Office Action 3: Non-Final Rejection (2023-12-01)
Rejection types: 103 - Obviousness

CLAIMS:
AMENDMENTS TO THE CLAIMS:

The following listing of claims replaces all prior listings:

1\. (Currently Amended) A system for wireless vital sign monitoring, comprising:
a transmitter configured to transmit a wireless signal through a wireless channel of a venue;
a receiver configured to receive the wireless signal through the wireless channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue; and
a processor configured for:obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal,
computing a time series of denoised CI (DCI) by denoising the time series of CI(TSCI),
computing a concatenation of the TSCI and the time series of DCI (TSDCI),
computing a two dimensional (2D) decomposition of the concatenation,
enhancing the 2D decomposition based on a vertical highpass filtering and a horizontal lowpass filtering, and
monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition.

2\. (Currently Amended) The system of claim 1, wherein the processor is further configured for:
computing a time series of CI-feature (TSCF) as the concatenation of the TSCI and the TSDCI, each CI-feature (CF) comprising a concatenation of a first basic feature of a respective CI and a second basic feature of a corresponding DCI
wherein the first basic feature of any CI comprises at least one of: the CI, a magnitude of the CI, a phase of the CI, a magnitude of a component of the CI, or a phase of the component of the CI,
wherein the second basic feature of any DCI comprises at least one of: the DCI, a magnitude of the DCI, a phase of the DCI, a magnitude of a component of the DCI, or a phase of the component of the DCI,
wherein the TSDCI is computed by processing the TSCI with a first denoising filter, the first denoising filter comprising at least one of: a low-pass filter, a bandpass filter, a highpass filter, a median filter, or a percentile filter; and
computing the 2D decomposition based on the TSCF.

3\. (Original) The system of claim 2, wherein the processor is further configured for:
determining a series of sliding time windows, each sliding time window being associated with a time stamp;
computing a respective one dimensional (1D) characteristic function of the CF of the TSCF in each sliding time window; and
constructing the 2D decomposition based on the respective 1D characteristic function in each sliding time window.
4\. (Original) The system of claim 3, wherein:
the respective 1D characteristic function is computed based on a transformation of the CF of the TSCF in each sliding time window; and
the transformation comprises at least one of:
a frequency transformation, a transformation to frequency domain, a short-time Fourier transform (STFT), a wavelet transform, a filter-bank representation,
a time transformation, a transformation to a time-shift domain, an auto-correlation function (ACF), an auto-covariance function, or a time-frequency decomposition.

5\. (Original) The system of claim 4, wherein the processor is further configured for:
constructing a 2D matrix by lining up and aligning the 1D characteristic functions of the sliding time windows in time order according to the associated time stamps, wherein i-th column of the 2D matrix represents the 1D characteristic function associated with i-th sliding time window,j-th row of the 2D matrix is associated with a time-shift of j time units,

a matrix element at the i-th column and the j-th row of the 2D matrix is a value of the 1D characteristic function in the i-th sliding time window with a time-shift of j time units, and
the 2D matrix is a result of the 2D decomposition.

6\. (Previously Presented) The system of claim 5, wherein the processor is further configured for:
enhancing the 2D decomposition by applying, to each matrix element of the 2D matrix, the vertical highpass filtering, and the horizontal lowpass filtering.

7\. (Original) The system of claim 6, wherein the processor is further configured for:
filtering each matrix element of the 2D matrix with a respective 2D vertical highpass horizontal lowpass filter that is either separable or non-separable, either linear or nonlinear.

8\. (Original) The system of claim 7, wherein the processor is further configured for:
enhancing the 2D decomposition by filtering each matrix element of the 2D matrix with at least one of: a respective vertical highpass filter or a respective horizontal lowpass filter,
wherein each of the respective vertical highpass filter and the respective horizontal lowpass filter is either linear or nonlinear.

9\. (Original) The system of claim 8, wherein the processor is further configured for enhancing the 2D decomposition by at least one of:
filtering part of each column of the 2D matrix with a respective first common vertical highpass filter;
filtering part of each row of the 2D matrix with a respective second common vertical highpass filter;
filtering part of each column of the 2D matrix with a respective first common horizontal lowpass filter; or filtering part of each row of the 2D matrix with a respective second common horizontal lowpass filter.

10\. (Previously Presented) The system of claim 9, wherein:
each vertical highpass filter is a directional 1D highpass filter applied in a vertical direction or in a near-vertical direction.

11\. (Previously Presented) The system of claim 10, wherein:
each horizontal lowpass filter is a directional 1D lowpass filter applied in a horizontal direction or in a near-horizontal direction.

12\. (Original) The system of claim 11, wherein the processor is further configured for:
filtering each matrix element of the 2D matrix with a succession of at least two 1D filters, each being either the vertical highpass filter or the horizontal lowpass filter.

