DETAILED ACTION
Priority
The current application claims benefit of provisional application 63/362,818, filed on April 11, 2022. Examiner acknowledges the applicant’s claim for priority.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 18th, 2024 is being considered by the examiner.
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 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 a judicial exception without significantly more.
Step 1
Claims 1-19 recite subject matter within a statutory category as a process, machine, and/or article of manufacture. Independent claim 20 recites ineligible subject matter because a computer readable medium, under broadest reasonable interpretation, includes transitory forms of signal transmission and amounts to signal per se. However, it will be shown in the following steps, that claims 1-20 are nonetheless unpatentable under 35 U.S.C. 101.
Step 2A Prong One
Claim 1 states:
A computer-implemented method of forecasting epidemic or pandemic related cases and/or deaths at a localized level, the method comprising:
obtaining data from a plurality of online databases;
preprocessing the obtained data;
extracting a plurality of feature vectors from the preprocessed data, wherein the plurality of feature vectors comprises mobility data over a rolling time period, vaccination data, stringency measures, epidemic or pandemic cases and/or deaths data, and demographic data;
training a machine learning model using the extracted features and preprocessed data;
validating the trained machine learning model; and
predicting future epidemic or pandemic cases and/or deaths at the localized level using the validated machine learning model.
The broadest reasonable interpretation of these steps includes mental processes and/or mathematical concepts because each bolded component can practically be performed by the human mind or with pen and paper. Other than reciting generic computer terms like “computer” or “machine learning model”, nothing in the claims precludes the bold-font portions from practically being performed in the mind. For example, but for the “computer” language, “extracting a plurality of feature vectors from the preprocessed data, wherein the plurality of feature vectors comprises mobility data over a rolling time period, vaccination data, stringency measures, epidemic or pandemic cases and/or deaths data, and demographic data” in the context of this claim encompasses a mental process of the user recalling specific case information from previous COVID 19 cases in a hospital setting during a pandemic to determine any potential patterns. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Thus:
method of forecasting epidemic or pandemic related cases and/or deaths at a localized level, the method comprising:
preprocessing the obtained data;
extracting a plurality of feature vectors from the preprocessed data, wherein the plurality of features vectors comprises mobility data over a rolling time period, vaccination data, stringency measures, epidemic or pandemic cases and/or deaths data, and demographic data;
predicting future epidemic or pandemic cases and/or deaths at the localized level
as drafted, could lay out a healthcare professional mentally reviewing previous cases during a pandemic and finding symptoms for contagious patients to avoid the spread of an infectious disease to other patients. Therefore, under the broadest reasonable interpretation, these steps include multiple abstract ideas that will be identified as a single abstract idea moving forward.
Independent claims 15 and 20 cover similar steps of receiving data, preprocessing data, extracting data from the preprocessed data, training a machine learning model, validating the machine learning model, and predicting future epidemics. These claims fall under the same category of an abstract idea and follows the same rationale as claim 1.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 9, reciting particular aspects of how “wherein the machine learning model comprises a plurality of gradient boosted decision tree algorithms” recite an abstract idea but for recitation of generic computer components).
Dependent claims 2, 3, 16, and 17 add additional elements to their parent claims which will be further inspected in the following steps for a practical application to their abstract idea.
