DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Rejections under 35 USC 112
Applicant's arguments filed 07/02/2026 with regard to the rejection of the claimed invention under 35 USC 112(a) written description and enablement have been fully considered but they are not persuasive.
Applicant argues that the specification provides adequate support for the claimed invention with respect to the written description requirements set forth in 35 USC 112(a), citing paragraphs [0086] and [0090-0091] for support. Examiner respectfully disagrees and asserts that the specification fails to provide adequate disclosure of the actual workings of the claimed deep learning algorithm.
Applicant additionally argues that the amended claims which includes the new limitations of “parameters of cardiovascular function” to parameters “traditionally derived from one or more of imaging, blood tests, and pressure measurements,” fulfill the enablement requirements set forth in 35 USC 112(a). Examiner respectfully disagrees and argues that while the new limitations do narrow the array of parameters of cardiovascular function, parameters “traditionally derived from one or more of imaging, blood tests, and pressure measurements” still applies to a wide array of conditions/parameters, which may each require a different method of derivation.
With this in consideration, the rejection under 35 USC 112(a) enablement and written description is maintained.
Prior Art Rejections in view of Terry
Applicant’s arguments, see Remarks filed 07/02/2026, with respect to the rejection of claim 1, 12, 18, 19, 21, 26, 37, 43, 44, and 46 under 35 USC 102 in view of Terry have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made over Trapero in view of Tang et al (US 20200029874 A1).
Prior Art Rejections in view of Trapero
Applicant's arguments filed 07/02/2026 have been fully considered but they are not persuasive.
Applicant argues that Trapero does not teach all of the limitations of the claimed invention because the plurality of electrodes taught by Trapero are not positioned in an ECG configuration; and that Trapero does not each prediction the surrogates of parameters of cardiovascular function wherein the surrogates of cardiovascular function being traditionally derived from one of more of imaging, blood tests, and pressure measurements.
Examiner respectfully disagrees and argues that the plurality of electrodes taught by Trapero are necessarily positioned in an ECG configuration as they are configured to measure ECG signals. Examiner points to paragraph [0013] wherein Trapero states: ‘Using at least one pair of electrodes, an ECG sensor can measure the surface potential difference between the electrodes. In some cases, the pair of electrodes can be placed in a suitable location (e.g., on a chest of a user, such as above a heart of the user) to measure electrical signals of the heart.’
Examiner also argues that Trapero does teach the prediction of surrogates of parameters of cardiovascular function wherein the surrogates of cardiovascular function being traditionally derived from one of more of imaging, blood tests, and pressure measurements, wherein the surrogate of parameters of cardiovascular function is a blood glucose level, traditionally derived from a finger prick blood test (Trapero [0018]).
With this in consideration, the rejection of the claimed invention in view of Trapero is maintained and further detailed below.
Information Disclosure Statement
The Information Disclosure Statements (IDS) filed 09/22/2023 and 02/27/2025 have been considered by the Examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 12, 15, 18, 19, 21, 25-29, 37, 40, 43, 44, 46, and 50-52 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
See MPEP 2163 which states:
The written description requirement has several policy objectives. "[T]he ‘essential goal’ of the description of the invention requirement is to clearly convey the information that an applicant [inventor] has invented the subject matter which is claimed." In re Barker, 559 F.2d 588, 592 n.4, 194 USPQ 470, 473 n.4 (CCPA 1977). Another objective is to convey to the public what the applicant claims as the invention. See Regents of the Univ. of Cal. v. Eli Lilly, 119 F.3d 1559, 1566, 43 USPQ2d 1398, 1404 (Fed. Cir. 1997), cert. denied, 523 U.S. 1089 (1998). "The ‘written description’ requirement implements the principle that a patent must describe the technology that is sought to be patented; the requirement serves both to satisfy the inventor’s obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee [inventor] was in possession of the invention that is claimed." Capon v. Eshhar, 418 F.3d 1349, 1357, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). Further, the written description requirement promotes the progress of the useful arts by ensuring that patentees adequately describe their inventions in their patent specifications in exchange for the right to exclude others from practicing the invention for the duration of the patent’s term.
To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See, e.g., Moba, B.V. v. Diamond Automation, Inc., 325 F.3d 1306, 1319, 66 USPQ2d 1429, 1438 (Fed. Cir. 2003); Vas-Cath, Inc. v. Mahurkar, 935 F.2d at 1563, 19 USPQ2d at 1116.