13\. (Original) The system of claim 12, wherein the processor is further configured for:
enhancing the 2D decomposition by applying histogram equalization to a number of consecutive columns of the 2D matrix to generate an enhanced 2D matrix.

14\. (Original) The system of claim 13, wherein the processor is further configured for:
computing a distance score based on dynamic time warping (DTW) between a reference column and a particular column of the enhanced 2D matrix associated with a particular time, wherein:the reference column is computed in a training session by clustering a number of training columns matched based on DTW,
the training columns are computed based on training TSCI obtained from training wireless signals that are received through the wireless channel when training objects with vital signs are present and impacting the wireless channel, and
when the distance score is larger than a first threshold, the periodic motion of the vital sign of the object is tentatively not detected at the particular time.

15\. (Original) The system of claim 14, wherein the processor is further configured for:
subtracting a quantity from each matrix element in the particular column of the enhanced 2D matrix; and
computing a zero-crossing count of the particular column,
wherein when the zero-crossing count is larger than a second threshold, the periodic motion of the vital sign of the object is tentatively not detected at the particular time.

16\. (Original) The system of claim 15, wherein the processor is further configured for:
computing multiple peaks of the particular column of the enhanced 2D matrix; and
computing an inter-peak distance based on the multiple peaks.

17\. (Original) The system of claim 16, wherein:
when the inter-peak distance is in an acceptable range, the periodic motion of the vital sign of the object is tentatively detected at the particular time, and the inter-peak distance is determined to be equal to a frequency or a period of the vital sign; and
when the inter-peak distance is not in an acceptable range, the periodic motion of the vital sign of the object is tentatively not detected at the particular time.

18\. (Original) The system of claim 17, wherein the processor is further configured for:
detecting the vital sign of the object when P is larger than a third threshold, wherein P is a percentage of the periodic motion of the vital sign being tentatively detected in a period of time.

19\. (Currently Amended) A wireless device of a system for wireless vital sign monitoring, comprising:
a processor;
a memory communicatively coupled to the processor; and
a receiver communicatively coupled to the processor, wherein:
an additional wireless device in the system is configured to transmit a wireless signal through a wireless channel of a venue,
the receiver is configured to receive the wireless signal through the wireless channel,
the received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue, and
the processor is configured for:obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal,
computing a time series of denoised CI (DCI) by denoising the time series of CI (TSCI),
computing a concatenation of the TSCI and the time series of DCI (TSDCI),
computing a two dimensional (2D) decomposition of the concatenation,
enhancing the 2D decomposition by applying a vertical highpass filter followed by a histogram equalization, and
monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition.

20\. (Currently Amended) A method for wireless vital sign monitoring, comprising:
transmitting a wireless signal through a wireless channel of a venue;
receiving the wireless signal through the wireless channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue;
obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal;
computing a time series of denoised CI (DCI) by denoising the time series of CI (TSCI);
computing a concatenation of the TSCI and the time series of DCI (TSDCI);
computing a two dimensional (2D) decomposition of the concatenation;
enhancing the 2D decomposition based on a vertical highpass filter and a histogram equalization; and
monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition.