Step 2A Prong Two
This judicial exception of “Mental Processes” or “Organizing Human Activity” is not integrated into a practical application. Independent claim 1’s method recites additional elements such as computer and machine learning model. In addition to the generic components and additional elements listed above, independent claims 15 and 20’s system and computer product also includes a processor and computer readable medium. The computer, processor, machine learning model, and computer readable medium will be treated as a generic computer component. In particular, these additional elements do not integrate the abstract idea into a practical application because the additional elements:
amount to mere instructions to apply an exception (such as recitation of “computer-implemented” and “the validated machine learning model” amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [0007] “a generalizable and automated machine learning based modelling pipeline” and [0047] “Aspects of embodiments can be implemented in the form of control logic using hardware (e.g. an application specific integrated circuit or field programmable gate array) and/or using computer software with a generally programmable processor”, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of “obtaining data from a plurality of online databases” amounts to mere data gathering, recitation of “training a machine learning model using the extracted features and preprocessed data” and “validating the trained machine learning model” amounts to insignificant application, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Additionally, claim 2 “recommending levels of health care resources at the localized level based on the predicted future epidemic or pandemic cases and/or deaths.” and claim 3 “recommending levels of epidemic or pandemic testing supplies at the localized level based on the predicted future epidemic or pandemic cases and/or deaths.” and claim 16 “recommend levels of health care resources at the localized level based on the predicted future epidemic or pandemic cases and/or deaths” and claim 17 “recommend levels of epidemic or pandemic testing supplies at the localized level based on the predicted future epidemic or pandemic cases and/or deaths.” amounts to necessary data outputting, see MPEP 2106.05(g). 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 improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The remaining dependent claims 4-14 and 18-19 do not recite additional elements or activity but further narrow or define the abstract idea embodied in the claims and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
The claims 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 amount to no more than mere instructions to apply an exception, and add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
By example, the claim recites an additional element of a machine learning model. Chickering et al. (US20020180805) demonstrates in [0047] “In general, the system and process of the present invention is started by obtaining a probabilistic model 300, such as by learning or creating one using conventional machine learning techniques” that machine learning models were conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
To elaborate:
“obtaining data from a plurality of online databases” , is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
“training a machine learning model using the extracted features and preprocessed data” Arranging a hierarchy of groups, and sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(vi)
“validating the trained machine learning model”, is equivalently, performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii);
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. These additional limitations amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
To elaborate:
claim 2 “recommending levels of health care resources at the localized level based on the predicted future epidemic or pandemic cases and/or deaths.” , is equivalently, Presenting offers and gathering statistics, OIP Techs., MPEP 2106.05(d)(II)(iv)
claim 3 “recommending levels of epidemic or pandemic testing supplies at the localized level based on the predicted future epidemic or pandemic cases and/or deaths.” , is equivalently, Presenting offers and gathering statistics, OIP Techs., MPEP 2106.05(d)(II)(iv)
claim 16 “recommend levels of health care resources at the localized level based on the predicted future epidemic or pandemic cases and/or deaths” , is equivalently, Presenting offers and gathering statistics, OIP Techs., MPEP 2106.05(d)(II)(iv)
claim 17 “recommend levels of epidemic or pandemic testing supplies at the localized level based on the predicted future epidemic or pandemic cases and/or deaths.” , is equivalently, Presenting offers and gathering statistics, OIP Techs., MPEP 2106.05(d)(II)(iv)
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 improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, 9-11, 15-17, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Achin et al (US 20220199266).
Regarding claim 1, Achin teaches.
A computer-implemented method of forecasting epidemic or pandemic related cases and/or deaths at a localized level, the method comprising: ([0012] “At least one aspect is directed to a method of modeling infectious diseases”)
obtaining data from a plurality of online databases; ([0012] “modeling at least one infectious disease, can include receiving, from one or more data sources, data including values associated with an occurrence of the infectious disease during a first time period”; see also [0099] “The shared resources and services can include, but not limited to, networks, network bandwidth, servers 195, processing, memory, storage, applications, virtual machines, databases” where the databases comprise online sources of information)
preprocessing the obtained data; ([0118] “A template may encode, for machine execution, pre-processing steps, model-fitting steps, and/or post-processing steps suitable for use with the template's predictive modeling algorithm(s).”)
extracting a plurality of feature vectors from the preprocessed data, ([0118] “Examples of pre-processing steps include, without limitation, imputing missing values, feature engineering (e.g., one-hot encoding, splines, text mining, etc.), feature selection (e.g., dropping uninformative features, dropping highly correlated features, replacing original features by top principal components, etc.).” where feature selection is extracting feature vectors) wherein the plurality of feature vectors comprises mobility data over a rolling time period, vaccination data, ([0009] “Present implementations can thus positively affect outcomes in past, ongoing, and future vaccine trial enrollment, vaccine distribution, and rapid antigen testing distribution” where vaccine distribution in ongoing trials comprises both mobility data over a time period and vaccination data) stringency measures, epidemic or pandemic cases and/or deaths data, and demographic data; ([0085] “Data that can be included in the epidemiological modeling includes, but is not limited to, any data relevant to disease spread, including but not limited to static data, such as socio-economic data, demographic data (i.e., higher population density in urban areas can lead to increased disease spread), as well as non-static data, such as (1) real-time reported cases, deaths, testing data, vaccination rates, and/or hospitalization rates from any suitable source, including from a domestic epidemiological entity or foreign equivalent, state health agencies, hospitals or health networks, etc.; (2) real-time mobility data (e.g., movement trends over time by geography across different categories of places, such as retail and recreation, groceries and pharmacies, parks, transit stations, including but not limited to airports, bus terminals, train stations, toll data, workplaces, and residential; (3) real-time climate and other environmental data known to be disease drivers (temperature, rainfall, etc.; remote sensing data); (4) big data derived from electronic health records, social media, the internet and other digital sources such as mobile phones.”; see also [0343] “Data elements can include, be based on, or the like, infection, death, testing, mobility, recovery, and the like, can be based on, inferred from, or the like, from policy action by public, non-public, or hybrid public-private actors, including restrictions on travel, movement, density, crowds, curfews, lockdowns, and the like” where lockdowns comprises stringency measures)
training a machine learning model using the extracted features and preprocessed data; ([0124] “a template's metadata may indicate the processing resources needed to train and/or test the modeling technique on a dataset of a given size, the effect on resource consumption of the number of cross-validation folds and the number of points searched in the hyper-parameter space, the intrinsic parallelization of the processing steps performed by the modeling technique, etc.”)
