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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/08/2026 has been entered.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 8, 12, 20 & 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kovacs et al. (U.S. Patent Application 2015/0359486 A1), and further in view of Kim et al. (U.S. Patent Application 2017/0172431 A1).
Claim 1: Kovacs teaches –
A method of estimating a blood pressure of a person [allowing the detection of blood pressure] (Para 0119) comprising:
collecting a signal of each of a plurality of load cells [load cells] (Para 0120 and Figure 1, Element 110) embedded in a patient support apparatus that is configured to support a patient in a plurality of postures supporting the person [body of a user 105 standing on a scale 110] (Para 0059) to determine a ballistocardiogram [information in a ballistocardiogram (BCG) and an impedance plethysmography (IPG) measurements] (Para 0046);
collecting a photoplethysmogram signal [photoplethysmogram (PPG) measurement circuit 1640, which collects data from the LED/photosensor 1610, can also provide data to the processor circuit 1645] (Para 0126) from an appendage of the person (Figure 1, Element 105; in the Figure the user is shown with their legs or in other words appendage being measured);
Examiner’s Note: Figure 16 of Kovacs is disclosed as being implemented with the other embodiments of Kovacs (Para 0125) and thus citing Figure 1 & 16 is not an improper combining of different embodiments.
Kovacs fails to teach applying a model. However, Kim teaches –
applying a model [The processor 104 extracts at least one feature from the pulse wave information and applies the at least one feature to the estimator] (Para 0059) to the signals [ballistocardiogram (BCG)] (Para 0056; BCG is disclosed as pulse wave information that the estimator uses in Para 0059) of each of the load cells of Kovacs and the photoplethysmogram signal [photoplethysmogram (PPG) signal] (Para 0077 and Figure 2, Element 220) to determine an estimate of the blood pressure of the person [calculating a final blood pressure of the user by applying the blood pressure calibration value to the basic blood pressure] (Claim 1),
wherein the model has been calibrated for the specific posture of the person [trains the estimator based on the pulse wave information corresponding to the posture and the reference blood pressure calibration values obtained to correspond to the estimated posture] (Para 0064) as the model is being applied [determines a blood pressure calibration value corresponding to the pulse wave information of the user using a pre-trained estimator] (Para 0078) in order to obtain a more accurate measurement of a blood pressure (Para 0006)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the blood pressure processing of Kovacs to include the model application as taught by Kim in order to obtain a more accurate measurement of a blood pressure (Para 0006).
Claim 8/1: Kovacs fails to teach calibration. However, Kim teaches wherein the method further comprises calibrating the model for the specific person [providing a guide for the user to measure the pulse wave information at different positions; measuring the pulse wave information at the different positions; obtaining reference blood pressure calibration values corresponding to the different positions; and training the estimator based on the pulse wave information and the reference blood pressure calibration values] (Para 0014) in order to a more accurate measurement of a blood pressure (Para 0006)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the blood pressure processing of Kovacs to include the calibration as taught by Kim in order to obtain a more accurate measurement of a blood pressure (Para 0006).
Claim 12: Kovacs teaches –
A method of estimating a blood pressure of a person [allowing the detection of blood pressure] (Para 0119) comprising:
collecting a signal of each of a plurality of load cells [load cells] (Para 0120 and Figure 1, Element 110) supporting the person [body of a user 105 standing on a scale 110] (Para 0059) to determine a ballistocardiogram [information in a ballistocardiogram (BCG) and an impedance plethysmography (IPG) measurements] (Para 0046);
collecting a photoplethysmogram signal [photoplethysmogram (PPG) measurement circuit 1640, which collects data from the LED/photosensor 1610, can also provide data to the processor circuit 1645] (Para 0126) from an appendage of the person (Figure 1, Element 105; in the Figure the user is shown with their legs or in other words appendage being measured);
Examiner’s Note: Figure 16 of Kovacs is disclosed as being implemented with the other embodiments of Kovacs (Para 0125) and thus citing Figure 1 & 16 is not an improper combining of different embodiments.