OFFICE ACTION:
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 .
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 07/31/2023 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (“Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi”) in view of Xu et al. (“Attention-based Walking Gait and Direction Recognition in Wi-Fi Networks”) and Li et al. (CN109684941).
To claim 1, Liu teach a system for wireless vital sign monitoring, comprising:
a transmitter configured to transmit a wireless signal through a wireless channel of a venue (Figs. 2, 10);
a receiver configured to receive the wireless signal through the wireless channel (Figs. 2, 10), wherein the received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue (pages 270-273, sections 4-6); and
a processor (Fig. 2) configured for:
obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal, computing a two dimensional (2D) decomposition of the time series of CI (TSCI), enhancing the 2D decomposition, and monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition (pages 269-272, sections 3.3-5, Figs. 1, 3, 5-8).
But, Liu do not expressly disclose computing a time series of denoised CI (DCI) by denoising the time series of CI (TSCI), computing a concatenation of the TSCI and the time series of DCI (TSDCI), and the 2D decomposition based on a vertical highpass filtering and a horizontal lowpass filtering.
Xu teach a system for human gait recognition in WiFi networks, comprising modules for: computing a time series of denoised CSI by denoising the time series of CSI (pages 5-6, IV-B, raw CSI processing), and computing a concatenation of the time series CSI and the time series denoised CSI (pages 7-8, IV-C, walking profile splicing), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu, in order to further process collected signals by design preference.
Li teach an image processing system comprising a 2D decomposition based on a vertical highpass filtering and a horizontal lowpass filtering (paragraphs 0012, 0019-0021) followed by a histogram equalization (paragraph 0012).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Li into the system of Liu and Xu, in order to further 2D decomposition with enhancement.
To claim 19, Liu, Xu and Li teach a wireless device of a system for wireless vital sign monitoring, comprising: a processor; a memory communicatively coupled to the processor; and a receiver communicatively coupled to the processor, wherein: an additional wireless device in the system is configured to transmit a wireless signal through a wireless channel of a venue, the receiver is configured to receive the wireless signal through the wireless channel, the received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue, and the processor is configured for: obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal, computing a two dimensional (2D) decomposition of the time series of CI (TSCI), enhancing the 2D decomposition by applying a vertical highpass filter followed by a histogram equalization, and monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition (as explained in response to claim 1 above).
To claim 20, Liu, Xu and Li teach a method for wireless vital sign monitoring, comprising: transmitting a wireless signal through a wireless channel of a venue; receiving the wireless signal through the wireless channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue; obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal; computing a two dimensional (2D) decomposition of the time series of CI (TSCI); enhancing the 2D decomposition based on a vertical highpass filter and a histogram equalization; and monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition (as explained in response to claim 1 above).
To claim 2, Liu, Xu and Li teach claim 1.
Liu, Xu and Li teach wherein the processor is further configured for: computing a time series of CI-feature (TSCF) as the concatenation of the TSCI and the TSDCI, each CI-feature (CF) comprising a concatenation of a basic feature of a respective CI and a second basic feature of a corresponding DCI, wherein the first basic feature of any CI comprises at least one of: the CI, a magnitude of the CI, a phase of the CI, a magnitude of a component of the CI, or a phase of the component of the CI (Liu, page 270, sections 4.1-4.2), wherein the second basic feature of any DCI comprises at least one of: the DCI, a magnitude of the DCI, a phase of the DCI, a magnitude of a component of the DCI, or a phase of the component of the DCI (Xu, pages 6-7), wherein the TSDCI is computed by processing the TSCI with a first denoising filter, the first denoising filter comprising at least one of: a low-pass filter, a bandpass filter, a highpass filter, a median filter, or a percentile filter (Liu, page 270, section 4.1 data calibration); and computing the 2D decomposition based on the TSCF (Liu, pages 270-271, section 4.2-4.3).
To claim 3, Liu, Xu and Li teach claim 2.
Liu teach wherein the processor is further configured for: determining a series of sliding time windows, each sliding time window being associated with a time stamp (page 270, sections 4.1-4.2, sliding window or moving time window, associating time stamp would have been an obvious feature); computing a respective one dimensional (1D) characteristic function of the CF of the TSCF in each sliding time window; and constructing the 2D decomposition based on the respective 1D characteristic function in each sliding time window (pages 271-272, Figs. 5-8).
To claim 4, Liu, Xu and Li teach claim 3.
Liu teach wherein: the respective 1D characteristic function is computed based on a transformation of the CF of the TSCF in each sliding time window; and the transformation comprises at least one of a frequency transformation, a transformation to frequency domain, a short-time Fourier transform (STFT), a wavelet transform, a filter-bank representation, a time transformation, a transformation to a time-shift domain, an auto-correlation function (ACF), an auto-covariance function, or a time-frequency decomposition (page 271, section 4.4, PSD transforms the time series of CSI measurements on each subcarrier to its power distribution in the frequency domain).
To claim 5, Liu, Xu and Li teach claim 4.