validating the trained machine learning model; and ([0203] “To facilitate cross-validation, predictive modeling system 200 may partition the dataset (or suggest a partitioning of the dataset) into K “folds”)
predicting future epidemic or pandemic cases and/or deaths at the localized level using the validated machine learning model. ([0082] “Another aspect of the present disclosure encompasses modeling and predicting the impact and spread of SARS-CoV-2 strains include the L strain, the S strain”)
Regarding claim 2, Achin teaches all of the limitations of claim 1. Achin also teaches:
recommending levels of health care resources at the localized level based on the predicted future epidemic or pandemic cases and/or deaths. ([0388] “Effective disease modeling can advantageously help governments control outbreaks by optimizing the allocation of resources such as tests, ventilators, and personnel.”)
Regarding claim 3, Achin teaches all of the limitations of claim 1. Achin also teaches
recommending levels of epidemic or pandemic testing supplies at the localized level based on the predicted future epidemic or pandemic cases and/or deaths. ([0343] Present implementations can include ML-powered long-term forecasting can reduce the time needed for successful vaccine trials, optimize the distribution of vaccines to areas that will see the largest impact, and provide targeting information for the distribution of rapid antigen tests to dampen the effects of future outbreaks”)
Regarding claim 4, Achin teaches all of the limitations of claim 1. Achin also teaches:
wherein the plurality of feature vectors further comprises calendar related data. ([0026] In an example system, the one or more processors are further configured to provide, based on the one or more second forecast and for display, at least one of a daily incidence level chart, a weekly incidence trend chart, an incidence level map, or a testing level chart.” Where the weekly incident chart comprises collecting calendar data for the incidences)
Regarding claim 5, Achin teaches all of the limitations of claim 1. Achin also teaches:
wherein the plurality of feature vectors further comprises socio-economic data. ([0085] Data that can be included in the epidemiological modeling includes, but is not limited to, any data relevant to disease spread, including but not limited to static data, such as socio-economic data,”)
Regarding claim 6, Achin teaches all of the limitations of claim 1. Achin also teaches:
wherein the feature vectors comprise weather data. ([0085] Data that can be included in the epidemiological modeling includes, but is not limited to, any data relevant to disease spread, including but not limited to static data, such as socio-economic data, demographic data (i.e., higher population density in urban areas can lead to increased disease spread), as well as non-static data, such as … (3) real-time climate and other environmental data known to be disease drivers (temperature, rainfall, etc.; remote sensing data)”)
Regarding claim 7, Achin teaches all of the limitations of claim 1. Achin also teaches:
wherein the mobility data comprises cellular network data and/or gps data. ([0194] “For interpreted variables (e.g., date, time, currency, measurement units, percentages, and location coordinates), examples of transformations include, without limitation, parsing a date string into a continuous time variable, day of week, month, and season to test each aspect of the date for predictive power.” Where location coordinates comprise gps data)
Regarding claim 9, Achin teaches all of the limitations of claim 1. Achin also teaches:
wherein the machine learning model comprises an optimized gradient boosting algorithm. ([0205] “reducing the size of the data samples to which the predictive models are fitted may reduce the amount of computing resources needed to tune the parameters of a predictive model or the hyper-parameters of a modeling technique. Hyper-parameters include variable settings for a modeling technique that can affect the speed, efficiency, and/or accuracy of model fitting process. Examples of hyper-parameters include, without limitation, the penalty parameters of an elastic-net model, the number of trees in a gradient boosted trees model”)
Regarding claim 10, Achin teaches all of the limitations of claim 9. Achin also teaches:
