DETAILED ACTION
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 .
Claims 21-37 are canceled.
Claims 1-20 are presented for examination.
The claims and only the claims form the metes and bounds of the invention. “Office personnel are to give claims their broadest reasonable interpretation in light of the supporting disclosure. In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed. Cir. 1997). Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969)” (MPEP p 2100-8, c 2, I 45-48; p 2100-9, c 1, l 1-4). The Examiner has full latitude to interpret each claim in the broadest reasonable sense. The Examiner will reference prior art using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-4 and 11-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-4 and 6-15 of U.S. Patent No. 11,967,428. Although the conflicting claims are not identical, they are not patentably distinct from each other because the claimed features of the claims 1-22 of U.S. Patent No. 11,403,408 can also be interpreted as claimed features as claimed in the claims 1-20 of the present application.
Although the claims at issue are not identical, they are not patentably distinct from each other because following the rationale in In re Goodman, cited above, where applicant has once been granted a patent containing a claim for the specific or narrower invention, applicant may not then obtain a second patent with a claim for the generic or broader invention without first submitting an appropriate terminal disclaimer.
Instant Application
US Patent 11,967,428
1. A computer system, comprising: a processing system comprising a processing device and computer storage;
a predictive model comprising computer program code processed by the processing system and having an input that receives data values for input features derived from data for an entity and an output that provides data representing a result from the predictive model processing the received data values for the input features,
wherein the result is a value indicative of an outcome for the entity;
a set of weights stored in the computer storage and associating, for a plurality of types of events in patient medical histories,
weights for tuples representing different combinations of a type of event with at least one of a relative time with respect to a reference time or an entity profile characteristic,
wherein the plurality of types of events include at least one of a medical diagnosis, a medical procedure, a medical treatment, a medical laboratory result, or a medication prescribed or purchased or administered for a patient;
a calculation module comprising computer program code processed by the processing system and having an input that receives event data for a set of events from a patient medical history for the entity, accesses the set of weights, based on the event data, relative times for events in the event data with respect to a reference time, and entity profile characteristics for that entity,
to retrieve weights for the events in the set of events, and outputs a result of a function of the retrieved weights and the set of the events for the entity; and
wherein the predictive model receives, as the input features, the results for the entity computed by the calculation module, and
wherein the predictive model computes the output for the entity based on the received results.
1. A computer system, comprising: a processing system comprising a processing device and computer storage;
a predictive model comprising computer program code processed by the processing system and having an input that receives data values for input features derived from event data from a patient medical history for an entity and an output that provides data representing a result from the predictive model processing the received data values for the input features,
wherein the result is a value indicative of a predicted outcome for the entity relative to an input reference time;
a timeline generation module comprising computer program code processed by the processing system and having an input to receive event data from a patient medical history for an entity, and an output that provides a plurality of timelines for the entity, each timeline comprising data representing a respective set of events from the received event data within a respective category of events from among a plurality of categories of events in patient medical histories;
a respective set of weights stored in the computer storage for each category of the plurality of categories of events in patient medical histories and wherein each respective set of weights for each category comprises a respective distinct weight for each tuple in a plurality of tuples, each tuple representing a respective distinct combination of at least a type of event, a respective relative time of the event with respect to a reference time, and one or more entity profile characteristics for an entity,
wherein categories of events include at least one of a medical diagnosis, a medical procedure, a medical treatment, a medical laboratory result, or a medication prescribed or purchased or administered for a patient and wherein types of events correspond to one or more event codes or one or more medical instances;
a calculation module comprising computer program code processed by the processing system and having a first input that receives the plurality of timelines provided by the timeline generation module for the entity and a second input that receives the input reference time, the calculation module: for each timeline, accessing, from the set of weights in the computer storage, a respective weight for each event in the timeline based on the type of the event, the respective relative time of the event with respect to the input reference time and one or more entity profile characteristics of the entity, for each timeline, computing a respective additional feature for the entity as a respective function of the retrieved respective weights for each event in the timeline; and
wherein the predictive model receives, as the input features, at least the data values derived from the event data from the patient medical history for the entity and the respective additional features computed for the timelines for the entity computed by the calculation module, and
wherein the predictive model computes the predicted outcome for the entity relative to the input reference time based on both the data values for the input features derived from the event data from the patient medical history for the entity and the additional features computed for the timelines for the entity.
