DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed December 03, 2025.
Claims 1-2, 4-17, and 21-26 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 .
Information Disclosure Statement(s)
Examiner has considered examination disclosure statement dated 12/03/2025
Response to Amendment
Claims 1-2, 4-17, and 21-26 are currently pending.
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 1-2, 4-17, and 21-26 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1, 16, 25, and 26:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent Claim 1 and 26 fall within the statutory category of method
Independent Claim 16 falls within the statutory category of system
Independent Claim 25 falls within the statutory category of article of manufacture
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 16, 25, and 26 claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in claim 1, 16, 25, and 26 (claim 1 as representative) is:
A method for adaptive monitoring of health data associated with an acute illness, over a network connected to one or more user devices, , the
(a) obtaining an unenriched study configuration for studying the acute illness, the unenriched study configuration comprising an unenriched pool of subjects;
(b) collecting,, health data relating to the unenriched pool of subjects, the health data comprising both
(i) one or more responses of the unenriched pool of subjects to one or more health queries, and (ii) geographic incidence data for the acute illness;
(c) predicting, , a risk of each subject of the unenriched pool of subjects for developing the acute illness during a time period based at least in part on the one or more responses and the geographic incidence data;
(d) comparing the risk of each subject of the unenriched pool of subjects for developing the acute illness to a predefined threshold, , thereby generating an enriched study configuration for studying the acute illness, the enriched study configuration comprising an enriched pool of subjects, wherein:
(i) the enriched pool of subjects has fewer subjects than the unenriched pool of subjects, and(ii) an average risk of each subject of the enriched pool of subjects developing the acute illness during the time period is higher than an average risk of each subject of the unenriched pool of subjects developing the acute illness during the time period;
(e) collecting, , additional health data associated with the acute illness
And (f) performing the study with the enriched study configuration
This abstract idea is “mental process” as it is making observations, evaluations, judgments, and opinions based on data to determine an enriched pool of subjects to perform a study configuration with. See MPEP § 2106.04(a).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claims 1, 16, 25, and 26 judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional claim elements below:
A computer
A network
A machine learning model
Wearable
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, a computer, is recited that is performing the abstract idea and merely recited as a tool or equivalent as “apply-it”
The additional element, a network, is recited that is performing the abstract idea and merely recited as a tool or equivalent as “apply-it” for data gathering
The additional element, a machine learning model, is recited as merely generally linking the abstract idea to the technological field of artificial intelligence
The additional element, a wearable, is recited as merely generally linking the abstract idea to the technological field of wearable computer devices
Independent claim 16 recites the additional claim elements not already recited in the independent claim 1 below:
one or more processors; and one or more memories storing computer-executable instructions
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, one or more processors; and one or more memories storing computer-executable instructions, is performing the abstract idea and is recited as merely a tool or equivalent as “apply-it”
Independent claim 25 recites the additional claim elements not already recited in the independent claim 1 below:
One or more non-transitory computer-readable media comprising computer-executable instructions and atleast one processor
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, One or more non-transitory computer-readable media comprising computer-executable instructions and atleast one processor, is performing the abstract idea and is recited as merely a tool or equivalent as “apply-it”
Independent claim 26 does not recite any additional elements not already recited in independent claim 1 therefore considered purely the abstract idea
Accordingly, independent claims 1, 16, 25, and 26 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements as analyzed above in step 2A prong 2, are merely applying the abstract idea or generally linking and therefore, do not amount to significantly more. The claims are patent ineligible.
Dependent Claims 2, 4-15, 17, and 21-24:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
The dependent claims 2 and 4-15 fall within the statutory category of method
The dependent claims 17, 21, 22, 23, and 24 fall within the statutory category of system
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2, 4-15, 17, and 21-24 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claims 1 and 16 abstract idea by (1) further limiting the analysis of data and the type of data. Therefore, the dependent claims inherit the same abstract idea which is “mental process” as it is making observations, evaluations, judgments, and opinions based on data to determine an enriched pool of subjects to perform a study configuration with. See MPEP § 2106.04(a).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2, 4-15, 17, and 21-24 this judicial exception is not integrated into a practical application.
The dependent claims recite the additional elements below not already recited in the independent claims:
A second machine learning model
Audio data or video data
A decision tree algorithm
A random forest model
a generative additive model
wearable devices
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, A second machine learning model, is generally linking the abstract idea to artificial intelligence
The additional element, audio or video data, is generally linking the abstract idea to implementation by computers
The additional element, a decision tree algorithm, is generally linking the abstract idea to artificial intelligence
The additional element, a random forest model, is generally linking the abstract idea to artificial intelligence
The additional element, a generative additive model, is generally linking the abstract idea to artificial intelligence
The additional element, wearable devices, is generally linking the abstract idea to wearable technology implementation
Accordingly, the dependent claims as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that amount to significantly more for the same reasons given in prong 2A-2. The claims are patent ineligible.
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.
Claims 1, 5, 9, 10, 16, 17, 21, 22, 25, and 26 are rejected to under 35 U.S.C. 103 as being unpatentable over Hopkins et. al (hereinafter Hopkins) (US20240185965A1) in view of Wolz et. al (hereinafter Wolz) (US20180190369A1)
As per claim 1, Hopkins teaches:
A method for adaptive monitoring of health data associated with an acute illness, over a network connected to one or more user devices, , the method comprising: (see Fig. 12 and see [0136] discloses, “The method utilizes within-subject PANSS data between two sequential assessment periods prior to randomization (screening and baseline). The ability to prognostically enrich for a specific dimension of psychopathology, independent of total item scores and, at the level of individual subjects is a powerful strategy for uncovering specific drug-treatment effects in clinical trials. Using clinical trial data evidence for specific treatment effects on certain symptom domains (e.g., negative symptom domain, anxiety symptom domains) may be demonstrated in trials of patients with an acute exacerbation of schizophrenia.”)
