DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 abstract idea
without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. However, as will be shown below, claims 1-20 are
nonetheless unpatentable under 35 U.S.C. 101.
Claim 1 is representative of the inventive concept and recites:
A method, implemented by one or more computers, for forecasting case counts for a future date in one or more geographic areas of persons infected by a disease associated with one or more pathogens, the presence of which in a biological sample is testable by a polymerase chain reaction (PCR) test such that a load of the one or more pathogens typically correlates with a PCR cycle at which a PCR test of the biological sample indicates presence of the one or more pathogens, such a PCR cycle referred to as a threshold cycle (Ct), the method comprising:
receiving, at one or more computers, data relevant to forecasting the case counts, the data comprising Ct data and other data, the Ct data comprising Ct values from PCR tests of biological samples from persons within the one or more geographic areas generated by PCR instruments performing thermal cycling of the biological samples;
generating, by the one or more computers, arrays of feature data for processing by a trained machine learning model implemented by the one or more computers, the feature data comprising Ct features obtained from the Ct data and other features obtained from the other data wherein the Ct features include statistical features derived from distributions of the Ct values comprising at least a mean and a skewness for respective dates;
and processing, by the one or more computers, the arrays of feature data using the machine learning model to generate at least one forecasted case count comprising a forecasted number of infected persons for the future date in the one or more geographic areas.
*Claims 18 and 19 recite similar limitations as claim 1, but for a non-transitory computer readable medium and system, respectively.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of generating and processing ) or using pen and paper. Other than reciting generic computer components/functions such as “computer” and “machine learning model”, “system”, and “non-transitory computer readable medium”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the generic computer language, the claim encompasses the user collecting data, processing it, and then making prediction based on the analysis of data. If a claim limitation, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process. The recitation of generic computer components/functions also covers behavioral or interactions between people (i.e. a computer and user interface), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case a person is able to physically follow the steps to collect and process data), hence the claim falls under “Certain Methods of Organizing Human Activity”.
Dependent claims 2-17 and 20 recite additional subject matter which further narrows or
defines the abstract idea embodied in the claims (such as claim 2, reciting what Ct data actually comprises, but for recitation of generic computer components/functions).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional limitations:
Claim 1 recites “computer and machine learning model” and “receiving, at one or more computers, data relevant to forecasting the case counts, the data comprising Ct data and other data, the Ct data comprising Ct values from PCR tests of biological samples from persons within the one or more geographic areas generated by PCR instruments performing thermal cycling of the biological samples”.
In particular, the additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations of
are recited as being performed by a computer and machine learning model. A computer is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The machine learning model is used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the recitation of “receiving, at one or more computers, data relevant to forecasting the case counts, the data comprising Ct data and other data, the Ct data comprising Ct values from PCR tests of biological samples from persons within the one or more geographic areas generated by PCR instruments performing thermal cycling of the biological samples”
Dependent claims 7, 8, and 9 recite recurrent neural network
Dependent claim 14 recites EpiEstim processing
Dependent claim 15 recites Hay model processing
Dependent claims 16 and 20 recite user device
In particular, the additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations
are recited as being performed by a recurrent neural network, user device, EpiEstim processing, and Hay model processing. They are each recited at a high level of generality and amount to no more than mere instructions to apply the exception using a generic computer. The recurrent neural network is used to generally apply the abstract idea without limiting how it functions.
Dependent claims 2-6, 10-13, and 17 do not include any additional elements beyond those already recited in independent claims 1, 18, and 19, and dependent claims 7, 8, 9, 14-15, 16, and 20, hence also do not integrate the aforementioned abstract idea into a practical application. Looking at the limitations as an ordered combination 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 machine learning model or improves any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 18, and 19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: A computer in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields as demonstrated by:
Recitation of receiving data which refers to acquiring information sent from another device or system (TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) in a manner that would be well-understood, routine, and conventional.
Dependent claims 2-6, 10-13, and 17 do not include any additional elements beyond those
already recited in independent claims 1, 18, and 19 and dependent claims 7, 8, 9, 14-15, 16, and 20. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 18, and 19 hence do not amount to “significantly more” than the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective function merely provide conventional computer implementation.
