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 Amendment
The amendments filed 28 November 2025 have been entered. Claims 1 – 19, and 23 are pending.
Applicant’s amendments to the claims have not overcome each and every objection to the specification previously applied in the office action dated 27 June 2025.
Applicant’s arguments and amendments have overcome each and every rejection to the claims under 35 U.S.C. 112(a) previously applied in the office action dated 27 June 2025.
Applicant’s amendments have overcome each and every rejection to the claims under 35 U.S.C. 112(b) previously applied in the office action dated 27 June 2025.
Information Disclosure Statement
There is a listing of 32 references in the “REFERENCES” section of the specification on pages 25 – 27. The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." As these references have not been cited by the examiner on form PTO-892, they have not been considered.
Specification
Related to the aforementioned “REFERENCES” section in page 25 – 27 specification, reference 3 continues to includes a hyperlink, www.ncbi.nlm.nih.gov/books/NBK365. The portion is browser-executable.
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 19 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding Claim 1, the claim recites "an act or step, or series of acts or steps" for analyzing respiratory kinematics, and is therefore a process, which is a statutory category of invention (Step 1). The claims are then analyzed to determine whether they are directed to any judicial exception (Step 2A, Prong 1).
Regarding Claims 12 and 23, the claims each recite an apparatus, which is one of the statutory categories of invention (Step 1). The claim is then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong 1).
Each of Claims 1 – 19 and 23 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1 – 19 and 23 recites at least one step or instruction for observations, evaluations, judgments, and opinions, which are grouped as a mental process under the 2019 PEG. The claimed invention involves making observations, evaluations, judgments, and opinions, which are concepts performed in the human mind under the 2019 PEG.
Accordingly, each of Claims 1 – 19 and 23 recites an abstract idea.
Specifically, Independent Claims 1, 12, and 23 recite (underlined are observations, judgements, evaluations, or opinions, which are grouped as a mental process under the 2019 PEG) (additional elements bolded, see Step 2A, prong 2);
Claim 1
A method for analyzing respiratory kinematics, the method comprising:
collecting a plurality of kinematic signal data streams from each of a respective plurality of inertial sensor devices applied to a subject, wherein the plurality of kinematic signal data streams are synchronized with each other;
transforming the plurality of kinematic signal data streams into a respective plurality of analytic signals;
determining landmark points associated with each of the plurality of analytic signals to identify individual breathing intervals associated with each of the plurality of inertial sensor devices; and
analyzing two or more of the individual breathing intervals associated with at least two of the plurality of inertial sensor devices to establish a magnitude-synchronicity relationship that is utilized to characterize breathing motion patterns exhibited in the subject.
Claim 12
A system for analyzing respiratory kinematics, the system comprising:
a plurality of inertial sensor devices applied to a subject;
a monitoring platform device including at least one processor and a memory; and
an analysis of respiratory kinematics (ARK) engine stored in the memory and implemented by the at least one processor that is configured for collecting a plurality of kinematic signal data streams from each of the plurality of inertial sensor devices, wherein the plurality of kinematic signal data streams are synchronized with each other, transforming the plurality of kinematic signal data streams into a respective plurality of analytic signals, determining landmark points associated with each of the plurality of analytic signals to identify individual breathing intervals associated with each of the plurality of inertial sensor devices, and analyzing two or more of the individual breathing intervals associated with at least two of the plurality of inertial sensor devices to establish a magnitude-synchronicity relationship that is utilized to characterize breathing motion patterns exhibited in the subject.
Claim 23
A non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising:
collecting a plurality of kinematic signal data streams from each of a respective plurality of inertial sensor devices applied to a subject, wherein the plurality of kinematic signal data streams are synchronized with each other;
transforming the plurality of kinematic signal data streams into a respective plurality of analytic signals;
determining landmark points associated with each of the plurality of analytic signals to identify individual breathing intervals associated with each of the plurality of inertial sensor devices; and
analyzing two or more of the individual breathing intervals associated with at least two of the plurality of inertial sensor devices to establish a magnitude-synchronicity relationship that is utilized to characterize breathing motion patterns exhibited in the subject.
(observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
These underlined limitations describe a mathematical calculation and/or a mental process, as a skilled practitioner is capable of performing the recited limitations and making a mental assessment thereafter. Examiner notes that nothing from the claims suggests that the limitations cannot be practically performed by a human with the aid of a pen and paper, or by using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner additionally notes that nothing from the claims suggests and undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps. For example, in Independent Claims 1, 12, and 23, these limitations include:
Evaluation to transform the plurality of kinematic signal data streams into a respective plurality of analytic signals;
Observation and judgment of landmark points associated with each of the plurality of analytic signals to identify individual breathing intervals associated with each of the plurality of inertial sensor devices;
Observation and judgment of two or more of the individual breathing intervals associated with at least two of the plurality of inertial sensor devices to establish a magnitude-synchronicity relationship that is utilized to characterize breathing motion patterns exhibited in the subject.
all of which are grouped as mental processes under the 2019 PEG.
Similarly, Dependent Claims 2 – 11, and 13 – 19 include the following abstract limitations, in addition the aforementioned limitations in Independent Claims 1, 12, and 23 (underlined observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG):
wherein an instantaneous phase angle from each of the analytic signals is used to make observation and judgment of the landmark points.
wherein an instantaneous phase angle from each of the analytic signals is used to make observation and judgment of the landmark points.
wherein the magnitude-synchronicity relationship is provided as input to a statistical model that is configured to generate the probability.
wherein the magnitude-synchronicity relationship is evaluated as input to a statistical model that is configured to evaluate the probability.
wherein the magnitude-synchronicity relationship is defined by at least a comparison of magnitudes of motion exhibited by the analytic signals associated with two or more inertial sensor devices.
wherein the magnitude-synchronicity relationship is defined by at least an observation or judgment to compare magnitudes of motion exhibited by the analytic signals associated with two or more inertial sensor devices.
wherein the breathing motion patterns are utilized to determine a probability of a presence of a respiratory condition existing in the subject.
wherein the breathing motion patterns are utilized to evaluate a probability of a presence of a respiratory condition existing in the subject.
wherein the landmark points are utilized to derive a respiratory rate time series.
wherein the landmark points are utilized to evaluate a respiratory rate time series.
wherein identifying individual breathing intervals includes identifying breathing intervals on a breath by breath basis or on a continuous signal strip basis.
wherein identifying individual breathing intervals includes observation and judgment of breathing intervals on a breath by breath basis or on a continuous signal strip basis.
wherein the magnitude-synchronicity relationship is defined by a quantification of the degree of synchronicity and phase relationships exhibited by the analytic signals associated with two or more inertial sensor devices.
wherein the magnitude-synchronicity relationship is evaluated by an observation, judgment, and evaluation of the degree of synchronicity and phase relationships exhibited by the analytic signals associated with two or more inertial sensor devices.
all of which are grouped as mental processes under the 2019 PEG.