2. The computer system of claim 1,
wherein the reference time is a current time.
2. The computer system of claim 1,
wherein the input reference time is a current time.
3. The computer system of claim 1,
wherein the reference time is a time associated with an event.
3. The computer system of claim 1,
wherein the input reference time is a time associated with an event.
4. The computer system of claim 1,
wherein the reference time is a time for which the outcome of the predicted model is computed.
4. The computer system of claim 1,
wherein the input reference time is a time for which the outcome of the predicted model is computed.
11. The computer system of claim 1, wherein the function is a linear function.
6. The computer system of claim 1, wherein the function is a linear function.
12. The computer system of claim 1, wherein the function is a non-linear function.
7. The computer system of claim 1, wherein the function is a non-linear function.
13. The computer system of claim 1,
wherein each unique tuple in the set of weights has a single weight.
8. The computer system of claim 1,
wherein each unique tuple in the set of weights has a single weight.
14. The computer system of claim 1, wherein at least one tuple in the set of weights has a plurality of weights, and the calculation module selects from among the plurality of weights.
9. The computer system of claim 1,
wherein at least one tuple in the set of weights has a plurality of weights, and the calculation module selects from among the plurality of weights.
15. The computer system of claim 1, wherein the entity comprises a patient, and the entity profile characteristic comprises
at least one of age, a comorbidity, a behavior, a characteristic from a family history, or genetic profile attribute of the patient.
10. The computer system of claim 1, wherein the entity comprises a patient, and the entity profile characteristic comprises
at least one of age, a comorbidity, a behavior, a characteristic from a family history, or genetic profile attribute of the patient.
16. The computer system of claim 1, wherein the set of weights comprises a plurality of weight tables, including a first weight table for a first outcome and a second weight table for a second outcome different from the first outcome, wherein a first predictive model generates values indicative of the first outcome using the first weight table, and a second predictive model generates values indicative of the second outcome using the second weight table.
11. The computer system of claim 1, wherein the set of weights comprises a plurality of weight tables, including a first weight table for a first outcome and a second weight table for a second outcome different from the first outcome, wherein a first predictive model generates values indicative of the first outcome using the first weight table, and a second predictive model generates values indicative of the second outcome using the second weight table.
17. The computer system of claim 1, wherein the set of weights comprises a weight table corresponding to a first outcome, and wherein the predictive model outputs a value indicative of a second outcome different from the first outcome.
12. The computer system of claim 1, wherein the set of weights comprises a weight table corresponding to a first outcome, and wherein the predictive model outputs a value indicative of a second outcome different from the first outcome.
18. The computer system of claim 17, wherein the second outcome is correlated with the first outcome.
13. The computer system of claim 12, wherein the second outcome is correlated with the first outcome.
19. The computer system of claim 1, wherein the set of weights comprises a plurality of weight tables, wherein the calculation module accesses the plurality of weight tables to compute the results provided as inputs to the predictive model.
14. The computer system of claim 1, wherein the set of weights comprises a plurality of weight tables, wherein the calculation module accesses the plurality of weight tables to compute the results provided as inputs to the predictive model.
20. The computer system of claim 1, wherein the predictive model generates a value indicative of a first outcome for an entity, wherein the first outcome is correlated to a second outcome, and the computer system reports a value indicative of the second outcome for the entity based on the value indicative of the first outcome for the entity.
15. The computer system of claim 1, wherein the predictive model generates a value indicative of a first outcome for an entity, wherein the first outcome is correlated to a second outcome, and the computer system reports a value indicative of the second outcome for the entity based on the value indicative of the first outcome for the entity.
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.
Claims 1-20 are 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 "wherein the predictive model receives, as the input features, the results for the entity computed by the calculation module, and wherein the predictive model computes the output for the entity based on the received results”.
There is insufficient antecedent basis for “the results for the entity computed by the calculation module” recited in the claim 1.
