DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 3 and 9-19 are objected to because of the following informalities:
Claim 3 line 3 should include a hyphen “-“ between “real time”;
Claims 9-11 recite the “system of claim 1…”, although the claims should recite the “method of claim 1…”, as claim 1 is a method claim;
Claim 12 line 11 should move the comma “,” after processor to read as “…convert, by the processor in real-time, the TAC…” to improve grammatical clarity;
Claims 13-19 recite the “system of claim 10…”, although the claims should recite the “method of claim 10…”, as claim 10 depends from claim 1;
Claim 15 line 2 should recite “converts” instead of “coverts”;
Claim 15 lines 2-3 include a comma “,” after “real-time”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 line 9 recites “…converting, using the processor, the TAC to BAC/BrAC”, which renders the claim indefinite. The inclusion of a forward slash “/” between BAC and BrAC creates multiple interpretations. One interpretation is the processor converts the TAC to BAC and/or BrAC. Another interpretation is the TAC is converted into a ratio of BAC/BrAC. Thus, the claim is indefinite as the scope of the claim cannot be determined. For purposes of compact prosecution, the examiner is interpreting “BAC/BrAC” as blood alcohol concentration (BAC) and/or breath alcohol concentration (BrAC). Claims 2-11 are rejected due to their dependence upon rejected claim 1.
Claim 12 line 11 recites “convert, by the processor, in real-time the TAC to BAC/BrAC”, which renders the claim indefinite. The inclusion of a forward slash “/” between BAC and BrAC creates multiple interpretations. One interpretation is the processor converts the TAC to BAC and/or BrAC. Another interpretation is the TAC is converted into a ratio of BAC/BrAC. Thus, the claim is indefinite as the scope of the claim cannot be determined. For purposes of compact prosecution, the examiner is interpreting “BAC/BrAC” as blood alcohol concentration (BAC) and/or breath alcohol concentration (BrAC). Claims 13-19 are rejected due to their dependence on rejected claim 12.
Claim 15 lines 2-3 recites “…wherein the processor coverts (interpreted as “converts”), in real-time the TAC to BAC/BrAC…”, which renders the claim indefinite. The inclusion of a forward slash “/” between BAC and BrAC creates multiple interpretations. One interpretation is the processor converts the TAC to BAC and/or BrAC. Another interpretation is the TAC is converted into a ratio of BAC/BrAC. Thus, the claim is indefinite as the scope of the claim cannot be determined. For purposes of compact prosecution, the examiner is interpreting “BAC/BrAC” as blood alcohol concentration (BAC) and/or breath alcohol concentration (BrAC).
Claim 20 line 7 recites “…the processor configured to convert TAC to BAC/BrAC…”, which renders the claim indefinite. The inclusion of a forward slash “/” between BAC and BrAC creates multiple interpretations. One interpretation is the processor converts the TAC to BAC and/or BrAC. Another interpretation is the TAC is converted into a ratio of BAC/BrAC. Thus, the claim is indefinite as the scope of the claim cannot be determined. For purposes of compact prosecution, the examiner is interpreting “BAC/BrAC” as blood alcohol concentration (BAC) and/or breath alcohol concentration (BrAC).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1 recites:
A method for converting transdermal alcohol concentration (TAC) to blood or breath alcohol concentration (BAC/BrAC), the method comprising:
measuring, using a biosensor, the TAC of a human;
receiving, by a processor, data corresponding to one or more drinking curves for a population of humans;
receiving, by the processor, data corresponding to at least one of (i) static characteristics of the human, (ii) physiological characteristics of the human, and (iii) current environmental conditions; and
converting, using the processor, the TAC to BAC/BrAC using the data from one or more drinking curves, and the at least one of (i) the static characteristics of the human, (ii) the physiological characteristics of the human, and (iii) the current environmental conditions.