Liu teach wherein the processor is further configured for: constructing a 2D matrix by lining up and aligning the 1D characteristic functions of the sliding time windows in time order according to the associated time stamps, wherein i-th column of the 2D matrix represents the 1D characteristic function associated with i-th sliding time window, j -th row of the 2D matrix is associated with a time-shift of j time units, a matrix element at the i-th column and the j-th row of the 2D matrix is a value of the 1D characteristic function in the i-th sliding time window with a time-shift of j time units, and the 2D matrix is a result of the 2D decomposition (despite lack of description on matrix and alignment, it would have been obvious in view of pages 271-272, vector representation on CSI measurement, claimed 2D matrix representations above are well-known in practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data analysis, hence Official Notice is taken).
To claim 6, Liu, Xu and Li teach claim 5.
Liu teach wherein the processor is further configured for: enhancing the 2D decomposition by applying, to each matrix element of the 2D matrix, a vertical highpass filtering, a horizontal lowpass filtering, or both (despite of lack of description in Liu, using particular filtering technique(s) on matrix data is well-known in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
To claim 7, Liu, Xu and Li teach claim 6.
Liu teach wherein the processor is further configured for: filtering each matrix element of the 2D matrix with a respective 2D vertical highpass horizontal lowpass filter that is either separable or non-separable, either linear or nonlinear (obvious in explanation in response to claim 6 above).
To claim 8, Liu, Xu and Li teach claim 7.
Liu teach wherein the processor is further configured for: enhancing the 2D decomposition by filtering each matrix element of the 2D matrix with at least one of a respective vertical highpass filter or a respective horizontal lowpass filter, wherein each of the respective vertical highpass filter and the respective horizontal lowpass filter is either linear or nonlinear (obvious in explanation in response to claim 6 above).
To claim 9, Liu, Xu and Li teach claim 8.
Liu teach wherein the processor is further configured for enhancing the 2D decomposition by at least one of filtering part of each column of the 2D matrix with a respective first common vertical highpass filter; filtering part of each row of the 2D matrix with a respective second common vertical highpass filter; filtering part of each column of the 2D matrix with a respective first common horizontal lowpass filter; or filtering part of each row of the 2D matrix with a respective second common horizontal lowpass filter (obvious in explanation in response to claim 6 above).
To claim 10, Liu, Xu and Li teach claim 9.
Liu teach wherein: a vertical highpass filter is a directional 1D highpass filter applied in a vertical direction or in a near-vertical direction (obvious in explanation in response to claim 6 above).
To claim 11, Liu, Xu and Li teach claim 10.
Liu teach wherein: a horizontal lowpass filter is a directional 1D lowpass filter applied in a horizontal direction or in a near-horizontal direction (obvious in explanation in response to claim 6 above).
To claim 12, Liu, Xu and Li teach claim 11.
Liu teach wherein the processor is further configured for: filtering each matrix element of the 2D matrix with a succession of at least two 1D filters, each being either the vertical highpass filter or the horizontal lowpass filter (obvious in explanation in response to claim 6 above).
To claim 13, Liu, Xu and Li teach claim 12.
Liu teach wherein the processor is further configured for: enhancing the 2D decomposition by applying histogram equalization to a number of consecutive columns of the 2D matrix to generate an enhanced 2D matrix (despite of lack of description in Liu, using particular histogram equalization technique(s) on matrix data is well-known in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
To claim 14, Liu, Xu and Li teach claim 13.
But, Liu do not expressly disclose wherein the processor is further configured for: computing a distance score based on dynamic time warping (DTW) between a reference column and a particular column of the enhanced 2D matrix associated with a particular time, wherein: the reference column is computed in a training session by clustering a number of training columns matched based on DTW, the training columns are computed based on training TSCI obtained from training wireless signals that are received through the wireless channel when training objects with vital signs are present and impacting the wireless channel, and when the distance score is larger than a first threshold, the periodic motion of the vital sign of the object is tentatively not detected at the particular time (despite of lack of description in Liu, having distance score on dynamic time warping with threshold detection is well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
To claim 15, Liu, Xu and Li teach claim 14.
Liu teach wherein the processor is further configured for: subtracting a quantity from each matrix element in the particular column of the enhanced 2D matrix; and computing a zero-crossing count of the particular column, wherein when the zero-crossing count is larger than a second threshold, the periodic motion of the vital sign of the object is tentatively not detected at the particular time (despite of lack of description in Liu, claimed particular matrix arrangement on threshold detection is well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
To claim 16, Liu, Xu and Li teach claim 15.
Liu teach wherein the processor is further configured for: computing multiple peaks of the particular column of the enhanced 2D matrix; and computing an inter-peak distance based on the multiple peaks (pages 270-271, section 4.3).
To claim 17, Liu, Xu and Li teach claim 16.
Liu teach wherein: when the inter-peak distance is in an acceptable range, the periodic motion of the vital sign of the object is tentatively detected at the particular time, and the inter-peak distance is determined to be equal to a frequency or a period of the vital sign; and when the inter-peak distance is not in an acceptable range, the periodic motion of the vital sign of the object is tentatively not detected at the particular time (despite of lack of description in Liu, having particular inter-peak distance arrangement for periodic motion detection is well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
Liu teach 18, Liu, Xu and Li teach claim 17.
Liu teach wherein the processor is further configured for: detecting the vital sign of the object when P is larger than a third threshold, wherein P is a percentage of the periodic motion of the vital sign being tentatively detected in a period of time (despite of lack of description in Liu, using particular threshold arrangement for periodic motion detection is well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
Conclusion

