DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 8/31/2022 for application number 17/900,793.
Claims 1-20 are pending.
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 .
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows: The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed applications, Application No. 17/532,966; 15/854,628; 14/738,711; 14/738,666; and 14/738,626 fail to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Independent claims 1 and 11 recite:
…training a DNN (deep neural network) with the plurality of users one or more indirect physiological parameters and where the direct physiological parameter is a training target; determining one or more groups from the users physiological parameters; associating each user in the user database with one or more groups; determine a calibration for each of the one or more groups; generating a parameter model that substantially matches the deep neural network; receiving a new user physiological parameters; determining a group distance of the new user from each of the one or more groups; determining the closest group; determining the error of the parameter model using the closest group and associated calibration; if the error is greater than a threshold, quantizing the new user physiological parameters; iterate one at a time each new user physiological parameters input to the parameter model; determining the one or more quantized new user physiological parameters that reduces error; and create new group based on the new user physiological parameters.
which is not disclosed in the prior-filed applications. Therefore, claims 1-20 are not entitled to the filing dates of the prior-filed applications.
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.
The term “substantially” in independent claims 1 and 11 is a relative term which renders the claim indefinite. The term “substantially” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It would be a matter of opinion as to how closely the parameter model would have match the DNN in order to be “substantially match[ing].”
Claims 9 and 19 recite the limitation "minimum and maximum values of the physiological." There is insufficient antecedent basis for this limitation in the claim. First, the Examiner assumes “the physiological parameters” was intended; however, the claims recite three different physiological parameters: the direct, the indirect, and new user physiological parameters, and it is unclear which is the antecedent basis of this limitation. For prior art purposes, the Examiner is assuming “the new user physiological parameters” was intended.
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 (which is representative of independent claim 11) recites:
A method for estimating a physiological parameter using a parameter model determined by a deep neural network, the method comprising: accessing a user database containing one or more indirect and direct physiological parameters for each of a plurality of users by at least one processor; training a DNN (deep neural network) with the plurality of users one or more indirect physiological parameters and where the direct physiological parameter is a training target; determining one or more groups from the users physiological parameters; associating each user in the user database with one or more groups; determine a calibration for each of the one or more groups; generating a parameter model that substantially matches the deep neural network; receiving a new user physiological parameters; determining a group distance of the new user from each of the one or more groups; determining the closest group; determining the error of the parameter model using the closest group and associated calibration; if the error is greater than a threshold, quantizing the new user physiological parameters; iterate one at a time each new user physiological parameters input to the parameter model; determining the one or more quantized new user physiological parameters that reduces error; and create new group based on the new user physiological parameters.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically a mental process and mathematical calculation. A human can mentally, with the aid of pen and paper, sort users into groups based on their physiological parameters, mathematically calculate a calibration for creating a parameter model for each group so that the parameter model corresponds to a DNN output (i.e. calculate coefficients or weights for a parametric model), mentally judge a closest group for a new user, mathematically calculate the error of the closest group’s model for the new user, and mentally judge of the error is greater than a threshold. (The Examiner notes that the remaining underlined limitations of the claim are contingent on the error being greater than a threshold. “The broadest reasonable interpretation of a method (or process) claim having contingent limitations … does not include steps that are not required to be performed because the condition(s) precedent are not met.” See MPEP 2111.04(II). Thus, the claim does not require the steps to be performed when the error is below the threshold. However, even assuming these limitations were required, they are further mental steps and mathematical calculations).
(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional elements of [a] accessing a database with physiological parameters, [b] training a DNN, [c] receiving new user parameters, [d] generic computer elements like a processor and memory for claim 11. Elements [a] and [c] are insignificant extra-solution activity because they amount to mere necessary data gathering for the for the abstract idea. Elements [b] and [d] are mere instructions to apply the exception. For [b], this element only recites the idea of an outcome (that a DNN is trained to predict a direct physiological parameter from indirect parameters) and not the details of how the solution is accomplished (there are no details on how the training works). For [d], this element merely adds generic computer components after the fact to the abstract idea. Even when elements [a] – [d] are considered together in the claim as a whole, they do not integrate the abstract idea into a practical application because they merely add insignificant extra-solution activity and mere instructions to apply the exception to the abstract idea.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Elements [a] and [c] are well-understood, routine, and conventional activity, analogous to storing and retrieving information in memory, see MPEP 2106.05(d) citing Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Elements [b] and [d] are mere instructions to apply the exception as explained above. Even when elements [a] – [d] are considered together with the abstract idea in the claim as a whole, they do not amount to significantly more than the judicial exception itself because they merely add insignificant extra-solution activity that is well-understood, routine, and conventional and mere instructions to apply the exception to the abstract idea.
With respect to dependent claims 2, 4-5, 8-9, 12, 14-15, and 18-19, (2A, prong 1) these claims add additional mathematical calculations to the abstract idea. Claims 2 and 12 recite determining groups by calculating a Euclidian distance. Claims 4-5 and 14-15 recite calculating scalar coefficients for inputs / outputs of the parameter model. Claims 8-9 and 18-19 recite performing calculations for quantization.