Dependent Claims 2-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph for the same reason as independent Claim 1 above.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that
form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless —
(a)(1) the claimed invention was patented, described in a printed publication, or
in public use, on sale, or otherwise available to the public before the effective
filing date of the claimed invention.
Claims 1-10 and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PGPUB 2017/0061093 by Amarasingham et al. (“Amarasingham”).
As to Claim 1, Amarasingham teaches a computer system, comprising: a processing system comprising a processing device and computer storage (Amarasingham: at least Fig. 1 shows processing devices);
a predictive model comprising computer program code processed by the processing system (Amarasingham: at least ¶0034; “clinical predictive and monitoring system 10”; ¶0042 also discloses “predictive modeling performed by the present system”) and having an input that receives data values for input features derived from data for an entity (Amarasingham: at least ¶0035; “data received by the clinical predictive and monitoring system 10 may include electronic medical records (EMR) that include both clinical and non-clinical data” and “EMR clinical data may be received from entities such as hospitals, clinics, pharmacies, laboratories, and health information exchanges, including: vital signs and other physiological data; data associated with comprehensive or focused history and physical exams by a physician, nurse, or allied health professional; medical history”; ¶0034 also discloses “clinical and non-clinical data relating to patients or individuals requiring care”; note: “patients or individuals requiring care” as entities) and an output that provides data representing a result from the predictive model processing the received data values for the input features, wherein the result is a value indicative of an outcome for the entity (Amarasingham: at least ¶0034; “these data are used to determine a disease risk score for selected patients so that they may receive more target intervention, treatment, and care that are better tailored and customized to their particular condition and needs. The system 10 is most suited for identifying particular patients who require intensive inpatient and/or outpatient care to avert serious detrimental effects of certain diseases and to reduce hospital readmission rates”; ¶0061 also discloses “one output from the disease/risk logic module 30 includes the risk scores of all the patients for particular disease or condition. In addition, the module 30 may rank the patients according to the risk scores, and provide the identities of those patients at the top of the list”);
a set of weights stored in the computer storage and associating, for a plurality of types of events in patient medical histories (Amarasingham: at least ¶0034; “a disease risk score for selected patients”; ¶0043 further discloses “for example, a clinician (healthcare personnel) may immediately generate a list of patients that have the highest congestive heart failure risk scores, e.g., top n numbers or top x %”; ¶0047 further “stored historical and real-time data related to the patients are analyzed by the clinical predictive and monitoring system and method 10 to identify specific diseases and conditions related to the patient, such as congestive heart failure”; ¶0061 further discloses “the predictive model for congestive heart failure may take into account a set of risk factors or variables, including the worst values for laboratory and vital sign variables such as: … temperature, pulse, diastolic blood pressure, and systolic blood pressure. Further, non-clinical factors are also considered, for example, the number of home address changes in the prior year, risky health behaviors (e.g., use of illicit drugs or substances), number of emergency room visits in the prior year, history of depression or anxiety, and other factors” and “predictive model specifies how to categorize and weight each variable or risk factor, and the method of calculating the predicted probably of readmission or risk score”; note: heart failure/heart problem and hospital admission/readmission as example of events), weights for tuples representing different combinations of a type of event with at least one of a relative time with respect to a reference time (Amarasingham: at least Fig. 10, ¶0092; “Data tab, is shown providing the patient name, review status, admission date, length of stay (LOS), working diagnosis, risk level, and location in the healthcare facility. FIG. 10 shows a list of patients that have been determined to be at high risk and very high risk for readmission to the hospital, preferably by the clinical predictive and monitoring system described above”; ¶0060 also discloses “risk levels for all internal medicine patients for any cause, an all-cause readmissions predictive model” and “HIV readmission, diabetes identification, risk for cardio-pulmonary arrest, kidney disease progression, acute coronary syndrome, pneumonia, cirrhosis, all-cause disease-independent readmission”; (¶0047 also discloses “within 24 hours of a patient's admission to the hospital, stored historical and real-time data related to the patients are analyzed by the clinical predictive and monitoring system and method 10 to identify specific diseases and conditions related to the patient, such as congestive heart failure. Further, the system 10 calculates a risk score for congestive heart failure for this particular patient within 24 hours of admission; ¶0087 also discloses “a number of data items associated with each patient in a list are displayed, such as … the admission or arrival date”; note: records in table as tuples with patient information) or an entity profile characteristic (Amarasingham: at least ¶0087; “… a number of patients identified as having risk of readmission to the