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 .
Status of Claims
This action is in reply to the claims filed on 15 October 2025.
Claims 1-6, 14-17, and 20 are amended.
Claims 1-20 are pending.
Priority
The effective filing date for the present application is recognized as 05/25/2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1 Analysis:
Claims 1-20 are directed to a method, apparatus, or system for tracking and encouraging adherence to healthcare instructions and therefore falls into one of the four statutory categories.
(Step 1: Yes, the claim falls into one of the four statutory categories).
Step 2A analysis: Prong one:
The independent Claims 1, 14, and 20, taking Claim 1 as example, recite the following limitations:
obtaining… real-time hospital admission data in a plurality of formats from one or more data sources, the real-time hospital admission data associated with a plurality of individuals;
standardizing…the real-time hospital admission data from the plurality of formats into a standard format to generate standardized real-time hospital admission data, the standard format for the standardized real-time hospital admission data including at least one patient identifier, at least one indicator of a disease of interest, and at least one admission date;
generating…a modified standardized real-time hospital admission dataset by removing extraneous data from the standardized real-time hospital admission data based on the at least one indicator of the disease of interest;
determining… a primary admission value based on the modified standardized real-time hospital admission dataset associated with the plurality of individuals and for the disease of interest;
determining… a readmission value based on the modified standardized real-time hospital admission dataset associated with the plurality of individuals;
determining…a disease-specific readmission rate based on the primary admission value and the readmission value,
wherein the primary admission value and the readmission value are based on a common indicator of a disease of interest; and
causing to output…data associated with the disease-specific readmission rate based on the modified standardized real-time hospital admission dataset.
The above claim describes a method for obtaining hospital admission data, determining a primary admission value based on the hospital admission data, determining a readmission value based on hospital admission data, determining a disease-specific readmission rate based on the primary admission value and readmission value, and outputting data associated with the disease-specific readmission rate.
Dependent Claims 2-13 and 15-19 are directed towards:
Determining the primary admission value based on the hospital admission data
Determining the readmission value based on hospital data
The data that the hospital admission data can include
The data that the primary admission value comprises
Determining the disease-specific readmission rate and training the machine learning model
The data that the primary admission value and the readmission value can include
Generating a trend prediction based on data
Accordingly, the claims recite “certain methods of organizing human activity,” which falls within the judicial exception of an abstract idea. (Step 2A – Prong one: Yes, the claim is abstract).
Step 2A analysis: Prong two:
Claims 1, 5, 13, 14, 17, and 20 recite additional elements beyond the abstract idea. The judicial exception is not integrated into a practical application. Claims 1, 5, 13, 14, 17, and 20 recite one or more processors and a graphical user interface of a user device; and Claim 14 recites one or more storage devices.
These additional elements are recited at a high level of generality (i.e., as a generic processor performing generic computer functions), such that it amounts to no more than mere instructions to apply the exceptions using a generic computer component. The claims recite the following additional elements: one or more processors; one or more storage devices; and a graphical user interface of a user device. The additional elements do not more than merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Accordingly, these additional elements when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore Claims 1, 5, 13, 14, 17, and 20 are directed to an abstract idea without practical application. (Step 2A – Prong two: No, the additional elements are not integrated into a practical application).
Step 2B
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements: (1) do not improve the functioning of a computer or any other technology or technical field, and (2) merely use the additional elements for implementing the abstract idea.
A. Improvements to the Functioning of a Computer or To Any Other Technology or Technical
Field. MPEP 2106.05(a)
The additional elements of Claims 1, 5, 13, 14, 17, and 20 do not integrate the abstract idea into a practical application and only use those elements for performing the abstract idea and not more than the judicial exception itself. None of the claims recite an “inventive concept” because the additional elements fail to improve the functioning of a computer or any other technology or technical field (See MPEP 2106.05(a)), which does not render a claim as being significantly more than the abstract idea.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing.
There is no indication in the claims or specification that the invention improves the performance, efficiency, security, or functionality of a computer system beyond using generic components to perform a judicial exception. The claims do not improve how computers store, retrieve, transmit, or process data, nor do they enhance any other technical field.
Thus, because the claims fail to recite an improvement to the functioning of a computer or any other technology or technical field, they do not amount to significantly more than the judicial exception itself.
B. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
The additional elements of Claims 1, 5, 13, 14, 17, and 20 do not integrate the abstract idea into a practical application and only use those elements for performing the abstract idea and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea (See MPEP 2106.05(f)).
The requirement to execute the claimed steps/functions using one or more processors, etc., are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of using a one or more processing elements, an administrator portal, a mobile application, a server, etc., (Claims 1-20) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (See MPEP 2106.05(f)). (Step 2B: No, the claims do not provide significantly more).
In conclusion, the claims are directed to the abstract idea for managing data and to receive data, retrieve data, process data, store data, update data, transmit data, display data, etc.. The claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology or contains instructions to implement the judicial exception, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to nonstatutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-13; 14-16 and 18; and 20 are rejected under 35 U.S.C 103 as being unpatentable over Chaudhuri et al. (US 2023/0215563 A1) in view of Schoenberg et al. (US 2010/0217621 A1).
With respect to Claim 1,
Chaudhuri teaches A computer-implemented method comprising:
obtaining, by one or more processors ("one or more processors" of Chaudhuri [0156]), hospital admission data in a plurality of formats from one or more data sources the real-time hospital admission data associated with a plurality of individuals; (“the patient’s electronic health record (EHR) may be queried so that feature values can be extracted and input into one or more machine learning models trained to predict readmission for AMI patients” in Chaudhuri [0019]; “feature values extractor 220 may assign a value, such as a binary value, for a particular category based on the patient's data (e.g., assign a feature value of “1” if the patient is married or does not live alone and a value of “0” otherwise). Feature values extractor 220 may also perform a calculation using the patient's data to derive a feature value.” in Chaudhuri [0044])
standardizing, by the one or more processors, the hospital admission data from the plurality of formats into a standard format to generate standardized admission data, the standard format for the hospital admission data including at least one patient identifier, at least one indicator of a disease of interest, and at least one admission date; ("AMI patient identifier 210 is generally configured to determine whether an individual is an AMI patient." in Chaudhuri [0037]; “The indicator may be a diagnosis, a lab result, a procedure, or an order that indicates the patient is having or has had an AMI within the recent past” in Chaudhuri [0038]; “feature values extractor 220 may assign a value, such as a binary value, for a particular category based on the patient's data…Feature values extractor 220 may also perform a calculation using the patient's data to derive a feature value.” in Chaudhuri [0044]; and “date of admission” in Chaudhuri [0045]; Examiner notes paragraph [0024] of Applicant’s specification describes patient identifier can encompass admission/readmission and diagnosis data.)
generating, by the one or more processors, a modified standardized hospital admission dataset by removing extraneous data from the standardized hospital admission data based on the at least one indicator of the disease of interest; (“different features are used for each model of readmission predictor 230…As can be seen, a greater number of feature values may be input into the second model than in the first model.” in Chaudhuri [0117]; and Fig. 4A-B and 5)
determining, by the one or more processors, a primary admission value based on the modified standardized hospital admission dataset associated with the plurality of individuals and for the disease of interest; ("readmission predictor 230 use one or more machine learning models to determine the risk of the AMI patient being readmitted in a future time interval…readmission predictor 230 includes an first model trained…the first model (which may be referred to as an “admit model”)"in Chaudhuri [0115])
determining, by the one or more processors, a readmission value based on the modified standardized hospital admission dataset associated with the plurality of individuals; ("readmission predictor 230 use one or more machine learning models to determine the risk of the AMI patient being readmitted in a future time interval…readmission predictor 230 includes…a second model trained…the second model (which may be referred to as a “discharge model”)" in Chaudhuri [0115])
determining, by the one or more processors, a disease-specific readmission rate based on the primary admission value and the readmission value, wherein the primary admission value and the readmission value are based on a common indicator of a disease of interest; ("Each of the first model and the second model may output a prediction that the AMI patient will be readmitted within the future time interval, and those two predictions may be combined to determine the overall prediction for the AMI patient." in Chaudhuri [0119]) and
causing to output, by the one or more processors, data associated with the disease-specific readmission rate via a graphical user interface of a user device based on the modified standardized hospital admission dataset. ("user/clinician interface 142 takes the form of a graphical user interface" in Chaudhuri [0026]; “ Decision support application 140 and/or user/clinician interface 142 also facilitates the display of results” in Chaudhuri [0028]; and "The output of readmission predictor 230 may be in the form of a quantitative risk score, such as a value between 0 and 1." in Chaudhuri [0122])
Chaudhuri does not explicitly disclose:
obtaining, by one or more processors, real-time hospital admission data;
Schoenberg teaches obtaining, by one or more processors, real-time hospital admission data (see “Patient records may be collected from one or more information sources and various hospital departments…the patient information including…real-time clinical data obtained from one or more medical devices” in Schoenberg [0032]). This step of Schoenberg is applicable to the method of Chaudhuri as they both share characteristics and capabilities, namely, they are directed to obtaining patient information. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the obtained hospital admission data as disclosed by the method of Chaudhuri to be obtained real-time as taught by Schoenberg. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Chaudhuri in order to update the patient’s medical information (see paragraph [0005] of Schoenberg).