wherein the machine learning model comprises a plurality of gradient boosted decision tree algorithms. ([0205] “reducing the size of the data samples to which the predictive models are fitted may reduce the amount of computing resources needed to tune the parameters of a predictive model or the hyper-parameters of a modeling technique. Hyper-parameters include variable settings for a modeling technique that can affect the speed, efficiency, and/or accuracy of model fitting process. Examples of hyper-parameters include, without limitation, the penalty parameters of an elastic-net model, the number of trees in a gradient boosted trees model”)
Regarding claim 11, Achin teaches all of the limitations of claim 1. Achin also teaches:
wherein the vaccination data comprises types of vaccines, ([0016-0017] “In an example method, the disease is selected from the group consisting of Anthrax, Arboviral diseases (diseases caused by viruses spread by mosquitoes, sandflies, ticks, etc.) such as West Nile virus, eastern and western equine encephalitis, Babesiosis, Botulism, Brucellosis, Campylobacteriosis, Chancroid, Chickenpox, Chlamydia, Cholera, Coccidioidomycosis, Coronavirus (COVID-19), Cryptosporidiosis, Cyclosporiasis, Dengue virus infections, Diphtheria, Ehrlichiosis, Foodborne disease outbreak, Giardiasis, Gonorrhea, Haemophilus influenza (invasive disease), Hantavirus pulmonary syndrome, Hemolytic uremic syndrome (post-diarrheal), Hepatitis A, Hepatitis B, Hepatitis C, HIV infection, Influenza-related infant deaths, Invasive pneumococcal disease, Lead (elevated blood level), Legionnaire disease (legionellosis), Leprosy, Leptospirosis, Listeriosis, Lyme disease, Malaria, Measles, Meningitis (meningococcal disease), Mumps, Novel influenza A virus infections, Pertussis, Pesticide-related illnesses and injuries, Plague, Poliomyelitis, Poliovirus infection (nonparalytic), Psittacosis, Q-fever, Rabies (human and animal cases), Rubella (including congenital syndrome), Salmonella paratyphi and typhi infections, Salmonellosis, Severe acute respiratory syndrome-associated coronavirus disease, Shiga toxin-producing Escherichia coli (STEC), Shigellosis, Smallpox, Syphilis (including congenital syphilis), Tetanus, Toxic shock syndrome (other than streptococcal), Trichinellosis, Tuberculosis, Tularemia, Typhoid fever, Vancomycin intermediate Staphylococcus aureus (VISA), Vancomycin resistant Staphylococcus aureus (VRSA), Vibriosis, Viral hemorrhagic fever (including Ebola virus, Lassa virus, among others), Waterborne disease outbreak, Yellow fever, and Zika virus disease and infection (including congenital). In an example method, the infectious disease includes at least one of COVID-19, a strain corresponding to COVID-19, or a variant of SARS-CoV-2.”; see also [0077] “More accurate predictions of influenza disease progression and types can also aid in selecting influenza strains to be included in the yearly influenza vaccination”) efficacy data for the types of vaccines, and vaccine administration data. ([0341] “distributing antigen tests a relative period of time could be on the order of weeks, while for choosing locations to enroll participants in the US COVID-19 vaccine trials, the relevant period of time could be months. It can be advantageous in US COVID-19 trials to measure primary efficacy endpoints 1-2 weeks after second dosage of a candidate vaccine is administered. This can indicate that incidence 4-6 weeks after the first administered dosage would be the time period of interest when incidence has the largest effect on vaccine evaluation” where the system utilizes efficacy data and vaccine data administration)
Regarding claim 15, Achin teaches:
A system for forecasting epidemic or pandemic related cases and/or deaths at a localized level, the system comprising: one or more processors programmed to: ([0021] “A system to model at least one infectious disease, can include a machine learning model executable on one or more processors coupled to memory”)
receive data from a plurality of online databases; ([0012] “modeling at least one infectious disease, can include receiving, from one or more data sources, data including values associated with an occurrence of the infectious disease during a first time period”; see also [0099] “The shared resources and services can include, but not limited to, networks, network bandwidth, servers 195, processing, memory, storage, applications, virtual machines, databases” where the databases comprise online sources of information)
preprocess the received data; ([0118] “A template may encode, for machine execution, pre-processing steps, model-fitting steps, and/or post-processing steps suitable for use with the template's predictive modeling algorithm(s).”)