healthcare facility due to congestive heart failure” and “a number of data items associated with each patient in a list are displayed, such as name, patient ID, gender, age”), wherein the plurality of types of events include at least one of a medical diagnosis, a medical procedure, a medical treatment, a medical laboratory result (Amarasingham: at least ¶0034; “these data are used to determine a disease risk score for selected patients so that they may receive more target intervention, treatment, and care that are better tailored and customized to their particular condition and needs. The system 10 is most suited for identifying particular patients who require intensive inpatient and/or outpatient care to avert serious detrimental effects of certain diseases and to reduce hospital readmission rates”; ¶¶0047-0048 further disclose “if this particular patient's risk score for congestive heart failure is above a certain risk threshold, then the patient is identified on a list of high-risk patients that is presented to an intervention coordination team” and “clinical predictive and monitoring system and method 10 may further automatically present a plan to include recommended intervention and treatment options”; ¶¶0060-0061 further discloses “risk levels for all internal medicine patients for any cause, an all-cause readmissions predictive model may be used to process the patient data” and “calculating the predicted probably of readmission or risk score”; ¶0092 further discloses “working diagnosis”), or a medication prescribed or purchased or administered for a patient;
a calculation module comprising computer program code processed by the processing system and having an input that receives event data for a set of events from a patient medical history for the entity (Amarasingham: at least ¶0035; “data received by the clinical predictive and monitoring system 10 may include electronic medical records (EMR) that include both clinical and non-clinical data” and “EMR clinical data may be received from entities such as hospitals, clinics, pharmacies, laboratories, and health information exchanges, including: vital signs and other physiological data; data associated with comprehensive or focused history and physical exams by a physician, nurse, or allied health professional; medical history”; ¶0034 also discloses “clinical and non-clinical data relating to patients or individuals requiring care”; note: “patients or individuals requiring care” as entities), accesses the set of weights, based on the event data, relative times for events in the event data with respect to a reference time (Amarasingham: at least ¶0092; “… review status, admission date, length of stay (LOS), working diagnosis, risk level, and location in the healthcare facility. FIG. 10 shows a list of patients that have been determined to be at high risk and very high risk for readmission to the hospital, preferably by the clinical predictive and monitoring system described above”), and entity profile characteristics for that entity, to retrieve weights for the events in the set of events (Amarasingham: at least ¶0034; “receive a variety of clinical and non- clinical data relating to patients or individuals requiring care” and “these data are used to determine a disease risk score for selected patients so that they may receive more target intervention, treatment, and care that are better tailored and customized to their particular condition and needs”; ¶0034 also discloses “EMR non-clinical data may include, for example, social, behavioral, lifestyle, and economic data; type and nature of employment; job history; medical insurance information; hospital utilization patterns; exercise information; addictive substance use; occupational chemical exposure; frequency of physician or health system contact; location and frequency of habitation changes”; note: determine scores which are affected by the entity or patient -- such as patient’s behaviors, lifestyle), and outputs a result of a function of the retrieved weights and the set of the events for the entity (Amarasingham: at least ¶0043; “for example, a clinician (healthcare personnel) may immediately generate a list of patients that have the highest congestive heart failure risk scores, e.g., top n numbers or top x %”; ¶0047 further “stored historical and real-time data related to the patients are analyzed by the clinical predictive and monitoring system and method 10 to identify specific diseases and conditions related to the patient, such as congestive heart failure”; ¶0061 further discloses “the predictive model for congestive heart failure may take into account a set of risk factors or variables” and “predictive model specifies how to categorize and weight each variable or risk factor, and the method of calculating the predicted probably of readmission or risk score”); and
wherein the predictive model receives, as the input features, the results for the entity computed by the calculation module, and wherein the predictive model computes the output for the entity based on the received results (Amarasingham: at least ¶0106; “showing a readmission risk factor reporting function for a particular selected patient according to the present disclosure. This screen summarizes those factors relating to a particular patient that contribute to a very high risk of readmission”; ¶0060 also explains “if the hospital desires to determine the risk levels for all internal medicine patients for any cause, an all-cause readmissions predictive model may be used to process the patient data. As another example, if the hospital desires to identify those patients at risk for short-term and long-term diabetic complications, the diabetes predictive model may be used to target those patients. Other predictive models may include HIV readmission, diabetes identification, risk for cardio-pulmonary arrest, kidney disease progression, acute coronary syndrome, pneumonia, cirrhosis, all-cause disease-independent readmission”).