With respect to dependent claims 3 and 13, (2A, prong 2) these claims add the additional element of the groups being determined utilizing a DNN. This additional element does not integrate the abstract idea into a practical application because it is a mere instruction to apply the exception because only recites the idea of an outcome (that a DNN is used for grouping) and not the details of how the solution is accomplished (how the DNN functions to determine the groups). (2B) This element does not amount to significantly more than the judicial exception itself because it is a mere instruction to apply, as explained above. Even when all of the additional elements are considered together with the abstract idea in the claim as a whole, they do not amount to significantly more than the judicial exception itself because they merely add insignificant extra-solution activity that is well-understood, routine, and conventional and mere instructions to apply the exception to the abstract idea.
With respect to dependent claims 6-7, 10, 16-17, and 20, (2A, prong 2) these claims add the additional element the indirect parameters being particular types of health data, and that the parameter model is used to determine an early warning score for a health status. These additional elements do not integrate the abstract idea into a practical application because they are field of use limitations. Specifically, these limitations confine the use of the mental steps and mathematical calculations to a healthcare environment, which is insufficient to integrate the abstract idea into a practical application. (2B) These additional elements do not amount to significantly more than the abstract idea itself because as explained above, they are field of use limitations. Even when all of the additional elements are considered together with the abstract idea in the claim as a whole, they do not amount to significantly more than the judicial exception itself because they merely add field of use limitations, insignificant extra-solution activity that is well-understood, routine, and conventional, and mere instructions to apply the exception to the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over DeMazumder et al. (US 2021/0272696 A1) in view of Elewitz et al. (US 2021/0110294 A1).
In reference to claim 1, DeMazumder teaches a method for estimating a physiological parameter … determined by a deep neural network (estimating risk score using DNN, para. 0111, 0144, 0060), the method comprising: accessing a user database containing one or more indirect and direct physiological parameters for each of a plurality of users by at least one processor (EHR with physiological parameters like blood pressure and pulse is accessed, para. 0057-61, 0106-09); training a DNN with the plurality of users one or more indirect physiological parameters and where the direct physiological parameter is a training target (DNN trained with physiological parameters like blood pressure and pulse as the input, and the risk score as the targeted output, para. 0057-61, 0106-09); determining one or more groups from the users physiological parameters (user can be grouped, para. 0061, 0117).
However, DeMazumder does not explicitly teach using a parameter model; … associating each user in the user database with one or more groups; determine a calibration for each of the one or more groups; generating a parameter model that substantially matches the deep neural network; receiving a new user physiological parameters; determining a group distance of the new user from each of the one or more groups; determining the closest group; determining the error of the parameter model using the closest group and associated calibration; if the error is greater than a threshold, quantizing the new user physiological parameters; iterate one at a time each new user physiological parameters input to the parameter model; determining the one or more quantized new user physiological parameters that reduces error; and create new group based on the new user physiological parameters.
Elewitz teaches using a parameter model (logistic model can be used to determine risk scores, para. 0126); … associating each user in the user database with one or more groups (each user is assigned to a cluster, para. 0160); determine a calibration for each of the one or more groups; generating a parameter model that substantially matches [the deep neural network] (for each group, a logistic model is created with coefficients – the coefficients are calibrations – and the logistic model is meant to match a complex model, para. 0161, as combined with DeMazumder which teaches a complex DNN model for the risk score, it would be obvious that the complex model would be the DNN of DeMazumder); receiving a new user physiological parameters (a validation user, which is the new user, is received, para. 0162); determining a group distance of the new user from each of the one or more groups; determining the closest group (closest group determined using Euclidian distance, para. 0162); determining the error of the parameter model using the closest group and associated calibration (root mean square error, or RMSE, is determined, para. 0162-65); if the error is greater than a threshold (the Examiner notes that this is a contingent limitation. “The broadest reasonable interpretation of a method (or process) claim having contingent limitations … does not include steps that are not required to be performed because the condition(s) precedent are not met.” See MPEP 2111.04(II). Thus, the contingent limitations do not need to be performed when the error is less than a threshold. This corresponds to the situation in Elewitz when the RMSE is minimized at step 620, fig. 6.) quantizing the new user physiological parameters; iterate one at a time each new user physiological parameters input to the parameter model; determining the one or more quantized new user physiological parameters that reduces error; and create new group based on the new user physiological parameters.
It would have been obvious to one of ordinary skill in art, having the teachings of DeMazumder and Elewitz before the earliest effective filing date, to modify the estimation as disclosed by DeMazumder to include the parameter model as taught by Elewitz.
One of ordinary skill in the art would have been motivated to modify the estimation of DeMazumder to include the parameter model of Elewitz because by creating the logistic models, it provides transparency into what features have the most impact on the risk score, whereas with complex models it may not be clear what features are contributing most to the score (Elewitz, para. 0155-56).
In reference to claim 2, DeMazumader does not teach the method of claim 1, wherein the one or more groups are determined by the euclidian distance of a user physiological parameters from a group's physiological parameters.
Elewitz teaches the method of claim 1, wherein the one or more groups are determined by the euclidian distance of a user physiological parameters from a group's physiological parameters (closest group determined using Euclidian distance, para. 0162).