Kovacs fails to teach applying a model. However, Kim teaches –
applying a model [The processor 104 extracts at least one feature from the pulse wave information and applies the at least one feature to the estimator] (Para 0059) to the signals [ballistocardiogram (BCG)] (Para 0056; BCG is disclosed as pulse wave information that the estimator uses in Para 0059) of each of the load cells of Kovacs and the photoplethysmogram signal [photoplethysmogram (PPG) signal] (Para 0077 and Figure 2, Element 220) to generate a blood pressure waveform [as a waveform, such as, a pulse wave] (Para 0053), and estimating at least one blood pressure value of the person from the generated blood pressure waveform, to determine an estimate of the blood pressure of the person [calculating a final blood pressure of the user by applying the blood pressure calibration value to the basic blood pressure] (Claim 1),
wherein the model has been calibrated for the specific posture of the person as the model is being applied [trains the estimator based on the pulse wave information corresponding to the posture and the reference blood pressure calibration values obtained to correspond to the estimated posture] (Para 0064) as the model is being applied [determines a blood pressure calibration value corresponding to the pulse wave information of the user using a pre-trained estimator] (Para 0078) in order to obtain a more accurate measurement of a blood pressure (Para 0006)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the blood pressure processing of Kovacs to include the model application as taught by Kim in order to obtain a more accurate measurement of a blood pressure (Para 0006).
Claim 20/12: Kovacs fails to teach calibration. However, Kim teaches wherein the method further comprises calibrating the model for the specific person [providing a guide for the user to measure the pulse wave information at different positions; measuring the pulse wave information at the different positions; obtaining reference blood pressure calibration values corresponding to the different positions; and training the estimator based on the pulse wave information and the reference blood pressure calibration values] (Para 0014) in order to a more accurate measurement of a blood pressure (Para 0006)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the blood pressure processing of Kovacs to include the calibration as taught by Kim in order to obtain a more accurate measurement of a blood pressure (Para 0006).
Claim 22/12: Kovacs fails to teach the bed. However, Kim teaches wherein each of a plurality of load cells is embedded in an adjustable hospital bed that supports the person in a plurality of postures including at least supine and lateral postures (Figure 7) in order to a more accurate measurement of a blood pressure (Para 0006)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the load cells of Kovacs to include the bed as taught by Kim in order to obtain a more accurate measurement of a blood pressure (Para 0006).
Claim(s) 2 & 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kovacs et al. (U.S. Patent Application 2015/0359486 A1) and Kim et al. (U.S. Patent Application 2017/0172431 A1), and further in view of Kim et al. (U.S. Patent Application 2018/0289288 A1; referred to herein as Kim‘288)
Claim 2/1: Kovacs teaches –
estimating the blood pressure of the person by calculating the inverse of the pulse transit time [PTT is often inversely correlated with BP] (Para 0004).
Kovacs fails to teach the filtering. However, Kim teaches –
processing the ballistocardiogram and photoplethysmogram signal to filter [amplifies and filters the measured pulse wave information] (Para 0072; filtered before the transmission to the mobile device which performs the further processing steps) the signals before feature extraction [The processor 104 extracts at least one feature from the pulse wave information and applies the at least one feature to the estimator] (Para 0059) in order to obtain a more accurate measurement of a blood pressure (Para 0006)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the blood pressure processing of Kovacs to include the filtering as taught by Kim in order to obtain a more accurate measurement of a blood pressure (Para 0006).