Independent Claim 12 recites:
A system for converting transdermal alcohol concentration (TAC) to blood or breath alcohol concentration (BAC/BrAC), wherein the converting is in real-time with progressive forecasting and modeling techniques and recursive updating methods, the system comprising:
a biosensor for measuring the TAC of a human; and
a processor configured to:
receive data from one or more drinking curves from a population of humans;
receive data corresponding to at least one of (i) static characteristics of the human,(ii) physiological characteristics of the human, and (iii) the current environmental conditions; and
convert, by the processor, in real-time the TAC to BAC/BrAC using the data from one or more drinking curves and the at least one of (i) the static characteristics of the human, (ii) the physiological characteristics of the human, and (iii) the current environmental conditions.
Independent claim 20 recites:
A biosensor device for converting transdermal alcohol concentration (TAC) to blood or breath alcohol concentration (BAC/BrAC), the device comprising:
a wearable sensor contactable to a human skin to measure the TAC of the human;
a processor connected to the wearable sensor and connectable to a network, the processor configured to receive, via the network, data corresponding to one or more drinking curves for a population of humans;
the processor configured to convert TAC to BAC/BrAC using (i) the data from one or more drinking curves and (ii) the measured TAC.
Step 1:
The examiner finds claim 1 as being drawn to a method and claims 12 and 20 are drawn to machines.
Step 2A Prong 1:
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018).
The claimed steps of measuring, receiving, and converting recite mental processes and mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
The step of “measuring” the TAC of a human is a mental process capable of being performed in the human mind. For example, the human mind is capable of estimating values for length or height by using learned experiences and spatial awareness. The step of “receiving” data is a mental process capable of being performed in the human mind. For example, the human mind is capable of receiving a multitude of stimuli from various sources, such as nerve endings. The step of “converting” the TAC to BAC/BrAC is both a mental process and mathematical concept. For example, as a mental process, the human mind is capable of converting light wavelengths into the perception of color. As a mathematical process, converting data is changing the representation of a value (i.e., performing dimensional analysis).
The claimed steps of measuring, receiving, and converting can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
“[T]he ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” MPEP 2106.04(a)(2) III. The pending claims merely recite steps for estimation that include observations, evaluations, and judgments.
Examples of ineligible claims that recite mental processes include:
• a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind,
Electric Power Group, LLC v. Alstom, S.A.;
• claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind,
University of Utah Research Foundation v. Ambry Genetics Corp.
• a claim to collecting and comparing known information, which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
Regarding the dependent claims 2-11 and 13-19, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
Step 2A Prong 2:
This judicial exception (abstract idea) in Claims 1-20 is not integrated into a practical application because:
• The abstract idea amounts to simply implementing the abstract idea on a computing device. For example, the recitations regarding the generic computing components for measuring, receiving, and converting merely invoke a computer as a tool.
• The data-gathering step (measuring and receiving) and the data-output step do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
• There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computing device that is used as a tool for measuring, receiving, and converting.
• The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to estimate bio-information.
• The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computing device for measuring, receiving, and converting. The claims do not apply the obtained prediction to a particular machine. Rather, the data is merely output in a post-solution step.
Step 2B:
The additional elements are identified as follows: biosensor, processor, and network.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
• Applicant’s specification (e.g. paragraph [0007]) which discloses that the processor(s) comprise generic computer components that are configured to perform the generic computer functions (e.g. measuring, receiving, and converting) that are well-understood, routine, and conventional activities previously known to the pertinent industry.
• Applicant’s specification (e.g., para. [0013]) which discloses that the network is merely a tool to transport data from one location to another;
• Lewis (US 20190388016 A1) para. 0038 which states the use of “conventional” biosensors to track a specific type of data (i.e., transdermal alcohol content);
• Applicant’s Background in the specification; and
• The non-patent literature of record in the application.
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional generic computer components as those claimed. See option III. A. 2. in the Berkheimer memorandum.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the units associated with the steps do not add meaningful limitation to the abstract idea. A computer, processor, memory, or equivalent hardware is merely used as a tool for executing the abstract idea(s). The process claimed does not reflect an improvement in the functioning of the computer.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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.