With respect to Claim 2,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 1
Chaudhuri teaches wherein determining the primary admission value based on the modified standardized real-time hospital admission dataset includes: determining at least one primary admission in at least one first time frame of interest, wherein each of the at least one first time frame of interest has zero or one primary admission; (“The computerized method of claim 16, wherein the first time period is 12 hours after admission of an AMI reference patient and the second time is at discharge of the AMI reference patient.”) (Chaudhuri [Claim 17]) (Examiner note: After admission is interpreted as the patient has been admitted and has one primary admission within the first 12 hours which is interpreted as the first time frame of interest) and (“FIG. 3 depicts a schematic illustration of example time intervals for features that may be utilized in predicting readmission for AMI patients, in accordance with an embodiment of the disclosure;”) (Chaudhuri [0007]) (Examiner note: Figure 3 contains a time frame of interest which is the 180 days up unto readmission which is interpreted as the second time frame of interest.)
and determining a total number of primary admissions in a second time frame of interest, wherein the second time frame of interest includes the at least one first time frame of interest. (“The computerized method of claim 16, wherein the first time period is 12 hours after admission of an AMI reference patient and the second time is at discharge of the AMI reference patient.”) (Chaudhuri [Claim 17]) (Examiner note: Number of admissions can be determined after patient is discharged and is interpreted as determining number of primary admissions in a second time frame)
With respect to Claim 3,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 2
Chaudhuri teaches wherein determining the readmission value based on the modified standardized real-time hospital admission data includes: determining a total number of readmissions associated with the at least one primary admission in the second time frame of interest. ("At step 640, an indication of whether a readmission occurred for each patient within the set of AMI reference patients may be determined. This readmission may be referred to herein as a “readmission encounter” and may include the first qualifying inpatient or observation visit within a time period, such as 30 days, of the index admission. In some aspects, an index admission may refer to any qualifying admission to a care facility (which may be limited to an acute care hospital in some embodiments) that is assessed for the outcome of whether a patient was readmitted or not within 30 days.") (Chaudhuri [0139])
With respect to Claim 4,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 1
Chaudhuri teaches wherein the real-time hospital admission data further includes at least one of: an International Classification of Diseases and Related Health Problems (ICD) diagnosis code; a Clinical Care Document (CCD) summary; Admission, Discharge, Transfer (ADT) data; a Health Level Seven (HL7) message; an individual medical record number; an individual demographical information; insurance claims data; an admitting hospital location; an individual residence location; or prior discharge data. ("In some embodiments, the indicator is a working diagnosis for the patient, which may be in the patient's EHR. For example, a working diagnosis may be determined at step 510 from a diagnosis code in the patient's EMR, such as any 121 ICD-10-CM diagnosis codes.") (Chaudhuri [0130]) (Examiner note: Since the limitation is recited in alternate form only one limitation is needed and the 121 ICD-10-CM diagnosis code is interpreted as the ICD diagnosis code comprising the admission data)
With respect to Claim 7,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 1
Chaudhuri teaches wherein the primary admission value is determined based on one indicator of a disease of interest. ("FIG. 5 depicts a flow chart of an example method for predicting whether an AMI patient will have a readmission within a future time interval, in accordance with an embodiment of the disclosure") (Chaudhuri [0009]) (Examiner note: AMI meaning acute myocardial infarction is considered a disease. The primary admission value includes a total number of primary admissions in a time period that may be predefined, in this example the patient is admitted because of an AMI which is interpreted as the disease of interest)
With respect to Claim 8,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 7
Chaudhuri teaches wherein the readmission value is determined based on the one indicator of a disease of interest. ("Some feature values may be for one or more time frames") (Chaudhuri [0045]) (Examiner note: A feature value can be an indicator of a disease of interest)
With respect to Claim 9,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 1
Chaudhuri teaches wherein the primary admission value and the readmission value are determined based on a first time frame of interest, wherein the first time frame of interest is a time frame beginning at a primary admission. ("At step 640, an indication of whether a readmission occurred for each patient within the set of AMI reference patients may be determined. This readmission may be referred to herein as a “readmission encounter” and may include the first qualifying inpatient or observation visit within a time period, such as 30 days, of the index admission.") (Chaudhuri [0139]) (Examiner note: First qualifying inpatient or observation visit which is the index admission is interpreted as the primary admission value, readmission encounter is interpreted as a readmission value, and both are based on a fist time frame of interest, for example, 30 days of the index admission)