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 1-3, 4, 7-8, 11-13, and 16-20 are rejected under 35 U.S.C. 103 is being unpatentable over Segal(US20200373018A1) in view of Hay(Science, 373, 16 July 2021, Pages 1-12)
Claim 1
Segal discloses:
A method, implemented by one or more computers, for forecasting(Para 0035, Segal discloses: “prediction” [PREDICTION CAN BE A FORECAST FOR A FUTURE DATE]) case counts(Para 0082, Segal discloses: “number of cases” [NUMBER OF CASES CAN BE CASE COUNTS]) for a future date in one or more geographic areas(Para 0040, Segal discloses: “multiple geographic areas”]) of persons(Para 0035, Segal disclose: “persons”) infected(Para 0067, Segal discloses: “infected”) by a disease(Para 0017, Segal discloses: “infected with a viral disease”) associated with one or more pathogens(Para 0112, Segal discloses: “ML model may be trained for other infectious diseases that spread from subject to subject, by one subject infecting other subjects, for example, other viruses (e.g., flu), prions, bacteria, parasites, and the like[ONE OR MORE PATHOGENS].”), the presence(Para 0068, Segal discloses: “presence[PRESENCE] of virus”) of which in a biological sample(Para 0068, Segal discloses: “analyze tissue samples[TISSUE SAMPLE CAN BE BIOLOGICAL SAMPLE] of the person for presence of virus) is testable by a polymerase chain reaction (PCR) test(Para 0010, Segal discloses: “ test for diagnosing the viral disease comprises PCR[PCR test]”) such that a load of the one or more pathogens typically correlates with a PCR cycle at which a PCR test(Para 0010, Segal discloses: “ test for diagnosing the viral disease comprises PCR[PCR test]”) of the biological sample(Para 0068, Segal discloses: “analyze tissue samples[TISSUE SAMPLE CAN BE BIOLOGICAL SAMPLE] of the person for presence of virus) indicates presence(Para 0068, Segal discloses: “presence[PRESENCE] of virus”) of the one or more pathogens(Para 0112, Segal discloses: “ML model may be trained for other infectious diseases that spread from subject to subject, by one subject infecting other subjects, for example, other viruses (e.g., flu), prions, bacteria, parasites, and the like[ONE OR MORE PATHOGENS].”), DATE]) case count(Para 0082, Segal discloses: “number of cases” [NUMBER OF CASES CAN BE CASE COUNTS]) comprising a forecasted(Para 0035, Segal discloses: “prediction” [PREDICTION CAN BE A FORECAST FOR A FUTURE DATE]) number of infected(Para 0067, Segal discloses: “infected”) persons(Para 0035, Segal disclose: “persons”) for the future date in the one or more geographic areas(Para 0040, Segal discloses: “multiple geographic areas”]).
Segal does not explicitly disclose: threshold cycle(Ct), Ct data, Ct values, wherein the Ct features include statistical features derived from distributions of the Ct values comprising a least a mean and a skewness for respective dates
Hay discloses: threshold cycle(Ct), Ct data, Ct values
threshold cycle (Ct)(Page 1, Hay discloses: “cycle threshold (Ct)”)
Ct data(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA])
Ct values(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA])
wherein the Ct features include statistical features derived from distributions of the Ct values comprising a least a mean(Figure 1, (D), Hay discloses Ct value mean) and a skewness(Figure 1, (F), Hay discloses Ct value skewness) for respective dates
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add data and values of threshold cycle(Ct), and wherein the Ct features include statistical features derived from distributions of the Ct values comprising a least a mean and a skewness for respective dates, as taught by Hay. One of ordinary skill would have been so motivated to provide a means to utilize the result of a test which detects the presence of a pathogen as a basis of measurement for the spread of the pathogen itself, but in this case for estimating epidemiologic dynamics using viral loads (Page 1, Hay discloses: “… current metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points…”).
Claim 2
Segal discloses:
The method of claim 1 wherein the Ct data comprises respective sets of Ct values from PCR tests(Para 0010, Segal discloses: “ test for diagnosing the viral disease comprises PCR[PCR test]”) conducted on respective dates, the PCR tests(Para 0010, Segal discloses: “ test for diagnosing the viral disease comprises PCR[PCR test]”) corresponding to persons(Para 0035, Segal disclose: “persons”) in the one or more geographic areas(Para 0040, Segal discloses: “multiple geographic areas”]).
