DETAILED ACTION
The present office action represents a nonfinal action on the merits.
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 .
Priority
This application claims the priority date of a 371 of PCT/EP2023/074712 of September 8, 2023 and a Provisional Application 63/404,949 of September 8, 2022.
Status of Claims
Claims 2-6, 9, 12, 14, and 17-22 are amended, claims 15-16 are cancelled and claims 1-14 and 17-22 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitations, “the first window.” There is insufficient antecedent basis for this limitation in the claim because claim 1 is an independent claim and the term is not referenced in claim 1. Examiner is interpreting “the first window” as “a first window.” Claims 2-14 and 17-22 are dependent on claim 1 and are similarly rejected. Appropriate correction is requested.
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 1-14 and 17-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-14 and 17-22 are drawn to a computer system for dynamically reporting to a user clinical data of patients, which is within the four statutory categories (i.e., machine).
Claims 1-14 and 17-22 recite a computer system for dynamically reporting to a user clinical data of patients, comprising a display interface and connected with at least two databases, at least one patient database comprising for each patient a set of clinical parameters including diagnostic status, each clinical parameter being associated with a clinical data for a given patient, and a second database comprising a set of display parameters for each diagnostic status, the computer system executing the steps of:
a. obtaining the clinical data for a selected patient from the patient database;
b. obtaining a set of display parameters based on the diagnostic status of the patient from the second database;
c. displaying on a first window the clinical data of the patient according to the set of display parameters, comprising at least:
i. clinical identifier of the selected patient;
ii. diagnostic status of the selected patient;
iii. timeline of selectable events associated with the selected patient;
wherein each selectable event can be further displayed in additional windows with the clinical data of the patient for the selected event.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity and/or mathematical concepts (e.g., gathering patient information; managing patient information, in this case predicting a clinical outcome based on patient clinical data.). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity and/or mathematical concepts) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-14 and 17-22 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Specifically, the dependent claims recite similar or additional limitations but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claim 1.
The additional elements from claim 1 include:
a computer system (apply it, MPEP 2106.05(f)).
a display interface (apply it, MPEP 2106.05(f)).
at least two databases (apply it, MPEP 2106.05(f)).
at least one patient database (apply it, MPEP 2106.05(f)).
a second database (apply it, MPEP 2106.05(f)).
a first window (apply it, MPEP 2106.05(f)).
additional windows (apply it, MPEP 2106.05(f)).
The additional elements from the dependent claims claim 12 include:
storing (apply it, MPEP 2106.05(f)).
a cohort database (apply it, MPEP 2106.05(f)).
Claims 1-14 and 17-22 are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of “a computer system”, “a display interface”, “at least two databases”, “at least one patient database”, “a second database”, “a first window”, and “additional windows”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g. see Specification Paragraphs [0105]-[0108], [0111], [0140]. (see MPEP 2106.05(f));
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., Paragraphs [[0105]-[0108], [0111], [0140], of the Specification discloses that the additional elements (i.e., a computer system, a display interface, at least two databases, at least one patient database, a second database, the patient database, the first window, and additional windows) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. MPEP 2106.05(d)(II):
Storing and retrieving information in memory, e.g., see Versata Dev. Group, Inc., v. SAP Am., Inc. – the current invention storing the cohorting parameters.
Dependent claims 2-14 and 17-22 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than those found in the independent claims.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves predictive outcome generation and patient monitoring computer system or any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-14 and 17-22 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 1-8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over De Vries (U.S. Pub. No. 2023/0170065 A1) in view of Hwang (U.S. Pub. No. 2021/0005321 A1).