With respect to Claim 10,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 1
wherein the primary admission value and the readmission value are determined based on a first time frame of interest ("At step 640, an indication of whether a readmission occurred for each patient within the set of AMI reference patients may be determined. This readmission may be referred to herein as a “readmission encounter” and may include the first qualifying inpatient or observation visit within a time period, such as 30 days, of the index admission.") (Chaudhuri [0139]) (Examiner note: First qualifying inpatient or observation visit which is the index admission is interpreted as the primary admission value, readmission encounter is interpreted as a readmission value, and both are based on a fist time frame of interest, for example, 30 days of the index admission)
and the disease-specific readmission rate is determined based on a second time frame of interest, the first time frame of interest being different from the second time frame of interest. ("FIGS. 4A and 4B depict example features that may be utilized in predicting readmission for AMI patients, in accordance with an embodiment of the disclosure") (Chaudhuri [0008]) (Examiner note: AMI readmission is interpreted as a disease specific readmission rate) and ("Some feature values may be for one or more time frames") (Chaudhuri [0045]) (Examiner note: Feature values taken from one or more time frames is being interpreted as the disease-specific readmission rate based on a second time from of interest)
With respect to Claim 11,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 1
Chaudhuri teaches Further comprising: generating, by the one or more processors, at least one trend prediction for an individual based on the disease-specific readmission rate, the at least one trend prediction for an individual including at least one of a readmission prediction, a disease progression prediction, a disease prognosis prediction, or a cost prediction, wherein the data associated with the disease-specific readmission rate includes the at least one trend prediction. ("FIG. 5 depicts a flow chart of an example method for predicting whether an AMI patient will have a readmission within a future time interval, in accordance with an embodiment of the disclosure") (Chaudhuri [0009]) (Examiner note: predicting whether an AMI patient will have a readmission in a future time interval is interpreted as generating a trend prediction based on the disease-specific readmission rate)
With respect to Claim 12,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 1
Chaudhuri teaches wherein generating the at least one trend prediction for an individual includes: obtaining trend data, the trend data including at least one of disease of interest readmission data, other disease readmission data, readmission cost data, or prognosis data; ("…train one or more machine learning models using, for each patient in at least a subset of the AMI reference patients, the indication of whether readmission occurred and the feature values; and deploying the one or more machine learning models in a decision support application for predicting readmission for AMI patients.") (Chaudhuri [Claim 15]) (Examiner note: Subset of the AMI reference patients and whether readmission occurred with the feature values is interpreted as obtaining trend data and AMI is interpreted as the disease of interest)
And determining, using a trained second machine learning model, the at least one trend prediction based on the trend data, wherein the trained second machine learning model has been trained by: receiving, as trend prediction training data, at least one of disease of interest readmission data, other disease readmission data, readmission cost data, or prognosis data associated with a plurality of users; and training a machine learning model, using the trend prediction training data, to infer at least one trend in disease-specific readmission for an individual. ("The computerized method of claim 15, wherein the one or more machine learning models comprise a first machine learning model trained using feature values for the set of reference AMI patients associated with a first time period during a patient encounter and a second machine learning model trained using feature values for the set of reference AMI patients associated with a second time period during a patient encounter.") (Chaudhuri [Claim 16]) (Examiner note: Second machine learning model training using feature values for reference AMI patients is interpreted as receiving prediction training data, and using trend prediction training data to infer at least one trend in disease-specific readmission for an individual)
With respect to Claim 13,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 1
Chaudhuri teaches further comprising: determining, by the one or more processors, a disease-any-cause readmission rate based on the primary admission value and the readmission value, wherein the primary admission value, the readmission value, and the disease-any-cause readmission rate are based on a plurality of indicators of diseases of interest. ("FIGS. 4A and 4B depict example features that may be utilized in predicting readmission for AMI patients, in accordance with an embodiment of the disclosure") (Chaudhuri [0008]) (Examiner note: Figure 4A depicts many kinds of causes for a readmission and from this data a disease-any-cause readmission rate can be determined)
Claims 14-16 and 18 –
Claims 14-16 and 18 are directed to a system. Claim 14-16 and 18 recite limitations that are parallel in nature as those addressed above for claims 1-3 and 11, which are directed towards a method. Claims 14-16 and 18 are therefore rejected for the same reasons as set forth above for claims 1-3 and 11, respectively.