Segal does not explicitly disclose: Ct data, Ct values
Hay discloses: Ct data, Ct values
Ct data(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA])
Ct values(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA])
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add data and values of threshold cycle(Ct), as taught by Hay. One of ordinary skill would have been so motivated to provide a means to utilize the result of a test which detects the presence of a pathogen as a basis of measurement for the spread of the pathogen itself, but in this case for estimating epidemiologic dynamics using viral loads (Page 1, Hay discloses: “… current metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points…”).
Claim 3
Hay discloses:
The method of claim 2 wherein generating comprises determining a mean(Page 2, Hay discloses: “mean”) and a skewness(Page 2, Hay discloses: “skewness”) of each of the respective sets of Ct values(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA]).
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add mean, skewness, and Ct data, as taught by Hay. One of ordinary skill would have been so motivated to provide a statistical methodology to assess Ct counts (or viral load) in helping to determine spread of disease, but in this case for estimating epidemiologic dynamics using viral loads (Page 1, Hay discloses: “… current metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points…”).
Claim 4
Hay discloses:
The method of claim 3 wherein generating further comprises determining a smoothed(Page 7, Hay discloses: “smoothed”) mean(Page 2, Hay discloses: “mean”) and a smoothed(Page 7, Hay discloses: “smoothed”) skewness(Page 2, Hay discloses: “skewness”) of each of the respective sets of Ct values(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA]) using Ct values(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA]) from a rolling window of dates around a date of each respective set of Ct values(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA]).
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add smoothed mean, smoothed skewness, and Ct values, as taught by Hay. One of ordinary skill would have been so motivated to provide a statistical methodology to assess Ct counts (or viral load) in helping to determine spread of disease, but in this case for estimating epidemiologic dynamics using viral loads (Page 1, Hay discloses: “… current metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points…”).
Claim 7
Segal discloses:
The method of claim 1 wherein the machine learning model(Para 0006, Segal discloses: “machine learning (ML) model) comprises a recurrent neural network(Para 0115, Segal discloses: “neural network”).
Claim 8
Segal discloses:
The method of claim 7 wherein the machine learning model(Para 0006, Segal discloses: “machine learning (ML) model) further comprises an autoregression model(Para 0193, Segal discloses: “regression models”[AUTOREGRESSION MODEL CAN BE A REGRESSION MODEL]) and an output multiplication function configured to multiply output of the recurrent neural network(Para 0115, Segal discloses: “neural network”) with output of the autoregression model(Para 0193, Segal discloses: “regression models”[AUTOREGRESSION MODEL CAN BE A REGRESSION MODEL]) to provide output of the machine learning model(Para 0006, Segal discloses: “machine learning (ML) model), wherein: some features(Para 0146, Segal discloses: “features”), including the Ct(Page 1, Hay discloses: “cycle threshold (Ct)”) features(Para 0146, Segal discloses: “features”), are processed by the recurrent neural network(Para 0115, Segal discloses: “neural network”); and at least one feature(Para 0146, Segal discloses: “features”) of the other features(Para 0146, Segal discloses: “features”) is processed by the autoregression model(Para 0193, Segal discloses: “regression models”[AUTOREGRESSION MODEL CAN BE A REGRESSION MODEL]).
Segal does not explicitly disclose: Ct
Hay discloses: Ct
Ct(Page 1, Hay discloses: “cycle threshold (Ct)”)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add Ct, as taught by Hay. One of ordinary skill would have been so motivated to provide a result of PCR testing to assess the presence of a pathogen in a sample, but in this case for estimating epidemiologic dynamics using viral loads (Page 1, Hay discloses: “… current metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points…”).
Claim 11
Segal discloses:
The method of claim 2 wherein the respective dates(Page 9, Hay discloses: “collection date”) corresponding to the respective sets of Ct data(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA]) are dates on which a sample(Para 0068, Segal discloses: “analyze tissue samples[SAMPLE] of the person for presence of virus) for a corresponding PCR test(Para 0010, Segal discloses: “ test for diagnosing the viral disease comprises PCR[PCR test]”) was collected.