Regarding claim 1, De Vries discloses a computer system for dynamically reporting to a user clinical data of patients, comprising a display interface and connected with at least two databases, at least one patient database comprising for each patient a set of clinical parameters including diagnostic status, each clinical parameter being associated with a clinical data for a given patient, and a second database (Examiner notes that the prior art does not explicitly state “first” or “second”, however it includes “multiple databases”.) comprising a set of display parameters for each diagnostic status, the computer system executing the steps of (Paragraphs [0005]-[0007], [0023]-[0024], and [0031] discuss systems for treatment recommendations based on health outcome evaluation determined for the patient based on the received healthcare information data from multiple external databases as a prescription history database and a medical history database, and can contain historical information on the patient’s medications, medical history and healthcare utilization, one or more of the clinical risk parameter, the social risk parameter, and the behavioral risk parameter can be dynamically updated based on received feedback data characterizing the patient, providing the treatment recommendation to a graphical user interface of a client device.):
a. obtaining the clinical data for a selected patient from the patient database (Paragraphs [0019] and [0022] discuss receive from databases data characterizing healthcare information associated with a patient.);
b. obtaining a set of display parameters based on the diagnostic status of the patient from the second database (Paragraphs [0020]-[0021] and [0059] discuss healthcare information data can be received from devices of healthcare providers and/or clinicians and/or external databases and include lab data and other vitals available to the provider, medications prescribed for specific patient types and diagnoses, information about their diagnoses or medications, or other healthcare outcomes evaluation data related to a patient.);
c. displaying on a first window the clinical data of the patient according to the set of display parameters, comprising at least (Paragraph [0039] discusses the clinical patient profile can include a graphical user interface that characterizes the attributes of a patient.):
ii. diagnostic status of the selected patient (Paragraphs [0028] and [0039] discuss the health outcome evaluation can include patient diagnoses.);
iii. timeline of selectable events associated with the selected patient (Paragraph [0039] discusses patient’s current and historical diagnoses, and their current and historical medication usage, as well as gaps in care.);
wherein each selectable event can be further displayed in additional windows with the clinical data of the patient for the selected event (Paragraph [0039] discusses the clinical patient profile can include a graphical user interface that characterizes the attributes of a patient, such as their current and historical diagnoses, and their current and historical medication usage, as well as gaps in care, non-optimal care plans, the patient’s current care and treatment plans, the patient’s lab tests and clinical values, and any recommended care plan changes for the patient.).
De Vries does not explicitly disclose:
i. clinical identifier of the selected patient.
Hwang teaches:
i. clinical identifier of the selected patient (Paragraph [0068] discusses results that include the name of the patient, patient ID or medical insurance number, doctor name, type of procedure.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, i. clinical identifier of the selected patient, as taught by Hwang, in order to assist doctors, surgeons, and other healthcare professionals to make decisions on whether the procedure should be performed on the patient, given the computed risk outcomes. (Hwang Paragraphs Abstract and [0004].).
Regarding claim 2, De Vries discloses wherein the selectable events are selected among:
patient demographic data, diagnosis status, performance status, genomics, analysis, histopathology analysis, blood analysis results, imaging examination, treatment plan, medical history, monitoring parameter, toxicology and/or progression (Paragraph [0019] and FIG 1 discuss data characterizing healthcare information associated with a patient can be received including data characterizing the patient’s prescription claims, medical insurance claims, healthcare utilization, diagnoses, behavior, demographics, prior authorizations, electronic medical records (e.g., lab data and medical chart data), and adherence to prescribed treatment plans, data obtained from wearable devices used for patient monitoring, health applications or software, answers to patient questionnaires provided by the patient, answers to risk assessments provided by the patient, patient geolocation data, and lab and/or genomic data characterizing patient attributes, data characterizing insurance claims billing, electronic health records, answers to algorithmically-derived health questions, case management, patient health plans, and drug coverage data.).
Regarding claim 3, De Vries discloses wherein the clinical patient information comprises, age, gender, current diagnostic status, performance status, last weight (Paragraphs [0042]-[0043] include obtain patient social factors including demographics information such as age, gender, occupational status, education, food security status, housing status, health insurance status, caregiver status, marital status, stressors, social support, physical activity, obesity, diet, and more.).
De Vries does not explicitly disclose:
the clinical patient identifier comprises patient ID; and
the clinical patient information comprises last height and/or body mass index (BMI).
Hwang teaches:
the clinical patient identifier comprises patient ID (Paragraph [0068] discusses results that include the name of the patient, patient ID or medical insurance number, doctor name, type of procedure.); and
the clinical patient information comprises last height and/or body mass index (BMI) (Paragraphs [0024] and [0039] discuss obtain patient test information including height and body mass index.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, the clinical patient identifier comprises patient ID and the clinical patient information comprises last height and/or body mass index (BMI), as taught by Hwang, in order to assist doctors, surgeons, and other healthcare professionals to make decisions on whether the procedure should be performed on the patient, given the computed risk outcomes. (Hwang Paragraphs Abstract and [0004].).
Regarding claim 4, De Vries discloses further comprising means to filter the events displayed in the timeline of selectable events (Paragraphs [0032] and [0059] discuss the health outcome evaluation can output data, based on one or more sources of data, that characterizes a patient’s healthcare utilization, trends in a patient’s clinical status, trends in a patient’s behaviors, and data characterizing risks faced by the patient, further events, including but not limited to new medical diagnoses, new medications prescribed, new lab values, new device information, hospitalizations and emergency room admissions, provider visits can be identified.).