(a)obtaining an unenriched study configuration for studying the acute illness, ([0056] discloses, “In some embodiments, provided is a method of identifying subject(s) of a clinical trial that conform to the expected symptom presentation of an expected study population. In some embodiments, provided is a method for reducing statistical noise within the study population by, for example, detecting and removing anomalous subjects. In some embodiments, provided is a method of screening a study population (e.g., for a bipolar depression clinical trial), wherein the method determines whether a patient exhibits an anomaly in their Montgomery-Åsberg Depression Rating Scale (MADRS) score. In some embodiments, provided is a method of screening or pre-screening patients for a treatment, clinical trial, or clinical study (e.g., screen out patients who should not respond to the medication treating the disease or condition). In some embodiments of the present disclosure, provided is a method that can be used to verify eligibility of a patient for a treatment, where such verification can be used for enriching a population for a treatment, identifying subjects that conform to the expected symptom presentation of a test, reducing heterogeneity of a population for a treatment, and rejecting anomalous subjects for a treatment.”)
the unenriched study configuration comprising an unenriched pool of subjects; ([0006] In some embodiments, provided herein is a method of verifying eligibility of a subject for a treatment comprising representing the subject's symptoms in a rating scale as a vector, computing an anomaly score based on the vector of the subject, and multiple vectors representing rating scales of symptoms of other subjects. In some embodiments, the method includes, based on the anomaly score, ranking the subject with a likelihood of contributing to a subgroup of patients having a common element structure of the rating scale. In some embodiments, the method includes enriching a study population in a clinical trial prior to randomization, such that the enriched study population has a reduced heterogeneity.” And see
(b) collecting, by the network, health data relating to the unenriched pool of subjects from the one or more user devices, the health data comprising both (i) one or more responses of the unenriched pool of subjects to one or more health queries, ([0015] discloses, “In some embodiments, a method for determining subject participation in a clinical trial includes receiving one or more rater inputs reflecting the rater's clinical evaluation of a severity of a previously diagnosed condition in a subject. In some embodiments, the method includes performing a computerized assessment of the subject to quantify severity of the previously diagnosed condition in the subject through a computerized interview that comprises presenting a plurality of questions to the subject and receiving a plurality of corresponding inputs from the subject in response thereto, based on plurality of inputs received from the subject, determining an anomaly score for the condition in the subject, and determining, via a processor, a recommendation of including or excluding the subject from the clinical trial.” And see [0125] discloses, “FIG. 5 is a block diagram 600 illustrating an example embodiment of the present disclosure. In some embodiments, a method is provided for a subject 604 submitting a questionnaire at a clinical site 602 (606). In some embodiments, the subject 604 can submit the questionnaire using an electronic clinical outcome assessment (eCOA) device (606). The results of the study can be sent to a eCOA central database (608). After a successful refresh of the database (610), the method sends screening and baseline MADRS scores and demographic information of the subject 604 to an analysis central database (612). Once stored in the central database, an anomaly score can be calculated and stored in the analysis central database 614 (616).”
and (ii) geographic incidence data for the acute illness; (see table 2, “geographies” and see [0150] discloses, “PANSS assessments prior to randomization (screening, baseline) were pooled for ITT populations of 13 studies in schizophrenia (Table 2).”)
(c) predicting, using a machine learning model, a risk of each subject of the unenriched pool of subjects for developing the acute illness during a time period based at least in part on the one or more responses and the geographic incidence data; (see table 2 and see [0043] In a first embodiment, an anomaly detector method reduces heterogeneity relative to information (e.g., MADRS scores) collected in clinical trials or other datasets…[…]…an needing a population at larger N for properties to emerge. While both the first and second embodiments employ variance-covariance-difference vector formalism, the first embodiment employs a machine learning method on that vector. [0044] The anomaly detector method of the first embodiment is considered a strategy to decrease variability (i.e., FDA strategy 1).” And see [0008] discloses, “In some embodiments, provided is a method of improving a clinical dataset, wherein the method includes, for a subject of the clinical dataset, computing an anomaly score based on a comparison of multiple elements of a diagnosis test to respective expected patterns. In some embodiments, provided is a method of improving a clinical dataset, wherein the method includes, for a subject of the clinical dataset, computing an anomaly score based on a comparison of the structure of psychiatric elements of the test administered to a respective expected structure. In some embodiments, the method includes, if the anomaly score of the subject is above a particular threshold, removing data corresponding to the subject from the clinical dataset, or if the anomaly score of the subject is below the particular threshold, including the data corresponding to the subject from the clinical dataset.” And see [0097] discloses, “In addition, an expected population having a diagnosis 106 has associated population vectors 108 having a vector of elements for each individual within the population 106. The population vectors 108 have the same elements as the subject vector 104 (e.g., the MADRS test), so that the subject vector 104 can be easily compared to the population vector 108. A classification module (e.g., a trained forest model) 110 compares the subject vector 104 to the population vector 108 by traversing decision trees and generates an anomaly score 112 based on the comparison, as described more in further detail herein. The anomaly score 112 is low when the subject vector 104 is close to the expected patterns as shown in the population vectors 108. However, the anomaly score is high when the subject vector 104 deviates from the population vectors 108. A person having ordinary skill in the art can recognize that, in some embodiments, the anomaly score 112 can be scaled differently, such that a high score allows the subject inclusion in the study/treatment and a low score excludes the subject from the study/treatment.” And see [0098] discloses, “A pre-screening module 114 then screens the subject 102 based on the anomaly score 112. If the anomaly score is within a tolerable range (e.g., low enough), the subject can be included in the study 116. In some embodiments, provided is a method to verify the diagnosis, or approve treatment. However, if the anomaly score is outside of the tolerable range (e.g., too high), the subject is excluded from the study 118, diagnosis contradicted, or treatment denied.” / examiner notes anomaly scores as taught in the prior art is interpreted by examiner by its plain understanding to someone of ordinary skill in the art as a way to identify and quantify risk in regards to a clinical study)