An applicant shows that the inventor was in possession of the claimed invention by describing the claimed invention with all of its limitations using such descriptive means as words, structures, figures, diagrams, and formulas that fully set forth the claimed invention. Lockwood v. Amer. Airlines, Inc., 107 F.3d 1565, 1572, 41 USPQ2d 1961, 1966 (Fed. Cir. 1997). Possession may be shown in a variety of ways including description of an actual reduction to practice, or by showing that the invention was “ready for patenting” such as by the disclosure of drawings or structural chemical formulas that show that the invention was complete, or by describing distinguishing identifying characteristics sufficient to show that the inventor was in possession of the claimed invention. See, e.g., Pfaff v. Wells Elecs., Inc., 525 U.S. 55, 68, 119 S.Ct. 304, 312, 48 USPQ2d 1641, 1647 (1998); Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1406; Amgen, Inc. v. Chugai Pharm., 927 F.2d 1200, 1206, 18 USPQ2d 1016, 1021 (Fed. Cir. 1991). An invention described solely in terms of a method of making and/or its function may lack written descriptive support where there is no described or art-recognized correlation between the disclosed function and the structure(s) responsible for the function.
In the case of the instant application, the claims are directed to a system/method for assessing cardiovascular functions of a patient comprising:
acquiring ECG data and BIA data concomitantly through a plurality of electrodes positioned in an ECG configuration,
the ECG data comprising a time-series of ECG measurements and the BIA data comprising a time- series of BIA measurements superposed with the time-series of ECG measurements, each BIA measurement stemming from an activated set of the plurality of electrodes that is spatially mapped to a distribution of anatomical structures based on an atlas of conduction paths specific for the ECG configuration and activation of the plurality of electrodes; and
predicting surrogates of parameters of cardiovascular function from the ECG data and the BIA data using a trained deep learning algorithms, the parameters of cardiovascular function being traditionally derived from one or more of imaging, blood tests, and pressure measurements; and
outputting the surrogates of the parameters of cardiovascular function in a clinical format.
The specification fails to disclose the actual workings of the “deep learning algorithm” only disclosing the structure of the deep learning model as “an ensemble of deep neural networks for signal time series classification and regression, including bilateral long-short-term memory (LSTM) recurrent neural networks” in paragraph [0090]. This does not amount to clearly conveying the information that an application has invented the claimed subject matter nor does it convey to the public what the applicant claims as the invention. The description provided in the specification does not satisfy the written description requirement and therefore does not adequately support the applicant’s possession of the full scope of the claimed invention.
Claims 1-4, 12, 15, 18, 19, 21, 25-29, 37, 40, 43, 44, 46, and 50-52 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
See MPEP 2164 which states:
The enablement requirement refers to the requirement of 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph that the specification describe how to make and how to use the invention. The invention that one skilled in the art must be enabled to make and use is that defined by the claim(s) of the particular application or patent.
The purpose of the requirement that the specification describe the invention in such terms that one skilled in the art can make and use the claimed invention is to ensure that the invention is communicated to the interested public in a meaningful way. The information contained in the disclosure of an application must be sufficient to inform those skilled in the relevant art how to both make and use the claimed invention. However, to comply with 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, it is not necessary to "enable one of ordinary skill in the art to make and use a perfected, commercially viable embodiment absent a claim limitation to that effect." CFMT, Inc. v. Yieldup Int’l Corp., 349 F.3d 1333, 1338, 68 USPQ2d 1940, 1944 (Fed. Cir. 2003) (an invention directed to a general system to improve the cleaning process for semiconductor wafers was enabled by a disclosure showing improvements in the overall system).
Detailed procedures for making and using the invention may not be necessary if the description of the invention itself is sufficient to permit those skilled in the art to make and use the invention. A patent claim is invalid if it is not supported by an enabling disclosure.
In making a determination as to whether an application has met the requirements for enablement under 35 U.S.C. 112(a), the following factors enumerated in In re Wands, 858 F.2d 731, 737, 8 USPQ2d 1400, 1404 (Fed. Cir. 1988), referred to as the Wands factors to assess whether any necessary experimentation required by the specification is "reasonable" or is "undue."
These factors include, but are not limited to:
(A) The breadth of the claims;
(B) The nature of the invention;
(C) The state of the prior art;
(D) The level of one of ordinary skill;
(E) The level of predictability in the art;
(F) The amount of direction provided by the inventor;
(G) The existence of working examples; and
(H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure.