APPLICANT REBUTTAL: REMARKS
Claims 1-20 are pending in the application. By this Response, claims 1-2 and 19-20 have been amended. No new matter is added by these amendments, which are supported by the Applicant's specification as filed, e.g. paragraphs [00198], [00255]. Applicant respectfully requests reconsideration and allowance of the pending claims in view of the remarks presented below.
Claim Rejections under 35 U.S.C. §103
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al.
("Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi"; hereinafter "Liu"), in view of Li et al. (CN 109684941).
Applicant respectfully traverses each of the above rejections, and does not agree on any Official Notice taken in the Office Action.
Without agreeing on the above rejections and solely for expediting the allowance of the application, independent claim 1 is amended to recite among other things:
obtaining a time series of channel information (CI) of the wireless channel based
on the received wireless signal,
computing a time series of denoised CI (DCI) by denoising the time series of
CI (TSCI),
computing a concatenation of the TSCI and the time series of DCI (TSDCI),
computing a two dimensional (2D) decomposition of the concatenation,
enhancing the 2D decomposition based on a vertical highpass filtering and a
horizontal lowpass filtering, and
monitoring the periodic motion of the vital sign based on the enhanced 2D
decomposition. (Emphasis added.)
The Office Action relied on Liu to teach the claimed "computing a two dimensional (2D) decomposition" (of the TSCI). See, Office Action, p. 3. Without agreeing to this teaching, Applicant respectfully submits that Liu does not teach or suggest: (1) computing a time series of
denoised CI (DCI) by denoising the TSCI; (2) computing a concatenation of the TSCI and the time series of DCI (TSDCI); and (3) computing a two dimensional (2D) decomposition of the concatenation. As such, Liu does not teach or suggest "computing a time series of denoised CI (DCI) by denoising the time series of CI (TSCI), computing a concatenation of the TSCI and the time series of DCI (TSDCI), computing a two dimensional (2D) decomposition of the concatenation," as recited in the amended claim 1.
It is well settled that establishing a prima facie case of obviousness requires that all claim limitations recited in a claim must be taught or suggested by the prior art. MPEP §2143.03. Clearly, taken individually or in combination, the cited references do not pass this test. Thus, a prima facie case of obviousness cannot be established with respect to claim 1. Thus, claim 1 is patentable.
Claims 2-18 depend from claim 1, and are thus likewise patentable for at least the same reasons as discussed above with respect to claim 1, and for the additional features recited therein.
Independent claims 19-20 have amended to include similar features as claim 1, and are thus likewise patentable for at least the same reasons as discussed above with respect to claim 1, and for the additional features recited therein.
In view of the above amendments and remarks, Applicant respectfully submits that all rejections under 35 U.S.C. 103 are overcome and should be withdrawn.
No Disclaimers or Disavowals
Although the present communication may include alterations to the application or claims, or characterizations of claim scope or referenced art, Applicant is not conceding in this application that previously pending claims are not patentable over the cited references. Rather,
any alterations or characterizations are being made to facilitate expeditious prosecution of this application. Applicant reserves the right to pursue at a later date any previously pending or other broader or narrower claims that capture any subject matter supported by the present disclosure, including subject matter found to be specifically disclaimed herein or by any prior prosecution. Accordingly, reviewers of this or any parent, child or related prosecution history shall not reasonably infer that Applicant has made any disclaimers or disavowals of any subject matter supported by the present application.
Conclusion

