary analysis ([0337] similarity score derivation) can be done using an artificial neural network. It would have been obvious to a person having ordinary skill in the art before the effective filing date of this invention to modify Natarajan with Xu because it would constitute simple substitution of the generic analysis model for one that specifically uses an artificial neural network as is common in the art of biosignal analysis.
Natarajan further teaches a multi-step process for analysis with a lightweight filtering module in the device and a higher compute ability processor externally for more complex analysis ([0019]). However, for the sake of completeness, a different prior art reference will be applied that explicitly claims a multi-step analysis process that performs the first, lighter weight analysis on the sensor device. Denison teaches wherein the server is configured to generate and distribute the primary analysis model to the device such that the primary analysis model is light-weighted to a level that enables real-time operation in the device ([0083] simple features, can be extracted from EEG data which have predictive value …particularly when used with other more complex feature analysis. Accordingly, through a combination of local algorithmic features, i.e. on the user's PED, and remote algorithmic features, i.e. those processing statistically or algorithmically extracted EEG data with machine learning classifiers running on remote servers, e.g. a cloud sourced backend, then these simpler features will essentially augment the more complex ones). It would have been obvious to a person having ordinary skill in the art before the effective filing date of this invention to modify the combination of Natarajan and Xu with Denison because there is some teaching, suggestion, or motivation to do so. Denison teaches that the local processing allows the remote processing to produce an overall greater classification accuracy.
Natarajan also does not specifically teach wherein a time period of the partial biosignal is specified with respect to a time point at which an abnormal event is determined to have occurred according to the result of performing the primary analysis, wherein the time period of the partial biosignal is specified to include a time period temporally preceding the time point and a time period temporally following the time point.
Fischell teaches a system and method for monitoring biosignals, detecting abnormal cardiac events and storing partial biosignal segments captured before, during and after an abnormal cardiac event for telemetering to another device, the stored data related to the event including partial biosignals specified with respect to a time point at which an abnormal event is determined to have occurred according to the result of performing the primary analysis, wherein the time period of the partial biosignal is specified to include a time period temporally preceding the time point and a time period temporally following the time point (e.g. [0042], [0234]), Therefore it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the partial (i.e. abnormal) biosignals in teachings of Natarajan and Xu with Denison with data collected before and after the abnormal event as taught by Fischell in order to provide the predictable results of providing a more accurate analysis of the collected data for a more accurate detection.
Regarding claims 5 and 10, the combination of Natarajan in view of Xu and Denison and Fischell teaches the invention as claimed and Natarajan further teaches a non-transitory computer-readable recording medium having stored thereon a computer program for executing the method of claim 1 or 2 ([0015] non-transitory machine-readable storage media).
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 MALLIKA DIPAYAN FAIRCHILD whose telephone number is (571)270-7043. The examiner can normally be reached Monday- Friday 8 am-5pm EST.
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/MALLIKA D FAIRCHILD/Primary Examiner, Art Unit 3792
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