Regarding claim 5, De Vries discloses wherein the set of display parameters comprises treatment plan, medical history, monitoring parameter and/or blood analysis results, and wherein clinical data for the selected patient are further displayed (Paragraphs [0019], [0021], [0072]-[0073] discuss output and display patient data on a graphical user interface, patient monitoring, patient history, lab data and other vitals available, adherence to treatment plan.).
De Vries does not explicitly disclose:
clinical data for the selected patient are further displayed in separate windows.
Hwang teaches:
clinical data for the selected patient are further displayed in separate windows (Paragraph [0017], [0082], and FIGS. 11A to 11J discuss use of additional screens generated by the graphical user interface for predicting patient risk outcomes that include patient data.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, clinical data for the selected patient are further displayed in separate windows, as taught by Hwang, in order to assist doctors, surgeons, and other healthcare professionals to make decisions on whether the procedure should be performed on the patient, given the computed risk outcomes. (Hwang Paragraphs Abstract and [0004].).
Regarding claim 6, De Vries discloses wherein the medical history of the patient comprises personal history, familial history and/or risk factors (Paragraphs [0007], [0021], and [0042]-[0043] discuss obtain patient data including family composition, patient clinical history, and historical patient risk data.).
Regarding claim 7, De Vries discloses wherein, in the case that the diagnostic status of the patient is associated with one disease, at least one clinical parameter for the selected patient is further displayed with the variation of the clinical data of said parameter over time (Paragraphs [0025], [0028], [0030], [0039], and [0072] discuss output and display patient data on a graphical user interface and the health outcome evaluation can include metrics that can be determined based on the received healthcare information data, including for example, clinical values (e.g., A1c for a diabetes patient, blood pressure for a hypertensive patient), disease profile (e.g., diagnoses, duration, utilization by disease, and abnormal lab values), the data can be received via an ongoing (real time) or regularly scheduled data feed, or ad-hoc and supplemented with additional data gathered directly from patients and providers over time.).
De Vries does not explicitly disclose:
clinical data for the selected patient are further displayed in separate windows.
Hwang teaches:
clinical data for the selected patient are further displayed in separate windows (Paragraph [0017], [0046], [0082], and FIGS. 4 discuss use of additional screens generated by the graphical user interface for predicting patient risk outcomes that include patient data for a number of time points.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, clinical data for the selected patient are further displayed in separate windows, as taught by Hwang, in order to assist doctors, surgeons, and other healthcare professionals to make decisions on whether the procedure should be performed on the patient, given the computed risk outcomes. (Hwang Paragraphs Abstract and [0004].).
Regarding claim 8, De Vries discloses wherein the detection of a change in one patient diagnostic status entails the retrieval of a set of display parameters corresponding to the new diagnostic status and updates of the displayed clinical data for the patient in the system (Paragraphs [0044] and [0074] discuss the risk prediction models can be dynamically updated by predictive modeling techniques that are configured to optimize the health outcome evaluation based on updated information and allow for real-time updating of the treatment plan changes and recommendations during the conversation with the patient or the provider. If the user of the software learns new information in the course of patient care or a conversation with the patient or provider, they can add the data to the patient or provider profile and analyze the additional data in context in real time to update the treatment profile in the system.).
De Vries does not explicitly disclose:
the displayed clinical data for the patient in the window or windows of the system.
Hwang teaches:
the displayed clinical data for the patient in the window or windows of the system (Paragraph [0017], [0046], [0082], and FIGS. 4 discuss use of additional screens generated by the graphical user interface for predicting patient risk outcomes that include patient data for a number of time points, data for different and/or additional timepoints (e.g., a 30-day timepoint) may be calculated and displayed.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, the displayed clinical data for the patient in the window or windows of the system, as taught by Hwang, in order to assist doctors, surgeons, and other healthcare professionals to make decisions on whether the procedure should be performed on the patient, given the computed risk outcomes. (Hwang Paragraphs Abstract and [0004].).
Regarding claim 11, De Vries discloses wherein the current diagnostic status comprises disease, stage of disease or subclassification (Paragraphs [0028] discuss the health outcome evaluation can include metrics that can be determined based on the received healthcare information data, metrics include disease profile (e.g., diagnoses, duration, utilization by disease).).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over De Vries in view of Hwang and in further view of Fieres (U.S. Pub. No. 2019/0065985 A1) and Barsoum (U.S. Pub. No. 2014/0279754 A1).