(d) comparing the risk of each subject of the unenriched pool of subjects for developing the acute illness to a predefined threshold, thereby generating an enriched study configuration for studying the acute illness, the enriched study configuration comprising an enriched pool of subjects, wherein: ([0101] discloses, “A population enrichment module 122 then screens the subject 102 based on the anomaly score 112. If the anomaly score is within a tolerable range (e.g., low enough), the subject, and the subject's associated data, can be included in the clinical dataset 124. However, ifthe anomaly score is outside of the tolerable range (e.g., too high), the subject and its associated data is excluded from the study 124.” And see [0102] discloses, “FIG. 1C is a block diagram 140 illustrating example embodiments of the present disclosure. The method screens a subject 102 for verifying a diagnosis. In some embodiments, the subject 102 has been diagnosed with a disease or condition. In some embodiments, provided is a method to verify or reject the diagnosis of the disease or condition. First, the subject 102 takes a diagnosis test having multiple elements, which generates a subject vector 104. As described above, the subject vector is a one-dimensional array of a plurality of values derived from the item scores in a rating scale for a single subject. As one example, the subject vector 104 can be individual elements from the subject taking the MADRS test.” And see [0103] discloses, “In addition, an expected population having a diagnosis 106 has associated population vectors 108 having a vector of elements for each individual within the population 106. The population vectors 108 have the same elements as the subject vector 104 (e.g., the MADRS test), so that the subject vector 104 can be easily compared to the population vector 108. A classification module (e.g., a trained forest model) 110 compares the subject vector 104 to the population vector 108 by traversing decision trees and generates an anomaly score 112 based on the comparison, as described more in further detail below. The anomaly score 112 is low when the subject vector 104 is close to the expected patterns as shown in the population vectors 108. However, the anomaly score is high when the subject vector 104 deviates from the population vectors 108. A person having ordinary skill in the art can recognize that, in some embodiments, the anomaly score 112 can be scaled differently, such that a high score allows the subject inclusion in the study/treatment and a low score excludes the subject from the study/treatment.” And see [0034] discloses, “In a second embodiment, an enrichment method pre-defines a desired factor structure (e.g., of a PANSS score) and enriches the population for that desired factor structure one subject at a time.” And see [0044] discloses, “The enrichment method of the second embodiment is considered a prognostic enrichment strategy (i.e., FDA strategy 2).” )
(i) the enriched pool of subjects has fewer subjects than the unenriched pool of subjects, (see Fig. 8 and Fig. 9 for example and see [0161] discloses, “FIG. 9 are graphs 1000 illustrating factor scores for a study of the drug lurasidone. Graph 1002 illustrates the Marder Negative PANSS Symptom Factor Score (MPNS) for all subjects as measured at each week during a doubleblind baseline study for 504 placebo patients and 1041 patients being administered lurasidone. Graph 1004 illustrates the Marder Negative PANSS Symptom Factor Score (MPNS) for subjects enriched for MPNS as measured at each week during a double-blind baseline study for 70 placebo patients and 148 patients being administered ulotaront. Graph 1006 illustrates the Marder Negative PANSS Symptom Factor Score (MPNS) for subjects deenriched for MPNS as measured at each week during a double-blind baseline study for 427 placebo patients and 887 patients being administered ulotaront. [0162] Graph 1008 is a graph illustrating UPSM-transformed factor scores. UPSM transformed factor scores illustrate drug v. placebo effect size to a 95% confidence interval at each endpoint. The graph illustrates the UPSM-transformed factor scores for positive, disorganized, negative, hostility, affective, and PANSS total scores. The graph illustrates the results for all subjects (in the diamond), enriched subjects, and de-enriched subjects.”)
and (ii) an average risk of each subject of the enriched pool of subjects developing the acute illness during the time period is higher than an average risk of each subject of the unenriched pool of subjects developing the acute illness during the time period; ([0059] discloses, “In some embodiments, a method of verifying eligibility of a subject for a treatment includes administering a test, to the subject, that measures multiple symptoms of the subject. In some embodiments, the method includes computing an anomaly score based on a comparison of each element of the test administered to a respective expected pattern. In some embodiments, the method includes, based on the anomaly score, assigning, to the subject, a likelihood of having a condition related to the treatment.” And see [0097] discloses, “However, the anomaly score is high when the subject vector 104 deviates from the population vectors 108. A person having ordinary skill in the art can recognize that, in some embodiments, the anomaly score 112 can be scaled differently, such that a high score allows the subject inclusion in the study/treatment and a low score excludes the subject from the study/treatment.” And see [0101] discloses, “A population enrichment module 122 then screens the subject 102 based on the anomaly score 112. If the anomaly score is within a tolerable range (e.g., low enough), the subject, and the subject's associated data, can be included in the clinical dataset 124. However, if the anomaly score is outside of the tolerable range (e.g., too high), the subject and its associated data is excluded from the study 124.” And see [0102] discloses, “FIG. 1C is a block diagram 140 illustrating example embodiments of the present disclosure. The method screens a subject 102 for verifying a diagnosis. In some embodiments, the subject 102 has been diagnosed with a disease or condition. In some embodiments, provided is a method to verify or reject the diagnosis of the disease or condition. First, the subject 102 takes a diagnosis test having multiple elements, which generates a subject vector 104. As described above, the subject vector is a one-dimensional array of a plurality of values derived from the item scores in a rating scale for a single subject. As one example, the subject vector 104 can be individual elements from the subject taking the MADRS test.” And see [0103] discloses, “In addition, an expected population having a diagnosis 106 has associated population vectors 108 having a vector of elements for each individual within the population 106. The population vectors 108 have the same elements as the subject vector 104 (e.g., the MADRS test), so that the subject vector 104 can be easily compared to the population vector 108. A classification module (e.g., a trained forest model) 110 compares the subject vector 104 to the population vector 108 by traversing decision trees and generates an anomaly score 112 based on the comparison, as described more in further detail below. The anomaly score 112 is low when the subject vector 104 is close to the expected patterns as shown in the population vectors 108. However, the anomaly score is high when the subject vector 104 deviates from the population vectors 108. A person having ordinary skill in the art can recognize that, in some embodiments, the anomaly score 112 can be scaled differently, such that a high score allows the subject inclusion in the study/treatment and a low score excludes the subject from the study/treatment.”/ examiner notes the prior art is teaching higher anomaly score is likelihood or risk of patient having a disease thus the higher the score the higher the risk and higher average risk. )