As to Claim 2, Amarasingham teaches the computer system of claim 1, wherein the reference time is a current time (Amarasingham: at least ¶0092; “providing the patient name, review status, admission date, length of stay (LOS)”; “the fact that Ethel Price has had three readmissions in the past year, and two emergency room visits in the last year are primary contributing factors that resulted in the very high risk of readmission”; note: length of stay is relative to a current time).
As to Claim 3, Amarasingham teaches the computer system of claim 1, wherein the reference time is a time associated with an event (Amarasingham: at least ¶0092; “providing the patient name, review status, admission date, length of stay (LOS)”; “the fact that Ethel Price has had three readmissions in the past year, and two emergency room visits in the last year are primary contributing factors that resulted in the very high risk of readmission”; note: time of admission).
As to Claim 4, Amarasingham teaches the computer system of claim 1, wherein the reference time is a time for which the outcome of the predicted model is computed (Amarasingham: at least ¶0061; “non-clinical factors are also considered, for example, the number of home address changes in the prior year, risky health behaviors (e.g., use of illicit drugs or substances), number of emergency room visits in the prior year, history of depression or anxiety, and other factors”; ¶0034 also discloses “These data are used to determine a disease risk score for selected patients so that they may receive more target intervention, treatment, and care that are better tailored and customized to their particular condition and needs”; note: prior year means one year before the current time – current time is when outcome is computed).
As to Claim 5, Amarasingham teaches the computer system of claim 1, wherein at least one tuple represents a combination of a type of event, a relative time (Amarasingham: at least ¶0047; “within 24 hours of a patient's admission to the hospital, stored historical and real-time data related to the patients are analyzed by the clinical predictive and monitoring system and method 10 to identify specific diseases and conditions related to the patient, such as congestive heart failure. Further, the system 10 calculates a risk score for congestive heart failure for this particular patient within 24 hours of admission; ¶0087 also discloses “a number of data items associated with each patient in a list are displayed, such as … the admission or arrival date”; note: tuples as a patient records), and an entity profile characteristic (Amarasingham: at least ¶0087; “… a number of patients identified as having risk of readmission to the healthcare facility due to congestive heart failure” and “a number of data items associated with each patient in a list are displayed, such as name, patient ID, gender, age”).
As to Claim 6, Amarasingham teaches the computer system of claim 1, wherein at least one tuple represents a combination of a type of event and a relative time (Amarasingham: at least ¶0047 further discloses “within 24 hours of a patient's admission to the hospital, stored historical and real-time data related to the patients are analyzed by the clinical predictive and monitoring system and method 10 to identify specific diseases and conditions related to the patient, such as congestive heart failure. Further, the system 10 calculates a risk score for congestive heart failure for this particular patient within 24 hours of admission; ¶0087 also discloses “a number of data items associated with each patient in a list are displayed, such as … the admission or arrival date”; tuples as a patient records).