In the case of the instant application the Wands Factors for enablement are considered below:
(A) The breadth of the claims – The claims are broadly written as it is not clear from the disclosure how the surrogates of parameters of cardiovascular function are predicted using ECG data and BIA data as inputs into a trained deep learning algorithm because the structure of the deep learning algorithm is not disclosed. The claims are also broadly written in the sense that “surrogates of parameters of cardiovascular function” may apply to a wide array of parameters, resulting in a vast scope of the claims, wherein each parameter would require a unique and separate method of analysis.
(B) The nature of the invention – The nature of the invention is inherently abstract as the surrogates of parameters of cardiovascular function may apply to a wide array of conditions/parameters, therefore the scope of the claimed invention may belong to various fields which may have a great separation in subject matter. Non-limiting examples stated in the specification including predicting a level of brain natriuretic peptide, central venous pressure, pulmonary venous congestion, or angle brachial index.
(C) The state of the prior art – Due to the vast possible scope of the claimed invention, the state of the prior art would necessarily include a large span of information, therefore lacking a defined area about which one skilled in the art would have known. Assessing/monitoring the state of the patient, including parameters of cardiovascular function, using acquired physiological signals including ECG and bioimpedance is well known in the art.
Ghaffari et al (US 20170095670 A1) establishes that it is well known/established in the art to monitor/assess the state of a patient using one or more sensing devices which collect signals to indicate one or more conditions of a subject. It is well establishes to use the state of a patient based on measured physiological signal(s) to predict/inform parameters of the patient’s condition and treatment plan.
(D) The level of one of ordinary skill – As discussed above with regards to the nature of the invention and the state of the prior art, the vast breadth of possible applications for the claimed invention would likely not be conducive for a single person to have the necessary level of skill in all relevant art due to the possible divergent nature of the different applications for the claimed invention. One skilled in the art for one application of the claimed invention, for example, generating impedance tomography images, may not be skilled in the art of another application of the claimed invention, for example, predicting central venous pressure.
(E) The level of predictability in the art – There is a lack of predictability in the art because one skilled in the art cannot readily anticipate the effect of a change within the subject matter to which the claimed invention pertains. See MPEP 2164.03. Biological processes including cardiovascular function have a high level of unpredictability due to individual factors which may affect each situation and conceivably effect its outcome. For example, modeling/predicting cardiovascular function of a patient has a high level of unpredictability due to factors including comorbidities, health history, pre-existing conditions, genetics, lifestyle, and more.
The claimed invention involves many unpredictable factors including physiological activity which may require disclosure of at least one species to provide adequate basis to support the claim. In art areas having a high degree of uncertainly, such as those pertaining to physiological activity, it is not reasonably predictable from the disclosure of a single species what other species will work.
(F) The amount of direction provided by the inventor – In order to comply with the enablement requirement, applicant must include sufficient information that teaches exactly how to use or make the claimed invention. The present specification fails to provide an adequate amount of direction to teach exactly how to use or make the claimed invention, specifically in regards to using a deep learning algorithm (such as the one depicted in Figs. 4 and 5 of the instant application) to predict a wide array of cardiovascular parameters. Each of the predicted surrogates of cardiovascular parameters would likely constitute a different method of analysis of the inputted data and Applicant does not include an adequate amount of direction to reasonably enable one of ordinary skill in the art to use/make the claimed invention.
(G) The existence of working examples –
DeMazumder (US 20210272696 A1) discloses a working example for dynamic predictive monitoring in the Critical Health Assessment and Outcomes Study which uses deep learning algorithms to analyze physiological signals to assess parameters of cardiovascular function and make predictions regarding a future state of the patient. DeMazumder offers a working example (Fig. 3) which demonstrates the way machine learning algorithms and other data operations are performed in order to analyze physiological signals and train a model to make predictions of a future condition.
(H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure – The claimed invention as disclosed in the specification would require an undue amount of experimentation in order to make and use the claimed invention due to the vast scope of applications of the claimed invention. The specification lists a multitude of scenarios in which a deep learning algorithm may be used to predict a cardiovascular parameter without disclosing the specific analysis that would necessarily be conducted to arrive at said prediction. Therefore, one of ordinary skill in the art would be required to perform experimentation for each and every application of the deep learning algorithm in order to make and use the claimed invention; an undue amount of experimentation.