…[…]…and(f) performing the study with the enriched study configuration. (Fig. 8 and Fig. 9 and see [0006] discloses, “In some embodiments, the method includes enriching a study population in a clinical trial prior to randomization, such that the enriched study population has a reduced heterogeneity.” And see [0159] discloses, “FIG. 8 are graphs 900 illustrating factor scores for a study of the drug ulotaront. Graph 902 illustrates the Marder Negative PANSS Symptom Factor Score (MPNS) for all subjects as measured at each week during a doubleblind baseline study for 125 placebo patients and 120 patients being administered ulotaront. Graph 904 illustrates the Marder Negative PANSS Symptom Factor Score (MPNS) for subjects enriched for MPNS as measured at each week during a double-blind baseline study for 29 placebo patients and 34 patients being administered ulotaront. Graph 906 illustrates the Marder Negative PANSs Symptom Factor Score (MPNS) for subjects de-enriched for MPNS as measured at each week during a double-blind baseline study for 96 placebo patients and 86 patients being administered ulotaront.” And see [0160] discloses, “Graph 908 is a graph illustrating UPSM-transformed factor scores. UPSM transformed factor scores illustrate drug v. placebo effect size to a 95% confidence interval at each endpoint. The graph illustrates the UPSM-transformed factor scores for positive, disorganized, negative, hostility, affective, and PANSS total scores. The graph illustrates the results for all subjects (in the diamond), enriched subjects, and de-enriched subjects.” And see [0161] discloses, “FIG. 9 are graphs 1000 illustrating factor scores for a study of the drug lurasidone. Graph 1002 illustrates the Marder Negative PANSS Symptom Factor Score (MPNS) for all subjects as measured at each week during a doubleblind baseline study for 504 placebo patients and 1041 patients being administered lurasidone. Graph 1004 illustrates the Marder Negative PANSS Symptom Factor Score (MPNS) for subjects enriched for MPNS as measured at each week during a double-blind baseline study for 70 placebo patients and 148 patients being administered ulotaront. Graph 1006 illustrates the Marder Negative PANSS Symptom Factor Score (MPNS) for subjects deenriched for MPNS as measured at each week during a double-blind baseline study for 427 placebo patients and 887 patients being administered ulotaront.”)
However, Hopkins does not explicitly teach:
(e) collecting, by the network, additional health data associated with the acute illness for each subject of the enriched pool of subjects for the time period, the additional health data comprising wearable data;
However, Wolz does explicitly teach:
(e) collecting, by the network, additional health data associated with the acute illness for each subject of the enriched pool of subjects for the time period, the additional health data comprising wearable data; (And see [0007] discloses, “Another aspect of the invention provides a method of selecting a patient for treatment , the method comprising the steps of : ( a ) collecting a first sub - set of patient specific data comprising at least two of : i ) demographic information ; ii ) medical history ; iii ) clinical symptoms ; iv ) subjective complaints and v ) activity from a wearable sensor ( b ) collecting a second sub - set of patient specific data comprising at least two of : vi ) clinical test results ; vii ) diagnostic imaging ; viii ) CSF analysis ; ix ) blood based markers and x ) genetic risk factors ; ( c ) combining the first sub - set of patient specific data and the second sub - set of patient specific data to define an enrichment indicator ; ( d ) comparing the enriched indicator with a set of pre - determined target indicators ; ( e ) characterizing one or more patients from which the first and second sub - sets of patient specific data were derived as being suitable or non - suitable for treatment of a neurodegenerative disease in accordance with step ( d ) ; and ( f ) selecting one or more patients for treatment.” And see [0032])
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkin’s teaching of enriching clinical study subjects by collecting data previously cited with Wolz teachings of enriching collecting data from a wearable, the motivation being that Wolz (see [0027]) and Hopkins ([0125]) are collecting data to enrich study populations, therefore the combination of Hopkins with the teachings of Wolz would increase the ease of collecting data and decrease resources needed to collect data to improve trials outcomes and speed furthering the providing of treatment in a cost effective and efficient manner.
As per claim 5, Hopkins further teaches:
The method of claim 1, wherein the one or more responses to the one or more health queries comprise physiological data that includes one or more of resting heart rate data, sleep data, step count data, blood pressure data, caloric data, nutrition data, or body temperature data. ([0047] discloses, “An element is a subject's response to a test or diagnostic test that is a measure of the subject's physical or mental state. The subject's response can be an answer to a question, such as rating symptoms on a numerical scale or a yes or no question (e.g., a binary element). The subject's response, in other embodiments, can be a physical response measured in an objective manner. Elements can include data such as a rating scale, latent variables, or domains of a rating scale, but can also include demographic information in some embodiments. In some embodiments, elements can be items of a MADRS or PANSS score, which are described further below. In some embodiments, elements can be inferred by the items of a MADRS or PANSS score, which are described further below. In some embodiments, an element is an unobserved variable that can be inferred from set of subject's responses. [0048] The Montgomery-Åsberg Depression Rating Scale (MADRS) score is a ten-item mood disorder diagnostic questionnaire, wherein each item is scored by a professional (e.g., a psychiatrist) on a scale of 0-6 for a total score of 0-60, with a higher score implies a worse symptom. The ten MADRS items are: 1. Apparent sadness; 2. Reported sadness; 3. Inner tension; 4. Reduced sleep; 5. Reduced appetite; 6. Concentration difficulties; 7. Lassitude; 8. Inability to feel; 9. Pessimistic thoughts; and 10. Suicidal thoughts. In general, many of the MADRS items are either highly correlated or highly inversely correlated. One example of a pair of highly correlated items is "apparent sadness" and "reported sadness." A person who scores high on "apparent sadness" is expected to also score high on "reported sadness" because those items are highly related. In other words, a person who appears sad should report sadness. From "apparent sadness" and "reported sadness", a latent element "sadness" can be inferred.”)
As per claim 9, Hopkins further teaches:
The method of claim 1, wherein the machine learning model comprises a decision tree algorithm. ([0097] discloses, “In addition, an expected population having a diagnosis 106 has associated population vectors 108 having a vector of elements for each individual within the population 106. The population vectors 108 have the same elements as the subject vector 104 (e.g., the MADRS test), so that the subject vector 104 can be easily compared to the population vector 108. A classification module (e.g., a trained forest model) 110 compares the subject vector 104 to the population vector 108 by traversing decision trees and generates an anomaly score 112 based on the comparison, as described more in further detail herein.”)