Accordingly, as indicated above, each of the above-identified claims recite an abstract idea.
Step 2A, Prong 2
The above-identified abstract ideas in each of Independent Claims 1, 12, and 23 (and their respective Dependent Claims 2 – 11 and 13 - 19) are not integrated into a practical application under 2019 PEG because the additional elements (identified above in Independent Claims 1, 12, and 23), either alone or in combination, generally link the use of the above-identified abstract ideas to a particular technological environment or field of use. More specifically, the additional elements of:
a plurality of inertial sensor devices
monitoring platform device
at least one processor
memory
analysis of respiratory kinematics (ARK) engine
non-transitory computer readable medium
processor
computer
Additional elements recited include a “plurality of inertial sensor devices”, “monitoring platform device”, “at least one processor” for “collecting” and to “execute”, “memory” for “storing”, analysis of respiratory kinematics (ARK) engine” to be “implemented”, “non-transitory computer readable medium” to “store”, and “computer” to “control” in the Independent Claims 1, 12, and 23 and their dependent claims. These components are recited at a high level of generality, , i.e., as a inertial sensors to generate signal measurements (the “plurality of kinematic signal data streams”), a processor to process data (the “collecting data streams”), a non-transitory computer readable medium for storing data (the “having stored thereon executable instructions”), and a computer for processing data (the “to perform steps comprising…collecting, transforming, determining, and analyzing”). These generic hardware component limitations for “plurality of inertial sensor devices”, “monitoring platform device”, “at least one processor”, “memory”, “analysis of respiratory kinematics (ARK) engine”, “non-transitory computer readable medium”, and “computer” are no more than mere instructions to apply the exception using generic computer and hardware components. As such, these additional elements do not impose any meaningful limits on practicing the abstract idea.
Further additional elements from Independent Claims 1, 12, and 23 include pre-solution activity limitations, such as:
collecting a plurality of kinematic signal data streams from each of a respective plurality of inertial sensor devices applied to a subject, wherein the plurality of kinematic signal data streams are synchronized with each other;
a plurality of inertial sensor devices applied to a subject;
a monitoring platform device including at least one processor and a memory; and
an analysis of respiratory kinematics (ARK) engine stored in the memory and implemented by the at least one processor that is configured for collecting a plurality of kinematic signal data streams from each of the plurality of inertial sensor devices, wherein the plurality of kinematic signal data streams are synchronized with each other,
A non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising:
In addition the aforementioned extra-solution activity limitations in Independent Claims 1 and 20, additional extra-solution activity limitations recited in Dependent Claims 2 -11 and 13 - 19 include:
wherein signal noise is removed from the plurality of kinematic signal data streams via one or more filters prior to transforming into the analytic signals.
wherein each of the plurality of inertial sensor devices is positioned in a separate location on the torso of the subject.
wherein each of the plurality of kinematic signal data streams is collected via wireless communications.
These pre-solution measurement elements are insignificant extra-solution activity, setting up the parameters of the system, and serve as data-gathering for the subsequent steps.
The “plurality of inertial sensor devices”, “monitoring platform device”, “at least one processor”, “memory”, “analysis of respiratory kinematics (ARK) engine”, “non-transitory computer readable medium”, and “computer” as recited in Independent Claims 1, 12, and 23 and their dependent claims are generically recited computer and hardware elements which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract ideas identified above in Independent Claims 1, 12, and 23 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer processor as claimed. In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in Independent Claims 1, 12, and 23 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, Independent Claims 1, 12, and 23 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B –
None of Claims 1 – 19 and 23 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: “plurality of inertial sensor devices”, “monitoring platform device”, “at least one processor”, “memory”, “analysis of respiratory kinematics (ARK) engine”, “non-transitory computer readable medium”, and “computer” as recited in Independent Claims 1, 12, and 23 and their dependent claims.
The additional elements of the “plurality of inertial sensor devices”, “monitoring platform device”, “at least one processor”, “memory”, “analysis of respiratory kinematics (ARK) engine”, “non-transitory computer readable medium”, and “computer” in Claims 1 – 19 and 23, as discussed with respect to Step 2A Prong Two, amounts to no more than mere instructions to apply the exception using generic computer and hardware components. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, the “plurality of inertial sensor devices” is described generically in [Page 10, Lines 6 - 8] with “Each of the inertial sensor devices 102 can also include a plurality of sensor elements, such as, accelerometers, gyroscopes (e.g., angular velocity in XYZ coordinate space), magnetometers, and the like.” and [Page 8, Line 16 - 18] “may be configured to record respiratory motion signals using very small but powerful motion sensor devices (e.g., microelectromechanical inertial measurement units (MEMS-IMUs)” The plurality of inertial sensor devices is presented as generic box element “sensor devices 102” in Figure 1.
Per Applicant’s specification, the “monitoring platform device” is described generically on [Page 9, Lines 24 - 28] “monitoring platform device 106 may comprise any computing device, such as a personal computer, a laptop computer, a smartphone device, a tablet device, and the like. In particular, monitoring platform device 106 is configured to receive signaling data that is captured by inertial sensor devices 102.” The monitoring platform device is presented as generic box element “monitoring platform device” 106 in Figure 1.
Per Applicant’s specification, the “at least one processor” is described generically on [Page 9, Lines 23 – 26] as one or more hardware processing units 110 of monitoring platform device 106.” where monitoring platform device 106 “may comprise any computing device, such as a personal computer…” The processor may also be according to [Page 10, Lines 16 – 17] “each sensor device 102 may have its own processor that can collect and store some amount of data in local memory” The “at least one processor” is presented as generic box element “processor” 110 in Figure 1.
Per Applicant’s specification, the “memory” is described generically at [Page 9, Lines 22 – 23] to store “a software algorithm that is stored in memory 107.“ Memory devices are also described at [Page 5, Lines 8 – 11] “Exemplary computer readable media suitable for implementing the subject matter described herein include non-transitory computer-readable media, such as disk memory devices, chip memory devices…” The memory is presented as generic box element “memory” 107 in Figure 1.
Per Applicant’s specification, the “analysis of respiratory kinematics (ARK) engine” is described generically as an application on [Page 11, Lines 9 – 10] “a host application (e.g., ARK engine 108) running on a computer device”. The application is presented as a generic box element “ARK Engine” 108 in Figure 1.
Per Applicant’s specification, the “non-transitory computer readable medium” is described generically in [Page 5, Lines 9 – 15] “Exemplary computer readable media suitable for implementing the subject matter described herein include non-transitory computer-readable media, such as disk memory devices, chip memory devices, programmable logic devices, and application specific integrated circuits. In addition, a computer readable medium that implements the subject matter described herein may be located on a single device or computing platform or may be distributed across multiple devices or computing platforms.” The “non-transitory computer readable medium” is not specifically shown in a figure.