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 1-4, 12, 15, 18, 19, 21, 25-29, 37, 40, 43, 44, 46, and 50-52 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 26 are directed to the abstract idea of predicting surrogates of parameters of cardiovascular function from ECG and BIA data which can generally be considered to be mathematical concepts relating to organizing information and manipulating information through mathematical correlations.
Step 1
Claim 1 is directed to a machine and claim 26 is directed to a method.
Step 2A, Prong 1
Claims 1 and 26 recite the limitations of predicting surrogates of parameters of cardiovascular function from the ECG data and the BIA data using a trained deep learning algorithm, the parameters of cardiovascular function being traditionally derived from one or more of imaging, blood tests, and pressure measurements. These steps, given their broadest reasonable interpretation, can be understood as the determination of a mathematical relationship between variables comprising organizing information and manipulating information through mathematical correlations.
Step 2A, Prong 2
Claims 1 and 26 do not include any additional elements that integrate the abstract idea into practical application.
Claims 1 and 26 include the additional elements of a processor and a non-transitory computer-readable medium having stored thereon program instructions executable by the processor;
acquiring ECG data and BIA data concomitantly through a plurality of electrodes positioned in an ECG configuration, the ECG data comprising a time-series of ECG measurements and the BIA data comprising a time- series of BIA measurements superposed with the time-series of ECG measurements, each BIA measurement stemming from an activated set of the plurality of electrodes that is spatially mapped to a distribution of anatomical structures based on an atlas of conduction paths specific for the ECG configuration and activation of the plurality of electrodes; and
outputting the surrogates of the parameters of cardiovascular function in a clinical format.
The additional elements of a processor and a non-transitory computer-readable medium having stored thereon program instructions executable by the processor amount to generic computer implementation of the abstract idea. MPEP 2106.05(f) states: “Use of 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 integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253.”
Acquiring ECG data and BIA data concomitantly through a plurality of electrodes positioned in an ECG configuration, the ECG data comprising a time-series of ECG measurements and the BIA data comprising a time- series of BIA measurements superposed with the time-series of ECG measurements, each BIA measurement stemming from an activated set of the plurality of electrodes that is spatially mapped to a distribution of anatomical structures based on an atlas of conduction paths specific for the ECG configuration and activation of the plurality of electrodes amounts to insignificant extra-solution activity of data gathering in the form of performing clinical tests, in this case the measurement if ECG and BIA data, to obtain input for an equation, wherein the equation is the trained deep learning algorithm which predicts surrogates of cardiovascular parameters using the ECG and BIA data as inputs. See MPEP 2106.05(g), In re Grams, 888 F.2d 835.
Outputting the surrogates of the parameters of cardiovascular function in a clinical format amounts to insignificant extra-solution activity in the form of necessary data output. See MPEP 2106.05(g).
Step 2B
Claims 1 and 26 do not include any additional elements that amount to significantly more than the abstract idea.
Claims 1 and 26 include the additional elements of a processor and a non-transitory computer-readable medium having stored thereon program instructions executable by the processor;
acquiring ECG data and BIA data concomitantly through a plurality of electrodes positioned in an ECG configuration, the ECG data comprising a time-series of ECG measurements and the BIA data comprising a time- series of BIA measurements superposed with the time-series of ECG measurements, each BIA measurement stemming from an activated set of the plurality of electrodes that is spatially mapped to a distribution of anatomical structures based on an atlas of conduction paths specific for the ECG configuration and activation of the plurality of electrodes; and
outputting the surrogates of the parameters of cardiovascular function in a clinical format.
The additional elements listed above have been identified as extra-solution activity and/or generic computer implementation of the abstract idea as discussed in detail under section Step 2A, Prong 2.
Additionally, the additional elements of a processor and a non-transitory computer-readable medium having stored thereon program instructions executable by the processor; acquiring ECG and BIA data, and outputting the surrogates of the parameters of cardiovascular function in a clinical format can be held to be routine, conventional, and well-understood in the art; and they are recited with a high level of generality which does not amount to significantly more than the abstract idea itself.
Claims 2-4, 12, 15, 18, 19, 21, 25, 27, 28, 37, 40, 43, 44, 46, and 50-52 further define the abstract idea/mathematical concept of predicting surrogates of parameters of cardiovascular functions.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 18, 25-27, 43, and 50-52 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Trapero Martin et al (US 20210228134 A1), hereinafter referred to as Trapero.