extract a plurality of feature vectors from the preprocessed data, ([0118] “Examples of pre-processing steps include, without limitation, imputing missing values, feature engineering (e.g., one-hot encoding, splines, text mining, etc.), feature selection (e.g., dropping uninformative features, dropping highly correlated features, replacing original features by top principal components, etc.).” where feature selection is extracting feature vectors) wherein the plurality of feature vectors comprises mobility data over a rolling time period, stringency measures, spatial data, vaccination data, epidemic or pandemic cases and/or deaths data, and demographic data; ([0009] “Present implementations can thus positively affect outcomes in past, ongoing, and future vaccine trial enrollment, vaccine distribution, and rapid antigen testing distribution” where vaccine distribution in ongoing trials comprises both mobility data over a time period and vaccination data; see also [0085] “Data that can be included in the epidemiological modeling includes, but is not limited to, any data relevant to disease spread, including but not limited to static data, such as socio-economic data, demographic data (i.e., higher population density in urban areas can lead to increased disease spread), as well as non-static data, such as (1) real-time reported cases, deaths, testing data, vaccination rates, and/or hospitalization rates from any suitable source, including from a domestic epidemiological entity or foreign equivalent, state health agencies, hospitals or health networks, etc.; (2) real-time mobility data (e.g., movement trends over time by geography across different categories of places, such as retail and recreation, groceries and pharmacies, parks, transit stations, including but not limited to airports, bus terminals, train stations, toll data, workplaces, and residential; (3) real-time climate and other environmental data known to be disease drivers (temperature, rainfall, etc.; remote sensing data); (4) big data derived from electronic health records, social media, the internet and other digital sources such as mobile phones.”; see also [0343] “Data elements can include, be based on, or the like, infection, death, testing, mobility, recovery, and the like, can be based on, inferred from, or the like, from policy action by public, non-public, or hybrid public-private actors, including restrictions on travel, movement, density, crowds, curfews, lockdowns, and the like” where lockdowns comprises stringency measures; see also [0194] “For interpreted variables (e.g., date, time, currency, measurement units, percentages, and location coordinates), examples of transformations include, without limitation, parsing a date string into a continuous time variable, day of week, month, and season to test each aspect of the date for predictive power.” Where location coordinates comprise spatial data)
train a machine learning model using the extracted features and preprocessed data; ([0124] “a template's metadata may indicate the processing resources needed to train and/or test the modeling technique on a dataset of a given size, the effect on resource consumption of the number of cross-validation folds and the number of points searched in the hyper-parameter space, the intrinsic parallelization of the processing steps performed by the modeling technique, etc.”; see also [0217] “In some embodiments, predictive modeling system 200 may offer one or more advantages in developing blended prediction models. First, blending may work better when a large variety of candidate models are available to blend. Moreover, blending may work better when the differences between candidate models correspond not simply to minor variations in algorithms but rather to major differences in approach, such as those among linear models, tree-based models, support vector machines, and nearest neighbor classification”)
validate the trained machine learning model; ([0203] “To facilitate cross-validation, predictive modeling system 200 may partition the dataset (or suggest a partitioning of the dataset) into K “folds”)
and predict future epidemic or pandemic cases and/or deaths at the localized level using the validated machine learning model. (“[0082] Another aspect of the present disclosure encompasses modeling and predicting the impact and spread of SARS-CoV-2 strains include the L strain, the S strain,”)
Regarding claim 16, Achin teaches all of the limitations of claim 15. Achin also teaches:
wherein the one or more processors are programmed to: recommend levels of health care resources at the localized level based on the predicted future epidemic or pandemic cases and/or deaths ([0388] “Effective disease modeling can advantageously help governments control outbreaks by optimizing the allocation of resources such as tests, ventilators, and personnel.”)