Per Applicant’s specification, the “computer” is described on [Page 9, Lines 25 – 27] with “monitoring platform device 106 may comprise any computing device, such as a personal computer, a laptop computer, a smartphone device, a tablet device, and the like.” The computer is not specifically shown as a unique item in a figure.
Accordingly, in light of Applicant’s specification, the claimed terms “plurality of inertial sensor devices”, “monitoring platform device”, “at least one processor”, “memory”, “analysis of respiratory kinematics (ARK) engine”, “non-transitory computer readable medium”, and “computer” are reasonably construed as a generic computing and hardware devices. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the “plurality of inertial sensor devices”, “monitoring platform device”, “at least one processor”, “memory”, analysis of respiratory kinematics (ARK) engine”, “non-transitory computer readable medium”, and “computer”. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1 – 19 and 23 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the apparatus and method of Claims 1 – 19 and 23 are directed to applying an abstract idea as identified above on a general-purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1 – 19 and 23 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements for Step 2A Prong 2 in Independent Claims 1, 12, and 23 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1 – 19 and 23 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1 – 19 and 23 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1 – 19 and 23 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 19 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Auerbach, (WO 2017/183039 A1), hereinafter Auerbach, in view of Colas, et. al. (US Patent Application Publication US 2020/0100727 A1), hereinafter Colas.
Regarding Claim 1, Auerbach discloses A method for analyzing respiratory kinematics ([Abstract]; Fig 13, [Page 12, Paragraph 2]), the method comprising:
collecting a plurality of kinematic signal data streams (Fig 13, Box 62 “Receiving signals generated by inertial sensors”; ([Page 11, All of Paragraph 2] including “generated signals S “)(Examiner notes that “signals” suggests a plurality of kinematic data streams) from each of a respective plurality of inertial sensor devices ([Page 11, All of Paragraph 2] including “Inertial sensors 56 and 57”; Fig 13, Box 62 “Receiving signals generated by inertial sensors”; “Inertial sensors 56 and 57…output a signal S…receiver 33…receives the generated signals S) applied to a subject (Fig 12, “Inertial sensors 56 and 57” on the person’s torso’ )
transforming ([Page 26, Paragraph 3] “Fourier transform”; “One method to extract the dominant frequency from the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal, is through analysis of time series windows which include several breaths. For instance, this can be achieved by calculating the Fourier transform over a moving window of, e.g., -20 sec every 1 sec.”) the plurality of kinematic signal data streams ([Page 11, All of Paragraph 2] including “generated signals S“) into a respective plurality of analytic signals ([Page 11, Paragraph 2] “…receives the generated signals S and transmits them to a computing device 37, which processes the generated signals S to calculate a sensor acceleration.” as one analytic signal, and [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion” as another; Fig 13; [Page 26, Paragraph 3] “the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal”)(Examiner notes that the sensor signals are transformed, or calculated, into acceleration and magnitude of displacement signals, or analytic signals, after they have been “cleaned”)
determining landmark points ([Page 11] “rigid marker 51”; [Page 29, Paragraph 2] “3D movement between the rigid chest and abdominal markers”; [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion”)(Examiner notes that “chest” and “abdominal” are multiple landmarks with a particular “magnitude of displacement” inflection points (or “peaks”) in the analytic signals) associated with each of the plurality of analytic signals (Fig 12, “Inertial sensors 56 and 57” on the person’s torso, with “inertial sensor 56” placed higher on the chest and “inertial sensor 57” placed lower toward the navel” and the [Page 29, Paragraph 2] “3D movement between the rigid chest and abdominal markers”)(Examiner notes that these “markers” are consistent with the torso location placements of “inertial sensors 56 and 57”) to identify individual breathing intervals ([Page 12, Paragraph 2] “the respiratory rate”) associated with each of the plurality of inertial sensor devices (Fig 13, [Page 12, Paragraph 2] “After the computing device receives the signals generated by the inertial sensors i step 62, each signal is filtered in step 64 to determine the continuous rotational component of the breathing motion and subsequently the respiratory rate.”); and
analyzing two or more of the individual breathing intervals ([Page 27, Paragraph 2] “signal windows (say 10 seconds)”; [Page 34. Paragraph 3] “The data from a monitored subject can be added to the database by adding it as time segments of e.g., 10 minutes)(Examiner notes that data can be taken from the order of 10 seconds to 10 minutes, which would be more than two breaths, or breathing intervals.) associated with at least two of the plurality of inertial sensor devices ([Page 27, Paragraph 2] “…from all available sensors (inertial and image sensors); [Page 11, Paragraph 1] “inertial or kinematic sensors 56 and 57”) to establish a magnitude-synchronicity relationship ([Page 6, c) iii)] “iii. compare current and maximum displacements. Iv. Calculate the tidal volume based on a calibration between said current and maximum displacements.) that is utilized to characterize breathing motion patterns exhibited in the subject ([Page 3, Item c) i)] “wherein the one or more computing devices is operable to: i. generate a first breathing pattern from the first signals,”; Fig 13; ([Page 6, c) iii)] “compare current and maximum displacements.” Where the analytic signals are [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion”; [Page 26, Paragraph 3] “the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal”) [Page 27, Paragraph 3] “Inhale time, Exhale time: For each marker displacement trace, the time evolved between a peak and the following trough can be used as a measure of the inhale or exhale time, depending on the marker location.” (Examiner notes that the data generated simultaneously by the in “inertial sensors” 56 and 57, is processed into acceleration data and displacement analytic data for determining the “peaks” for the breathing rate patterns and respiratory characteristics like tidal volume.)
Auerbach does not specifically disclose wherein the plurality of kinematic signal data streams are synchronized with each other. Auerbach broadly discloses that ([Page 40; Paragraph 7] – [Page 41, Paragraph 1] “The pose of a subject is deduced in real time as described herein.”; [End of Page 7 – Page 8, Paragraph 1] “The signal generating elements may include a first group of one or more kinematic signal generating elements selected from an accelerometer, a gyroscope, or a combination thereof,… one or more computing devices operable to calculate the magnitude of a maximum displacement and of a current displacement in conjunction with both the first and second group of signal generating elements.” Signal data is received from the inertial sensors and used to calculate respiratory characteristics in “real-time”, indicating that the signal data streams are synchronized. The calculations are made “in conjunction with”, or synchronized with, the sensors.
Colas teaches a sensor system, including accelerometers attached to a patient’s torso, for determining respiratory characteristics during sleep. Specifically for Claim 1, Colas teaches wherein the plurality of kinematic signal data streams are synchronized with each other ([Abstract] “A first accelerometer for a thoracic position, a second accelerometer synchronized with the first and for an abdominal position...”; “Synchronization” description from [0134] – [0136], including “The (raw or filtered) data coming from the second accelerometer is synchronized with the (raw or filtered) data coming from the first accelerometer and the data from the optional sensors.”)