Segal does not explicitly disclose: respective date of collection, Ct data
Hay discloses: respective date of collection, Ct data
respective dates(Page 9, Hay discloses: “collection date”)
Ct data(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA])
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add respective date of collection and Ct data, as taught by Hay. One of ordinary skill would have been so motivated to provide a result of PCR testing and the associated testing date to better understand the timing of pathogen spread, but in this case for estimating epidemiologic dynamics using viral loads (Page 1, Hay discloses: “… current metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points…”).
Claim 12
Segal discloses:
The method of claim 1 wherein the one or more geographic areas(Para 0040, Segal discloses: “multiple geographic areas”]) comprises a plurality of respective geographic areas(Para 0173, Segal discloses: “the aggregation is for multiple geographic zones, or one large zones which may include multiple geographic zones therein, for example, a county, a province, a state, or a country…”) and further wherein the at least one case count(Para 0082, Segal discloses: “number of cases” [NUMBER OF CASES CAN BE CASE COUNTS]) comprises a plurality of respective case counts(Para 0082, Segal discloses: “number of cases” [NUMBER OF CASES CAN BE CASE COUNTS]) each corresponding to a different one of the respective geographic areas(Para 0006, Segal discloses: “for each of a plurality of subjects: the first subset of the plurality of answers, an indication of a certain geographic zone of the plurality of geographic zones”).
Claim 13
Segal discloses:
The method of claim 12 wherein the respective geographic areas(Para 0040, Segal discloses: “geographic areas”]) are counties(Para 0173, Segal discloses: “county”).
Claim 16
Segal discloses:
The method of claim 1, further comprising providing a real-time or near real- time notification(Para 0109, Segal discloses: “alerts” [AN ALERT CAN BE CONIDERED A NOTIFICATION]) of the forecasted(Para 0035, Segal discloses: “prediction” [PREDICTION CAN BE A FORECAST]) case count(Para 0082, Segal discloses: “number of cases” [NUMBER OF CASES CAN BE CASE COUNTS])
to a user device(Para 0095, Segal discloses: “mobile device” [MOBILE DEVICE CAN BE CONSIDERED A USER DEVICE]).
Claim 17
Segal discloses:
A computer program product comprising executable code stored in a non- transitory computer readable medium, the executable code being executable on one or more computer processors(Para 0006, Segal discloses: “…at least one hardware processor executing a code…”) to execute the method of claim 1.
Claim 18
Claim 18 recites similar limitations as claim 1. See claim 1 analysis
Claim 19
Claim 19 recites similar limitations as claim 1. See claim 1 analysis
Claim 20
Segal discloses:
The system of claim 19, wherein the plurality of computer readable instructions, upon execution by the one or more processors, further perform the step of providing a real-time or near real-time notification(Para 0109, Segal discloses: “alerts” [AN ALERT CAN BE CONIDERED A NOTIFICATION]) of the forecasted(Para 0035, Segal discloses: “prediction” [PREDICTION CAN BE A FORECAST]) case count(Para 0082, Segal discloses: “number of cases” [NUMBER OF CASES CAN BE CASE COUNTS]) to a user device(Para 0095, Segal discloses: “mobile device” [MOBILE DEVICE CAN BE CONSIDERED A USER DEVICE]).
Claims 5, 6, and 14-15 are rejected under 35 U.S.C. 103 is being unpatentable over Segal(US20200373018A1) in view of Hay(Science, 373, 16 July 2021, Pages 1-12) further in view of Cori et al (American Journal of Epidemiology, Volume 178, Issue 9, 1 November 2013, Pages 1505–1512)
Claim 5
Hay discloses:
The method of claim 2 wherein generating further comprises: using the respective sets of Ct values(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA]) to determine respective sets of estimated incident rates(Page 7, Hay discloses: “rate of incident infections”[INCIDENT RATES]); using the respective sets of estimated incident rates(Page 7, Hay discloses: “rate of incident infections”[INCIDENT RATES]) to determine respective sets of estimated effective reproductive rate (Rt) time series values; and determining a mean(Page 2, Hay discloses: “mean”) and a skewness(Page 2, Hay discloses: “skewness”) of each respective set of Rt time series values.