Claim 20 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Swette (US 5944661 A).
Regarding claim 20, Swette teaches a biosensor device for converting transdermal alcohol concentration (TAC) to blood or breath alcohol concentration (BAC/BrAC), the device comprising:
a wearable sensor contactable to a human skin [col. 2 lns. 26-29 “…it is a principal objective of this invention to provide an electrochemical sensor based on a solid polymer electrolyte in the proton exchange form integrated with a recording device that is wearable…”] to measure the TAC of the human [col. 2 lns. 31-32 “…passive tracking of very low concentrations of transdermal alcohol…”];
a processor [col. 6 lns. 27-31] connected to the wearable sensor and connectable to a network [This limitation is being interpreted as intended use. The processor of Swette is capable of being connected to a network as the processor is capable of transmitting data (see col. 11 lns. 38-42)], the processor configured to receive, via the network, data corresponding to one or more drinking curves for a population of humans [Figs. 10 and 11];
the processor configured to convert TAC to BAC/BrAC using (i) the data from one or more drinking curves and (ii) the measured TAC [Figs. 10 and 11, see also col. 11 lns. 24-25 “…the digital data are converted to engineering units of temperature and BAC…”].
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.
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 1-4 and 12 are rejected under 35 U.S.C. 102(a)(1) as anticipated by Swette (US 5944661 A) or, in the alternative, under 35 U.S.C. 103 as obvious over Swette in view of Rothschild (US 20170229149 A1).
Regarding claim 1, Swette teaches a method for converting transdermal alcohol concentration (TAC) to blood or breath alcohol concentration (BAC/BrAC), the method comprising:
measuring, using a biosensor [col. 2 ln. 27 “electrochemical sensor”], the TAC of a human [col. 2 lns. 31-32 “…passive tracking of very low concentrations of transdermal alcohol…”];
receiving, by a processor [col. 6 lns. 27-31], data corresponding to one or more drinking curves for a population of humans [Figs. 10 and 11];
receiving, by the processor, data corresponding to at least one of (i) static characteristics of the human, (ii) physiological characteristics of the human [col. 2 lns. 59-60 “skin properties”], and (iii) current environmental conditions; and
converting, using the processor, the TAC to BAC/BrAC using the data from one or more drinking curves [Figs. 10 and 11, see also col. 11 lns. 24-25 “…the digital data are converted to engineering units of temperature and BAC…”], and the at least one of (i) the static characteristics of the human, (ii) the physiological characteristics of the human [Figs. 10 and 11, see also col. 7 lns. 2-4 “…convert the ethanol concentration-related current to a temperature compensated voltage signal…”], and (iii) the current environmental conditions.
In the alternative, static characteristics of the human and environmental conditions are required by the method, in which Swette fails to teach either.
Rothschild teaches receiving static characteristics [0004 “…age, weight, height…”] of the human and environmental conditions [0004 “environment temperature”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and incorporate the teachings of Rothschild to include receiving static characteristics of the human and environmental conditions. Doing so configures the system to record and/or correct data that may be skewed from additional/external factors, thus providing a more accurate diagnosis of the patient’s condition.
Regarding claim 2, Swette and Rothschild teach the method of claim 1, wherein the data corresponding to the one or more drinking curves includes a measurement of TAC and a measurement of at least one of BAC and BrAC [Swette Figs. 10 and 11].
Regarding claim 3, Swette and Rothschild teach the method of claim 1, wherein the data corresponding to the one or more drinking curves includes a time sequence of measurements of TAC and a time sequence of measurements of BAC or BrAC [Swette Figs. 10 and 11, time is on the X-axis], and wherein the method is performed in real time [Swette col. 11 lns. 16-21 “The data acquisition/logic circuit 35 is programmed to sample three analog signals from the control circuit 14, convert these to digital signals and store the three signals (ethanol concentration, temperature and skin contact) at preset intervals together with real time data”].
