DETAILED ACTION
Status of Claims
This action is in reply to the application filed on 7 March, 2025.
Claims 7 and 14 have been cancelled; Claims 16 – 20 have been added; and Claims 1, 3, 4, 6, 8 – 11, 13 and 15 have been amended by a preliminary amendment filed with the application.
Claims 1 – 6, 8 - 13 and 15 - 20 are currently pending and have been examined.
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 .
Specification
The disclosure is objected to because of the following informalities: The specification describes three equations (Equation 1, 2 and 3 @ paragraph 0049 – 0051) at the heart of the invention, including for estimating LOS, and the probability of discharge. However, the characters in the equations, other than punctuation marks, are incomprehensible – appearing as dots. Appropriate correction is required.
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.
Claims 3 and 11 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 3 and 11 recite: “wherein the . . . plurality of . . . prediction features . . . comprises one or more of [list]. Examiner cannot determine the metes and bounds of the claim. A plurality requires at least two elements, where the claims allow for only one. Appropriate correction or clarification is required.
Claim 5 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claim 5 recites: “wherein the patient is a historical patient”. Examiner cannot determine the metes and bounds of the claim. Initially, Examiner notes that there is no reference point for being “historical”. Here Examiner presumes that the patient is a historical ICU patient – i.e. an ICU patient that was discharged, or died. For example, the specification contrasts “historical and/or current ICU patients” (@ 0037, 0068). Nonetheless, even with that interpretation, “the patient” in Claim 5 refers to “a patient in an intensive care unit” as recited in Claim 1. A patient in an ICU is not a historical ICU patient. It make little sense to predict an ICU LOS for a historical patient. Appropriate correction or clarification is required.
Claim 6 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claim 6 recites: “predict the ICU LOS for the patient when one or more not required ICU LOS prediction features are missing from the obtained plurality of records.” Examiner cannot determine the metes and bounds of the claim. The specification discloses that the extracted prediction features may be “manually curated” to identify prediction features that are “required” or “not required”. However, it is unclear what distinguishes these two states. It is unclear what element “requires or does not require” the prediction features.
For purposes of this examination, Examiner presumes that a “required prediction feature” is one that contributes in a meaningful way (i.e. as determined by a curator) to the prediction accuracy. Conversely, a prediction feature that is “not required” is one that does not contribute to the prediction accuracy. These are conventional feature selection techniques that would be known to one of ordinary skill in the art. However, the claims recite performing the prediction when not required features are missing. It is unclear how missing features that are not needed for the prediction can have any effect thereon. Appropriate correction or clarification is required.
Claims 16 and 18 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 16 and 18 recite: “curating . . . to identify . . . which of the different health features are required . . . and which of the different health features are not required”. Examiner cannot determine the metes and bounds of the claim. The specification discloses that the extracted prediction features may be “manually curated” to identify prediction features that are “required” or “not required”. However, it is unclear what distinguishes these two states. It is unclear what element “requires or does not require” the prediction features.
For purposes of this examination, Examiner presumes that a “required prediction feature” is one that contributes in a meaningful way (i.e. as determined by a curator) to the prediction accuracy. Conversely, a prediction feature that is “not required” is one that does not contribute to the prediction accuracy. These are conventional feature selection techniques that would be known to one of ordinary skill in the art. However, the claims recite training the prediction model “using the plurality of . . . prediction features. It is unclear if training is performed using required features, not required features, or both. Appropriate correction or clarification is required.
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 – 6, 8 - 13 and 15 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept – i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea..
Claim 1 is representative. Claim 1 recites:
A method for predicting a length of stay (LOS) for a patient in an intensive care unit (ICU), the method comprising:
obtaining, from an electronic medical records database, a plurality of records for the patient in the ICU covering at least a first time period;
extracting, from the obtained plurality of records, a plurality of different defined ICU LOS prediction features for the patient;
analyzing the extracted plurality of different defined ICU LOS prediction features using a trained ICU LOS prediction model; and
predicting, by the trained ICU LOS prediction model, an ICU LOS for the patient,
wherein the predicted ICU LOS comprises a predicted probability of the patient leaving the ICU at a plurality of different timepoints
Claim 20 recites medium with instructions executed by a processor, and Claim 10 recites a system that executes the steps of the method recited in Claim 1.