Colas teaches a prior art respiratory kinematics test device upon which the claimed invention (synchronizing the kinematic signal data streams) can be seen as an “improvement.” (Auerbach only broadly discloses that the sensors are used in “real-time” for pose analysis and that calculations are made “in conjunction with” the groups of signal generating elements.)
Colas teaches a prior art comparable device (respiratory kinematics test device) with synchronized accelerometers attached to the abdomen and thoracic regions. Colas teaches a motivation to combine the disclosure of Auerbach with Colas’ sensor synchronization teaching at [0136] and [0137] with “…the position of the individual may be determined during sleep. This position may be synchronized with the determined characteristics (respiratory, cardiac, snoring)…” and “Using data coming from the accelerometers…it is possible to determine, by frequency band, a set of at least one characteristic representative of a sleep disorder normally taking place in a sleeping phase of the individual, together with the time, in other words the moment in time or the period of time, at or during which the characteristic was extracted.” A person of ordinary skill in the art before the effective filing date of the claimed invention would recognize that synchronizing the sensor data would yield simpler and more accurate analysis of the sensor data relative to the movement of the person at a given time, as measured on a multitude of sensors.
Thus, the manner of enhancing a particular device (respiratory kinematics test device) was made part of the ordinary capabilities of one skilled in the art based upon the teaching improvement in Colas. Accordingly, one of ordinary skill in the art would have been capable of applying this known “improvement” technique in the same manner to the prior art respiratory kinematics device of Auerbach, and the results would have been predictable to one of ordinary skill in the art. Namely, one skilled in the art would have readily recognized that the kinematics sensors’ and their data (Auerbach’s “inertial sensors 56 and 57” with “kinematic signal” and Colas’ “first accelerometer for a thoracic position” and “second accelerometer for a thoracic position” with “raw or filtered data”) could be synchronized to simplify consistent data analysis and time-link measurements from multiple sensors to physiological movements. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the “inertial sensors 56 and 57” reading “kinematic signal” disclosed in Auerbach and the synchronization of “first accelerometer for a thoracic position” and “second accelerometer for a thoracic position” reading “raw or filtered data taught by Colas, creating a single apparatus for measuring respiratory kinematics at a specific time with multiple sensors. The use of a known technique to improve similar devices (methods or products) in the same way is likely to be obvious.
Regarding Claim 12, Auerbach discloses A system for analyzing respiratory kinematics ([Abstract]; [Page 5, Paragraph 5] “a system for monitoring the respiratory activity of a subject”), the system ([Page 5, Paragraph 5] “a system for monitoring the respiratory activity of a subject”) comprising:
a plurality of inertial sensor devices applied to a subject (Fig 12, “Inertial sensors 56 and 57” on the person’s torso, with “inertial sensor 56” placed higher on the chest and “inertial sensor 57” placed lower toward the navel”).
a monitoring platform device including at least one processor and a memory ([Page 10, Paragraph 2] “An optional remote application e.g. at a central nurse station or on a mobile device that can be used to control the signal generating element in the subject’s vicinity and receive data from the signal generating element”)(Examiner notes that mobile devices (cell phones) have at least one processor and a memory, and the “remote application” is used to monitor the patient’s respiratory kinematics.); and
an analysis of respiratory kinematics (ARK) engine ([Abstract]; [Page 5, Paragraph 5] “a system for monitoring the respiratory activity of a subject” including [Page 3, all of c] Steps of determining respiratory characteristics) stored in the memory ([Page 9, Paragraph 6] “A computing device that is connected either wirelessly or wired to the receiver…part of all of the signal or image processing may be performed on a device such as a microprocessor or a computer board wired to the receiver.”) [Page 10, Paragraph 6 (bb)] “Data storage devices, which be located on the receiver, on a computing device, or at a remote location, for storing data…”)((Examiner notes that the “analysis of respiratory kinematics (ARK) engine is described within Applicant’s specification as an “application”. A computing device would routinely store “applications” in the memory in order to be able to perform “signal or image processing.”) and implemented by the at least one processor ([Page 9, All of Paragraph 6] including “part or all of the signal or image processing may be performed on a device such as a microprocessor”) that is configured for
collecting a plurality of kinematic signal data streams (Fig 13, Box 62 “Receiving signals generated by inertial sensors”; ([Page 11, All of Paragraph 2] including “generated signals S “)(Examiner notes that “signals” suggests a plurality of kinematic data streams) from each of the plurality of inertial sensor devices ([Page 11, All of Paragraph 2] including “Inertial sensors 56 and 57”; Fig 13, Box 62 “Receiving signals generated by inertial sensors”; “Inertial sensors 56 and 57…output a signal S…receiver 33…receives the generated signals S)
transforming ([Page 26, Paragraph 3] “Fourier transform”; “One method to extract the dominant frequency from the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal, is through analysis of time series windows which include several breaths. For instance, this can be achieved by calculating the Fourier transform over a moving window of, e.g., -20 sec every 1 sec.”) the plurality of kinematic signal data streams ([Page 11, All of Paragraph 2] including “generated signals S“) into a respective plurality of analytic signals ([Page 11, Paragraph 2] “…receives the generated signals S and transmits them to a computing device 37, which processes the generated signals S to calculate a sensor acceleration.” as one analytic signal, and [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion” as another; Fig 13; [Page 26, Paragraph 3] “the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal”)(Examiner notes that the sensor signals are transformed, or calculated, into acceleration and magnitude of displacement signals, or analytic signals, after they have been “cleaned”)
determining landmark points ([Page 11] “rigid marker 51”; [Page 29, Paragraph 2] “3D movement between the rigid chest and abdominal markers”; [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion”)(Examiner notes that “chest” and “abdominal” are multiple landmarks with a particular “magnitude of displacement” inflection points (or “peaks”) in the analytic signals) associated with each of the plurality of analytic signals (Fig 12, “Inertial sensors 56 and 57” on the person’s torso, with “inertial sensor 56” placed higher on the chest and “inertial sensor 57” placed lower toward the navel” and the [Page 29, Paragraph 2] “3D movement between the rigid chest and abdominal markers”)(Examiner notes that these “markers” are consistent with the torso location placements of “inertial sensors 56 and 57”) to identify individual breathing intervals ([Page 12, Paragraph 2] “the respiratory rate”) associated with each of the plurality of inertial sensor devices (Fig 13, [Page 12, Paragraph 2] “After the computing device receives the signals generated by the inertial sensors i step 62, each signal is filtered in step 64 to determine the continuous rotational component of the breathing motion and subsequently the respiratory rate.”); and
analyzing two or more of the individual breathing intervals ([Page 27, Paragraph 2] “signal windows (say 10 seconds)”; [Page 34. Paragraph 3] “The data from a monitored subject can be added to the database by adding it as time segments of e.g., 10 minutes)(Examiner notes that data can be taken from the order of 10 seconds to 10 minutes, which would be more than two breaths, or breathing intervals.) associated with at least two of the plurality of inertial sensor devices ([Page 27, Paragraph 2] “…from all available sensors (inertial and image sensors); [Page 11, Paragraph 1] “inertial or kinematic sensors 56 and 57”) to establish a magnitude-synchronicity relationship ([Page 6, c) iii)] “iii. compare current and maximum displacements. Iv. Calculate the tidal volume based on a calibration between said current and maximum displacements.) that is utilized to characterize breathing motion patterns exhibited in the subject ([Page 3, Item c) i)] “wherein the one or more computing devices is operable to: i. generate a first breathing pattern from the first signals,”; Fig 13; ([Page 6, c) iii)] “compare current and maximum displacements.” Where the analytic signals are [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion”; [Page 26, Paragraph 3] “the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal”) [Page 27, Paragraph 3] “Inhale time, Exhale time: For each marker displacement trace, the time evolved between a peak and the following trough can be used as a measure of the inhale or exhale time, depending on the marker location.” (Examiner notes that the data generated simultaneously by the in “inertial sensors” 56 and 57, is processed into acceleration data and displacement analytic data for determining the “peaks” for the breathing rate patterns and respiratory characteristics like tidal volume.)