Regarding claim 4, Swette and Rothschild teach the method of claim 1, wherein the data corresponding to the static characteristics includes a measurement of at least one of age [Rothschild 0004 “…age, weight, height…”], sex, ethnicity, height [Rothschild 0004], weight [Rothschild 0004], body fat and muscle, skin color, skin thickness, and skin tortuosity,
wherein the data corresponding to the physiological characteristics includes a measurement of at least one of sweat, skin conductance [Swette col. 2 lns. 59-60 “…skin properties such as temperature and ionic conductivity…”], skin hydration, exercise, heart rate, blood pressure, blood flow, and stomach content, and
wherein the data corresponding to the current environmental conditions includes a measurement of at least one of ambient temperature [Rothschild 0004 “environment temperature”], humidity, pressure, GPS, weather, and climate.
Regarding claim 12, Swette teaches a system for converting transdermal alcohol concentration (TAC) to blood or breath alcohol concentration (BAC/BrAC), wherein the converting is in real-time with progressive forecasting and modeling techniques and recursive updating methods, the system comprising:
a biosensor [col. 2 ln. 27 “electrochemical sensor”] for measuring the TAC of a human [col. 2 lns. 31-32 “…passive tracking of very low concentrations of transdermal alcohol…”]; and
a processor [col. 6 lns. 27-31] configured to:
receive data from one or more drinking curves from a population of humans [Figs. 10 and 11];
receive data corresponding to at least one of (i) static characteristics of the human, (ii) physiological characteristics of the human [col. 2 lns. 59-60 “skin properties”], and (iii) the current environmental conditions; and
convert, by the processor, in real-time the TAC to BAC/BrAC using the data from one or more drinking curves [Figs. 10 and 11, see also col. 11 lns. 24-25 “…the digital data are converted to engineering units of temperature and BAC…”] and the at least one of (i) the static characteristics of the human, (ii) the physiological characteristics of the human [Figs. 10 and 11, see also col. 7 lns. 2-4 “…convert the ethanol concentration-related current to a temperature compensated voltage signal…”], and (iii) the current environmental conditions.
In the alternative, static characteristics of the human and environmental conditions are required by the method, in which Swette fails to teach either.
Rothschild teaches receiving static characteristics [0004 “…age, weight, height…”] of the human and environmental conditions [0004 “environment temperature”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and incorporate the teachings of Rothschild to include receiving static characteristics of the human and environmental conditions. Doing so configures the system to record and/or correct data that may be skewed from additional/external factors, thus providing a more accurate diagnosis of the patient’s condition.
Claims 5-6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Swette and Rothschild as applied to claim 1 above, and further in view of Goldner (US 20210391081 A1).
Regarding claim 5, Swette and Rothschild teach the method of claim 1, wherein Swette teaches converting the data [Swette Figs. 10 and 11, see also col. 11 lns. 24-25], but fail to teach using a deterministic or stochastic finite dimensional autoregressive moving average with exogenous input (ARMAX) input/output model.
Goldner teaches using a deterministic or stochastic finite dimensional autoregressive moving average with exogenous input (ARMAX) input/output model [0037 “Examples of machine learning models suitable for use with the present technology include, but are not limited to: (…), autoregressive moving average with exogenous inputs (ARMAX) models…”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and Rothschild and incorporate the teachings of Goldner to include using a deterministic or stochastic finite dimensional autoregressive moving average with exogenous input (ARMAX) input/output model. Doing so configures the system to utilize a mathematical model that has enhanced forecasting accuracy by incorporating external factors alongside a system's internal dynamics.
Regarding claim 6, Swette and Rothschild teach the method of claim 1, wherein Swette teaches converting the data [Swette Figs. 10 and 11, see also col. 11 lns. 24-25], but fail to teach using a blind or Bayesian deconvolution scheme.