Kovacs and Kim fail to teach processing between fiducial points. However, Kim‘288 teaches –
determining a timing delay [foot IPG timings are used to extract intervals between timing fiducials of the improved BCG (typically the I-wave) and the Foot IPG for the purpose of computing the PTT and the (PWV)] (Para 0112) between the fiducial points in the ballistocardiogram signal and the photoplethysmogram signal to determine a pulse transit time [signal processing steps to obtain fiducial references from the individual Leg IPG “beats,” which are subsequently used to obtain fiducials in the Foot IPG] (Para 011); and
estimating the blood pressure of the person by calculating the pulse transit time [timings extracted from the BCG for the purpose of computing the PTT] (Para 0115) [robust timing extraction of the blood pressure pulse in the foot which is achieved by means of a two-step processing] (Para 0100) in order to enable a low-cost screening of vascular disease (Para 0032)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the blood pressure processing of Kovacs and Kim to include the filtering and processing as taught by Kim‘288 in order to enable a low-cost screening of vascular disease (Para 0032).
Claim 13/12: Kovacs teaches wherein the method further includes:
estimating the blood pressure of the person by calculating the inverse of the pulse transit time [PTT is often inversely correlated with BP] (Para 0004).
Kovacs fails to teach the filtering. However, Kim teaches –
processing the ballistocardiogram signal and the photoplethysmogram signal to filter [amplifies and filters the measured pulse wave information] (Para 0072; filtered before the transmission to the mobile device which performs the further processing steps) the signals before feature extraction [The processor 104 extracts at least one feature from the pulse wave information and applies the at least one feature to the estimator] (Para 0059) in order to obtain a more accurate measurement of a blood pressure (Para 0006)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the blood pressure processing of Kovacs to include the filtering as taught by Kim in order to obtain a more accurate measurement of a blood pressure (Para 0006).
Kovacs and Kim fail to teach processing between fiducial points. However, Kim‘288 teaches –
determining a timing delay [foot IPG timings are used to extract intervals between timing fiducials of the improved BCG (typically the I-wave) and the Foot IPG for the purpose of computing the PTT and the (PWV)] (Para 0112) between the fiducial points in the ballistocardiogram signal and the photoplethysmogram signal to determine a pulse transit time [signal processing steps to obtain fiducial references from the individual Leg IPG “beats,” which are subsequently used to obtain fiducials in the Foot IPG] (Para 011); and
estimating the blood pressure of the person by calculating the pulse transit time [timings extracted from the BCG for the purpose of computing the PTT] (Para 0115) [robust timing extraction of the blood pressure pulse in the foot which is achieved by means of a two-step processing] (Para 0100) in order to enable a low-cost screening of vascular disease (Para 0032)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the blood pressure processing of Kovacs and Kim to include the filtering and processing as taught by Kim‘288 in order to enable a low-cost screening of vascular disease (Para 0032).
Claim(s) 3-7 & 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kovacs et al. (U.S. Patent Application 2015/0359486 A1) and Kim et al. (U.S. Patent Application 2017/0172431 A1) and further in view of Athaya et al. (Athaya, T.; Choi, S. An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach. Sensors 2021, 21, 1867; enclosed prior)
Claim 3/1: Kovacs teaches a learning program [a learning program] (Para 0074) and Kim teaches to infer a person’s blood pressure (Claim 1). However, Kovacs and Kim fail to teach a deep learning model. However, Athaya teaches wherein the model includes: applying a deep learning model [a U-net deep learning architecture] (Abstract) in order to the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kimto include the deep learning as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Claim 4/3/1: Kovacs and Kim fail to teach wherein the deep learning model includes a contractive path and an expansive path. However, Athaya teaches wherein the deep learning model [Deep Learning Architecture] (3.3 Title on Page 7) includes a contractive path and an expansive path [This architecture consists of three paths: contracting, bottleneck, and expansive path] (Page 7, Section 3.3 Deep Learning Architecture) in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kim to include the contractive and expansive paths as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Claim 5/4/3/1: Kovacs and Kim fail to teach wherein the contractive path reduces the dimension of signal by half while doubling the number of channels for each layer. However, Athaya