Auerbach does not specifically disclose wherein the plurality of kinematic signal data streams are synchronized with each other. Auerbach broadly discloses that ([Page 40; Paragraph 7] – [Page 41, Paragraph 1] “The pose of a subject is deduced in real time as described herein.”; [End of Page 7 – Page 8, Paragraph 1] “The signal generating elements may include a first group of one or more kinematic signal generating elements selected from an accelerometer, a gyroscope, or a combination thereof,… one or more computing devices operable to calculate the magnitude of a maximum displacement and of a current displacement in conjunction with both the first and second group of signal generating elements.” Signal data is received from the inertial sensors and used to calculate respiratory characteristics in “real-time”, indicating that the signal data streams are synchronized. The calculations are made “in conjunction with”, or synchronized with, the sensors.
Colas teaches a sensor system, including accelerometers attached to a patient’s torso, for determining respiratory characteristics during sleep. Specifically for Claim 12, Colas teaches wherein the plurality of kinematic signal data streams are synchronized with each other ([Abstract] “A first accelerometer for a thoracic position, a second accelerometer synchronized with the first and for an abdominal position...”; “Synchronization” description from [0134] – [0136], including “The (raw or filtered) data coming from the second accelerometer is synchronized with the (raw or filtered) data coming from the first accelerometer and the data from the optional sensors.”)
As explained in greater detail in the rejection for Claim 1, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the “inertial sensors 56 and 57” reading “kinematic signal” disclosed in Auerbach and the synchronization of “first accelerometer for a thoracic position” and “second accelerometer for a thoracic position” reading “raw or filtered data taught by Colas, creating a single apparatus for measuring respiratory kinematics at a specific time with multiple sensors. The use of a known technique to improve similar devices (methods or products) in the same way is likely to be obvious.
Regarding Claim 23, Auerbach discloses A non-transitory computer readable medium ([Page 11, Paragraph 2] “computing device 37”) having stored thereon executable instructions that when executed by a processor of a computer ([Page 11, Paragraph 2] “computing device 37, which processes the generated signals S….”; [Abstract] describes functions)(Examiner notes that a computing device would have a processor to “process” and storage upon which instructions are stored) control the computer to perform steps ([Page 11, Paragraph 2] describes steps, including “a computing device 37, which processes…to calculate…”) comprising:
collecting a plurality of kinematic signal data streams (Fig 13, Box 62 “Receiving signals generated by inertial sensors”; ([Page 11, All of Paragraph 2] including “generated signals S “)(Examiner notes that “signals” suggests a plurality of kinematic data streams) from each of a respective plurality of inertial sensor devices ([Page 11, All of Paragraph 2] including “Inertial sensors 56 and 57”; Fig 13, Box 62 “Receiving signals generated by inertial sensors”; “Inertial sensors 56 and 57…output a signal S…receiver 33…receives the generated signals S) applied to a subject (Fig 12, “Inertial sensors 56 and 57” on the person’s torso’ )
transforming ([Page 26, Paragraph 3] “Fourier transform”; “One method to extract the dominant frequency from the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal, is through analysis of time series windows which include several breaths. For instance, this can be achieved by calculating the Fourier transform over a moving window of, e.g., -20 sec every 1 sec.”) the plurality of kinematic signal data streams ([Page 11, All of Paragraph 2] including “generated signals S“) into a respective plurality of analytic signals ([Page 11, Paragraph 2] “…receives the generated signals S and transmits them to a computing device 37, which processes the generated signals S to calculate a sensor acceleration.” as one analytic signal, and [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion” as another; Fig 13; [Page 26, Paragraph 3] “the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal”)(Examiner notes that the sensor signals are transformed, or calculated, into acceleration and magnitude of displacement signals, or analytic signals, after they have been “cleaned”)
determining landmark points ([Page 11] “rigid marker 51”; [Page 29, Paragraph 2] “3D movement between the rigid chest and abdominal markers”; [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion”)(Examiner notes that “chest” and “abdominal” are multiple landmarks with a particular “magnitude of displacement” inflection points (or “peaks”) in the analytic signals) associated with each of the plurality of analytic signals (Fig 12, “Inertial sensors 56 and 57” on the person’s torso, with “inertial sensor 56” placed higher on the chest and “inertial sensor 57” placed lower toward the navel” and the [Page 29, Paragraph 2] “3D movement between the rigid chest and abdominal markers”)(Examiner notes that these “markers” are consistent with the torso location placements of “inertial sensors 56 and 57”) to identify individual breathing intervals ([Page 12, Paragraph 2] “the respiratory rate”) associated with each of the plurality of inertial sensor devices (Fig 13, [Page 12, Paragraph 2] “After the computing device receives the signals generated by the inertial sensors i step 62, each signal is filtered in step 64 to determine the continuous rotational component of the breathing motion and subsequently the respiratory rate.”); and
analyzing two or more of the individual breathing intervals ([Page 27, Paragraph 2] “signal windows (say 10 seconds)”; [Page 34. Paragraph 3] “The data from a monitored subject can be added to the database by adding it as time segments of e.g., 10 minutes)(Examiner notes that data can be taken from the order of 10 seconds to 10 minutes, which would be more than two breaths, or breathing intervals.) associated with at least two of the plurality of inertial sensor devices ([Page 27, Paragraph 2] “…from all available sensors (inertial and image sensors); [Page 11, Paragraph 1] “inertial or kinematic sensors 56 and 57”) to establish a magnitude-synchronicity relationship ([Page 6, c) iii)] “iii. compare current and maximum displacements. Iv. Calculate the tidal volume based on a calibration between said current and maximum displacements.) that is utilized to characterize breathing motion patterns exhibited in the subject ([Page 3, Item c) i)] “wherein the one or more computing devices is operable to: i. generate a first breathing pattern from the first signals,”; Fig 13; ([Page 6, c) iii)] “compare current and maximum displacements.” Where the analytic signals are [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion”; [Page 26, Paragraph 3] “the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal”) [Page 27, Paragraph 3] “Inhale time, Exhale time: For each marker displacement trace, the time evolved between a peak and the following trough can be used as a measure of the inhale or exhale time, depending on the marker location.” (Examiner notes that the data generated simultaneously by the in “inertial sensors” 56 and 57, is processed into acceleration data and displacement analytic data for determining the “peaks” for the breathing rate patterns and respiratory characteristics like tidal volume.)