Goldner teaches using a Bayesian deconvolution scheme [0037 “Examples of machine learning models suitable for use with the present technology include, but are not limited to: (…), Bayesian algorithms…”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and Rothschild and incorporate the teachings of Goldner to include using a Bayesian deconvolution scheme. Doing so configures the system to use a model that offers enhanced handling of noise and uncertainty and the ability to incorporate prior knowledge, thus producing more robust and accurate results.
Regarding claim 8, Swette and Rothschild teach the method of claim 1, wherein Swette teaches converting the data [Swette Figs. 10 and 11, see also col. 11 lns. 24-25] and transmitting the data to the processor wherein the processor is remote from the biosensor [col. 11 lns. 38-42], but fail to teach using an artificial neural network (ANN) by the processor, wherein the processor is connected to the biosensor by a network.
Goldner teaches using an artificial neural network (ANN) by the processor [0023 “The system 100 can include processors”, see also 0037 “Examples of machine learning models suitable for use with the present technology include, but are not limited to: (…), artificial neural networks…”],
wherein the processor is connected to the biosensor by a network [0123 “The network 1301 can transmit data between the user devices”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and Rothschild and incorporate the teachings of Goldner to include using an artificial neural network (ANN) by the processor, wherein the processor is connected to the biosensor by a network. Doing so configures the system to identify complex patterns, learn from large amounts of data, and accurately analyze non-linear problems in an automated fashion, thus enabling an efficient means to diagnose the patient’s condition.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Swette and Rothschild as applied to claim 1 above, and further in view of Tong (US 20030171661 A1).
Regarding claim 7, Swette and Rothschild teach the method of claim 1, wherein Swette teaches converting the data [Swette Figs. 10 and 11, see also col. 11 lns. 24-25], but fail to teach using a lattice filter-based recursive identification scheme.
Tong teaches using a lattice filter-based recursive identification scheme [0042 “…a controller 24 can be included with the electrode system 10 that has an adaptive noise cancellation circuit or routine 24a such as Least Means Squared (LMS), Recursive Least Squares (RLS), and Lattice algorithms”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and Rothschild and incorporate the teachings of Tong to include using a lattice filter-based recursive identification scheme. Doing so configures the system to use a model that allows for the efficient, on-line computation of exact least squares solutions with superior numerical stability and a modular structure that simplifies order determination.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Swette and Rothschild as applied to claim 1 above, and further in view of Poltorak (US 20220160309 A1).
Regarding claim 9, Swette and Rothschild teach the system of claim 1, wherein Swette teaches converting the data [Swette Figs. 10 and 11, see also col. 11 lns. 24-25], but fail to teach using a hidden Markov model (HMM) or a physics-informed hidden Markov model (PIHMM) by the processor.
Poltorak teaches using a hidden Markov model (HMM) [0500 “The model may be a statistical model, and be predictive of future states, such as a hidden Markov model (HMM)”] by the processor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and Rothschild and incorporate the teachings of Poltorak to include using a hidden Markov model (HMM) by the processor. Doing so configures the system to use a model that analyzes sequential data where the underlying states are hidden, recognizing patterns and temporal dependencies, thus improving the efficiency of converting and analyzing the data.
Claims 10 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Swette and Rothschild as applied to claim 1 above, and further in view of Budiman (US 20100298765 A1).
Regarding claim 10, Swette and Rothschild teach the system of claim 1, wherein Swette teaches converting the data [Swette Figs. 10 and 11, see also col. 11 lns. 24-25], but fail to teach using a deconvolution filter based on output feedback linear quadratic Gaussian tracking gain computed by the processor.
Budiman teaches using a deconvolution filter based on output feedback linear quadratic Gaussian tracking gain computed by the processor [0072 “Any of a variety of controller design methodologies, such as PID systems, full state feedback systems with state estimators, output feedback systems, (Linear-Quadratic-Gaussian) controllers, LQR (Linear-Quadratic-Regulator) controllers, eigenvalue/eigenstructure controller systems, and the like, could be used to design algorithms to perform physiological control”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and Rothschild and incorporate the teachings of Budiman to include using a deconvolution filter based on output feedback linear quadratic Gaussian tracking gain computed by the processor. Doing so configures the system to use a model that enables optimal control and accurate target tracking in real-world systems where not all internal states can be perfectly or directly measured.
