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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 18, 2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2020/0402665 A1, “Zhang”) in view of Wald et al. (US 2020/0411193 A1, “Wald”) and Devaraja et al. (US 12,205,695 B1, “Devaraja”).
As to claims 1, 8, 14, Zhang discloses a method (Fig. 1) comprising:
receiving a patient record for a patient admitted to a healthcare facility (patient data 102 for a patient admitted to an inpatient healthcare facility, para. 0002, 0054-0056), the patient record including social information determined for the patient (patient data 102 includes various non-clinical patient factors that can influence a likelihood of readmission, such as patient’s demographics, socioeconomics, personal patient support, and patient lifestyle, para. 0061-0062);
applying a first machine learning model to the patient record to predict a readmission risk profile of the patient (applying a readmission risk forecasting model 106 which comprises one or more machine learning models trained to predict likelihood of readmission of a patient following discharge, para. 0053); and
outputting an indication of the readmission risk profile of the patient (actionable care plan module 116 can generate an indication of a patient’s risk level based on readmission risk scores of similar cases, Fig. 3, para. 0115, and output a recommended actionable care plan 122 based on the determined readmission risk profile information, para. 0116, via a graphical user interface, para. 0117-0118);
determining, based on processing the readmission risk profile using a second machine learning model, one or more remedial actions to be taken for the patient, prior to discharging the patient from the healthcare facility, to reduce a readmission risk level of the patient (an actionable care plan may be rendered to the clinician and patient prior to discharging to mitigate the risk of readmission, para. 0038, 0053, 0112, 0117-0118, 0125, 0147), wherein the one or more remedial actions comprise providing modified or additional medical treatments or services to the patient; and
in response to determining that the one or more remedial actions have been performed, outputting an indication via a graphical user interface flagging that the one or more remedial actions have been performed.
Zhang differs from claims 1, 8, 14 in that it does not disclose the above underlined limitations.
Wald teaches wherein the one or more remedial actions comprise providing modified or additional medical treatments or services to the patient: predicting the probability of a patient’s readmission based on information available prior to discharge, and initiating one or more intervening actions based on the prediction to reduce the probability of readmission (Abstract), the intervening actions may include ordering additional testing prior to discharge, ordering one or more medications for the patient, examination prior to discharge, etc. (para. 0015, 0019, 0155). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang with the above teaching of Wald in order to effectuate an actual reduction in the readmission risk (Wald: para. 0001).
Zhang in view of Wald teaches: determining, based on processing the readmission risk profile, one or more remedial actions to be taken for the patient (Zhang: an actionable care plan is determined based on the readmission risk score, para. 0112), but differs from the claims in that it does not teach using a second machine learning model. Devaraja teaches using a machine learning model to determine interventions for a patient (col. 23, lines 5-15). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Wald with the above teaching of Devaraja in order to use a machine learning model that can be improved with updated input (Devaraja: col. 23, lines 9-15).
Devaraja further teaches in response to determining that the one or more remedial actions have been performed, outputting an indication via a graphical user interface flagging that the one or more remedial actions have been performed: user interface 1000 allows personnel to document the intervention and what was completed (Fig. 10A, note checked boxes to indicate intervention actions that have been performed; col. 25, lines 4-13). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Wald with the above teaching of Devaraja so that a completed remedial action can indicated and documented.
As to claims 2, 9, 15, Zhang in view of Wald and Devaraja discloses:
receiving a plurality of patient records of patients previously discharged from the healthcare facility, the plurality of patient records including social information and readmission information (Zhang: patient medical history information includes internal medical history information for patients associated with a single healthcare organization, as well as medical history information aggregated for patients across various disparate healthcare organizations, para. 0054; data includes pre and post discharge information for various patients, patient demographics, lifestyle factors, etc., para. 0107; Wald: patient history, patient demographics, patient’s past encounters, socioeconomic status, hospitalization history, etc., para. 0027, 0030, 0047, 0092);
training the first machine learning model using training data comprising a first subset of the plurality of patient records (historical patient data for past patients corresponding to the patient data 102 can be used to train and develop the readmission forecasting model 106, para. 0104, 0107); and
validating the first machine learning model using validation data comprising a second subset of the plurality of patient records (production test dataset is used to validate model performance, para. 0033, 0135-0137).