As to Claim 7, Amarasingham teaches the computer system of claim 1, wherein the calculation module receives a plurality of different sets of events (Amarasingham: at least ¶0034; “receive a variety of clinical and non-clinical data relating to patients or individuals requiring care. The variety of data include real-time data streams and historical or stored data from hospitals and healthcare entities 14, non-health care entities 15, health information exchanges 16, and social-to-health information exchanges and social services entities 17, for example”; ¶0035 also discloses “data received by the clinical predictive and monitoring system 10 may include electronic medical records (EMR) that include both clinical and non-clinical data” and “EMR clinical data may be received from entities such as hospitals, clinics, pharmacies, laboratories, and health information exchanges, including: vital signs and other physiological data; data associated with comprehensive or focused history and physical exams by a physician, nurse, or allied health professional; medical history; prior allergy and adverse medical reactions; family medical history; prior surgical history; emergency room records; medication administration records; culture results; dictated clinical notes and records; gynecological and obstetric history; mental status examination; vaccination records; radiological imaging exams; invasive visualization procedures; psychiatric treatment history”).
As to Claim 8, Amarasingham teaches the computer system of claim 1, wherein the calculation module applies a function to each set of events in the plurality of different sets of events (Amarasingham: at least ¶0060; “the disease/risk logic module 30 further comprises a predictive model process 36 that is adapted to predict the risk of particular diseases or condition of interest according to one or more predictive models” and “if the hospital desires to determine the level of risk for future readmission for all patients currently admitted with heart failure, the heart failure predictive model may be selected for processing patient data. However, if the hospital desires to determine the risk levels for all internal medicine patients for any cause, an all-cause readmissions predictive model may be used to process the patient data”; ¶0034 also discloses “receive a variety of clinical and non-clinical data relating to patients or individuals requiring care” and “variety of data include real-time data streams and historical or stored data from hospitals and healthcare entities; note: patients have different sets of events/medical history).
As to Claim 9, Amarasingham teaches the computer system of claim 8, wherein the calculation module applies a different function to different sets of events (Amarasingham: at least ¶0060; “the disease/risk logic module 30 further comprises a predictive model process 36 that is adapted to predict the risk of particular diseases or condition of interest according to one or more predictive models” and “if the hospital desires to determine the level of risk for future readmission for all patients currently admitted with heart failure, the heart failure predictive model may be selected for processing patient data. However, if the hospital desires to determine the risk levels for all internal medicine patients for any cause, an all-cause readmissions predictive model may be used to process the patient data”; ¶0034 also discloses “receive a variety of clinical and non-clinical data relating to patients or individuals requiring care” and “variety of data include real-time data streams and historical or stored data from hospitals and healthcare entities; note: different models that serve different functions).
As to Claim 10, Amarasingham teaches the computer system of claim 8, wherein the calculation module applies a plurality of functions to each set of events (Amarasingham: at least ¶0060; “the disease/risk logic module 30 further comprises a predictive model process 36 that is adapted to predict the risk of particular diseases or condition of interest according to one or more predictive models” and “if the hospital desires to determine the level of risk for future readmission for all patients currently admitted with heart failure, the heart failure predictive model may be selected for processing patient data. However, if the hospital desires to determine the risk levels for all internal medicine patients for any cause, an all-cause readmissions predictive model may be used to process the patient data” and “other predictive models may include HIV readmission, diabetes identification, risk for cardio-pulmonary arrest, kidney disease progression, acute coronary syndrome, pneumonia, cirrhosis, all-cause disease-independent readmission, colon cancer pathway adherence, and others”; ¶0034 also discloses “receive a variety of clinical and non-clinical data relating to patients or individuals requiring care” and “variety of data include real-time data streams and historical or stored data from hospitals and healthcare entities; note: different models that can be used applied).
As to Claim 13, Amarasingham teaches the computer system of claim 1, wherein each unique tuple in the set of weights has a single weight (Amarasingham: at least ¶0061; “those patients at the highest risks are automatically identified so that targeted intervention and care may be instituted. One output from the disease/risk logic module 30 includes the risk scores of all the patients for particular disease or condition” and “… identities of those patients at the top of the list”; note: note: records for patients as tuples).