Regarding claim 9, De Vries discloses further comprising connection to a trained prediction machine learning models according to the diagnostic status and a list of inputting parameters for a trained prediction machine learning model according to diagnostic status, the computer system executing the steps of (Paragraphs [0007] and [0042]-[0043] discuss use of the risk prediction models trained for providing the risk factor in response to the querying based on historical patient risk data, querying a treatment recommendation rules engine for a recommendation parameter based on at least one of the determined risk factor, the health outcome evaluation, and/or the received healthcare information data.):
b. retrieving the list of input parameters for the patient according to the trained model (Paragraph [0007] discusses the questionnaire rules engine modified by a questionnaire predictive model that identifies a predictor variable based on the received healthcare information data and revises the questionnaire rules engine based on the identified predictor variables.);
c. inputting the clinical data for the selected patient into the trained model for prediction of clinical outcome according to the diagnostic status of the patient (Paragraphs [0007], [0049], and [0062] discuss analyze inputs that can include the metrics/data outputs generated as part of the health outcome evaluation, predictive model can evaluate patient demographic and socioeconomic data, the name of the medication, diagnoses, any patient comorbidities, any side effects reported by the patient and other relevant information including historical behaviors..);
d. generating prediction of clinical outcome (Paragraph [0062] discusses the predictive model can perform this analysis by assessing this data and determining predictive variables that are significant predictors of a successful treatment outcome and identify other patients who have a high likelihood of achieving a successful treatment outcome.); and
e. displaying on a fourth window the prediction of clinical outcome and confidence of the model (Paragraphs [0039], [0063], and [0073] discuss the clinical patient profile can include a graphical user interface that characterizes the attributes of a patient and risk prediction generation process can provide for display output data characterizing the generated risk predictions to the recommendation generation process, which can generate one or more of the recommendations and provide a dynamic risk score.).
De Vries does not explicitly disclose:
further comprising connection to a database of trained prediction machine learning models;
a. selecting the trained model for prediction of clinical outcome according to the diagnostic status of the selected patient;
Fieres teaches:
further comprising connection to a database of trained prediction machine learning models (Paragraph [0005] discusses the trained machine learning model may be sent to the database management system.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, further comprising connection to a database of trained prediction machine learning models, as taught by Fieres, in order to merge multiproviders. (Fieres Paragraph [0003].).
Barsoum teaches:
a. selecting the trained model for prediction of clinical outcome according to the diagnostic status of the selected patient (Paragraphs [0017]-[0018] discuss a model selector can receive a set of predictors from an input source as well as a clinical outcome parameter to be predicted, the input source can provide the set of predictors directly or by selecting an existing patient record from the database.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, a. selecting the trained model for prediction of clinical outcome according to the diagnostic status of the selected patient, as taught by Barsoum, in order to provide a self- evolving model that retrains the model as new data becomes available to ensure that the model remains relevant in the face of both new medical developments as well as its own predictions. (Barsoum Paragraph [0013].).
Regarding claim 10, De Vries does not explicitly disclose wherein a list of features contributing to the prediction are further displayed in the additional window.
Hwang teaches:
wherein a list of features contributing to the prediction are further displayed in the additional window (Paragraph [0017], [0036]-[0039], [0082], and FIGS. 3-4, 11A to 11J discuss use of additional screens generated by the graphical user interface for predicting patient risk outcomes that include patient data, a section on the display may include patient data used in the prediction, for example, a second section may include a first area indicating a number of modifiable factors that may allow a user to set or change values corresponding to one or more inputs used to generate the risk outcomes, the modifiable factors include body mass index (BMI), opiate prescription(s), hypertension prescription(s), and anticoagulation prescription(s).).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, wherein a list of features contributing to the prediction are further displayed in the additional window, as taught by Hwang, in order to assist doctors, surgeons, and other healthcare professionals to make decisions on whether the procedure should be performed on the patient, given the computed risk outcomes. (Hwang Paragraphs Abstract and [0004].).
Claims 12-14 and 17-22 are rejected under 35 U.S.C. 103 as being unpatentable over De Vries in view of Hwang and in further view of Birnbaum (U.S. Pub. No. 2020/0058382).