As to claims 3, 10, 16, Zhang in view of Wald and Devaraja discloses:
determining that the readmission risk profile indicates that the readmission risk level of the patient exceeds a threshold (Zhang: patient is likely to be readmitted based on a readmission risk score being above a set threshold, para. 0110, 0112; Wald: intervening actions may be initiated automatically when the probability of readmission satisfies a threshold level, para. 0157); and
outputting an indication of the one or more remedial actions (Zhang: outputs can be provided to one or more end users via suitable user interface or graphical user interface, para. 0117; Wald: intervening recommendation or notification is presented via user/clinician interface, para. 0154).
As to claims 5, 12, 18, Zhang in view of Wald and Devaraja discloses: wherein the readmission risk profile includes at least one of a readmission risk level or a readmission risk factor determined for the patient (Zhang: readmission risk score 108, Fig. 1, para. 0106-0107; Wald: readmission probability threshold level, para. 0157).
As to claims 6, 13, 19, Zhang in view of Wald and Devaraja discloses: wherein determining the readmission risk profile comprises: predicting, for the patient, a plurality of risk levels including a respective risk level of each of a plurality of readmission risk factors; and determining the readmission risk level based on the based on the plurality of risk levels (Zhang: risk classification level is determined based on the importance scores of each contributing factor, Fig. 2, para. 0111-0112; Wald: overall risk level may be the average probability output from the plurality of ML models, para. 0163).
Claim(s) 4, 11, 17, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Wald and Devaraja, as applied to claim 1 above, and further in view of Ling et al. (US 2016/0125159 A1, “Ling”).
Zhang in view of Wald and Devaraja differs from claims 4, 11, 17, 20 in that although it teaches extracting information from various data sources and the socioeconomic factors as including home zip code, rural-urban community area code associated with the patient’s home location (para. 0060-0062), it does not specifically teach:
receiving a plurality of social records from one or more social data sources;
determining the social information for the patient by mapping the patient record to the plurality of social records based on residential information included in the patient record, wherein the social information includes at least one of demographic information or urban information; and
augmenting the patient record to include the social information determined for the patient.
Ling teaches a method of identifying individuals having a risk level of repeated visits to a medical facility (Abstract) in which census data from the US Dept. of Commerce Census Bureau may be integrated into the patient data warehouse to provide approximation on a patient’s socioeconomic status based on residence zip codes (para. 0036).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Wald and Devaraja with the above teaching of Ling in order to determine a patient’s approximate socioeconomic data based on home location by using an official data source.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Wald and Devaraja, as applied to claim 1 above, and further in view of Elidan et al. (US 2024/0153639 A1, “Elidan”) and Chopra et al. (US 2023/0112369 A1, “Chopra”).
Zhang in view of Wald and Devaraja differs from claim 7 in that although it discloses extracting patient data from clinical notes, files, reports and the like, as input to the readmission risk forecasting model (para. 0055-0059), it does not teach: parsing the patient record by: tokenizing the text in the patient record and normalizing the tokenized text; converting the tokenized text into an object that is represented numerically using at least one of one-hot encodings or word embedding vectors; and processing the object using a natural language processing algorithm.
Elidan teaches the well known use of Natural Language Processing to extract data from patient medical files (para. 0084). Chopra teaches the well known use of tokenizing text by cleaning the text, e.g. removal of stopwords, conversion to lower case, consolidate white space, etc., and using a one-hot encoder to encode the plurality of tokens into interaction embedding (para. 0008) in order to pre-process unstructured text for input into a model (para. 0042-0055). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Wald and Devaraja with the above teachings of Elidan and Chopra in order to effectively extract and process patient information before input to the readmission risk forecasting model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pappada (US 2015/0227710 A1) teach a graphical user interface presenting interventions performed (para. 0134-0135, 0145-0146).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stella L Woo whose telephone number is (571)272-7512. The examiner can normally be reached Monday - Friday, 8 a.m. to 5 p.m.
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/Stella L. Woo/ Primary Examiner, Art Unit 2693