As per claim 10, Hopkins further teaches:
The method of claim 9, wherein the decision tree algorithm comprises a random forest model. ([0097] discloses, “In addition, an expected population having a diagnosis 106 has associated population vectors 108 having a vector of elements for each individual within the population 106. The population vectors 108 have the same elements as the subject vector 104 (e.g., the MADRS test), so that the subject vector 104 can be easily compared to the population vector 108. A classification module (e.g., a trained forest model) 110 compares the subject vector 104 to the population vector 108 by traversing decision trees and generates an anomaly score 112 based on the comparison, as described more in further detail herein.”)
As per claim 16 it is a system claim which repeat the same limitations of claim 1, the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Hopkins and Wolz as well as the motivations to combine disclose the underlying process steps that constitute the methods of claim 1, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claim 16 is rejected for the same reasons given above for claim 1.
As per claim 17, Hopkins does not explicitly teach:
The system of claim 16, wherein the wearable data for each subject of the enriched pool of subjects is collected via the network receiving data from a plurality of wearable devices, wherein each device of the plurality of wearable devices is associated with a subject of the enriched pool of subjects
However, Wolz does teach:
The system of claim 16, wherein the wearable data for each subject of the enriched pool of subjects is collected via the network receiving data from a plurality of wearable devices, wherein each device of the plurality of wearable devices is associated with a subject of the enriched pool of subjects (see Fig. 7 and see [0027] discloses, “Embodiments of the present invention seek to combine data collected in multiple locations which might include data held by general practitioners , data held by other medical professional , but may also include data collected by the patient themselves ( eg : wearable devices ) to provide an enrichment indicator.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkins’ teaching of larger unenriched population for tracking acute illness information such as for example clinical studies as previously cited with Wolz teachings of enriching study groups for a study configuration, for the same reasons given above for claim 1.
As per claim 21, Hopkins further teaches:
The system of claim 16, wherein the collecting at (b) further comprises obtaining demographic data corresponding to the unenriched pool of subjects ([0125] discloses, “FIG. 5 is a block diagram 600 illustrating an example embodiment of the present disclosure. In some embodiments, a method is provided for a subject 604 submitting a questionnaire at a clinical site 602 (606). In some embodiments, the subject 604 can submit the questionnaire using an electronic clinical outcome assessment (eCOA) device (606). The results of the study can be sent to a eCOA central database (608). After a successful refresh of the database (610), the method sends screening and baseline MADRS scores and demographic information of the subject 604 to an analysis central database (612).”)
As per claim 22, Hopkins further teaches:
The system of claim 21, wherein the demographic data comprises one or more of race data, ethnicity data, gender data, education data, or age data. (see table 2 “age group”)
As per claim 25, it is an article of manufacture claim which repeats the same limitations of claim 1, the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Hopkins and Wolz as well as the motivations to combine disclose the underlying process steps that constitute the method of claim 1, it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claim 25 are rejected for the same reasons given above for claim 1
As per claim 26, it is a method claim which repeats the same limitations of claim 1, the corresponding method claim with one new added limitation mapped below. Since the teachings of Hopkins and Wolz as well as the motivations to combine disclose the underlying process steps that constitute the method of claim 1, it is respectfully submitted that they cover the repeated limitations of claim 1. As such, the limitations of claim 26 are rejected for the same reasons given above for claim 1.
Hopkins further teaches:
i) at least one subject of the unenriched pool of subjects is not at risk of developing the acute illness during the time period, ([0037] discloses, “By removing anomalous patients, the drug effect is more accurately measured because the drug or placebo is not given to an individual who does not actually have the disease or condition the study is testing against.”)
Claims 6, 7, 8, 15, 23, and 24 are rejected to under 35 U.S.C. 103 as being unpatentable over Hopkins et. al (hereinafter Hopkins) (US20240185965A1) in view of Wolz et. al (hereinafter Wolz) (US20180190369A1) and in further view of Jain et. al (hereinafter Jain) (US11504011B1)
As per claim 6, Hopkins and Wolz does not teach:
The method of claim 1, wherein the acute illness is an infectious disease.
However, Jain does teach:
The method of claim 1, wherein the acute illness is an infectious disease. (Col. 2 lines 43-51 discloses, infectious diseases such as COVID-19)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkin’s teaching of collecting geographic data and using machine learning to determine enrichment prior to randomization for a clinical trial as previously cited and Wolz teachings of enriching clinical data with Jain’s teaching the disease being studied as infectious COVID-19, the motivation being that Wolz (see [0010]) and Hopkins ([0042]) are collecting data to enrich study populations through predictive means and understanding of geographies, therefore the combination of Hopkins and Wolz with Jain’s teaching of infectious disease would be a simple substitution following the same methods of enriching strategies.
As per claim 7, Hopkins and Walz do not teach:
The method of claim 6, wherein the infectious disease is COVID-19 or a flu.
However, Jain does teach:
The method of claim 6, wherein the infectious disease is COVID-19 or a flu. (Col. 2 lines 43-51 discloses, infectious diseases such as COVID-19)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkin’s teaching of collecting geographic data and using machine learning to determine enrichment prior to randomization for a clinical trial as previously cited and Wolz teachings of enriching clinical data with Jain’s teaching the disease being studied as infectious COVID-19 for the same reasons given for claim 6.
As per claim 8, Hopkins and Wolz do not teach:
The method of claim 1, wherein the one or more responses to the one or more health queries comprise one or both of audio data or video data.
However, Jain does teach:
The method of claim 1, wherein the one or more responses to the one or more health queries comprise one or both of audio data or video data. (Col. 129 lines 64-67 and Col. 130 lines 1-29 discloses, any data used or responded with can be video or audio style data)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkin’s teaching of collecting health query data as previously cited and Wolz teachings of sensor data from wearables with Jain’s teaching of the data being audio or video, the motivation being it is choice data and would be predictable to use electronic devices found in Hopkins, Wolz, and Jain to collect audio or video data with no unpredictable results and improve the quality of data used for enrichment strategies.
As per claim 15, Hopkins and Wolz do not teach:
The method of claim 1, wherein the one or more health queries comprise one or more of a household composition query, an occupation query, a residence query, or an infected contact query.