Regarding claim 17, Achin teaches all of the limitations of claim 15. Achin also teaches:
wherein the one or more processors are programmed to: recommend levels of epidemic or pandemic testing supplies at the localized level based on the predicted future epidemic or pandemic cases and/or deaths. ([0343] Present implementations can include ML-powered long-term forecasting can reduce the time needed for successful vaccine trials, optimize the distribution of vaccines to areas that will see the largest impact, and provide targeting information for the distribution of rapid antigen tests to dampen the effects of future outbreaks”)
Regarding claim 19, Achin teaches all of the limitations of claim 15. Achin also teaches:
wherein the feature vectors comprise weather data. ([0085] Data that can be included in the epidemiological modeling includes, but is not limited to, any data relevant to disease spread, including but not limited to static data, such as socio-economic data, demographic data (i.e., higher population density in urban areas can lead to increased disease spread), as well as non-static data, such as … (3) real-time climate and other environmental data known to be disease drivers (temperature, rainfall, etc.; remote sensing data)”)
Regarding claim 20, Achin teaches:
A computer readable medium storing instructions for causing one or more processors to: ([0030] “At least one aspect is directed to a computer readable medium including one or more instructions stored thereon and executable by a processor to model infectious diseases”)
receive data from a plurality of online databases; ([0012] “modeling at least one infectious disease, can include receiving, from one or more data sources, data including values associated with an occurrence of the infectious disease during a first time period”; see also [0099] “The shared resources and services can include, but not limited to, networks, network bandwidth, servers 195, processing, memory, storage, applications, virtual machines, databases” where the databases comprise online sources of information)
preprocess the received data; [0118] “A template may encode, for machine execution, pre-processing steps, model-fitting steps, and/or post-processing steps suitable for use with the template's predictive modeling algorithm(s).”)
extract a plurality of feature vectors from the preprocessed data, ([0118] “Examples of pre-processing steps include, without limitation, imputing missing values, feature engineering (e.g., one-hot encoding, splines, text mining, etc.), feature selection (e.g., dropping uninformative features, dropping highly correlated features, replacing original features by top principal components, etc.).” where feature selection is extracting feature vectors) wherein the plurality of feature vectors comprises mobility data over a rolling time period, stringency measures, spatial data, epidemic or pandemic cases and/or deaths data, vaccination data, and demographic data; ([0009] “Present implementations can thus positively affect outcomes in past, ongoing, and future vaccine trial enrollment, vaccine distribution, and rapid antigen testing distribution” where vaccine distribution in ongoing trials comprises both mobility data over a time period and vaccination data; see also [0085] “Data that can be included in the epidemiological modeling includes, but is not limited to, any data relevant to disease spread, including but not limited to static data, such as socio-economic data, demographic data (i.e., higher population density in urban areas can lead to increased disease spread), as well as non-static data, such as (1) real-time reported cases, deaths, testing data, vaccination rates, and/or hospitalization rates from any suitable source, including from a domestic epidemiological entity or foreign equivalent, state health agencies, hospitals or health networks, etc.; (2) real-time mobility data (e.g., movement trends over time by geography across different categories of places, such as retail and recreation, groceries and pharmacies, parks, transit stations, including but not limited to airports, bus terminals, train stations, toll data, workplaces, and residential; (3) real-time climate and other environmental data known to be disease drivers (temperature, rainfall, etc.; remote sensing data); (4) big data derived from electronic health records, social media, the internet and other digital sources such as mobile phones.”; see also [0343] “Data elements can include, be based on, or the like, infection, death, testing, mobility, recovery, and the like, can be based on, inferred from, or the like, from policy action by public, non-public, or hybrid public-private actors, including restrictions on travel, movement, density, crowds, curfews, lockdowns, and the like” where lockdowns comprises stringency measures see also [0194] “For interpreted variables (e.g., date, time, currency, measurement units, percentages, and location coordinates), examples of transformations include, without limitation, parsing a date string into a continuous time variable, day of week, month, and season to test each aspect of the date for predictive power.” Where location coordinates comprise spatial data)
train a machine learning model using the extracted features and preprocessed data; ([0124] “a template's metadata may indicate the processing resources needed to train and/or test the modeling technique on a dataset of a given size, the effect on resource consumption of the number of cross-validation folds and the number of points searched in the hyper-parameter space, the intrinsic parallelization of the processing steps performed by the modeling technique, etc.”; [0217] “In some embodiments, predictive modeling system 200 may offer one or more advantages in developing blended prediction models. First, blending may work better when a large variety of candidate models are available to blend. Moreover, blending may work better when the differences between candidate models correspond not simply to minor variations in algorithms but rather to major differences in approach, such as those among linear models, tree-based models, support vector machines, and nearest neighbor classification”)
validate the trained machine learning model; ([0203] “To facilitate cross-validation, predictive modeling system 200 may partition the dataset (or suggest a partitioning of the dataset) into K “folds”)
and predict future epidemic or pandemic cases and/or deaths at the localized level using the validated machine learning model. (“[0082] Another aspect of the present disclosure encompasses modeling and predicting the impact and spread of SARS-CoV-2 strains include the L strain, the S strain”)
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.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Achin et al. (US20230051833) in view of Jafri et al. (Pat. 11011003).