Regarding claim 12, De Vries does not explicitly disclose wherein said system further comprises a cohort database comprising a plurality of cohorts, each cohort being represented by a set of cohorting parameters selected among the clinical parameters, each cohorting parameter having a predefined cohorting inclusion criteria said system being configured to execute the steps of:
a. selecting, from the patient database, a group of patients to study for which the clinical data match a set of study inclusion criteria of a set of study clinical parameters;
b. defining a set of cohorting inclusion criteria of a set of cohorting parameters including the set of study clinical parameters and associated inclusion criteria, said set of cohorting parameters and the inclusion criteria representing a cohort;
c. parsing the clinical data of the patient's database to retrieve the list of patients included in the cohort;
d. storing the cohorting parameters and the inclusion criteria for the cohort in the cohort database; and
e. storing in the cohort database the list of patients included in the cohort.
Birnbaum teaches:
wherein said system further comprises a cohort database comprising a plurality of cohorts, each cohort being represented by a set of cohorting parameters selected among the clinical parameters, each cohorting parameter having a predefined cohorting inclusion criteria said system being configured to execute the steps of (Paragraphs [0020], [0056]-[0059] discuss systems that store records received from data sources and client devices in one or more databases for one or more cohorts, that include any grouping of data (people, articles, objects, etc.) that shares at least one common characteristic or that exhibit attributes meeting a predefined set of criteria.):
a. selecting, from the patient database, a group of patients to study for which the clinical data match a set of study inclusion criteria of a set of study clinical parameters (Paragraph [0059] discusses select one or more cohorts that includes any grouping of data (people, articles, objects, etc.) that shares at least one common characteristic or that exhibit attributes meeting a predefined set of criteria.);
b. defining a set of cohorting inclusion criteria of a set of cohorting parameters including the set of study clinical parameters and associated inclusion criteria, said set of cohorting parameters and the inclusion criteria representing a cohort (Paragraph [0059] discusses a cohort may include individuals that exhibit at least one common characteristic from a medical perspective (e.g., demographic or clinical characteristics).);
c. parsing the clinical data of the patient's database to retrieve the list of patients included in the cohort (Paragraphs [0056] and [0059] discuss the system may query database to identify one or more patients matching the query parameter, individuals from a population determined to have a certain type of disease, or more specifically, certain characteristics associated with that disease (e.g., breast cancer in stage IV) may be identified and placed in a common cohort.);
d. storing the cohorting parameters and the inclusion criteria for the cohort in the cohort database (Paragraphs [0019], [0024], [0056]-[0057] discuss computer-readable storage medium may store instructions for execution by at least one processor, including instructions for causing the processor to perform steps or stages based on structured criteria and query database to identify one or more patients matching the cohort query parameters and transmit medical records associated with the matching patient(s) over network to client devices.); and
e. storing in the cohort database the list of patients included in the cohort (Paragraphs [0022] and [0052]-[0056] discuss query database to identify one or more patients matching the query parameters and transmit medical records associated with the matching patient(s) over network to client devices and store patient records received from data sources and client devices in one or more databases.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, wherein said system further comprises a cohort database comprising a plurality of cohorts, each cohort being represented by a set of cohorting parameters selected among the clinical parameters, each cohorting parameter having a predefined cohorting inclusion criteria said system being configured to execute the steps of: a. selecting, from the patient database, a group of patients to study for which the clinical data match a set of study inclusion criteria of a set of study clinical parameters; b. defining a set of cohorting inclusion criteria of a set of cohorting parameters including the set of study clinical parameters and associated inclusion criteria, said set of cohorting parameters and the inclusion criteria representing a cohort; c. parsing the clinical data of the patient's database to retrieve the list of patients included in the cohort; d. storing the cohorting parameters and the inclusion criteria for the cohort in the cohort database; and e. storing in the cohort database the list of patients included in the cohort, as taught by Birnbaum, in order to avoid risks of unintended bias and reduce the time required for cohort selection, and a need to reduce the costs associated with cohort selection by reducing the reliance on manual analysis. (Birnbaum Paragraphs [0002] and [0004].).
Regarding claim 13, De Vries does not explicitly disclose wherein the selecting of the group of patients include only the patients for which the clinical data of the cohorting clinical parameters is known.
Birnbaum teaches:
wherein the selecting of the group of patients include only the patients for which the clinical data of the cohorting clinical parameters is known (Paragraphs [0024]-[0025] discuss patients are sorted by model, one or more rules that identify characteristics of patients based on associated medical records and use those characteristics to sort patients into subgroup (“In cohort”).).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, wherein the selecting of the group of patients include only the patients for which the clinical data of the cohorting clinical parameters is known, as taught by Birnbaum, in order to avoid risks of unintended bias and reduce the time required for cohort selection, and a need to reduce the costs associated with cohort selection by reducing the reliance on manual analysis. (Birnbaum Paragraphs [0002] and [0004].).