Regarding claims 1 and 26, Trapero teaches a system/method for assessing cardiovascular functions of a patient, comprising:
a processor (524); and
a non-transitory computer-readable medium (526) having stored thereon program instructions executable by the processor (see [0090]; memory 526 can be used to store machine-readable instructions) for:
acquiring electrocardiogram (ECG) data (808) and bioelectrical impedance analysis (BIA) data (810) concomitantly (see [0017]; ECG and BI data are measured simultaneously) through a plurality of electrodes (340) positioned in an ECG configuration (see Fig. 8; flowchart depicting a process 800 for performing measurements using an ECG/BI measurement system, [0142]; the inner electrodes may be shared between measuring impedance voltage and measuring ECG signals);
the ECG data comprising a time-series of ECG measurements and the BIA data comprising a time-series of BIA measurements superposed with the time-series of ECG measurements (see [0096]; the ECG measurements and BI measurements can be collected simultaneously as a single signal that is then filtered to extract an ECG-related part and a BI-related part),
each BIA measurement stemming from an activated set of the plurality of electrodes that is spatially mapped to a distribution of anatomical structures based on an atlas of conduction paths specific for the ECG configuration and activation of the plurality of electrodes (see Fig. 8, [0137-0139]; in some cases simultaneous with receiving electrode data output at block 802, an injection current can be provided at block 804 in order to measure BI measurements, see also Fig. 9, [0161]; at block 1012 stimulating electrodes can be activated to provide an injection current into the skin of the user at block 1014 and simultaneously at block 1016, an electrode signal can be received from at least one pair of electrodes);
and predicting surrogates of parameters of cardiovascular function from the ECG data and the BIA data using a trained deep learning algorithms (see Fig. 6, [0115]; data captured by smart patch 600, e.g. ECG data and BI data, can be transmitted to healthcare server 666, [0120-0121]; healthcare server 666 can include an AI engine that provides predictive insights on patient conditions, trained models can enable high accuracy detection of health conditions/states with minimal errors, [0227]; “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, the apparatus and execution environment can realize various different computing model infrastructures such as deep learning and artificial intelligence computing infrastructures);
the parameters of cardiovascular function being traditionally derived from one or more of imaging, blood tests, and pressure measurements (see [0018]; the acquisition of ECG and BI data facilitates the accurate measure of blood glucose levels without the need for invasive techniques, such as finger pricks, blood glucose level can be estimated using ECG data alone, BI data alone, or both ECG and BI data) and
outputting the surrogates of parameters of cardiovascular function in a clinical format (see Fig. 14, [0190]; the ECG-based blood glucose levels may be combined with the BI-based blood glucose levels to output an adapted glucose level which is then displayed at step 14330).
Regarding claims 2 and 27, Trapero teaches the system of claim 1 and the method of claim 26, wherein predicting the surrogates of parameters of cardiovascular function comprises generating impedance plethysmography (IPG) data (see [0060]; BI data can be used to perform impedance plethysmography) representative of cardiovascular function over time from the ECG data and BIA data (see [0136]; patient’s aggregate health data can be centrally stored and then processed and analyzed on a continual basis by the healthcare server’s AI engine, Fig. 14, [0190]; the ECG-based blood glucose levels may be combined with the BI-based blood glucose levels to output an adapted glucose level which is then displayed at step 14330).
Regarding claims 18 and 43, Trapero teaches the system of claim 1 and the method of claim 26, wherein the program instructions are further executable by the processor (524) for applying the deep learning algorithms for detecting a likelihood of cardiovascular disease or cardiotoxicity (see [0020]; acquisition of ECG data and BI data from a single, wearable device can also facilitate analysis, estimation, and prediction about other physiological metrics or medical conditions of an individual, [0060-0061]; BI data can be used to determine various physiological metrics such as predicting cardiovascular-respiratory diseases, ECG data can be used to determine various physiological metrics such as determining the presence of any damage to the heart's muscle cells or conduction system) and an associated severity (see [0122]; healthcare server 666 can triage predictions from the AI engine using a scoring system which determines how urgently the patient may need healthcare provider attention).
Regarding claims 25 and 50, Trapero teaches the system of claim 1 and the method of claim 26, wherein the program instructions are further executable by the processor for predicting a readiness for hospital discharge or a need for hospital admission (see [0122]; healthcare server 666 can triage predictions from the AI engine using a scoring system which determines how urgently the patient may need healthcare provider attention, e.g. emergency condition or low priority situation; where it can be appreciated that an emergency condition would demonstrate a need for hospital admission).