However, Jain does teach:
The method of claim 1, wherein the one or more health queries comprise one or more of a household composition query, an occupation query, a residence query, or an infected contact query. (Col. 80 lines 26-30 and Col. 106 lines 29-37 discloses, questions to the user on whether other users are wearing masks and if they have been diagnosed with COVID-19 which based on basic definition as no specific definition is given in the instant application is being interpreted as infected contact query)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkin’s teaching of collecting health query data as previously cited and Wolz teachings of sensor data from wearables with Jain’s teaching of the data for infected contact query for the same reasons given for claim 8.
As per claim 23, Hopkins and Wolz do not teach:
The system of claim 16, wherein the computer-executable instructions, when executed, further cause the one or more processors to perform operations (a) and (b) on a repeating basis at a first time interval.
However, Jain does teach:
The system of claim 16, wherein the computer-executable instructions, when executed, further cause the one or more processors to perform operations (a) and (b) on a repeating basis at a first time interval. (Col. 2 lines 12-14 and see Col. 77 lines 26-35 discloses data can be collected repeatedly over time intervals ongoingly)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkins’ teaching of larger unenriched population for tracking acute illness information such as for example clinical studies as previously cited and Wolz teachings of enriching study groups for a study configuration with Jain’s teachings of obtaining an unenriched study configuration and collecting health queries, the motivation being the data collection methods would not change and would be predictable to be repeated as many times as needed to get the data needed to improve and finalize the enriched study prior to randomization.
As per claim 24, Hopkins and Wolz do not teach:
The system of claim 23, wherein the computer-executable instructions, when executed, further cause the one or more processors to perform operations (c) and (d) on a repeating basis at a second time interval that is longer than the first time interval.
However, Jain does teach:
The system of claim 23, wherein the computer-executable instructions, when executed, further cause the one or more processors to perform operations (c) and (d) on a repeating basis at a second time interval that is longer than the first time interval. (Col. 2 lines 12-14 and Col. 52 lines 52-55 discloses the modeling is repeatedly updated and predictions made on an ongoing basis which is any time interval as required)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkins’ teaching of larger unenriched population for tracking acute illness information such as for example clinical studies as previously cited and Wolz teachings of enriching study groups for a study configuration with Jain’s teachings of obtaining an unenriched study configuration and collecting health queries, for the same reasons given for claim 23.
Claim 2 and 4 are rejected to under 35 U.S.C. 103 as being unpatentable over Hopkins et. al (hereinafter Hopkins) (US20240185965A1) in view of Wolz et. al (hereinafter Wolz) (US20180190369A1), in further view of Jain et. al (hereinafter Jain) (US11504011B1) and in even further view of Achin et. al (hereinafter Achin) (US20230051833Al)
As per claim 2, Hopkins and Wolz do not teach:
The method of claim 1, further comprising: predicting, using a second machine learning model, an incidence rate for the acute illness for a population that comprises at least a portion of the unenriched pool of subjects; and displaying the incidence rate for the acute illness over the population.
However, Jain does teach:
The method of claim 1, further comprising: predicting, using a second machine learning model, an incidence rate for the acute illness for a population that comprises at least a portion of the unenriched pool of subjects;…[…]… (see Col. 36 lines 47-48 and Col. 37 lines 38-50 discloses, obtaining disease prevalence rates from community members data and see Fig. 1 230, and Col. 34 lines 44-49 discloses, where multiple models are used and a different model for each prediction is used)
However, Jain does not teach the underlined portion:
and displaying the incidence rate for the acute illness over the population.
However, Achin does teach the underlined portions::
and displaying the incidence rate for the acute illness over the population. (see [0026])
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkins and Wolz teachings of enriching study groups for a study configuration and Jain’s teaching of larger unenriched population and determining a prevalence rate as previously cited and an infection rate for an acute illness with Achin’s explicit teachings of displaying incidence rate over a population for an acute illness, the motivation being that Hopkins and Wolz discloses geographies of incidence and determining the rate of progression for the patient cohort including unenriched subjects (table 2 and [0052] respectively) and Jain already discloses displaying a score in relation to susceptibility, prevalence rate, and infection rate (see fig. 11 , 1123 and Fig. 12 and Col. 37 lines 39-50) therefore the prevalence/ incidence rate is already determined and it would be obvious to use this information to display to a user in order to decrease the number of resources needed to initiate key communications with the user which can influence the outcome of their health and improve the ease of use of the system as it would be predictable to do this electronic display on the mobile devices already in use in Hopkins and Wolz.
As per claim 4, Hopkins and Wolz do not teach:
The method of claim 2, wherein the incidence rate is predicted for a future time period.
However, Jain does teach:
The method of claim 2, wherein the incidence rate is predicted for a future time period. (Col. 2 lines 27-33 discloses the determination is if the prevalence will increase in a community indicating the future)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkin’s teaching of collecting geographic data and using machine learning to determine enrichment prior to randomization for a clinical trial as previously cited and Wolz teachings of enriching clinical data with Jain’s teaching of predicting for a future time period, the motivation being that Wolz (see [0010]) and Hopkins ([0042]) are collecting data to enrich study populations through predictive means and understanding of geographies, therefore the combination of Hopkins and Wolz with Jain would increase the accuracy of enrichment and decrease the resources needed to collect data to improve trials outcomes and speed furthering the providing of treatment in a cost effective and efficient manner.
Claim 11, 12, 13, and 14 are rejected to under 35 U.S.C. 103 as being unpatentable over Hopkins et. al (hereinafter Hopkins) (US20240185965A1) in view of Wolz et. al (hereinafter Wolz) (US20180190369A1) and in further view of WENJUN et. al (hereinafter WENJUN) (CN111430040A)
As per claim 11, Hopkins and Wolz do not teach:
The method of claim 1, wherein the machine learning model comprises a generative additive model.
However, WENJUN does teach:
The method of claim 1, wherein the machine learning model comprises a generative additive model. (page 2 para. 3 discloses, “Based on the multi-source data of hand, foot and mouth disease cases, meteorology and etiology, use the generalized additive model method of time series to construct a hand, foot and mouth disease prediction model;”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkin’s teaching of machine learning to enrich study populations and Wolz teachings of enriching studies utilizing machine learning with WENJUN’s teachings of a specific generative additive machine learning model, the motivation being that the data can be ran predictably through another model such as a GAM model which would possibly improve the analysis of the data to outperform linear methods which in machine learning can lead to more efficient ways to come to the best prediction with the data available.