Regarding claim 14, De Vries does not explicitly disclose further comprising at least one of steps:
a. comparing for at least one clinical parameter, the clinical data of the patients included in the cohort with the clinical data of the group of patients not included in the cohort;
b. displaying in a graphical form or via statistical report the result of the comparison;
c. determining at least one comparison metric representing a statistical significance of the difference between the clinical data of the patients within the cohort and the clinical data of the group of patients not included in the cohort; or
d. a combination thereof.
Birnbaum teaches:
discloses further comprising at least one of steps:
a. comparing for at least one clinical parameter, the clinical data of the patients included in the cohort with the clinical data of the group of patients not included in the cohort (Paragraphs [0023]-[0026], [0032], and [0041]-[0042] discuss identify characteristics of patients based on associated medical records and use those characteristics to sort patients into group (“Not in cohort”) and group (“Maybe in cohort”), comparing the characteristics of the cohort to the patient.);
b. displaying in a graphical form or via statistical report the result of the comparison (Paragraphs [0033], [0048]-[0052] and FIGS 4A-4B discuss based on the comparison, an alert may be generated for a user of model, alerting the user to the possibility of bias and display information over the network and include graphs of characteristic distributions over time.);
c. determining at least one comparison metric representing a statistical significance of the difference between the clinical data of the patients within the cohort and the clinical data of the group of patients not included in the cohort (Paragraphs [0024]-[0025], [0048]-[0052] and FIGS 4A-4B discuss rules that identify characteristics of patients based on associated medical records and use those characteristics to sort patients into group (“Not in cohort”) and group (“Maybe in cohort”) and based on the comparison, an alert may be generated for a user of model, alerting the user to the possibility of bias and display information over the network and include graphs of characteristic distributions over time.); or
d. a combination thereof.
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, further comprising at least one of steps: a. comparing for at least one clinical parameter, the clinical data of the patients included in the cohort with the clinical data of the group of patients not included in the cohort; b. displaying in a graphical form or via statistical report the result of the comparison; c. determining at least one comparison metric representing a statistical significance of the difference between the clinical data of the patients within the cohort and the clinical data of the group of patients not included in the cohort; or d. a combination thereof, as taught by Birnbaum, in order to avoid risks of unintended bias and reduce the time required for cohort selection, and a need to reduce the costs associated with cohort selection by reducing the reliance on manual analysis. (Birnbaum Paragraphs [0002] and [0004].).
Regarding claim 17, De Vries does not explicitly disclose further comprising the steps of:
a. parsing the clinical data of the patient's database to update the list of patients included in the cohort; and
b. if the list of patients has changed compared to the stored list of patients, updating the comparison metric and displaying the list of new patients and the corresponding comparison metric.
Birnbaum teaches:
further comprising the steps of:
a. parsing the clinical data of the patient's database to update the list of patients included in the cohort (Paragraphs [0005] and [0072] discuss obtain new patient information for real-time assessment of models as new patient information becomes available.); and
b. if the list of patients has changed compared to the stored list of patients, updating the comparison metric and displaying the list of new patients and the corresponding comparison metric (Paragraphs [0024]-[0025] and [0072] discuss rules that identify characteristics of patients based on associated medical records and use those characteristics to sort patients into group (“Not in cohort”) and group (“Maybe in cohort”); the system may receive data representing a third plurality of individuals labeled as within the cohort, the data including the at least one characteristic of the third plurality of individuals; compare the selected subset and the third plurality of individuals along the at least one characteristic; and when the comparison results in a difference between the selected subset and the third plurality of individuals greater than the threshold, transmit the alert to the user of the deployed model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, further comprising the steps of: a. parsing the clinical data of the patient's database to update the list of patients included in the cohort; and b. if the list of patients has changed compared to the stored list of patients, updating the comparison metric and displaying the list of new patients and the corresponding comparison metric, as taught by Birnbaum, in order to avoid risks of unintended bias and reduce the time required for cohort selection, and a need to reduce the costs associated with cohort selection by reducing the reliance on manual analysis. (Birnbaum Paragraphs [0002] and [0004].).