Regarding claims 51 and 52, Trapero teaches the system of claim 1 and the method of claim 26, wherein predicting the surrogates of parameters of cardiovascular function comprises predicting a level or proxy of at least one of the parameters of cardiovascular function (see [0020]; acquisition of ECG data and BI data from a single, wearable device can also facilitate analysis, estimation, and prediction about other physiological metrics or medical conditions of an individual) to represent a likelihood and associated severity (see [0122]; healthcare server 666 can triage predictions from the AI engine using a scoring system which determines how urgently the patient may need healthcare provider attention) of a diagnosis of heart failure (see [0060]; BI data can be used to identify or predict cardiovascular-respirator diseases e.g. congestive heart failure), whereby severity informs a prognosis and treatment of the patient (see [0121]; generated predictions/insights generated by the AI engine can support clinicians in their decision making and allow them to deliver more timely and accurate treatment by providing access to predictive insights, relevant patient information, and pertinent medical knowledge related to the patient's condition).
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.
Claims 1, 2, 12, 18, 19, 21, 26, 27, 37, 43, 44, and 46 are rejected under 35 U.S.C. 103 as being unpatentable over Terry et al (US 20190307405 A1) in view of Tang et al (US 20200029874 A1).
Regarding claims 1 and 26, Terry teaches a system/method for assessing cardiovascular functions of a patient, comprising:
a processor (analytics engine 20/30); and
a non-transitory computer-readable medium having stored thereon program instructions executable by the processor (see Fig. 1; protocol for real-time clinical data aggregation) for:
acquiring ECG data (see [0078]; vital sign monitors 18 include electrocardiographs) and BIA data (see [0184]; impedance sensor including electrodes attached to the patient’s skin surface); and
predicting surrogates of parameters of cardiovascular function from the ECG data and the BIA data (see [0080]; analytics engine 20 processes the data received from sources 12 and performs risk assessments where the risk assessment may include determining the risk of a patient developing sepsis; where sepsis may be regarded as a surrogate of a parameter of cardiovascular health) using a trained deep learning algorithms (see [0136]; analytics platform 20 that implements artificial intelligence), the parameters of cardiovascular function being traditionally derived from one or more of imaging, blood tests, and pressure measurements (it can be appreciated that the presence of sepsis is usually determined via a combination of imaging and/or blood tests) and;
outputting the parameters of cardiovascular function in a clinical format (see [0095]; if one or more of the calculated risk scores increases the analytics engine 20 alerts a caregiver wherein the alert may be sent to a mobile device or displayed on a graphical display associated with the patient).
Terry is silent regarding wherein the ECG and BIA data are acquired concomitantly through a plurality of electrodes positioned in an ECG configuration, and
wherein the ECG data comprising a time-series of ECG measurements and the BIA data comprising a time-series of BIA measurements superposed with the time-series of ECG measurements, each BIA measurement stemming from an activated set of the plurality of electrodes that is spatially mapped to a distribution of anatomical structures based on an atlas of conduction paths specific for the ECG configuration and activation of the plurality of electrodes.
Tang teaches a device for assessing cardiovascular functions of a patient comprising a body-worn sensor patch which measures electrocardiogram, impedance plethysmogram (IPG), photoplethysmogram (PPG), and phonocardiogram (PCG) waveforms, and collectively processes these to determine the vital signs and hemodynamic parameters (Tang, Abstract),
wherein the ECG and impedance data are acquired concomitantly through a plurality of electrodes (41, 42, 47, 48) positioned in an ECG configuration (see Tang [0054]; patch sensor 10 contacts the patient’s chest and includes two pairs of leads for measuring ECG and IPG waveforms), and
wherein the ECG data comprising a time-series of ECG measurements and the impedance data comprising a time-series of impedance measurements superposed with the time-series of ECG measurements (see Tang [0069]; the patch sensor determines BP by collectively processing time-dependent ECG, IPG, PPG, and PCG waveforms) , each impedance measurement stemming from an activated set of the plurality of electrodes that is spatially mapped to a distribution of anatomical structures based on an atlas of conduction paths specific for the ECG configuration and activation of the plurality of electrodes (see Tang [0054]; patch sensor 10 includes two pairs of electrodes 41, 42, 47, 48 wherein one of the leads 41, 47 in each pair injects electrical current to measure IPG waveforms and the other leads 42, 48 in each pair sense bio-electrical signals that are then processed by electronics to determine ECG and IPG waveforms).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the means for acquiring ECG and BIA data as taught by Terry with the sensor patch taught by Tang. One of ordinary skill in the art would have been motivated to make this modification in order to improve the monitoring of patients in hospitals, clinics, and the home by using Tang’s patch sensor to non-invasively measure vital signals along with complex hemodynamic parameters without the use of multiple machines, cuffs, or wires (Tang [0005]).