Regarding claim 16, Swette, Rothschild, and Budiman teach system of claim 10, wherein the data corresponding to the one or more drinking curves includes a measurement of TAC and a measurement of at least one of BAC and BrAC [Swette Figs. 10 and 11].
Regarding claim 17, Swette, Rothschild, and Budiman teach the system of claim 10, wherein the data corresponding to the static characteristics includes a measurement of at least one of age [Rothschild 0004 “…age, weight, height…”], sex, ethnicity, height [Rothschild 0004], weight [Rothschild 0004], body fat and muscle, skin color, skin thickness, and skin tortuosity,
wherein the data corresponding to the physiological characteristics includes a measurement of at least one of sweat, skin conductance [Swette col. 2 lns. 59-60 “…skin properties such as temperature and ionic conductivity…”], skin hydration, exercise, heart rate, blood pressure, blood flow, and stomach content, and
wherein the data corresponding to the current environmental conditions includes a measurement of at least one of ambient temperature [Rothschild 0004 “environment temperature”], humidity, pressure, GPS, weather, and climate.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Swette and Rothschild as applied to claim 1 above, and further in view of LaConte (US 20090279736 A1) and Goldner.
Regarding claim 11, Swette and Rothschild teach the system of claim 1, wherein Swette teaches converting the data [Swette Figs. 10 and 11, see also col. 11 lns. 24-25] and distributions fit to population BrAC/TAC data [Swette Figs. 10 and 11], but fail to teach using first principles physics-based forward model with random parameters and wherein the fitting the distributions is based on a naive pooled or mixed effects statistical model using either maximum likelihood, method of moments, or Bayesian techniques by the processor.
LaConte teaches using first principles physics-based forward model with random parameters [0034 “…alternative regression approaches include empirically derived regression models, models based on first principles of MR physics and tissue material properties, among others”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and Rothschild and incorporate the teachings of LaConte to include using first principles physics-based forward model with random parameters. Doing so configures the system to use a model that integrates understanding of physical laws with the ability to quantify uncertainty, improve generalization, and require less data than purely empirical or machine learning models.
Goldner teaches wherein the fitting the distributions is based on a naive pooled or mixed effects statistical model using Bayesian techniques by the processor [0037 “Examples of machine learning models suitable for use with the present technology include, but are not limited to: (…), Bayesian algorithms (e.g., naïve Bayes, Gaussian naïve Bayes, multinomial naïve Bayes, averaged one-dependence estimators, Bayesian belief networks, Bayesian networks).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and Rothschild and incorporate the teachings of Goldner to include wherein the fitting the distributions is based on a naive pooled or mixed effects statistical model using Bayesian techniques by the processor. Doing so configures the system to use a model that offers enhanced handling of noise and uncertainty and the ability to incorporate prior knowledge, thus producing more robust and accurate results.
Claims 13, 15, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Swette, Rothschild, and Budiman as applied to claim 10 above, and further in view of Goldner.
Regarding claim 13, Swette, Rothschild, and Goldner teach the system of claim 10, wherein the processor is remote from the biosensor [Swette col. 11 lns. 38-42], but fails to teach the processor is connected to the biosensor via a network.
Goldner teaches the processor is connected to the biosensor via a network [0123 “The network 1301 can transmit data between the user devices”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette, Rothschild, and Budiman and incorporate the teachings of Goldner to include the processor is connected to the biosensor via a network. Doing so configures the system to identify complex patterns, learn from large amounts of data, and accurately analyze non-linear problems in an automated fashion, thus enabling an efficient means to diagnose the patient’s condition. Doing so enables the sensor to send/receive data in real-time, thus improving the speed in which a diagnosis of the patient’s condition can be performed.