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add Ct values, incident rates, mean, and skewness, as taught by Hay. One of ordinary skill would have been so motivated to provide a means to measure incident rates using Ct values and utilizing statistics analysis to understand the potential accuracy of the prediction, but in this case for estimating epidemiologic dynamics using viral loads (Page 1, Hay discloses: “… current metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points…”).
Hay does not explicitly disclose: reproductive rate (Rt), time series value
Cori discloses: reproductive rate (Rt), time series value
reproductive rate (Rt)(Page 1506, Cori discloses: “instantaneous reproduction number, Rt”)
time series values(Page 1510, Cori discloses: “numbers from incidence time series” [TIME SERIES VALUES])
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the model for estimating epidemiologic dynamics using viral loads of Hay to add reproductive rate (Rt) and time series value, as taught by Cori. One of ordinary skill would have been so motivated to understand epidemic growth on a time-series basis for accurate prediction of spread, but in this case for time-varying reproduction numbers during epidemics(Page 1505, Cori discloses:” The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses.”).
Claim 6
Hay discloses:
The method of claim 5 wherein generating further comprises: determining a smoothed(Page 7, Hay discloses: “smoothed”) mean(Page 2, Hay discloses: “mean”) and a smoothed(Page 7, Hay discloses: “smoothed”) skewness(Page 2, Hay discloses: “skewness”) of each respective set of Rt time series values.
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add smoothed mean and smoothed, as taught by Hay. One of ordinary skill would have been so motivated to provide a means to utilize statistical analysis to understand the potential accuracy of the prediction, but in this case for estimating epidemiologic dynamics using viral loads (Page 1, Hay discloses: “… current metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points…”).
Hay does not explicitly disclose: reproductive rate (Rt), time series value
Cori discloses: reproductive rate (Rt), time series value
reproductive rate (Rt)(Page 1506, Cori discloses: “instantaneous reproduction number, Rt”)
time series values(Page 1510, Cori discloses: “numbers from incidence time series” [TIME SERIES VALUES])
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the model for estimating epidemiologic dynamics using viral loads of Hay to add reproductive rate (Rt) and time series value, as taught by Cori. One of ordinary skill would have been so motivated to understand epidemic growth on a time-series basis for accurate prediction of spread, but in this case for time-varying reproduction numbers during epidemics(Page 1505, Cori discloses:” The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses.”).
Claim 14
Hay discloses:
The method of claim 13 wherein using the respective sets of estimated incident rates(Page 7, Hay discloses: “rate of incident infections”[INCIDENT RATES]) to determine respective sets of estimated effective reproductive rate (Rt) time series values comprises using EpiEstim processing.
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add incident rate, as taught by Hay. One of ordinary skill would have been so motivated to evaluate incidence rate when determining the presence and spread of an epidemic, but in this case for estimating epidemiologic dynamics using viral loads (Page 1, Hay discloses: “… current metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points…”).
Hay does not explicitly disclose: reproductive rate (Rt), time series value, EpiEstim
Cori discloses: reproductive rate (Rt), time series value, EpiEstim
reproductive rate (Rt)(Page 1506, Cori discloses: “instantaneous reproduction number, Rt”)
time series values(Page 1510, Cori discloses: “numbers from incidence time series” [TIME SERIES VALUES])
EpiEstim(Page 1507, Cori discloses: “EpiEstim”)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the model for estimating epidemiologic dynamics using viral loads of Hay to add reproductive rate (Rt), time series value, and EpiEstim, as taught by Cori. One of ordinary skill would have been so motivated to understand epidemic growth on a time-series basis for accurate prediction of spread using a model specific for reproductive rate, but in this case for time-varying reproduction numbers during epidemics(Page 1505, Cori discloses:” The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses.”).
Claim 15
Hay discloses:
The method of claim 14 wherein using the respective sets of Ct values(Page 1, Hay discloses: “cycle threshold (Ct) values”[Ct VALUES CAN BE CONSIDERED Ct DATA]) to determine respective sets of estimated incident rates(Page 7, Hay discloses: “rate of incident infections”[INCIDENT RATES]) comprises using Hay model(Page 1, Hay discloses: “ this work provides a new method for estimating the epidemic growth rate and framework for robust epidemic monitoring” [METHOD CAN BE CONSIDERED HAY MODEL]) processing.