Claim 20 –
Claim 20 is directed to a non-transitory medium. Claim 20 recites limitations that are parallel in nature as those addressed above for claim 1 which is directed towards a method. Claim 20 is therefore rejected for the same reasons as set forth above for claim 1.
Claims 5; and 17 are rejected under 35 U.S.C 103 as being unpatentable over Chaudhuri et al. (US 2023/0215563 A1) in view of Schoenberg et al. (US 2010/0217621 A1) and further in view of Eishiro (JP 2004021699).
With respect to Claim 5,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 1
Chaudhuri in view of Schoenberg do not teach the limitation below.
However, Eishiro teaches wherein determining the primary admission value comprises: determining, by the one or more processors, whether any of the plurality of individuals has died…; and upon determining at least one of the plurality of individuals has died, removing, by the one or more processors, data associated with the at least one of plurality of individuals that has died from the hospital admission data. (“Next, an operation example of the present invention will be described with reference to the flowcharts shown in FIGS. FIG. 2 illustrates a process in which the patient terminal 10 deletes personal data stored in the storage device 11 when the patient no longer uses the patient terminal 10 that has been used because of discharge or death.”) (Eishiro [0015]) (Examiner note: Deleting personal data because of discharge or death is interpreted as removing data associated with the at least one of plurality of individuals that has died from the hospital admission data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined method as taught by Chaudhuri in view of Schoenberg to include the ability to remove patient data after a patient death, as taught by Eishiro with the motivation providing “a method for deleting or moving personal data by a PC terminal received from a PC via a network.” (Eishiro [0003]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add ability to remove patient data after a patient death.
Chaudhuri teaches …within a third time frame of interest… ("Additionally, both models may be run at any time, not only at a predetermined time (e.g., 12 hours) after admission and/or at discharge.") (Chaudhuri [0115]) (Examiner note: Since the models can be run at any time it is interpreted that they can run during a third time frame of interest) and (“FIG. 3 depicts a schematic illustration of example time intervals for features that may be utilized in predicting readmission for AMI patients, in accordance with an embodiment of the disclosure”) (Chaudhuri [0007]) (Examiner note: Figure three shows multiple examples of separate time intervals that can be used for data and is interpreted as a third time frame of interest)
Claim 17 –
Claim 17 is directed to a system. Claim 17 recites limitations that are parallel in nature as those addressed above for claim 5, which is directed towards a method. Claims 17 is therefore rejected for the same reasons as set forth above for claim 5.
Claims 6; and 19 are rejected under 35 U.S.C 103 as being unpatentable over Chaudhuri et al. (US 2023/0215563 A1) in view of Schoenberg et al. (US 2010/0217621 A1) and further in view of Chaudhuri et al. (US 2023/0215563 A1).