Auerbach does not specifically disclose wherein the plurality of kinematic signal data streams are synchronized with each other. Auerbach broadly discloses that ([Page 40; Paragraph 7] – [Page 41, Paragraph 1] “The pose of a subject is deduced in real time as described herein.”; [End of Page 7 – Page 8, Paragraph 1] “The signal generating elements may include a first group of one or more kinematic signal generating elements selected from an accelerometer, a gyroscope, or a combination thereof,… one or more computing devices operable to calculate the magnitude of a maximum displacement and of a current displacement in conjunction with both the first and second group of signal generating elements.” Signal data is received from the inertial sensors and used to calculate respiratory characteristics in “real-time”, indicating that the signal data streams are synchronized. The calculations are made “in conjunction with”, or synchronized with, the sensors.
Colas teaches a sensor system, including accelerometers attached to a patient’s torso, for determining respiratory characteristics during sleep. Specifically for Claim 23, Colas teaches wherein the plurality of kinematic signal data streams are synchronized with each other ([Abstract] “A first accelerometer for a thoracic position, a second accelerometer synchronized with the first and for an abdominal position...”; “Synchronization” description from [0134] – [0136], including “The (raw or filtered) data coming from the second accelerometer is synchronized with the (raw or filtered) data coming from the first accelerometer and the data from the optional sensors.”)
As explained in greater detail in the rejection for Claim 1, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the “inertial sensors 56 and 57” reading “kinematic signal” disclosed in Auerbach and the synchronization of “first accelerometer for a thoracic position” and “second accelerometer for a thoracic position” reading “raw or filtered data taught by Colas, creating a single apparatus for measuring respiratory kinematics at a specific time with multiple sensors. The use of a known technique to improve similar devices (methods or products) in the same way is likely to be obvious.
Regarding Claims 2 and 13, Auerbach discloses as described above, The system of claim 1 and The system of claim 12, respectively. For the remainder of Claims 2 and 13, Auerbach discloses wherein signal noise is removed from the plurality of kinematic signal data streams ([Page 26, Paragraph 3]) “from the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal”) via one or more filters prior to transforming into the plurality of analytic signals (Fig 13, Box 64; [Page 12, Paragraph 2] “After the computing device receives the signals generated by the inertial sensors in step 62, each signal is filtered in step 64.”) OR ([Page 26, Paragraph 3] “One method to extract the dominant frequency from the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal, is through analysis of time series windows which include several breaths. For instance, this can be achieved by calculating the Fourier transform over a moving window of, e.g., -20 sec every 1 sec.”)(Examiner notes that the filtering occurs “prior” to Step 66, “integrating signals to determine velocity and displacements over time for each cycle”, creating the analytic signals.)
Regarding Claims 3 and 14, Auerbach discloses as described above, The system of claim 1 and The system of claim 12, respectively. For the remainder of Claims 3 and 14, Auerbach discloses wherein each of the plurality of inertial sensor devices (Fig 12, “Inertial sensors 56 and 57) is positioned in a separate location on the torso of the subject (Fig 12, “Inertial sensors 56 and 57” on the person’s torso, with “inertial sensor 56” placed higher on the chest and “inertial sensor 57” placed lower toward the navel”).
Regarding Claims 4 and 15, Auerbach discloses as described above, The system of claim 1 and The system of claim 12, respectively. For the remainder of Claims 4 and 15, Auerbach discloses wherein an instantaneous phase angle ([Page 27, Paragraph 1] “For example, this can be achieved by taking the Fourier Transform of each segment separately and obtaining the phase difference between the relevant complex Fourier component of each of the two markers. The relevant component is the one that correspond to the extracted respiratory rate. Another method to find the phase difference between two markers is to average the time lag between their peaks and express it as a fraction of the cycle time; 50%, for example, corresponds to a 180° phase lag.”) from each of the plurality of analytic signals is used to determine the landmark points ([Page 27, Paragraph 1] “…phase difference between two markers is to average the time lag between their peaks and express it as a fraction of the cycle time.”; [Page 27, Paragraph 3] “Inhale time, Exhale time: For each marker displacement trace, the time evolved between a peak and the following trough can be used as a measure of the inhale or exhale time, depending on the marker location.”); (Examiner notes that the point in space where the landmark is located at a given time is found in the “peaks” in the calculated Fourier signal for each of the inertial sensors, one at the abdomen and one at the chest. Examiner further notes that the phase delay between the sensors is used to find positioning for the marker peaks)
Regarding Claims 5 and 16, Auerbach discloses as described above, The system of claim 1 and The system of claim 12, respectively. For the remainder of Claims 5 and 16, Auerbach discloses wherein the magnitude-synchronicity relationship is provided as input ([Page 33, “Signal Features” section] “average amplitudes of the respiratory signal peaks and troughs over 20 second intervals and their derivative over time can be used. The initial set of features can be reduced further in the training stage to reduce over-fitting of the classifier using standard methods.”)(Examiner notes that the “peaks” are used as an input to the “training stage”) to a statistical model ([Page 36] “..training stage” …”mathematical formulae”) that is configured to generate a probability ([Page 36, Paragraph 3] “The output of the training stage are mathematical formulae whose input are feature vectors and whose output is a class probability - the probability that the feature belongs to a specific class of the classifier.”)
Regarding Claims 6 and 17, Auerbach discloses as described above, The system of claim 1 and The system of claim 12, respectively. For the remainder of Claims 6 and 17, Auerbach discloses wherein each of the plurality of kinematic signal data streams is collected via wireless communications ([Page 11, Paragraph 2] “Output signal S is generally a wireless signal, but may also be transmitted by a wired connection.”, “generated signals S”; (Fig 13, Box 62 “Receiving signals generated by inertial sensors”).
Regarding Claims 7 and 18, Auerbach discloses as described above, The system of claim 1 and The system of claim 12, respectively. For the remainder of Claims 7 and 18, Auerbach discloses wherein the magnitude-synchronicity relationship is defined by at least a comparison of magnitudes of motion exhibited by the plurality of analytic signals (Fig 13; ([Page 6, c) iii)] “compare current and maximum displacements.” Where the analytic signals are [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion”; [Page 26, Paragraph 3] “the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal”) associated with the at least two of the plurality of inertial sensor devices ([Page 5, a)] “a) one or more signal generating elements being movement sensors”; ([Page 11, Paragraph 1] “inertial or kinematic sensors 56 and 57”).