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add incident rate, Hay model, and Ct values, as taught by Hay. One of ordinary skill would have been so motivated to utilize the Hay model to evaluate incidence rate using Ct values when determining the presence and spread of an epidemic, but in this case for estimating epidemiologic dynamics using viral loads (Page 1, Hay discloses: “… current metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points…”).
Claims 9 and 10 are rejected under 35 U.S.C. 103 is being unpatentable over Segal(US20200373018A1)
In view of Hay(Science, 373, 16 July 2021, Pages 1-12) in view of Cori et al (American Journal of Epidemiology, Volume 178, Issue 9, 1 November 2013, Pages 1505–1512), further in view of Frank(US10813559B2)
Claim 9
Segal discloses:
The method of claim 7 wherein the recurrent neural network(Para 0115, Segal discloses: “neural network”) comprises two long term short term memory (LSTM) layers.
Segal, Hay, and Cori do not explicitly disclose: long term short term memory (LSTM)
Frank discloses: long term short term memory (LSTM)
long term short term memory (LSTM)(Col. 36, Line 36, Frank discloses: “LSTM”)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add LSTM, as taught by Frank. One of ordinary skill would have been so motivated to specify the type of recurrent neural network being utilized for epidemic analysis, but in this case for detecting respiratory tract infections(Col. 2, Line 58, Frank discloses: “…there is a need for an ambulatory system to monitor coughing, which can capture audio in real-world settings, in order to accurately assess extents of coughing to help determine the severity of an RTI.”).
Claim 10
Frank discloses:
The method of claim 9 wherein the two LSTM(Col. 36, Line 36, Frank discloses: “LSTM”) layers have a hidden state size of two.
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the method of diagnosing pathogenesis of viral infection for epidemic prevention of Segal to add LSTM, as taught by Frank. One of ordinary skill would have been so motivated to specify the type of recurrent neural network being utilized for epidemic analysis, but in this case for detecting respiratory tract infections(Col. 2, Line 58, Frank discloses: “…there is a need for an ambulatory system to monitor coughing, which can capture audio in real-world settings, in order to accurately assess extents of coughing to help determine the severity of an RTI.”).
Response to Arguments
35 U.S.C. 101
(Pages 7-8) The claims do not recite a mental process
Applicant's arguments filed have been fully considered but they are not persuasive. Defining how the Ct values are produced is irrelevant to how the 101 is applied. The Ct values are data. Receiving data can be considered human activity and performing statistical math can be performed mentally or using pen and paper.
(Page 8) The claims integrate into a practical application
Applicant's arguments filed have been fully considered but they are not persuasive. The additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which amount to Amount to mere instructions to apply an exception (MPEP 2106.05(f)) and add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea.
(Pages 8-9) The claims recite significantly more than the abstract idea
Applicant's arguments filed have been fully considered but they are not persuasive. If the machine learning was removed from the claim, the data processing (which is the inferred invention), can be performed by a human. The claim provides no technical detail on how the machine learning operates but merely aims to define the inputs and outputs.
35 U.S.C. 103
(Pages 9-10) The claims as amended are not obvious over Segal and Hay
Applicant's arguments filed have been fully considered but they are not persuasive. The claims as written merely utilize generic machine learning to perform abstract functions (mental process and human activity). The addition of a thermal cycler and PCR instrumentation only describes how the Ct values are generated. Statistical features (such as mean and skewness) only act to define what the features are. The claims aim to only define the input and outputs of the system without describing technically how the system works, therefore the application of Segal and Hay for art applies. The claims as interpreted under BRI aim to combine generic or existing concepts and apply them to a specific field.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Achin(US 20230051833 A1): A method for using machine learning for epidemiological modeling
Sabeti(US20210050116A1): A technology that uses aggregated health data and outbreak models to provide risk assessments that indicate a likelihood of contracting COVID-19.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERYL GOPAL PATEL whose telephone number is (703)756-1990. The examiner can normally be reached Monday - Friday 5:30am to 2:30pm PST.
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, Kambiz Abdi can be reached at 571-272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685