With respect to Claim 6,
Chaudhuri in view of Schoenberg teaches the limitations of Claim 1
Chaudhuri suggests/teaches wherein determining the disease-specific readmission rate comprises: dividing the readmission value by the primary admission value; ("FIGS. 4A and 4B depict example features that may be utilized in predicting readmission for AMI patients, in accordance with an embodiment of the disclosure") (Chaudhuri [0008]) (Examiner note: In order for Chaudhuri to obtain a prediction readmission for AMI patients they would need to have both the primary admission value and the readmission value data, and in order to determine a specific readmission prediction rate for an AMI patient the readmission value can be divided by the primary admission value. Chaudhuri teaches a readmission rate, and a rate is a division of two numerical pieces of data, so this could be the division of the number primary admissions of AMI patients by the number of readmissions of AMI patients. Deeming a patient as an AMI (acute myocardial infarction) is interpreted as the disease specific value and in predicting the readmission rate for an AMI patient the primary admission values and readmission values are used to determine the specific AMI (disease specific) readmission rate by dividing the readmission value by the primary value)
or determining, using a trained first machine learning model, the disease-specific readmission rate based on at least a portion of the hospital admissions data associated with an individual, wherein the trained first machine learning model has been trained by: receiving, as disease-specific readmission rate training data, the hospital admission data including a plurality of admission dates associated with a plurality of users and a plurality of indicators of diseases of interest corresponding to the plurality of admission dates, and training a first machine learning model, using the disease-specific readmission rate training data, to infer the disease-specific readmission rate. ("FIG. 6 depicts a flow chart of an example method for training and deploying a machine learning system to predict readmission for AMI patients, in accordance with an embodiment of the disclosure") (Chaudhuri [0010]) (Examiner note: Machine learning system is interpreted as a trained first machine learning model) and ("At step 610, data for a set of reference individuals is received.") (Chaudhuri [0136]) (Examiner note: Data received includes disease-specific data and admission dates)
It would have been obvious for one of ordinary skill in the art to modify the teachings of Chaudhuri to calculate a rate based on a primary admission value and a readmission value for the purpose of determining a disease specific readmission rate with the motivation to “increase the effectiveness of interventions to reduce a readmission likelihood” (Chaudhuri [0019]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to divide to values to determine a rate.
Claim 19 –
Claim 19 is directed to a system. Claim 19 recites limitations that are parallel in nature as those addressed above for claim 6, which is directed towards a method. Claims 19 is therefore rejected for the same reasons as set forth above for claim 6.
Response to Arguments
Applicant's arguments filed 15 August 2025, with respect to 35 USC § 101, have been fully considered but they are not persuasive. With regard to claims 1-20, the applicant argues that the claims integrate the abstract idea into a practical application because they provide the technical improvement to better determine readmission rates in a patient population.
In response to the argument of claims 1-20, the Examiner respectfully disagrees. In order for a claim to be integrated into a practical application Examiners must evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. USPTO guidance uses the term ‘‘additional elements’’ to refer to claim features, limitations, and/or steps that are recited in the claim beyond the identified judicial exception. As noted in the rejection above, the limitations of: obtaining… real-time hospital admission data in a plurality of formats from one or more data sources, the real-time hospital admission data associated with a plurality of individuals; standardizing…the real-time hospital admission data from the plurality of formats into a standard format to generate standardized real-time hospital admission data,…; generating…a modified standardized real-time hospital admission dataset by removing extraneous data from the standardized real-time hospital admission data based on the at least one indicator of the disease of interest; and causing to output…data associated with the disease-specific readmission rate based on the modified standardized real-time hospital admission dataset are not considered to be “additional elements” because they are directed towards the judicial exception (i.e. an abstract idea). The limitations argued by the applicant are considered to be an abstract idea and fall under the methods of organizing human activity bucket because it is a concept directed to managing personal behavior. It is a concept directed to managing personal behavior because these are steps that can be performed by a medical professional in order to determine/predict if a patient may be readmitted to a hospital. This is similar to concepts that the court have found to be abstract such as a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). The only limitations that go beyond the judicial exception are one or more processors; one or more storage devices; and a graphical user interface of a user device. These limitations do no more than merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which is not considered to be a technical improvement. Therefore, the examiner maintains the claims are ineligible.
Applicant's arguments for claims 1-20 with respect to 35 USC § 102 and 103 fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RESHA DESAI whose telephone number is (571)270-7792. The examiner can normally be reached M-F 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RESHA DESAI/ Supervisory Patent Examiner, Art Unit 3628