Regarding Claims 8 and 19, Auerbach discloses as described above, The system of claim 1 and The system of claim 12, respectively. For the remainder of Claims 8 and 19, Auerbach discloses wherein the breathing motion patterns are utilized to determine a probability (Fig 13; [Page 33, “Signal Features” section] “average amplitudes of the respiratory signal peaks and troughs over 20 second intervals and their derivative over time can be used. The initial set of features can be reduced further in the training stage to reduce over-fitting of the classifier…)((Examiner notes that the “peaks” are part of breathing patterns) of a presence of a respiratory condition existing in the subject ([Page 27, Paragraph 2] “Periods of disordered breathing including apnea events can be found by building a classifier on signal windows (say 10 seconds) from all available sensors (inertial and image sensors), [Page 36, Paragraph 3] “The output of the training stage are mathematical formulae whose input are feature vectors and whose output is a class probability - the probability that the feature belongs to a specific class of the classifier.”)(Examiner notes that the “class” of the “classifier” pertains to a specific condition, such as “apnea.”)
Regarding Claim 9, Auerbach discloses as described above, The system of claim 1. For the remainder of Claim 9, Auerbach discloses wherein the landmark points are utilized to derive a respiratory rate time series ([Page 26, Paragraph 3] “One method to extract the dominant frequency from the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal, is through analysis of time series windows which include several breaths.”; Fig 13; Box 66, “Integrating signals to determine velocity and displacements over time for each cycle”)(Examiner notes that the landmark points are part of the processed signals from each of the inertial sensors at the abdomen and chest, where the peaks are part of the Fourier transform and time series calculation process.)
Regarding Claim 10, Auerbach discloses as described above, The system of claim 1. For the remainder of Claim 10, Auerbach discloses wherein identifying individual breathing intervals includes identifying breathing intervals on a breath by breath basis ([Page 12, Paragraph 2] “In order to reduce integration error, the integration is carried out between peaks and troughs of single breaths by enforcing regularization constraints such as zero velocity at peaks and troughs of the respiratory signal.”; Fig 13; Box 66, “Integrating signals to determine velocity and displacements over time for each cycle”)(Examiner notes that the “integration” part of the calculation can be done on the “breath-by-breath basis” as part of identifying characteristics about the breathing interval) or on a continuous signal strip basis ([Page 11, Paragraph 2] – [Page 12, Paragraph 1] “…calculating the Euler angles of marker 51 continuously and applying Kalman filtering to reduce noise. During breathing, the Euler angles were found to show a cyclic behaviour at the respiratory rate, thus providing an independent measurement of the respiratory rate.”)(Examiner notes that the breathing intervals determine a rate of breaths, and that is determined overall with calculations that are performed “continuously”).
Regarding Claim 11, Auerbach discloses as described above, The system of claim 1. For the remainder of Claim 11, Auerbach discloses wherein the magnitude-synchronicity relationship is defined by a quantification ([Page 6, iv) “tidal volume”)] of the degree of synchronicity ([Page 6, c) iii)] “compare current and maximum displacements. Iv. Calculate the tidal volume based on a calibration between said current and maximum displacements.)(Examiner notes that the degree of synchronicity between the peaks read at the abdominal inertial sensor and chest sensor affect the “tidal volume.”) and phase relationships ([Page 27; All of Paragraph 1] including “…the phase difference between abdominal and thoracic markers..”) exhibited by the analytic signals (Fig 13; ([Page 6, c) iii)] “compare current and maximum displacements.” Where the analytic signals are [end of Page 5] – [Page 6, Paragraph 1] “i. calculate, in response to the received generated signals, a magnitude of a maximum displacement in 3D space of the one or more signal generating elements during a cycle of the breathing motion”; [Page 26, Paragraph 3] “the marker's cleaned (filtered, cleaned of noise and after non-breathing motion removal) signal”) associated with the at least two of the plurality of inertial sensor devices ([Page 11, Paragraph 1] “inertial or kinematic sensors 56 and 57”).
Response to Arguments
Applicant's arguments filed 28 November 2025 have been fully considered but they are not persuasive.
In regard to the 35 U.S.C. 101 analysis:
Applicant argues at [Page 10, Paragraph 2] that the office has an overly narrow reading of the that the independent claims 1, 12, and 23 claim language as a whole, considering gerunds of “transforming”, “determining”, and “analyzing” without the overall content of each term. There is nothing positively recited in the independent claims that is more than a person having ordinary skill in the art experienced in analyzing respiratory kinematics would be able to perform. The overall phrase associated with each of the gerunds includes actions that are performed by researchers and students in college labs examining respiratory data. For “transforming the plurality of kinematic signal data streams into a respective plurality of analytic signals”. Given a print-out of graph of inertial sensor data, a person with ordinary skill in the art in the process of analyzing inertial respiratory data could “transform” these data into broad “analytic signals” by a filtering mathematical operation or by tracing a smoother approximation line with a pen. Then, the person with ordinary skill in the art could circle peaks in the respiratory sensor data, thus “determining landmark points associated with each of the plurality of analytic signals to identify individual breathing intervals associated with each of the plurality of inertial sensor devices.” Further, the broadness of “analyzing two or more of the individual breathing intervals…to establish a magnitude synchronicity relationship” includes a person having ordinary skill in the art aligning the time scales on the data to determine if the peaks on the sensor data line up. In a medical-environment example, a doctor could observe breathing rate (counting breaths with a watch), the amount and consistency of rise and fall of the chest (by touch); determine landmark points (how fast or consistent of a rate, how shallow of a chest rise and fall), and draw a conclusion over the course of two or more breaths in and out about the presence or a lack of synchronicity relationship (such as with shallow or labored breathing) to satisfy the abstract ideas themselves. The analysis steps are not specifically claimed to be anything outside the realm of human capabilities. The argument is not persuasive.
Applicant argues at [Page 10, Paragraph 1] and [Page 10, Bottom] that the independent claims 1, 12, and 23 are not directed to abstract ideas due to disclosure in Applicant’s specification that the analyzing respiratory kinematics (ARK) solves a problem of inexperienced caregivers missing red flags in respiratory monitoring or remote respiratory monitoring. As recited in the amended claims and using broadest reasonable interpretation, there is no particular limitation that indicates that the invention is accomplishing such a task relative to flagging red flags associated with respiratory monitoring. Further, there is no recitation of “remote monitoring” in the independent claims. As described above, a person having ordinary skill in the art experienced in analyzing respiratory kinematics would be able to perform the ideas as claimed, such as in a bedside assessment by an experienced clinician. Looking to MPEP 2111.01(II), it is improper to import claim limitations from the specification. The argument is not persuasive.