Regarding claims 2 and 27, Terry in view of Tang teaches the system of claim 1 and the method of claim 26. Terry is silent regarding wherein predicting the surrogates of the parameters of cardiovascular function comprises generating impedance plethysmography (IPG) data representative of cardiovascular function over time from the ECG data and BIA data.
Tang teaches wherein predicting the surrogates of the parameters of cardiovascular function comprises generating impedance plethysmography (IPG) data representative of cardiovascular function over time from the ECG data and BIA data (see Tang [0055]; a voltage passes through a series of electrical circuits featuring analog filters and differential amplifiers to, respectively, filter out and amplify signal components related to the ECG waveform and the IPG waveform).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the means for acquiring ECG and BIA data as taught by Terry with the sensor patch taught by Tang. One of ordinary skill in the art would have been motivated to make this modification in order to improve the monitoring of patients in hospitals, clinics, and the home by using Tang’s patch sensor to non-invasively measure vital signals along with complex hemodynamic parameters without the use of multiple machines, cuffs, or wires (Tang [0005]).
Regarding claims 12 and 37, Terry in view of Tang teaches the system of claim 1 and the method of claim 26, wherein the program instructions are further executable by the processor for
modeling changes in the parameters of cardiovascular function (see Fig. 2, [0092-0093]; dynamic patient risk assessment includes monitoring whether the received patient data is consistent or if changes are detected) to support pharmacologic treatment decisions and monitor treatment effects (see Figs. 1-2), and
presenting, based on the modeling of the changes in the parameters of cardiovascular function (see [0096]; displays a message indicating that one or more risk factors has increased), one or more suggestions for administration and tailored dosage of one of diuretic drugs and intravenous fluids in accordance with an anticipated hemodynamics responsiveness (see Fig. 4A [0115]; if indicated that the patient has sepsis then a 3-hour bundle is indicated at block 96 which may include administration of broad spectrum antibiotics and/or crystalloid for hypotension or lactate).
Regarding claims 18 and 43, Terry in view of Tang teaches the system of claim 1 and the method of claim 26, wherein the program instructions are further executable by the processor for applying the deep learning algorithms (see [0136]; analytics platform 20 that implements artificial intelligence) for detecting a likelihood of cardiovascular disease or cardiotoxicity and an associated severity (see [0080]; analytics engine 20 processes the data received from sources 12 and performs risk assessments where the risk assessment may include determining the risk of a patient developing sepsis; where sepsis may be regarded as a parameter of cardiovascular health).
Regarding claims 19 and 44, Terry in view of Tang teaches the system of claim 18 and the method of claim 43, wherein the program instructions are further executable by the processor applying the deep learning algorithms for detecting a likelihood of frailty and an associated severity (see Fig. 4B, [0121]; box 126 advanced analytics to augment clinical decision making and early detection of patient deterioration; where patient deterioration is considered to be equivalent to frailty, [0154]; second risk score box 230 shows a modified early warning score, MEWS);
and predicting a likelihood of ancillary comorbid diagnoses having a correlation with the likelihood of frailty (see [0141]; analytics engine 20 calculates a risk score or performs a risk assessment of the patient in substantially real time based on one or more of the patient demographics data, the comorbidity data, and the physiological data, [0159]; the AVPU portion of the MEWS indicated whether a person is alert, responsive to voice, responsive to pain, or unresponsive).
Regarding claims 21 and 46, Terry in view of Tang teaches the system of claim 19 and the method of claim 46, wherein the program instructions are further executable by the processor for predicting a likelihood of future adverse health events (see [0110]; analytics for predicting patient outcomes) using at least one of the likelihood of cardiovascular disease and the likelihood of frailty (see [0080]; determining the risk of the patient developing sepsis, [0154]; second risk score box 230 shows a modified early warning score, MEWS), and for predicting a probability of death, decompensation, readmission, or other adverse health events (see [0159]; with regards to the calculated MEWS, a score of 5 or greater indicates a likelihood of death).
Claims 3 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Trapero Martin et al (US 20210228134 A1) in view of Holzhacker (US 20170143224 A1).