As to Claim 14, Amarasingham teaches the computer system of claim 1, wherein at least one tuple in the set of weights has a plurality of weights (Amarasingham: at least ¶0055; “calculate a risk score associated with an identified disease or condition for each patient”; ¶¶0060-0061 also disclose “predict the risk of particular diseases” and “other predictive models may include HIV readmission, diabetes identification, risk for cardio-pulmonary arrest, kidney disease progression, acute coronary syndrome, pneumonia, cirrhosis, all-cause disease-independent readmission, colon cancer pathway adherence, and others”), and the calculation module selects from among the plurality of weights (Amarasingham: at least ¶0061; “those patients at the highest risks are automatically identified so that targeted intervention and care may be instituted. One output from the disease/risk logic module 30 includes the risk scores of all the patients for particular disease or condition” and “… identities of those patients at the top of the list”).
As to Claim 15, Amarasingham teaches the computer system of claim 1, wherein the entity comprises a patient (Amarasingham: at least ¶0034; “clinical and non-clinical data relating to patients or individuals requiring care”; note: “patients or individuals requiring care” as entities), and the entity profile characteristic comprises at least one of age, a comorbidity, a behavior (Amarasingham: at least ¶0061; “the predictive model for congestive heart failure may take into account a set of risk factors or variables, including the worst values for laboratory and vital sign variables such as: … temperature, pulse, diastolic blood pressure, and systolic blood pressure. Further, non-clinical factors are also considered, for example, the number of home address changes in the prior year, risky health behaviors (e.g., use of illicit drugs or substances), number of emergency room visits in the prior year, history of depression or anxiety, and other factors” and “predictive model specifies how to categorize and weight each variable or risk factor, and the method of calculating the predicted probably of readmission or risk score”; note: heart failure/heart problem and hospital admission/readmission as example of events), a characteristic from a family history, or genetic profile attribute of the patient (Amarasingham: at least ¶0035; “data received by the clinical predictive and monitoring system 10 may include electronic medical records (EMR) that include both clinical and non-clinical data” and “genetic information”).
As to Claim 16, Amarasingham teaches the computer system of claim 1, wherein the set of weights comprises a plurality of weight tables, including a first weight table for a first outcome (Amarasingham: at least ¶0017; a list of patients matching certain criteria (e.g., high risk and very high risk for congestive heart failure)) and a second weight table for a second outcome different from the first outcome (Amarasingham: at least ¶0016; patient worklist that displays a list of patients matching certain criteria (e.g., high risk and very high risk for readmission), wherein a first predictive model generates values indicative of the first outcome using the first weight table (Amarasingham: at least ¶0061; “predictive model specifies how to categorize and weight each variable or risk factor, and the method of calculating the predicted probably of readmission or risk score”), and a second predictive model generates values indicative of the second outcome using the second weight table (Amarasingham: at least ¶0060; “predict the risk of particular diseases or condition of interest according to one or more predictive models”; ¶0073 further discloses “the list of patients that are at risk for 30-day readmission due to congestive heart failure (CHF) are identified and listed in the active congestive heart failure list. Details of the exemplary screen are provided below”; note: predicting readmission or recurrence is different model than predicting the disease itself).
As to Claim 17, Amarasingham teaches the computer system of claim 1, wherein the set of weights comprises a weight table corresponding to a first outcome (Amarasingham: at least ¶0017; “list of patients matching certain criteria (e.g., high risk and very high risk for congestive heart failure)”), and wherein the predictive model outputs a value indicative of a second outcome different from the first outcome (Amarasingham: at least ¶0042; “identify patients at high-risk of readmission or disease recurrence become much more robust and accurate”; ¶0073; “the list of patients that are at risk for 30-day readmission due to congestive heart failure (CHF) are identified and listed in the active congestive heart failure list. Details of the exemplary screen are provided below).
As to Claim 18, Amarasingham teaches the computer system of claim 17, wherein the second outcome is correlated with the first outcome (Amarasingham: at least ¶0042; “identify patients at high-risk of readmission or disease recurrence become much more robust and accurate”; ¶0073; “the list of patients that are at risk for 30-day readmission due to congestive heart failure (CHF) are identified and listed in the active congestive heart failure list. Details of the exemplary screen are provided below”; note: second outcome is readmission).