Regarding claim 18, De Vries does not explicitly disclose wherein the computer system executes the additional steps of:
a. selecting one patient;
b. displaying and selecting at least one cohort from the cohort database for which the selected patient is part of the list of patients;
c. retrieving the clinical data of the selected patient;
d. selecting at least one clinical parameter of the selected patient to be compared with the corresponding clinical parameter of the patients included in the selected cohort;
e. retrieving the clinical data for the selected clinical parameter of the plurality of patients included in the cohort;
f. computing the distribution of the retrieved clinical data; and
g. positioning and displaying the clinical data of the selected patient in the computed distribution of the retrieved clinical data.
Birnbaum teaches:
wherein the computer system executes the additional steps of:
a. selecting one patient (Paragraph [0004] and [0028] discuss selection of patient in a cohort, each patient may have an associated score.);
b. displaying and selecting at least one cohort from the cohort database for which the selected patient is part of the list of patients (Paragraphs [0022]-[0024] and [0047]-[0048] discuss database of patient records that are sorted into a cohort and a report may include one or more graphs of characteristic distributions and/or characteristics over time of the cohort.);
c. retrieving the clinical data of the selected patient (Paragraph [0022]-[0024] discuss obtain from a database patient records that are sorted by the model that identify characteristics of patients for a cohort.);
d. selecting at least one clinical parameter of the selected patient to be compared with the corresponding clinical parameter of the patients included in the selected cohort (Paragraph [0022]-[0024] and [0028] discuss obtain from a database patient records that are sorted by the model that identify characteristics of patients for a cohort, model may extract features from records to produce scores associated with the records indicating a likelihood that the patient is a viable candidate for a cohort.);
e. retrieving the clinical data for the selected clinical parameter of the plurality of patients included in the cohort (Paragraph [0028] discusses model may extract features from records to produce scores associated with the records indicating a likelihood that the patient is a viable candidate for a cohort.);
f. computing the distribution of the retrieved clinical data (Paragraphs [0028] and [0045] discuss model may extract features from records to produce scores associated with the records, each patient may have an associated score (e.g., 5 out of 10, 80% probability, 0.8 out of 1.0, “likely” on a scale from “not likely” to “somewhat likely” to “likely” to “very likely,” or a similar scale) indicating a likelihood that the patient is a viable candidate for a cohort. indicating a likelihood that the patient is a viable candidate for a cohort, a report may include a graph showing distribution of scores determined using the model for various sets of patients.); and
g. positioning and displaying the clinical data of the selected patient in the computed distribution of the retrieved clinical data (Paragraphs [0028], [0048]-[0050] discuss a report may include one or more graphs of characteristic distributions and/or characteristics over time (e.g., survival probability over time, overall survival over time, time of initial diagnosis, time of follow-up after initial diagnosis, time of advanced diagnosis, time of follow-up after advanced diagnosis, or the like) comparing the reference standard (as described above) to a cohort, labels may, for example, indicate whether the patient is viable for a cohort and may have been determined using other computerized models and extract features from records to produce scores associated with the records.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, wherein the computer system executes the additional steps of: a. selecting one patient; b. displaying and selecting at least one cohort from the cohort database for which the selected patient is part of the list of patients; c. retrieving the clinical data of the selected patient; d. selecting at least one clinical parameter of the selected patient to be compared with the corresponding clinical parameter of the patients included in the selected cohort; e. retrieving the clinical data for the selected clinical parameter of the plurality of patients included in the cohort; f. computing the distribution of the retrieved clinical data; and g. positioning and displaying the clinical data of the selected patient in the computed distribution of the retrieved clinical data, as taught by Birnbaum, in order to avoid risks of unintended bias and reduce the time required for cohort selection, and a need to reduce the costs associated with cohort selection by reducing the reliance on manual analysis. (Birnbaum Paragraphs [0002] and [0004].).
Regarding claim 19, De Vries does not explicitly disclose wherein the computer system further comprises selecting one cohort among the plurality of cohorts based on a match between the selected patient's clinical data and the cohort inclusion criteria.
Birnbaum teaches:
wherein the computer system further comprises selecting one cohort among the plurality of cohorts based on a match between the selected patient's clinical data and the cohort inclusion criteria (Paragraphs [0020]-[0021] discuss systems for monitoring models used to select one or more cohorts and a user includes any individual who may wish to access and/or analyze patient data and/or perform an experiment using a selected cohort, for example, patients may comprise a plurality of breast cancer patients, a portion of which may be metastatic, where metastatic status is an attribute relevant to cohort selection.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, wherein the computer system further comprises selecting one cohort among the plurality of cohorts based on a match between the selected patient's clinical data and the cohort inclusion criteria, as taught by Birnbaum, in order to avoid risks of unintended bias and reduce the time required for cohort selection, and a need to reduce the costs associated with cohort selection by reducing the reliance on manual analysis. (Birnbaum Paragraphs [0002] and [0004].).