Regarding claims 3 and 28, Trapero teaches the system of claim 1 and the method of claim 26, wherein predicting the surrogates of parameters of cardiovascular function comprises generating impedance data representative of cardiovascular or pulmonary function (see Trapero Fig. 8; flowchart depicting a process 800 for performing measurements using an ECG/BI measurement system) over time from the ECG data and BIA data (see Trapero [0136]; patient’s aggregate health data can be centrally stored and then processed and analyzed on a continual basis by the healthcare server’s AI engine, Fig. 14, [0190]; the ECG-based blood glucose levels may be combined with the BI-based blood glucose levels to output an adapted glucose level which is then displayed at step 14330e).
Trapero is silent regarding wherein the prediction of surrogates of cardiovascular function comprises generating impedance tomography (ITG) data comprising static or dynamic images of a heart and a lung of the patient.
Holzhacker teaches a module for detecting cardiovascular and pulmonary signals using electrodes which detect one or more of a cardiac electrical signal and body impedance signal (Holzhacker Fig. 2, [0052-0057]),
wherein the module generates impedance tomography (ITG) data (see Holzhacker [0044]; body impedance signals are used for electrical impedance tomography) comprising static or dynamic images of a heart and a lung of the patient (see Holzhacker [0002]; induced voltages captured by the elctrodes are submitted to treatment by a software allowing the generation of images that represent ventilation and perfusion phenomena in the body of interest).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Trapero’s system/method for predicting parameters of cardiovascular function with Holzhacker’s impedance tomography image data. One of ordinary skill in the art would have been motivated to make this modification in order to visualize internal structures and functions of a patient’s cardiovascular system as input data to predict cardiovascular function. It can be appreciated that electrical impedance tomography as an imagine technique is well understood in the art as well as the advantages of synchronizing the collection of impedance data with electrocardiogram data (Holtzhacker [0002-0005]).
Claims 15 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Trapero Martin et al (US 20210228134 A1) in view of Grady (US 20120136266 A1).
Regarding claims 15 and 40, Trapero teaches the system of claim 1 and the method of claim 26. Trapero is silent regarding modeling changes in parameters of body composition to support pharmacologic treatment decisions and monitor treatment-effects; and
predicting, based on the modeling of the changes in the parameters of body composition,
a level or proxy of hydrophilic drug concentrations in a body of the patient and suggestions for tailored dosage of hydrophilic drugs,
the hydrophilic drugs comprising anticoagulant drugs and/or chemotherapy drugs.
Grady teaches a system/method for predicting cardiovascular function of a patient using an ECG analysis system which may produce an output indication whether a myocardial infarction is indicated, and a subsequent thrombolytic therapy predictive indicator (Grady [0003-0006]), comprising:
modeling changes in parameters of body composition to support pharmacologic treatment decisions and monitor treatment-effects (see Grady Figs. 7-8); and
predicting, based on the modeling of the changes in the parameters of body composition, a level or proxy of hydrophilic drug concentrations in a body of the patient and suggestions for tailored dosage of hydrophilic drugs, the hydrophilic drugs comprising anticoagulant drugs and/or chemotherapy drugs (see Grady [0052]; the ECG analysis sequence pictured in Fig. 8 may also run thrombolytic predictive instrument analysis to assist a physician in assessing patient risk and outcome when treated with thrombolytic therapy such as blood thinners).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Trapero’s system/method for predicting parameters of cardiovascular function with Grady’s thrombolytic predictive instrument analysis. One of ordinary skill in the art would have been motivated to make this modification in order to assess the risk and outcome for treating a patient with thrombolytic therapy including hydrophilic drugs such as anticoagulants (Grady [0052]).
Allowable Subject Matter
Claims 4 and 29 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.
Claims 4 and 29 are allowable over the closest prior art of Terry and Trapero Martin. Terry and Trapero Martin each teach the system of claim 1 and the method of claim 26. They are silent regarding predicting the surrogates of parameters of cardiovascular function from the ECG data and the BIA data using deep learning algorithms where the parameters include predicting a level or proxy of brain natriuretic peptide (BNP) or N-terminal brain natriuretic peptide (NT-proBNP) to represent a likelihood and associated severity of a diagnosis of heart failure, whereby severity informs a prognosis and treatment of the patient.
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.
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/A.J.S./Examiner, Art Unit 3792
/LYNSEY C Eiseman/Primary Examiner, Art Unit 3796
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