As to Claim 19, Amarasingham teaches the computer system of claim 1, wherein the set of weights comprises a plurality of weight tables (Amarasingham: at least ¶¶0016-0017; “patient worklist that displays a list of patients matching certain criteria (e.g., high risk and very high risk for readmission” and “a list of patients matching certain criteria (e.g., high risk and very high risk for congestive heart failure)”), wherein the calculation module accesses the plurality of weight tables to compute the results provided as inputs to the predictive model (Amarasingham: at least ¶0061; “rank the patients according to the risk scores, and provide the identities of those patients at the top of the list. For example, the hospital may desire to identify the top 20 patients most at risk for congestive heart failure readmission, and the top 5% of patients most at risk for cardio-pulmonary arrest in the next 24 hours”; ¶0114 further discloses “identify highest risk patients, identify adverse events, coordinate and alert practitioners, and monitor patient outcomes across time and space”; note: using risk scores to predict patients of highest risks).
As to Claim 20, Amarasingham teaches the computer system of claim 1, wherein the predictive model generates a value indicative of a first outcome for an entity (Amarasingham: at least ¶0047; “within 24 hours of a patient's admission to the hospital, stored historical and real-time data related to the patients are analyzed by the clinical predictive and monitoring system and method 10 to identify specific diseases and conditions related to the patient, such as congestive heart failure. Further, the system 10 calculates a risk score for congestive heart failure for this particular patient within 24 hours of admission. If this particular patient's risk score for congestive heart failure is above a certain risk threshold, then the patient is identified on a list of high-risk patients that is presented to an intervention coordination team”), wherein the first outcome is correlated to a second outcome (Amarasingham: at least ¶0042; “identify patients at high-risk of readmission or disease recurrence become much more robust and accurate”; ¶0073; “the list of patients that are at risk for 30-day readmission due to congestive heart failure (CHF) are identified and listed in the active congestive heart failure list. Details of the exemplary screen are provided below”; note: second outcome is readmission), and the computer system reports a value indicative of the second outcome for the entity based on the value indicative of the first outcome for the entity (Amarasingham: at least ¶0073; “the list of patients that are at risk for 30-day readmission due to congestive heart failure (CHF) are identified”).
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB 2017/0061093 by Amarasingham et al. (“Amarasingham”) in view of USPGPUB 2007/0172907 by Volker et al. (“Volker”).
As to Claim 11, Amarasingham teaches the computer system of claim 1.
Amarasingham does not explicitly disclose, but Volker discloses wherein the function is a linear function (Volker: at least ¶¶0028, 0044; “wherein patient predictor values are calculated by inputting data for two or more blood markers, e.g., two or more plasma or serum markers, and optionally one or more supplementary markers, into a linear or nonlinear function algorithm derived by correlating reference liver histopathological and blood markers, e.g., plasma or serum marker data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Volker’s feature of wherein the function is a linear function (Volker: at least ¶¶0028, 0044) with Amarasingham’s computer system.
The suggestion/motivation for doing so would have been to “aid in the diagnosis of the status or progress of a liver disease in a patient” (Volker: at least ¶0020).
As to Claim 12, Amarasingham teaches the computer system of claim 1.
Amarasingham does not explicitly disclose, but Volker discloses, wherein the function is a non-linear function (Volker: at least ¶¶0028, 0044; “wherein patient predictor values are calculated by inputting data for two or more blood markers, e.g., two or more plasma or serum markers, and optionally one or more supplementary markers, into a linear or nonlinear function algorithm derived by correlating reference liver histopathological and blood markers, e.g., plasma or serum marker data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Volker’s feature of wherein the function is a non-linear function (Volker: at least ¶¶0028, 0044) with Amarasingham’s computer system.
The suggestion/motivation for doing so would have been to “aid in the diagnosis of the status or progress of a liver disease in a patient” (Volker: at least ¶0020).
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Huen Wong whose telephone number is (571) 270-3426. The examiner can normally be reached on Monday - Friday (10:30AM EST - 6:30PM EST). If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Charles Rones can be reached on (571) 272-4085. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300 for regular communications and after final communications.
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/H .W./
Examiner, AU 2168
15 January 2025
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168