teaches wherein the contractive path reduces the dimension of signal by half [the number on the left side of each block denotes the input vector size of that block] (Page 8, Section 3.3 Deep Learning Architecture and Figure 8) while doubling the number of channels for each layer [The number of feature channels after each block doubles in the contracting path so that the architecture can learn the complicated structures effectively. The number on the upper corner side of a block denotes the number of feature vectors or channels] (Page 8, Section 3.3 Deep Learning Architecture and Figure 8) in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Examiner’s Note: Figure 8 discloses the input size of the signal being 256 on the very top left-hand side and reducing by half 128, 64, 32 & 16. Figure 8 discloses the number of channels being 64 on the top left-hand side over a bit from the signal input number and doubling as shown by the numbers of 64, 128, 256, 512 & 1024.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kim to include the contractive path as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Claim 6/4/3/1: Kovacs and Kim fail to teach wherein the contractive path includes cascaded layers of convolution, batch normalization, and non-linear activation. However, Athaya teaches wherein the contractive path includes cascaded layers of convolution (See Figure 8) in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kim to include the contractive and expansive paths as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Claim 7/6/4/3/1: Kovacs and Kim fail to teach wherein the expansive path includes de-convolutions. However, Athaya teaches wherein the expansive path includes de-convolutions (Figure 8) in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Examiner’s Note: While Athaya does not use the term de-convolution, it is understood that convolution occurs on the left contracting side of Figure 8 of Athaya. On the expansion side of Figure 8 of Athaya, the expansion operation is de-convoluting the previously performed convolution. The expansion is the inverse of the contraction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kimto include the contractive and expansive paths as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Claim 14/12: Kovacs fails to teach a model. However, Kim teaches wherein the method further includes: a model [The processor 104 extracts at least one feature from the pulse wave information and applies the at least one feature to the estimator] (Para 0059) using the ballistocardiogram signal [ballistocardiogram (BCG)] (Para 0056; BCG is disclosed as pulse wave information that the estimator uses in Para 0059) and the photoplethysmogram signal [photoplethysmogram (PPG) signal] (Para 0077 and Figure 2, Element 220) and to generate the blood pressure waveform [as a waveform, such as, a pulse wave] (Para 0053) from which the blood pressure value is derived [calculating a final blood pressure of the user by applying the blood pressure calibration value to the basic blood pressure] (Claim 1) in order to obtain a more accurate measurement of a blood pressure (Para 0006)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the blood pressure processing of Kovacs to include the model application as taught by Kim in order to obtain a more accurate measurement of a blood pressure (Para 0006).
Kovacs and Kim fail to teach U-Net architecture. However, Athaya teaches applying a deep learning model comprising a U-Net architecture [a U-net deep learning architecture] (Abstract) in order to the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kim to include the U-Net architecture as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Claim 15/14/12: Kovacs teaches ballistocardiogram signal [information in a ballistocardiogram (BCG) and an impedance plethysmography (IPG) measurements] (Para 0046) and the photoplethysmogram signal [photoplethysmogram (PPG) measurement circuit 1640] (Para 0126). Kovacs fails to teach the model. However, Kim teaches applying a model to the ballistocardiogram signal and the photoplethysmogram signal (See Rejected Claim 12). Kovacs and Kim fail to teach U-Net architecture. However, Athaya teaches wherein the U-Net architecture of the deep learning model [Deep Learning Architecture] (3.3 Title on Page 7) includes a contractive path and an expansive path [This architecture consists of three paths: contracting, bottleneck, and expansive path] (Page 7, Section 3.3 Deep Learning Architecture), wherein the contractive path receives as input the ABP signal (obvious to modify to the BCG signal of Kovacs and Kim) and the photoplethysmogram signal stacked along a channel dimension [input dimension] (Page 7, Section 3.3 Deep Learning Architecture and Figure 8) in order to the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kim to include the contractive as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Claim 16/15/14/12: Kovacs and Kim fail to teach wherein the contractive path reduces the dimension of