NEXT OFFICE ACTION: 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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b)  CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA  35 U.S.C. 112, the applicant), regards as the invention.
In claims 1 and 19-20, applicant amended “… computing a time series of concatenated CI (TSCCI) based on a concatenation of the TSCI and the time series of DCI (TSDCI), each concatenated CI (CCI) being computed by concatenating a respective CI and a respective DCI corresponding to the CI…”  Based on disclosure of paragraphs 00198 and 00255 of filed specification, wording of said amendment appears to be redundant, which is indefinite.  Also, said citation of filed specification supports a concatenation of a basic feature of a respective CI and another basic feature of a corresponding denoised CI, wherein basic feature can be any related feature.  For examination, the broadest reasonable interpretation is taken based on filed specification.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (“Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi”) in view of Xu et al. (“Attention-based Walking Gait and Direction Recognition in Wi-Fi Networks”) and Suyambukesan et al. (US2006/0077306).
To claim 1, Liu teach a system for wireless vital sign monitoring, comprising:
a transmitter configured to transmit a wireless signal through a wireless channel of a venue (Figs. 2, 10);
a receiver configured to receive the wireless signal through the wireless channel (Figs. 2, 10), wherein the received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue (pages 270-273, sections 4-6); and
a processor (Fig. 2) configured for:
obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal, computing a two dimensional (2D) decomposition of the time series of CI (TSCI), enhancing the 2D decomposition, and monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition (pages 269-272, sections 3.3-5, Figs. 1, 3, 5-8).
But, Liu do not expressly disclose computing a time series of concatenated CI (TSCCI) based on a concatenation of the TSCI and the time series of DCI (TSDCI), each concatenated CI (CCI) being computed by concatenating a respective CI and a respective DCI corresponding to the CI, computing a one dimensional (1D) characteristic function of CCI of the TSCCI in a sliding time window, computing a two dimensional (2D) decomposition of the TSCCI based on the 1D characteristic function in the sliding time window, enhancing the 2D decomposition based on a non-separable vertical highpass horizontal lowpass filtering.
Xu teach a system for human gait recognition in WiFi networks, comprising modules for: obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal (page 5, IV-B-1, removing paths with long delays from an obtained time series of CSI measurements, and outputting a time series of concatenated CSI), computing a time series of denoised CI (DCI) by denoising the time series of CI (TSCI) (pages 5-6, IV-B-2, denoising the time series of concatenated CSI), computing a time series of concatenated CI (TSCCI) based on a concatenation of the TSCI and the time series of DCI (TSDCI) (pages 5-6, CSI denoising), each concatenated CI (CCI) being computed by concatenating a respective CI and a respective DCI corresponding to the CI (pages 5-6, IV-B, refined time series of CSI is based on denoised concatenated CSI), computing a one dimensional (1D) characteristic function of CCI of the TSCCI in a sliding time window, computing a two dimensional (2D) decomposition of the TSCCI based on the 1D characteristic function in the sliding time window (pages 6-8, IV-C; page 9, V-B; walking profile splicing with sliding window segmentation), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu, in order to further process collected signal characteristics by design preference.
Suyambukesan teach a system for frequency content motion detection by decomposing a raw field data into frequency data, which comprising an operation of concurrently applying a vertical low pass filter and a horizontal high pass filter for enhancing the appearance of motion (Fig. 4, paragraph 0028).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Suyambukesan into the system of Liu and Xu, in order to further 2D decomposition with enhancement.
To claim 19, Liu, Xu and Suyambukesan teach a wireless device of a system for wireless vital sign monitoring (as explained in response to claim 1 above).
To claim 20, Liu, Xu and Suyambukesan teach a method for wireless vital sign monitoring (as explained in response to claim 1 above).
To claim 2, Liu, Xu and Suyambukesan teach claim 1.
Liu, Xu and Suyambukesan teach wherein the processor is further configured for: computing each CCI as a concatenation of a first basic feature of the respective CI and a second basic feature of the corresponding DCI, wherein the first basic feature of any CI comprises at least one of: CI, a magnitude of the CI, a phase of the CI, a magnitude of the CI, a phase of the component of the CI, or a function of any magnitude or phase of the CI (Liu, page 270, sections 4.1-4.2), wherein the second basic feature of any DCI comprises at least one of: the DCI, a magnitude of the DCI, a phase of the DCI, a magnitude of a component of the DCI, or a phase of the component of the DCI, or a function of any magnitude or phase of the CI (Xu, pages 6-7), wherein the TSDCI is computed by processing the TSCI with a first denoising filter, the first denoising filter comprising at least one of: a low-pass filter, a bandpass filter, a highpass filter, a median filter, or a percentile filter (Liu, page 270, section 4.1 data calibration).
To claim 3, Liu, Xu and Suyambukesan teach claim 2.
Liu teach wherein the processor is further configured for: determining a series of sliding time windows, each sliding time window being associated with a time stamp (page 270, sections 4.1-4.2, sliding window or moving time window, associating time stamp would have been an obvious feature); computing a respective one dimensional (1D) characteristic function of the CCI of the TSCCI in each sliding time window; and constructing the 2D decomposition based on the respective 1D characteristic function in each sliding time window (pages 271-272, Figs. 5-8).
To claim 4, Liu, Xu and Suyambukesan teach claim 3.
Liu teach wherein: the respective 1D characteristic function is computed based on a transformation of the CCI of the TSCCI in each sliding time window; and the transformation comprises at least one of a frequency transformation, a transformation to frequency domain, a short-time Fourier transform (STFT), a wavelet transform, a filter-bank representation, a time transformation, a transformation to a time-shift domain, an auto-correlation function (ACF), an auto-covariance function, or a time-frequency decomposition (page 271, section 4.4, PSD transforms the time series of CSI measurements on each subcarrier to its power distribution in the frequency domain).
To claim 5, Liu, Xu and Suyambukesan teach claim 4.