Regarding claim 20, De Vries does not explicitly disclose wherein the system is configured to select the cohort according to the patient's diagnostic status.
Birnbaum teaches:
wherein the system is configured to select the cohort according to the patient's diagnostic status (Paragraphs [0020]-[0021] and [0032] discuss systems for monitoring models used to select one or more cohorts based on particular characteristics, such as disease stage at diagnosis, for example, patients may comprise a plurality of breast cancer patients, a portion of which may be metastatic, where metastatic status is an attribute relevant to cohort selection.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, wherein the system is configured to select the cohort according to the patient's diagnostic status, as taught by Birnbaum, in order to avoid risks of unintended bias and reduce the time required for cohort selection, and a need to reduce the costs associated with cohort selection by reducing the reliance on manual analysis. (Birnbaum Paragraphs [0002] and [0004].).
Regarding claim 21, De Vries does not explicitly disclose wherein it is configured to execute the steps of:
a. parsing the plurality of cohorting inclusion criteria with the patient's clinical data to determine the cohorts matching the patient's clinical data; and
b. displaying and sorting the list of cohorts matching the patient's clinical data.
Birnbaum teaches:
wherein it is configured to execute the steps of:
a. parsing the plurality of cohorting inclusion criteria with the patient's clinical data to determine the cohorts matching the patient's clinical data (Paragraphs [0021] discuss two-step cohort selection filter comprise a plurality of patients, a portion of which may be viable for inclusion in a cohort. For example, patients may comprise a plurality of breast cancer patients, a portion of which may be metastatic, where metastatic status is an attribute relevant to cohort selection.); and
b. displaying and sorting the list of cohorts matching the patient's clinical data (Paragraphs [0046]-[0049] discusses overlay a plurality of models on the same graph and the models may be compared across one or more dimensions along the same time period, the report may include a graph of sensitivity versus one or more characteristics of the patient.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, wherein it is configured to execute the steps of: a. parsing the plurality of cohorting inclusion criteria with the patient's clinical data to determine the cohorts matching the patient's clinical data; and b. displaying and sorting the list of cohorts matching the patient's clinical data, as taught by Birnbaum, in order to avoid risks of unintended bias and reduce the time required for cohort selection, and a need to reduce the costs associated with cohort selection by reducing the reliance on manual analysis. (Birnbaum Paragraphs [0002] and [0004].).
Regarding claim 22, De Vries does not explicitly disclose wherein it is configured to execute the steps of:
a. comparing the clinical data of the patients with the cohorting inclusion criteria to update the list of cohorts matching the patient's clinical data; and
b. notifying the user with the difference with the previous list of cohorts.
Birnbaum teaches:
wherein it is configured to execute the steps of:
a. comparing the clinical data of the patients with the cohorting inclusion criteria to update the list of cohorts matching the patient's clinical data (Paragraphs [0005]-[0006] and [0032] discuss compare particular characteristics of the reference standard to the same characteristics of model cohort, allow for real-time assessment of models as new information becomes available and compare the selected subset and the second plurality of individuals along the at least one characteristic and determine whether the comparison results in a difference between the selected subset and the second plurality of individuals greater than a threshold.); and
b. notifying the user with the difference with the previous list of cohorts (Paragraphs [0006], [0048] discuss compare the selected subset and the second plurality of individuals along the at least one characteristic and determine whether the comparison results in a difference between the selected subset and the second plurality of individuals greater than a threshold, the report may include one or more graphs of characteristic distributions and/or characteristics over time comparing the reference standard to a cohort selected by the model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify De Vries to include, wherein it is configured to execute the steps of: a. comparing the clinical data of the patients with the cohorting inclusion criteria to update the list of cohorts matching the patient's clinical data; and b. notifying the user with the difference with the previous list of cohorts, as taught by Birnbaum, in order to avoid risks of unintended bias and reduce the time required for cohort selection, and a need to reduce the costs associated with cohort selection by reducing the reliance on manual analysis. (Birnbaum Paragraphs [0002] and [0004].).
Conclusion
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/DAWN T. HAYNES/
Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
