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 .
Claim Objections
Claims 1 and 5 are objected to because of the following informalities:
Claim 1 recites “configured to extracting a segment” in lines 8-9. This should read “configured to extract a segment”.
Claim 5 recites “sending matching waveforms from the correlation coefficients that exceed the threshold value” in lines 10-11. This should read “sending matching waveforms corresponding to the correlation coefficients that exceed the threshold value”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“input waveform module” first recited in claim 1.
“matching module” first recited in claim 1.
“machine learning module” first recited in claim 1.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The corresponding structure for “input waveform module” is identified as the machine learning module being provided on one or more separate devices, such as cloud server 134, the networked device 136, or the like, as described in par. [0036]. The input waveform module is interpreted as any device, server, or networked device capable of performing the functional limitations of the input waveform module, or equivalents thereof.
The corresponding structure for “matching module” is identified as the machine learning module being provided on one or more separate devices, such as cloud server 134, the networked device 136, or the like, as described in par. [0036]. The matching module is interpreted as any device, server, or networked device capable of performing the functional limitations of the matching module, or equivalents thereof.
The corresponding structure for “machine learning module” is identified as the machine learning module being provided on one or more separate devices, such as cloud server 134, the networked device 136, or the like, as described in par. [0036]. The machine learning module is interpreted as any device, server, or networked device capable of performing the functional limitations of the machine learning module, or equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-4 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.
Claim 1 recites “one or more receive antennas that receive return RF waves resulting from the transmitted RF waves into the person and from which a pulse wave signal is generated” in lines 4-5. It is unclear what structure is responsible for generating the pulse wave signal. The receive antennas do not comprise a recited circuit or processor to generate a pulse wave signal. Clarification is requested.
For the purposes of examination, the device is interpreted as comprising a circuit or processor configured to generating a pulse wave signal.
All claims not explicitly addressed above are rejected under 35 U.S.C. 112(b) are rejected by virtue of their dependency on a rejected base claim.
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 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication 2022/0192511 by Leabman, hereinafter “Leabman” in view of US Patent Publication 2015/0112152 by Ryan et al., hereinafter “Ryan” in view of US Patent Publication 2003/0083711 by Yonce et al., hereinafter “Yonce”.
Regarding claim 1, Leabman teaches a heath parameter monitoring system (Fig. 37, RF-based sensor system 3710) comprising: a device that includes one or more transmit antennas configured to transmit radio frequency (RF) waves from one or more transmit antennas into a person over a range of frequencies (TX antennas 3744 are similar to the TX antennas in Figs. 5-7 ([0224]), wherein the antennas are configured to transmit and receive millimeter range radio waves. For example, the antennas are configured to transmit radio waves in the 122-126 GHz frequency range, e.g., wavelengths in the range of 2.46-2.38 mm [0109-0116]. The radio waves are transmitted into the skin as shown in Figs. 36A-B), and one or more receive antennas that receive return RF waves resulting from the transmitted RF waves into the person (RX antennas 3746 are similar to the RX antennas in Figs. 5-7 ([0224]), wherein the antennas are configured to receive millimeter range radio waves. The received radio waves are received from the skin as shown in Figs. 36A-B) and from which a pulse wave signal is generated ([0223]; “The RF-based sensor system is configured to coherently combine signals across the two-dimensional array of RX antennas and across the range of radio frequencies to generate the pulse wave signal.”); a memory that stores the pulse wave signal (For the subsequent operations performed in the pulse wave signal processor and feature extractor, the pulse wave signal must be stored in a memory); a machine learning module with a machine learning algorithm that is configured to input into the machine learning algorithm waveforms ([0209]; The trained model database receives waveforms that are produced from the RF signals and generate an output corresponding to health parameter data).
Leabman does not teach a standard waveform database stored in the memory; an input waveform module in communication with the memory that is configured to extract a segment of the pulse wave signal to generate an extracted segment; a matching module in communication with the memory and that is configured to receive the extracted segment, compare the extracted segment to the waveforms in the standard waveform database thereby creating matched data, assign a correlation coefficient to each matched data, and determine which correlation coefficients exceed a threshold value.