As per claim 12, Hopkins and Wolz does not teach:
The method of claim 11, wherein the geographic incidence data is state-wide data.
However, Jain does teach:
The method of claim 11, wherein the geographic incidence data is state-wide data. (Col. 52 lines 26-30 discloses the geographic data is taken from a state level and see Col. 68 lines 55-58 discloses community as inclusive of the state wide and see Col. 37 lines 38-50 discloses, the prevalence rate is determined for the community previously defined as inclusive of the state)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkin’s teaching of collecting geographic data and using machine learning to determine enrichment prior to randomization for a clinical trial as previously cited and Wolz teachings of enriching clinical data with Jain’s teaching of being state wide, the motivation being that Wolz (see [0010]) and Hopkins ([0042]) are collecting data to enrich study populations through predictive means and understanding of geographies, therefore the combination of Hopkins and Wolz with Jain would increase the accuracy of enrichment and decrease the resources needed to collect data to improve trials outcomes and speed furthering the providing of treatment in a cost effective and efficient manner.
As per claim 13, Hopkins and Wolz does not teach:
The method of claim 11, wherein the geographic incidence data is county-wide data.
However, Jain does teach:
The method of claim 11, wherein the geographic incidence data is county-wide data. (Col. 52 lines 26-30 discloses the geographic data is taken from a county level and see Col. 68 lines 55-58 discloses community as inclusive of the county wide and see Col. 37 lines 38-50 discloses, the prevalence rate is determined for the community previously defined as inclusive of the county)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkin’s teaching of collecting geographic data and using machine learning to determine enrichment prior to randomization for a clinical trial as previously cited and Wolz teachings of enriching clinical data with Jain’s teaching of being county wide for the same reasons given for claim 12.
As per claim 14, Hopkins and Walz do not teach:
The method of claim 11, wherein the geographic incidence data is associated with a plurality of zip code tabulation areas (ZCTAs).
However, Jain does teach:
The method of claim 11, wherein the geographic incidence data is associated with a plurality of zip code tabulation areas (ZCTAs). (Col. 52 lines 26-30 and see Col. 37 lines 38-50 discloses, the prevalence rate is determined for the community previously defined as inclusive of zip codes in the plural and also see and see Col. 68 lines 55-58 and col. 100 lines 36-39)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hopkin’s teaching of collecting geographic data and using machine learning to determine enrichment prior to randomization for a clinical trial as previously cited and Wolz teachings of enriching clinical data with Jain’s teaching of being county wide for the same reasons given for claim 12.
Response to Arguments Regarding 35 U.S.C § 101 Rejection
The applicant argues on pages 1-6 of the submitted remarks that the rejection of claims 1-2, 4-17, and 21-26 under 35 U.S.C § 101 should be withdrawn in light of the below arguments.
A. The Amended Claims are Analogous to Claim 1 of USPTO Example 40 Like USPTO Example 40 of "Subject Matter Eligibility Examples: Abstract Ideas" ("2019 PEG Examples"), which provides guidance on exemplary claims and technologies that are patent-eligible, here, the claims provide a practical application to any alleged judicial exceptions by limiting collection of additional health data comprising wearable data to when initially collected health data is reflective of an increased risk for an acute illness, thereby avoiding excess network traffic volume associated with collection of wearable data. In USPTO Example 40 - Adaptive Monitoring of Network Traffic Data from the 2019 PEG Examples - the U.S. Patent and Trademark Office states Claim 1 of Example 40 is patent- eligible because: "the claim as a whole is directed to a particular improvement in collecting traffic data. Specifically, the method limits collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance." 2019 PEG Examples pp. 11. The invention of amended claim 1 is analogous, for the purposes of eligibility analysis, to that in Example 40 - Claim 1. Indeed, Applicant respectfully submits that the similarities between Example 40 - Claim 1 and amended claim 31 are striking. For example:
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(Emphases added).
The USPTO's guidance explains that Claim 1 of Example 40 is subject matter eligible because:
Specifically, the method limits collection of additional Netfiow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance. The collected data can then be used to analyze the cause of the abnormal condition. This provides a specific improvement over prior systems, resulting in improved network monitoring. (Emphases added).
Drawing further similarity to Example 40, amended claim 1 recites an analogous combination of additional elements that limits collection of additional health data comprising wearable data to when initially collected health data is reflective of an increased risk for an acute illness, thereby avoiding excess network traffic volume associated with collection of wearable data. Accordingly, just as claim 1 of Example 40 integrates the alleged abstract idea into practical application (Step 2A, Prong 2), the amended independent claim 1 also integrates the alleged abstract idea into practical application.
B. The Disclosure of the Present Application Supports the Claimed Practical Application The background for Example 40 explains: One industry standard network visibility protocol is NetFlow. In a typical setup, a NetFlow exporter generates and exports network traffic statistics (in the form of NetFlow records) to at least one NetFlow collector that analyzes the statistics. Because NetFlow records are very large, the continual generation and export of NetFlow records in such a setup substantially increases the traffic volume on the network, which hinders network performance.
(Emphases added). Similarly, the originally filed Specification also supports that continual collection and transmission of wearable data, which is understood by a person of ordinary skill in the art as being relatively large data (e.g., as compared to responses to health queries or geographic incidence data) increases traffic volume on the network, hindering network performance. For example, Applicant points to the network (120) of FIG. 1, which is reproduced below.