Regarding claim 8, Achin teaches all of the limitations of claim 1. Achin also teaches:
wherein the stringency measures comprise data on curfews, lockdowns, business closures, school closures, [0085] “Data that can be included in the epidemiological modeling includes, but is not limited to, any data relevant to disease spread, including but not limited to static data, such as socio-economic data, demographic data (i.e., higher population density in urban areas can lead to increased disease spread), as well as non-static data, such as (1) real-time reported cases, deaths, testing data, vaccination rates, and/or hospitalization rates from any suitable source, including from a domestic epidemiological entity or foreign equivalent, state health agencies, hospitals or health networks, etc.; (2) real-time mobility data (e.g., movement trends over time by geography across different categories of places, such as retail and recreation, groceries and pharmacies, parks, transit stations, including but not limited to airports, bus terminals, train stations, toll data, workplaces, and residential; (3) real-time climate and other environmental data known to be disease drivers (temperature, rainfall, etc.; remote sensing data); (4) big data derived from electronic health records, social media, the internet and other digital sources such as mobile phones.”; see also [0343] “Data elements can include, be based on, or the like, infection, death, testing, mobility, recovery, and the like, can be based on, inferred from, or the like, from policy action by public, non-public, or hybrid public-private actors, including restrictions on travel, movement, density, crowds, curfews, lockdowns, and the like” where lockdowns comprises stringency measures)
Regarding claim 8, Achin does not explicitly teach, as taught by Jafri:
and masking. ([33] “user monitoring module 402 to detect one or more suspicious activities within or proximate to facility 114 such as user 108 not wearing personal protective equipment (mask, gloves, face-shield, etc.),… one embodiment, user monitoring module 402 is configured to receive the user data from server 102 collected directly from mobile computing device 110 allowing user monitoring module 402 to perform one or more analyses based on a plurality of interaction data relating to interactions between user 108 and the plurality of geographic designators associated with facility 114.” where detecting the use of masks is information for determining the stringency measures associated with forecasting epidemic or pandemic related cases”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Achin with the teachings of Jafri, with a reasonable expectation of success, by explicitly incorporating mask information into the information being analyzed. This would have increased the accuracy of how policy metrics are factored while predicting epidemic forecasting. Jafri is adaptable to Achin as both inventions use machine learning algorithms to track a variety of information to predict disease progression in a region. Achin would have found Jafri after searching for solutions to avoid the next pandemic, as Jafri explains “Pandemics not only cause significant economic, social, and political disruption, but also require drastic modifications to the everyday lives of individuals” see [0002].
Distinguishable Subject Matter
Claims 12, 13, 14 and 18, in combination with the claims in which they depend upon, distinguish claims from the applied art, though the references still recite ineligible subject matter under 35 U.S.C. 101. Claim 12’s combination of elements recites that the feature vectors comprise a first derivative in a number of cases over a time period of at least one day, a stringency index, an effective reproduction number, a contact index, a daily number of cases, a location, a calendar subcycle, and a number of people that travelled less than one mile over a rolling period of seven days. Claims 13 further adds to this list by reciting that a calendar subcycle is encoded with at least one of sine and cosine transformations. Similarly, claim 14’s claims state feature vectors further comprises a change in a number of cases over a period of three days, an effective reproduction number, a daily number of cases, a daily number of cases as determined by a seven day rolling average, a rate of change in a number of cases over a seven day period, a first derivative in a number of cases over a time period of at least one day, a percent of people fully vaccinated, and a day of the week cycle. The disclosed prior art does not teach these combination of limitations. Claim 18 recites similar recitations as claim 12 while being dependent on independent claim 15’s system.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Achin et al. (US20230051833) discloses an epidemiological modeling system who utilizes machine learning to model the progression of a pandemic based on simulations of historical data.
Meyerson et al. (US 20220013241) discloses a decision system that uses a machine learning model, trained with historical data to recommend choices with the intent to mitigate the spread of diseases.
Yan et al. (US20220083932) discloses a system which tracks event risk based on location, number of attendees, policy mitigating risk, and more.
Conclusion
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/R.A.S/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685