Applicant argues at [Page 10, Bottom] – [Page 11, 1st Full Paragraph] that the independent claims 1, 12, and 23 are more than performing basic functions of automating mental tasks, as there are claim-required transformations executed by the system as a whole. As recited and described above, the abstract ideas of independent claims 1, 12, and 23 are those that can be, and are performed by researchers using mathematical analysis and output marking, and by experienced clinicians in a bedside assessment of a respiratory patient. As recited, it is the manipulation and observation of respiratory data variables themselves that comprise the abstract idea for claims 1, 12, and 23. Using inertial sensors, it is within the realm of human capability to analytically observe the outputs of the inertial sensors, focus on the analytic points of interest in the kinematic signal data, determine landmark points in the data, and analyze synchronicity of the data (how much it lines up. The claims recite a series of limitations that encompass an abstract idea of manipulating variables obtained from electronic components used in a usual way, and that variable manipulation can be accomplished with the aid of time, equations, and paper. Examiner notes that nothing from the claims suggests that the limitations cannot be practically performed by a human with the aid of a pen and paper, or by using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. The argument is not persuasive.
Applicant argues at [Page 11, 2nd Full Paragraph] – [Page 11, 4th Full Paragraph] that the required factual determination, supported by evidence, are “well-understood, routine, and conventional” has not been made, since the well-known components are claimed to perform non-conventional operations. Applicant further notes the claimed hardware are “hardware components (processors, memory, and computer-readable media) that appear in virtually every software and computing patent.” The hardware recited in the independent claims 1, 12, and 23 is being used for well-understood, routine, and conventional actions performed by these components. The inertial sensors are measuring data in a standard way. The memory is being used to store an “analysis of respiratory kinematics (ARK) engine” (a program). The processor is collecting synchronized data streams (collecting data), transforming the collected data signals into “analytic data signals” (either processing data using programmed mathematical functions, or assigning data streams a label of “analytic data signal”),determining landmark points in the data (such as processing data using a programmed mathematical function to find maxima and/or minima), and analyzing two or more of the intervals (processing data using programmed mathematical functions) to establish a magnitude-synchronicity relationship (assign a variable name label). These functions of receiving, processing, labelling data are routine functions of the hardware elements of the processor, memory, and non-transitory computer readable medium. Examples of these components performing these functions appears in the respiratory monitoring prior art cited herein (Auerbach: [Page 9, “microprocessor for performing some signal processing of the sensors”), and in US 2007/0118054 A1 cited in Applicant’s IDS (Pinhas: [0177] “…a digital signal processor…breathing pattern analysis module 22…adapted to extract breathing patterns from the raw data generated by data acquisition module 20, and to perform processing and classification of the breathing patterns.”; [0186] “…low passed data…stored in memory”). The argument is not persuasive.
Applicant argues at [Page 12, 1st Paragraph] that the 35 U.S.C. 101 rejection of the instant independent claims 1, 12, and 23 should be withdrawn. Based on the 35 U.S.C. 101 analysis and discussion above, the argument is not persuasive.
Regarding the 35. U.S.C. 103 Rejections:
Applicant argues at [Page 12, Bottom Paragraph] – [Page 13, Paragraph 2] that Auerbach’s disclosure cannot be applied to the claimed subject matter because the claimed matter represents a specific technological improvement and distinct capability to “move from respiration rate to other breathing motion patterns”, and that Auerbach does not teach or suggest “other breathing motion patterns” than “respiration rate”. There is nothing positively recited in the independent claims 1, 12, and 23 about “other breathing motion patterns.” Looking to Auerbach (and the citations above), the disclosure concerns ([Abstract]) “monitoring the respiratory activity of a subject” using one or more movement sensors (Fig 12), and determining a respiratory pattern by dividing a respiratory cycle into a plurality of portions. As part of the disclose for the background of Auerbach (2017), it states “Continuous monitoring of respiration beyond the respirator[y] rate (such as breathing patterns, breathing depth, accurate minute ventilation and apnea) is achieved nowadays using contact and invasive sensors.” This indicates that other respiratory patterns that are more than just respiratory rate were known and achieved at the time of Auerbach’s disclosure using a plurality of sensors. Auerbach describes one determining patterns associated with “Apnea and Hypo-apnea events” by showing “the phase difference between abdominal and thoracic markers”, and that “periods of disordered breathing” can be “…found by building a classifier on signal windows.” (Auerbach [Page 26, Bottom] – [Page 27, 1st Full Paragraph]). The patterns disclosed in Auerbach are broadly “other breathing patterns” to a “normal” breathing pattern, including more than a numerical respiratory rate. The argument is not persuasive.
Applicant argues at [Page 13, Paragraph 3] – [Page 14, 1st Full Paragraph] that the combination of Auerbach and Colas are improper because Colas arises from a different area of medicine, is not related to diagnostics, and has no motivation to be combined with Auerbach. As cited and described in the 35 U.S.C. 103 rejection above in more detail, Colas is a prior art reference that uses a set of synchronized sensors placed on the chest and abdomen (Colas: [0021]) that are used to calculate an “apnea-hypopnea index” (AHI) [0045]. Apnea is a temporary cessation of breathing, and this change in the respiratory pattern is often discovered while someone sleeps. Apnea patterns of breathing are also determined in Auerbach (Auerbach [Page 26, Bottom] – [Page 27, 1st Full Paragraph]) and discussed in Applicant’s specification as an “abnormal breathing pattern” of “apnea (breath interval variability)” at [Page 7, 1st full paragraph]. Colas also teaches at (Colas: [0004]) that “the present disclosure…is notably applicable in the collection and analysis of data that may be used later in the establishment of a diagnosis relating to the quality of sleep”. Importantly, the combination of Auerbach and Colas is for the “collection of data” concept, particularly for the synchronizing data from chest and abdomen-worn inertial sensors. As described in greater detail above, Colas teaches a prior art comparable device (respiratory kinematics test device) with synchronized accelerometers attached to the abdomen and thoracic regions. Auerbach also teaches a device with inertial sensors on the thoracic and abdomen regions (Fig 12). One skilled in the art would have readily recognized that the kinematics sensors’ and their data (Auerbach’s “inertial sensors 56 and 57” with “kinematic signal” and Colas’ “first accelerometer for a thoracic position” and “second accelerometer for a thoracic position” with “raw or filtered data”) could be synchronized to simplify consistent data analysis and time-link measurements from multiple sensors to physiological movements. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the “inertial sensors 56 and 57” reading “kinematic signal” disclosed in Auerbach and the synchronization of “first accelerometer for a thoracic position” and “second accelerometer for a thoracic position” reading “raw or filtered data taught by Colas, creating a single apparatus for measuring respiratory kinematics at a specific time with multiple sensors. The argument is not persuasive.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MELISSA J MONTGOMERY whose telephone number is (571)272-2305. The examiner can normally be reached Monday - Friday 7:30 - 5:00 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexander Valvis can be reached at (571) 272 - 4233. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MELISSA JO MONTGOMERY/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
Prosecution Insights