Regarding claim 15, Swette, Rothschild, and Budiman teach the system of claim 10, wherein the system comprises a plurality of further biosensors connected to the processor [Swette col. 7 lns. 59-61 “…a pair of exposed platinum pin sensors 17 located on the front surface of the sensor assembly 11 is used”], wherein the processor coverts, in real-time the TAC to BAC/BrAC for each of the plurality of further biosensors [see Swette Fig. 7, col. 13 lns. 34-37], but fail to teach the biosensors are connected to the processor via a network.
Goldner teaches the biosensors are connected to the processor via a network [0123 “The network 1301 can transmit data between the user devices”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette, Rothschild, and Budiman and incorporate the teachings of Goldner to include the biosensors are connected to the processor via a network. Doing so configures the system to identify complex patterns, learn from large amounts of data, and accurately analyze non-linear problems in an automated fashion, thus enabling an efficient means to diagnose the patient’s condition. Doing so enables the sensor to send/receive data in real-time, thus improving the speed in which a diagnosis of the patient’s condition can be performed.
Regarding claim 18, Swette, Rothschild, and Budiman teach the system of claim 10, wherein converting the data is performed [Swette Figs. 10 and 11, see also col. 11 lns. 24-25] in real-time [Swette col. 11 lns. 16-21 “The data acquisition/logic circuit 35 is programmed to sample three analog signals from the control circuit 14, convert these to digital signals and store the three signals (ethanol concentration, temperature and skin contact) at preset intervals together with real time data”], but fail to teach using a deterministic or stochastic finite dimensional autoregressive moving average with exogenous input (ARMAX) input/output model.
Goldner teaches using a deterministic or stochastic finite dimensional autoregressive moving average with exogenous input (ARMAX) input/output model [0037 “Examples of machine learning models suitable for use with the present technology include, but are not limited to: (…), autoregressive moving average with exogenous inputs (ARMAX) models…”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette, Rothschild, and Budiman and incorporate the teachings of Goldner to include using a deterministic or stochastic finite dimensional autoregressive moving average with exogenous input (ARMAX) input/output model. Doing so configures the system to utilize a mathematical model that has enhanced forecasting accuracy by incorporating external factors alongside a system's internal dynamics.
Regarding claim 19, Swette, Rothschild, and Budiman teach the system of claim 10, wherein converting the data is performed [Swette Figs. 10 and 11, see also col. 11 lns. 24-25], but fail to teach using an artificial neural network (ANN) or a physics-informed neural network (PINN) by the processor.
Goldner teaches using an artificial neural network (ANN) by the processor [0023 “The system 100 can include processors”, see also 0037 “Examples of machine learning models suitable for use with the present technology include, but are not limited to: (…), artificial neural networks…”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette and Rothschild and incorporate the teachings of Goldner to include using an artificial neural network (ANN) by the processor. Doing so configures the system to identify complex patterns, learn from large amounts of data, and accurately analyze non-linear problems in an automated fashion, thus enabling an efficient means to diagnose the patient’s condition.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Swette, Rothschild, and Budiman as applied to claim 10 above, and further in view of Hawthorne (US 20040236199 A1).
Regarding claim 14, Swette, Rothschild, and Budiman teach the system of claim 10, but fail to teach a remote database containing the one or more drinking curves from the population of humans connected to the processor via a network.
Hawthorne teaches a remote database containing the one or more drinking curves from the population of humans connected to the processor via a network [claim 9 “…a web-hosted database server for storing said plurality of transdermal alcohol concentration readings, said tamper indicators, said errors, and said diagnostic data”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Swette, Rothschild, and Budiman and incorporate the teachings of Hawthorne to include a remote database containing the one or more drinking curves from the population of humans connected to the processor via a network. Doing so configures the system to centralize data, enhance scalability, improve cost savings, provide better data security, and global accessibility, allowing users to access up-to-date info from anywhere and reducing local hardware needs.
Conclusion
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/JONATHAN M HANEY/ Examiner, Art Unit 3791
/JUSTIN XU/Primary Examiner, Art Unit 3791