signal by half while doubling the number of channels for each layer. However, Athaya teaches wherein the contractive path reduces the dimension of signal by half [the number on the left side of each block denotes the input vector size of that block] (Page 8, Section 3.3 Deep Learning Architecture and Figure 8) while doubling the number of channels for each layer [The number of feature channels after each block doubles in the contracting path so that the architecture can learn the complicated structures effectively. The number on the upper corner side of a block denotes the number of feature vectors or channels] (Page 8, Section 3.3 Deep Learning Architecture and Figure 8) in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Examiner’s Note: Figure 8 discloses the input size of the signal being 256 on the very top left-hand side and reducing by half 128, 64, 32 & 16. Figure 8 discloses the number of channels being 64 on the top left-hand side over a bit from the signal input number and doubling as shown by the numbers of 64, 128, 256, 512 & 1024.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kim to include the contractive path as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Claim 17/15/14/12: Kovacs and Kim fail to teach wherein the contractive path includes cascaded layers of convolution, batch normalization, and non-linear activation. However, Athaya teaches wherein the contractive path includes cascaded layers of convolution (See Figure 8) in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kim to include the contractive path as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Claim 18/17/15/14/12: Kovacs and Kim fail to teach wherein the expansive path includes de-convolutions. However, Athaya teaches wherein the expansive path includes de-convolutions (Figure 8) in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Examiner’s Note: While Athaya does not use the term de-convolution, it is understood that convolution occurs on the left contracting side of Figure 8 of Athaya. On the expansion side of Figure 8 of Athaya, the expansion operation is de-convoluting the previously performed convolution. The expansion is the inverse of the contraction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kim to include the expansive paths as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Claim 19/15/14/12: Kovacs and Kim fail to teach expansive and contractive paths. However, Athaya teaches wherein the expansive path includes skip connections that concatenate feature maps from corresponding layers in the contractive path [After each expansion block, the number of feature channels used by the convolutional layer is halved to maintain symmetry. Moreover, the output of the 2 × 1 convolution layer is concatenated with the feature channels from the corresponding contracting path] (Page 7, Section 3.3 Deep Learning Architecture and Figure 8) in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Kovacs and Kim to include the expansive and contractive paths as taught by Athaya in order to have the advantage of a non-invasive, continuous, and rapid approach (Page 3, Section Introduction).
Allowable Subject Matter
Claim(s) 9 & 21 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments, see Page 6, filed 08/05/2026, with respect to the Claim Objection have been fully considered and are persuasive. The Objection of the Claims has been withdrawn.
Applicant’s arguments, see Page 6, filed 08/05/2026, with respect to the Rejections of the Claims under 35 USC § 112(b) have been fully considered and are persuasive. The Rejections under 35 USC § 112(b) of the Claims have been withdrawn.
Applicant's arguments filed 08/05/2026 have been fully considered but they are not persuasive. The Applicant submitted arguments that teaches a weighting scale platform with electrodes for impedance-based measurements, not a patient support apparatus configured to support a patient in multiple postures. The Applicant interprets postures as supine, lateral or sitting positions. The Examiner respectfully disagrees. The weighting scale platform of Kovacs does support a patient in that a patient is supported when standing on the scale. The claim term, support, has no differentiation between standing, sitting or laying on the support apparatus. The claim term, postures, given its broadest most reasonable interpretation is not limited to just sitting or lying postures. One of ordinary skill in the art would recognize a standing slouch posture or a standing straighten posture. Both postures would be within the limits of the claim as standing is a posture along with sitting and lying down. Additionally, Kim explains the various different postures: a posture of standing or sitting with an arm stretched upwards, a posture of standing at attention with hands lowered, a posture of standing or sitting with a hand held at a height of the heart, and a posture of lying down (Para 0085), which include different standing, sitting and lying down postures. The argument is unconvincing.