It would have been obvious to one of ordinary skill in art, having the teachings of DeMazumder and Elewitz before the earliest effective filing date, to modify the estimation as disclosed by DeMazumder to include the parameter model as taught by Elewitz.
One of ordinary skill in the art would have been motivated to modify the estimation of DeMazumder to include the parameter model of Elewitz because by creating the logistic models, it provides transparency into what features have the most impact on the risk score, whereas with complex models it may not be clear what features are contributing most to the score (Elewitz, para. 0155-56).
In reference to claim 3, DeMazumder teaches the method of claim 1, wherein the one or more groups are determined by utilizing a DNN (DNN, para. 0111, 0144, 0060 can be used to group, para. 0061, 0117).
In reference to claim 4, DeMazumader does not teach the method of claim 1, wherein the calibration for a group utilizes scalar coefficients applied to the physiological parameters of a user in a group.
Elewitz teaches the method of claim 1, wherein the calibration for a group utilizes scalar coefficients applied to the physiological parameters of a user in a group (coefficients are applied to inputs to logistic model, para. 0126).
It would have been obvious to one of ordinary skill in art, having the teachings of DeMazumder and Elewitz before the earliest effective filing date, to modify the estimation as disclosed by DeMazumder to include the parameter model as taught by Elewitz.
One of ordinary skill in the art would have been motivated to modify the estimation of DeMazumder to include the parameter model of Elewitz because by creating the logistic models, it provides transparency into what features have the most impact on the risk score, whereas with complex models it may not be clear what features are contributing most to the score (Elewitz, para. 0155-56).
In reference to claim 5, DeMazumader does not teach the method of claim 1, wherein the calibration for a group utilizes scalar coefficients applied to the output of the parameter model.
Elewitz teaches the method of claim 1, wherein the calibration for a group utilizes scalar coefficients applied to the output of the parameter model (coefficients are applied to parameters of logistic model, which in turn determine the output of the model, para. 0126).
It would have been obvious to one of ordinary skill in art, having the teachings of DeMazumder and Elewitz before the earliest effective filing date, to modify the estimation as disclosed by DeMazumder to include the parameter model as taught by Elewitz.
One of ordinary skill in the art would have been motivated to modify the estimation of DeMazumder to include the parameter model of Elewitz because by creating the logistic models, it provides transparency into what features have the most impact on the risk score, whereas with complex models it may not be clear what features are contributing most to the score (Elewitz, para. 0155-56).
In reference to claim 6, DeMazumder teaches the method of claim 1, wherein the one or more indirect physiological parameters are one or more of respiratory rate, an oxygen saturation, a temperature, a derived blood pressure, and a pulse rate (respiration rate, oxygen saturation, temperature, pulse para. 0014).
In reference to claim 7, DeMazumder teaches the method of claim 6, wherein the indirect physiological parameter is a directly measured blood pressure (invasive and non-invasive blood pressure readings, para. 0014, which are directly measured).
In reference to claim 8, Elewitz teaches the method of claim 1, wherein the quantizing the new user physiological parameters selects four parameter levels for each new user physiological parameter, the quantized parameter levels being twenty and ten percent below the new user physiological parameter and ten and twenty percent above the new user physiological parameter (this limitation is contingent on the error being above a threshold as explained in the rejection of claim 1 above; Elewitz teaches the error being below a threshold, so this limitation does not need to be performed).
In reference to claim 9, Elewitz teaches the method of claim 1, wherein the quantizing the new user physiological parameters selects four parameter levels equally spaced between minimum and maximum values of the physiological (this limitation is contingent on the error being above a threshold as explained in the rejection of claim 1 above; Elewitz teaches the error being below a threshold, so this limitation does not need to be performed).
In reference to claim 10, DeMazumder teaches the method of claim 1, wherein the parameter model is used for determining an early warning score for a health status of a user (risk score for early identification of adverse health events, para. 0040-42).
In reference to claim 11, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 12, this claim is directed to a system associated with the method claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 13, this claim is directed to a system associated with the method claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 14, this claim is directed to a system associated with the method claimed in claim 4 and is therefore rejected under a similar rationale.
In reference to claim 15, this claim is directed to a system associated with the method claimed in claim 5 and is therefore rejected under a similar rationale.
In reference to claim 16, this claim is directed to a system associated with the method claimed in claim 6 and is therefore rejected under a similar rationale.
In reference to claim 17, this claim is directed to a system associated with the method claimed in claim 7 and is therefore rejected under a similar rationale.
In reference to claim 18, this claim is directed to a system associated with the method claimed in claim 8 and is therefore rejected under a similar rationale.
In reference to claim 19, this claim is directed to a system associated with the method claimed in claim 9 and is therefore rejected under a similar rationale.
In reference to claim 20, this claim is directed to a system associated with the method claimed in claim 10 and is therefore rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhong (US 20180277246 A1), Lanzrath (US 10679294 B1), Wang (US 10811139 B1), Krause (US 11355246 B2), and Warner (US 10643749 B1) all teach predictive models for health risk scoring.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571-272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144