As illustrated in FIG. 1, data from N devices (110(1), 110(2), ... 110(N)) may be transmitted to a computer system (130) and modeling unit (135) via the network (120). The N devices may comprise one or more wearable devices (e.g., 110(1)), which may transmit wearable data as traffic over the network (120). See, for example, paragraph [0047]: "the user devices 110(1)-110(N) may be any device suitable for collecting data such as wearable device data." Crucially, the originally filed Specification supports claim 1 in reducing the volume of wearable data transmitted over the network (120). For example, the Specification, at paragraph [0008] states: Advantageously, the machine learning model may enable trials or studies (e.g., for developing vaccines) to cut their candidate populations by the same factor (four to seven times) or allow studies or trials to be performed much more quickly, thereby reducing costs, saving resources, and potentially improving public health and safety. Accordingly, this cutting of candidate populations means that, rather than collecting wearable data from all N devices of (110(1)-110(N)), wearable data is instead transmitted from between
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and devices, thereby reducing the amount of wearable data transmitted over the network (120) of FIG. 1 by between a factor of about four to seven. By way of another example, the Specification, at paragraph [0060] states: By enriching the vaccine trial with more subjects who are likely to become infected, the overall pool of subjects recruited for the vaccine trial may be smaller. For example, if a study that may have otherwise had 10,000 subjects (e.g., to achieve statistically significant results) could be enriched by a factor of 2 (as in, the pool of subjects are twice as likely as the control pool to become infected), then the study may only need to recruit 5,000 subjects.
Again, reducing the pool from N=10,000 to N=5,000 means the wearable data from half as manyof devices 110(1
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10(N) are transmitted over the network 120 to the computer system 130.Indisputably, this reduces the amount of wearable data transmitted as traffic over the network (120) of FIG. 1. Therefore, amended claim 1 recites an analogous combination of additional elements to Example 40, and provides an analogous improvement to health monitoring by limiting collection of additional health data comprising wearable data to when initially collected health data is reflective of an increased risk for an acute illness, thereby avoiding excess network traffic volume associated with collection of wearable data. Applicant respectfully submits that FIG. 1, showing the network (120) as the central component to the system 100 over which all data flows, combined with the disclosure at [0008] and [0060] that the number of devices (110(1)-i10(N)) for which wearable data is collected is reduced, would lead a person of ordinary skill in the art to understand that this reduction in number of devices also reduces traffic on the network (120). For at least the reasons above, Applicant respectfully submits that independent claim 1, as well as independent claims 16 and 25, which include similar language, recite patent eligible subject matter under Step 2A, Prong 2, integrating any alleged abstract idea into practical application. Accordingly, Applicant respectfully submits that the independent claims 1, 16, and 25, and their corresponding dependent claims, are subject-matter eligible and Applicant respectfully requests the 35 U.S.C. § 101 rejection of the claims be withdrawn.
Examiner appreciates applicant’s arguments but does not find them persuasive. The MPEP § 2106.04 III states The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. A discussion of concepts performed in the human mind, as well as concepts that cannot practically be performed in the human mind and thus are not "mental processes", is provided below with respect to point A. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Mental processes performed by humans with the assistance of physical aids such as pens or paper are explained further below with respect to point B. Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
MPEP 2106.05(a) states the abstract idea cannot provide the improvement but rather the additional elements must provide the improvement. It also recites. “It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016) (a method of translating a logic circuit into a hardware component description of a logic circuit was found to be ineligible because the method did not employ a computer and a skilled artisan could perform all the steps mentally). Similarly, a claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017) (process for encoding/decoding facial data using image codes assigned to particular facial features held ineligible because the process did not require a computer).” Therefore, examiner notes the amended claim limitations argued are abstract and thus cannot bring forth the improvement even with the recitation of a computer performing the steps.
The MPEP 2106.05(a) also recites, “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016). After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"). The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology (e.g., the improvement described in the specification). In making this determination, it is critical that examiners look at the claim "as a whole," in other words, the claim should be evaluated "as an ordered combination, without ignoring the requirements of the individual steps." When performing this evaluation, examiners should be "careful to avoid oversimplifying the claims" by looking at them generally and failing to account for the specific requirements of the claims. McRO, 837 F.3d at 1313, 120 USPQ2d at 1100. An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107.
Examiner in response to applicant’s arguments that the amended claims are integrated into a practical application because they are strikingly the same as example 40 from the USPTO, examiner respectfully disagrees. Examiner notes there is no nexus between the instant application and example 40. Example 40 integrates the abstract idea into a practical application through additional elements by improving the general purpose computer environment in which the claims are confined. Furthermore, the example 40 is clearly linked to a technological problem and the claims reflect a technological improvement to the network traffic data collection itself. The instant application specification gives no evidence or description of a technical problem or technical solution for network traffic or even mentions this problem in regards within the claims or specification which are confined to a general purpose computer (see [091] and [0104] for e.g. of the general purpose computer disclosed). There is no nexus between example 40 and the instant application claims as the example 40 background was wholly different and the claims recited additional elements directed to the practical application improvement to a computer because the claim construction and outcome of the invention was constrained to the computer. The instant application claims are reciting enriching a study configuration which can be completed by pen and paper or with aid of a computer but is not confined to the computer as claimed merely the computer is a tool. The instant application sets forth improvement to the abstract idea and the abstract idea cannot bring forth the practical application improvement to a technical field or computer.
The 35 U.S.C § 101 rejection is maintained.
Response to Arguments Regarding 35 U.S.C § 103 Rejections
Applicant argues on pages 7-9 of the remarks that claims 1-2, 4-17, and 21-26 rejected under 35 U.S.C § 103 should be withdrawn for the following arguments.
Applicant’s arguments with respect to claims 1-2, 4-17, and 21-26 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Prior Art not cited but made of record
Wennberg - US20060129427A1
A system for predicting healthcare risk events including the process of accessing patient data associated with one or more patents, accessing geographic and healthcare system data, filtering the patient data, geographic data, and health care system data into clean data, and applying a predictive risk model to the clean data to generate patient profile data and to identify a portion of the patients susceptible to one or, more risk events.
JUTLA et. al – US20240029894Al
The present disclosure provides systems and methods for predicting the occurrence of an outbreak of an infectious disease. One such method includes acquiring environmental risk factor data associated with a particular disease, wherein the environmental risk factor data corresponds to a particular geographic region; acquiring social risk factor data associated with the particular disease; applying a prediction algorithm to the environmental and social risk factor data to generate a disease risk model for generating at least a trigger prediction and a transmission prediction for a disease causing pathogen at the particular geographic region; generating the trigger prediction by applying the disease risk model to a forecast of data for a first lead time for the particular geographic region; and/or generating the transmission prediction by applying the disease risk model to the transmission prediction for a second lead time for the particular geographic region. Other methods and systems are also provided.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 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. Should you have questions on access to the Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687