Liu teach wherein the processor is further configured for: constructing a 2D matrix by lining up and aligning the 1D characteristic functions of the sliding time windows in time order according to the associated time stamps, wherein i-th column of the 2D matrix represents the 1D characteristic function associated with i-th sliding time window, j -th row of the 2D matrix is associated with a time-shift of j time units, a matrix element at the i-th column and the j-th row of the 2D matrix is a value of the 1D characteristic function in the i-th sliding time window with a time-shift of j time units, and the 2D matrix is a result of the 2D decomposition (despite lack of description on matrix and alignment, it would have been obvious in view of pages 271-272, vector representation on CSI measurement, claimed 2D matrix representations above are well-known in practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data analysis, hence Official Notice is taken).
To claim 6, Liu, Xu and Suyambukesan teach claim 5.
Liu teach wherein the processor is further configured for: enhancing the 2D decomposition by applying, to each matrix element of the 2D matrix, the vertical highpass horizontal lowpass filtering (despite of lack of description in Liu, using particular filtering technique(s) on matrix data is well-known in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
To claim 7, Liu, Xu and Suyambukesan teach claim 6.
Liu teach wherein the processor is further configured for: filtering each matrix element of the 2D matrix with a respective 2D vertical highpass horizontal lowpass filter that is either separable or non-separable, either linear or nonlinear (obvious in explanation in response to claim 6 above).
To claim 8, Liu, Xu and Suyambukesan teach claim 7.
Liu teach wherein the processor is further configured for: enhancing the 2D decomposition by filtering each matrix element of the 2D matrix with at least one of a respective vertical highpass filter or a respective horizontal lowpass filter, wherein each of the respective vertical highpass filter and the respective horizontal lowpass filter is either linear or nonlinear (obvious in explanation in response to claim 6 above).
To claim 9, Liu, Xu and Suyambukesan teach claim 8.
Liu teach wherein the processor is further configured for enhancing the 2D decomposition by at least one of filtering part of each column of the 2D matrix with a respective first common vertical highpass filter; filtering part of each row of the 2D matrix with a respective second common vertical highpass filter; filtering part of each column of the 2D matrix with a respective first common horizontal lowpass filter; or filtering part of each row of the 2D matrix with a respective second common horizontal lowpass filter (obvious in explanation in response to claim 6 above).
To claim 10, Liu, Xu and Suyambukesan teach claim 9.
Liu teach wherein: a vertical highpass filter is a directional 1D highpass filter applied in a vertical direction or in a near-vertical direction (obvious in explanation in response to claim 6 above).
To claim 11, Liu, Xu and Suyambukesan teach claim 10.
Liu teach wherein: a horizontal lowpass filter is a directional 1D lowpass filter applied in a horizontal direction or in a near-horizontal direction (obvious in explanation in response to claim 6 above).
To claim 12, Liu, Xu and Suyambukesan teach claim 11.
Liu teach wherein the processor is further configured for: filtering each matrix element of the 2D matrix with a succession of at least two 1D filters, each being either the vertical highpass filter or the horizontal lowpass filter (obvious in explanation in response to claim 6 above).
To claim 13, Liu, Xu and Suyambukesan teach claim 12.
Liu teach wherein the processor is further configured for: enhancing the 2D decomposition by applying histogram equalization to a number of consecutive columns of the 2D matrix to generate an enhanced 2D matrix (despite of lack of description in Liu, using particular histogram equalization technique(s) on matrix data is well-known in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
To claim 14, Liu, Xu and Suyambukesan teach claim 13.
But, Liu do not expressly disclose wherein the processor is further configured for: computing a distance score based on dynamic time warping (DTW) between a reference column and a particular column of the enhanced 2D matrix associated with a particular time, wherein: the reference column is computed in a training session by clustering a number of training columns matched based on DTW, the training columns are computed based on training TSCI obtained from training wireless signals that are received through the wireless channel when training objects with vital signs are present and impacting the wireless channel, and when the distance score is larger than a first threshold, the periodic motion of the vital sign of the object is tentatively not detected at the particular time (despite of lack of description in Liu, having distance score on dynamic time warping with threshold detection is well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
To claim 15, Liu, Xu and Suyambukesan teach claim 14.
Liu teach wherein the processor is further configured for: subtracting a quantity from each matrix element in the particular column of the enhanced 2D matrix; and computing a zero-crossing count of the particular column, wherein when the zero-crossing count is larger than a second threshold, the periodic motion of the vital sign of the object is tentatively not detected at the particular time (despite of lack of description in Liu, claimed particular matrix arrangement on threshold detection is well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
To claim 16, Liu, Xu and Suyambukesan teach claim 15.
Liu teach wherein the processor is further configured for: computing multiple peaks of the particular column of the enhanced 2D matrix; and computing an inter-peak distance based on the multiple peaks (pages 270-271, section 4.3).
To claim 17, Liu, Xu and Suyambukesan teach claim 16.
Liu teach wherein: when the inter-peak distance is in an acceptable range, the periodic motion of the vital sign of the object is tentatively detected at the particular time, and the inter-peak distance is determined to be equal to a frequency or a period of the vital sign; and when the inter-peak distance is not in an acceptable range, the periodic motion of the vital sign of the object is tentatively not detected at the particular time (despite of lack of description in Liu, having particular inter-peak distance arrangement for periodic motion detection is well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
Liu teach 18, Liu, Xu and Suyambukesan teach claim 17.
Liu teach wherein the processor is further configured for: detecting the vital sign of the object when P is larger than a third threshold, wherein P is a percentage of the periodic motion of the vital sign being tentatively detected in a period of time (despite of lack of description in Liu, using particular threshold arrangement for periodic motion detection is well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Liu for data enhancement, hence Official Notice is taken).
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 extension fee 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 date of this final action.