The Applicant submitted arguments that the claimed patient support apparatus avoids the standing-posture limitation entirely by supporting patients in recumbent positions. The Examiner respectfully disagrees and contends that the claim limitation as recited in Claim 1 cannot be interpreted in such a limiting manner. The Examiner acknowledges that Claim 22 can be interpreted as limiting to a hospital bed.
The Applicant submitted arguments with respect to Kim et al. (U.S. Patent Application 2018/0289288). This appears to be an error and Kim‘288 fails to teach what the Applicant is arguing that Kim‘288 discloses. The argument is unconvincing.
The Applicant submits arguments that the disclosure of Kim‘431 relates to compensating for variation in the posture of the subject”. Specifically, the Applicant contends that compensating the sensor output for postural variation, not calibrating a model for a specific posture. The Examiner respectfully disagrees. Para 0062 of Kim ‘431 states:
processor 104 measures the pulse wave information at the plurality of different positions and obtains reference blood pressure calibration values corresponding to each of the plurality of different positions
The processor obtains a reference blood pressure calibration values for each of the specific postures. The calibration values are then used to calibrate the model for the specific postures by training the model with the calibration values, which is disclosed in Para 0063:
The processor 104 trains the estimator based on the reference blood pressure calibration values and the pulse wave information measured at the plurality of different positions
The Applicant further argues that Kim‘431 teaches compensating based on hydrostatic pressure differences and not ballistocardiogram heartbeat morphology. The Examiner contends that may be the case, however, the claim states, “wherein the model has been calibrated for the specific posture of the person”. The claim limitation makes no distinction with regards to the hydrostatic pressure differences and the ballistocardiogram heartbeat morphology. The argument is unconvincing.
The Applicant on Page 9 states that Kovacs in view of Kim ‘431 is hindsight biased. The Examiner respectfully disagrees. MPEP § 2141(I) states, “When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability”. The Examiner contends that the prior art references are within the same field of endeavor and thus would be obvious to one of ordinary skill in the art. The argument is unconvincing.
The Applicant submitted arguments that Athaya teaches a U-Net architecture that uses PPG signals alone to generate an ABP waveform. The Applicant presented arguments that one of ordinary skill in the art would not have been motivated to modify Athaya single modal PPG-based system to use a multi-model input as taught by the disclose of the Applicant. The Examiner contends that such a line of thinking would make sense if Athaya was the primary reference. However, the primary references are Kovacs in view of Kim which already establishes the benefits of using both PPG and BCG. Thus modifying Kovacs in view of Kim and ignoring the benefits of both PPG and BCG would not be obvious. Thus the Examiner contends that Kovacs and Kim in view of Athaya teaches the claimed invention and would have been obvious to one of ordinary skill in the art. The arguments are unconvincing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Huijbregts et al. (U.S. Patent Application 2018/0070837 A1) – Huijbregts teaches an apparatus for measuring the blood pressure, BP, of a user, the apparatus comprising a volume-clamp BP monitoring device that comprises a first pressure device for applying pressure to a first part of the body of the user, a first photoplethysmogram, PPG, sensor for obtaining a first PPG signal from the first part of the body of the user, and a control unit that is configured to analyse the first PPG signal and to control the pressure of the first pressure device; wherein the control unit is configured to adjust the pressure of the first pressure device to maintain the first PPG signal at a constant level and to determine the BP of the user from the pressure of the first pressure device; and a second sensor, separate from the first PPG sensor, for measuring a physiological characteristic of the user in a second part of the body of the user, wherein the second part of the body is separate from the first part of the body; wherein the apparatus is configured to analyse the measured physiological characteristic to determine a measure of the blood perfusion in the second part of the body of the user, and to determine whether to perform a recalibration of the volume-clamp BP monitoring device on the basis of changes in the blood perfusion.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HELENE C BOR whose telephone number is (571)272-2947. The examiner can normally be reached Mon - Fri 10:30 - 6:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christopher Koharski can be reached at (571) 272-7230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Helene Bor/Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797