Ryan teaches a system configured to determine the severity of a stenosis in a blood vessel. The system is configured to identify irregular heartbeat cycles by comparing heartbeat data (ECG traces) to a library (i.e., previously stored database) of heartbeat data. The heartbeat data is determined to match (or not match) with data from the library based on deviation of the heartbeat data from the library heartbeat data compared to being above (or below) a threshold based on an evaluation metric ([0049-0052]). The irregular heartbeats are identified such that they can be selectively processed (i.e., removed) from subsequent calculations to not adversely affect health parameter calculations and evaluation ([0056]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the system of Leabman as taught by Ryan such that the device comprises a standard waveform database stored in the memory; an input waveform module in communication with the memory that is configured to extract a segment of the pulse wave signal to generate an extracted segment; a matching module in communication with the memory and that is configured to receive the extracted segment, compare the extracted segment to the waveforms in the standard waveform database thereby creating matched data, assign an evaluation metric to each matched data, and determine which evaluation metrics exceed a threshold value, in order to identify irregular heartbeats and remove them from subsequent calculations to not adversely affect health parameter calculations and evaluation ([0056]).
It is noted that Ryan teaches the comparison of heartbeat cycles, therefore also teaches extracting each cycle from the data. This is analogous to generating an extracted segment. Further, in the combination of Leabman and Ryan, any subsequent processing of data, such as inputting waveforms into the machine learning model, is completed with the matched waveforms.
Leabman in view of Ryan does not teach wherein the evaluation metric is a correlation coefficient.
Yonce teaches a method of using a cross-correlation method to obtain correlation coefficients between measured heartbeat (ECG) waveforms and a plurality of template waveforms ([0029, 0055]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the system taught by Leabman in view of Ryan such that the evaluation metric is a correlation coefficient, as taught by Yonce ([0055]). This combination comprises combining prior art elements according to known methods to yield predictable results. See MPEP 2143-I-A. It is noted that Ryan teaches that any suitable comparative mathematical data evaluation technique can be used to determine whether the data matches or does not match ([0050]), and is therefore compatible with the teachings of Yonce.
Regarding claim 2, the combination of Leabman, Ryan, and Yonce teaches the heath parameter monitoring system of claim 1, wherein the matching waveforms comprise the waveforms from the standard waveform database, or waveforms resulting from a convolution and/or cross-correlation of the extracted segment and the waveforms from the standard waveform database (The matching waveforms are determined based on the correlation coefficients resulting from the cross-correlation (as taught by Ryan and Yonce) of the extracted segments and the waveforms from the standard waveform database).
Regarding claim 3, the combination of Leabman, Ryan, and Yonce teaches the heath parameter monitoring system of claim 1, wherein each waveform in the standard waveform database includes an associated label (Ryan, [0050]; “It is understood that the library of ECG traces may be selected based on patient characteristics, recognizing that a "normal" ECG trace for a heartbeat cycle can be different as a result of a particular patient's circumstances or conditions. Likewise, the library of ECG traces can include examples of irregular heartbeat cycles such that if the ECG trace matches an irregular heartbeat cycle trace it is identified or tagged as such.” Therefore, the library contains waveforms that are labeled either irregular or normal).
Regarding claim 4, the combination of Leabman, Ryan, and Yonce teaches the heath parameter monitoring system of claim 1, wherein the threshold value is user settable or is automatically set (Yonce, [0029]; “The exact correlation values that should optimally be used in deciding whether or not a test waveform and template waveform match may be selected on the basis of empiric testing as the optimum values may vary for an individual patient”. Therefore, the threshold is settable based on empiric testing).
Regarding claim 5, Leabman teaches a heath parameter monitoring method comprising: transmitting radio frequency (RF) waves from one or more transmit antennas into a person over a range of frequencies (TX antennas 3744 are similar to the TX antennas in Figs. 5-7 ([0224]), wherein the antennas are configured to transmit millimeter range radio waves into the skin.), and receiving, using one or more receive antennas, a pulse wave signal that results from the RF waves transmitted into the person (RX antennas 3746 are similar to the RX antennas in Figs. 5-7 ([0224]), wherein the antennas are configured to receive millimeter range radio waves. The received radio waves are received from the skin as shown in Figs. 36A-B, [0223]; “The RF-based sensor system is configured to coherently combine signals… to generate the pulse wave signal.”); sending waveforms to a machine learning module and inputting the waveforms into a machine learning algorithm ([0209]; The trained model database receives waveforms that are produced from the RF signals and generate an output corresponding to health parameter data.).