CITED REFERENCE SUMMARIES: Ref CN109684941: Claims:
one kind picks region partitioning method based on MATLAB image procossing litchi fruits, which is characterized in that include the following steps:
S1, the tree crown image for acquiring lichee tree crownï¼
S2, denoising sound preconditioning is carried out to collected tree crown image, specifically:
S2.1, original fruit image are broken down into tri- color images of R, G, B, each color image in RGB color
For two dimensional imageï¼
S2.2, each color image are broken down into low frequency and high frequency section using two-dimentional QMF compression algorithm, then utilize histogram
Figure equalization and contrast are promoted handles low frequency part and high frequency section respectivelyï¼
S2.3, two dimensional image is merged into using tower restructing algorithm to treated low frequency and high frequency section, is respectively completed three
The illumination equalization of color image is handledï¼
S2.4, to illumination equalization treated three color images, be merged into color image in RGB color, complete water
The pretreatment of fruit color imageï¼
S3, the AdaBoost classifier trained by the local binary patterns LBP feature of fruit and non-fruit are to doubtful fruit area
Domain and non-fruit region carry out error detection, and the identification of litchi fruits is completed after merging testing resultï¼
S4, lichee edge extracting and marginalisation complete positioning and extract lichee coordinate in imageï¼
S5, it obtains using K-means clustering method to carry out clustering to the region after the coordinate information of lichee, after classification
Central point to each piece of division region mass center as picking position.
picking region partitioning method based on MATLAB image procossing litchi fruits according to claim 1, which is characterized in that
In step S1, picking shooting is carried out to lichee tree crown using industrial camera and obtains tree crown image.
picking region partitioning method based on MATLAB image procossing litchi fruits according to claim 1, which is characterized in that
In step S2.2, the two dimension QMF compression algorithm specifically:
Wherein, x, y represent the transverse and longitudinal coordinate of pixel, and k, l are integers, and g, h are high-pass filter and low-pass filter respectively,
c j It is a 2D signal, And c j+1 It is vertical high frequency part, horizontal high-frequent part, diagonal high frequency respectively
Part and low frequency part.
picking region partitioning method based on MATLAB image procossing litchi fruits according to claim 3, which is characterized in that
In step S2.3, the progress according to the following equation of illumination equalization processing is carried out:
Wherein, x, y represent the transverse and longitudinal coordinate of pixel, and k, l are integers, and g, h are high-pass filter and low-pass filter respectively,
c' j+1 (k, l) is the image low frequency part after histogram equalization, It is hanging down after contrast is promoted respectively
Straight high frequency section, horizontal high-frequent part, diagonal high frequency section；c' j (x, y) is then the two dimensional image after illumination equalization.
picking region partitioning method based on MATLAB image procossing litchi fruits according to claim 1, which is characterized in that
Step S3 includes the training stage specifically:
S3.1, N width fruit illumination equalization image are randomly chosen, artificially to choose fruit region and non-fruit area
Domain,
Quantity M of S3.2, selected fruit region and non-fruit administrative division map piece, picture size is setting pixelï¼
S3.3, the LBP feature calculation in fruit region and non-fruit region is come outï¼
S3.4, it using AdaBoost structural classification device and is trained.
picking region partitioning method based on MATLAB image procossing litchi fruits according to claim 5, which is characterized in that
In step S3, the specific steps that are detected are as follows:
S3.5, doubtful fruit region by with CHT method extract, so that entire image is divided into two parts, that is, include
The image of CHT detection circle and the image round not comprising CHT detectionï¼
S3.6, CHT detection circular image are determined whether with the classifier of training stage for fruit regionï¼
S3.7, the image that CHT testing result is free of with one 10 Ã 10 wicket scanning whole picture, to search for by branches and leaves, fruit
The fruit target for the zonule blocked in factï¼
S3.8, two-part result merge, and obtain final recognition result.
picking region partitioning method based on MATLAB image procossing litchi fruits according to claim 1, which is characterized in that
Step S4 specifically:
Two cameras are collinearly placed in parallel, the imaging plane of plane determined by two camera photocentres and two cameras is put down respectively
Row is coplanar, and two cameras only have amount of movement, i.e. b in x-axis direction, and b is also referred to as baseline, the optical center company of spatial point A and two cameras
Line hands over imaging plane in point al and ar respectively, which is mapped in after two-dimensional surface, according to the triangle theory of similarity
It can be concluded that the three-dimensional coordinate of A point has following relationship:
Wherein, f is focal length, u 0 、v 1 、v 0 It is camera internal parameter, C with ay l Left and right camera, O are respectively indicated with Cr l Then distinguish with Or
It is the optical center of two cameras.
picking region partitioning method based on MATLAB image procossing litchi fruits according to claim 1, which is characterized in that
In step S5, clustering is carried out to the region using K-means clustering method specifically:
On obtaining tree crown after the coordinate of litchi fruits, randomly selecting k cluster center of mass point is μ 1 , μ 2 ..., μ k ∈R n , repeat
Following procedure is until convergence
{ for each sample i, calculate its class that should belong to
For each class j, such mass center is recalculated
K is the cluster numbers of setting, c (i) Represent sample i that class nearest with distance in k class, c (i) Value be 1 one into k
It is a, mass center μ j The conjecture to the center of a sample's point for belonging to same class is represented, the point in k tree crown is randomly selected first, as k
The mass center in a region, then the first step calculates its each distance into k mass center for each lichee coordinate, then selects
That point for taking distance nearest is as c (i) , have affiliated region by each lichee coordinate of the first step in this way；Second step
For each region, its mass center μ is recalculated j , the iteration first step and second step are until mass center is constant or variation
Very little.