Leabman does not teach extracting a segment of the pulse wave signal to generate an extracted segment; comparing the extracted segment to waveforms in a standard waveform database thereby creating matched data; assigning a correlation coefficient to each matched data, and determining which correlation coefficients exceed a threshold value.
Ryan teaches a method of determining the severity of a stenosis in a blood vessel. The method comprises to identifying irregular heartbeat cycles by comparing heartbeat data (ECG traces) to a library (i.e., previously stored database) of heartbeat data. The heartbeat data is determined to match (or not match) with data from the library based on deviation of the heartbeat data from the library heartbeat data compared to a threshold based on an evaluation metric ([0049-0052]). The irregular heartbeats are identified such that they can be selectively processed (i.e., removed) from subsequent calculations to not adversely affect health parameter calculations and evaluation ([0056]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Leabman to have comprised extracting a segment of the pulse wave signal to generate an extracted segment; comparing the extracted segment to waveforms in a standard waveform database thereby creating matched data; assigning an evaluation metric to each matched data, and determining which evaluation metrics exceed a threshold value, in order to identify irregular heartbeats and remove them from subsequent calculations to not adversely affect health parameter calculations and evaluation ([0056]).
It is noted that Ryan teaches the comparison of heartbeat cycles, therefore also teaches extracting each cycle from the data. This is analogous to generating an extracted segment. Further, in the combination of Leabman and Ryan, any subsequent processing of data, such as inputting waveforms into the machine learning model, is completed with the matched waveforms.
Leabman in view of Ryan does not teach wherein the evaluation metric is a correlation coefficient.
Yonce teaches a method of using a cross-correlation method to obtain correlation coefficients between measured heartbeat (ECG) waveforms and a plurality of template waveforms ([0029, 0055]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the system taught by Leabman in view of Ryan such that the evaluation metric is a correlation coefficient, as taught by Yonce ([0055]). This combination comprises combining prior art elements according to known methods to yield predictable results. See MPEP 2143-I-A. It is noted that Ryan teaches that any suitable comparative mathematical data evaluation technique can be used to determine whether the data matches or does not match ([0050]), and is therefore compatible with the teachings of Yonce..
Regarding claim 6, the combination of Leabman, Ryan, and Yonce teaches the heath parameter monitoring method of claim 5, wherein the matching waveforms comprise the waveforms from the standard waveform database, or waveforms resulting from a convolution and/or cross-correlation of the extracted segment and the waveforms from the standard waveform database (The matching waveforms are determined based on the correlation coefficients resulting from the cross-correlation (as taught by Ryan and Yonce) of the extracted segments and the waveforms from the standard waveform database).
Regarding claim 7, the combination of Leabman, Ryan, and Yonce teaches the heath parameter monitoring method of claim 5, wherein each waveform in the standard waveform database includes an associated label (Ryan, [0050]; “It is understood that the library of ECG traces may be selected based on patient characteristics, recognizing that a "normal" ECG trace for a heartbeat cycle can be different as a result of a particular patient's circumstances or conditions. Likewise, the library of ECG traces can include examples of irregular heartbeat cycles such that if the ECG trace matches an irregular heartbeat cycle trace it is identified or tagged as such.” Therefore, the library contains waveforms that are labeled either irregular or normal).
Regarding claim 8, the combination of Leabman, Ryan, and Yonce teaches the heath parameter monitoring method of claim 5, wherein the threshold value is user settable or is automatically set (Yonce, [0029]; “The exact correlation values that should optimally be used in deciding whether or not a test waveform and template waveform match may be selected on the basis of empiric testing as the optimum values may vary for an individual patient”. Therefore, the threshold is settable based on empiric testing).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Publication 2020/0121214 by Hyde et al. teaches a system that uses transmit antennas and receiving antennas to transmit and receive radio frequency signals into the body to generate cardiac parameters such as pulse shape.
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/NELSON ALEXANDER GLOVER/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791
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