STEP 1
The claims are directed to a device, a method and non-transitory computer readable medium which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea including:
analyzing the extracted plurality of different defined ICU LOS prediction features; and
predicting an ICU LOS for the patient, wherein the predicted ICU LOS comprises a predicted probability of the patient leaving the ICU at a plurality of different timepoints.
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “mental processes” grouping – concepts performed in the human mind including observation, evaluation, judgment and opinion. The claims recite receiving a plurality of records for the patient from an EMR database, extracting a plurality of defined ICU LOS prediction features, and analyzing the extracted prediction features to predict an ICU LOS for the patient.
Collecting information, including when limited to particular content, is within the realm of abstract ideas, and analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, are mental processes within the abstract idea category (Electric Power Group v. Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016).
The specification discloses that predicting ICU LOS for a patient is purely conventional in medicine. The specification discloses a table (i.e. Table 1) listing the various prediction features, which are also listed in Claim 3. The claims encompass analyzing a patient’s age and diagnostic group, as an example of “one or more” prediction features. Predicting a patient’s length of stay based on age and diagnostic group is a process that can be performed in the human mind. For example, Guidelines detailing expected lengths of stay, including ICU stays, are old and well known. As such, the claims recite an abstract idea within the mental process grouping.
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “certain methods of organizing human activity” grouping –
fundamental economic principles or practices including hedging, insurance, mitigating risk;
managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims recite receiving a plurality of records for the patient from an EMR database, extracting a plurality of defined ICU LOS prediction features, and analyzing the extracted prediction features to predict an ICU LOS for the patient. Predicting a patient’s ICU LOS is typical in medicine, according to the specification, (@ 0002, 0003) and is process that merely organizes this human activity. Predictions may be used for planning and benchmarking – i.e. insuring that ICU lengths of stay do not exceed treatment guidance standards or affect insurance reimbursements. (See MPEP 2016.04 (a)(2) II C finding that “a mental process that a neurologist should follow when testing a patient for nervous system malfunctions” is a method of organizing human activity, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). As such, the claims recite an abstract idea within the certain methods of organizing human activity grouping.
The claims, as illustrated by Claim 1, also recite limitations that encompass an abstract idea within the mathematical formula or relationship grouping. The claims recite: “predicting . . . an ICU LOS for the patient, wherein the predicted ICU LOS comprises a predicted probability of the patient leaving the ICU at a plurality of different timepoints.” The specification discloses Equations for performing these steps (@ 0049 - 0051). The expressly disclose equations are mathematical relationships. As such, the claims recite a mathematical formula or relationship.
STEP 2A PRONG TWO
The claims recite limitations that include additional elements beyond those that encompass the abstract idea above including:
obtaining, from an electronic medical records database, a plurality of records for the patient in the ICU covering at least a first time period;
extracting, from the obtained plurality of records, a plurality of different defined ICU LOS prediction features for the patient;
analyzing . . . using a trained ICU LOS prediction model; predicting by the trained ICU LOS prediction model
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with the MPEP. (see MPEP 2106.05)
Obtaining records from and EMR and extracting prediction features using conventional techniques described in the specification, such as natural language processing techniques, are insignificant extra-solution activities – i.e. a data gathering step.
The claims recite using a trained prediction model that applies established methods of machine learning to an abstract prediction process in a new data environment – i.e. applying a trained model to the prediction features extracted from the patient record. The specification teaches that the model may be trained by others to predict ICU LOS using the extracted prediction features; using a well-known deep learning model such as DeepHit. (@ 0005, 0036, 0045). Machine learning limitations reciting broad, functionally described, well-known techniques executed by generic and conventional computing devices does not provide a practical application of the abstract diagnostic process. “Today we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under §101.” (Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)).
Nothing in the claim recites specific limitations directed to an improved technology or technological process. A general purpose computer that applies a judicial exception by use of conventional computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a generic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claim do not integrate the abstract ICU LOS prediction process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract ICU LOS prediction process. Obtaining patient records from a database and extracting prediction features therefrom, is a well-understood, routine and conventional computer function – i.e. receiving or transmitting data over a network as in Symantec, TLI, OIP and buySAFE. Considered as an ordered combination the limitations recited in the claims add nothing that is not already present when the steps are considered individually. As such, the additional elements recited in the claim do not provide significantly more than the abstract ICU LOS prediction process, or an inventive concept.
The dependent claims add additional features including:
those that merely serve to further narrow the abstract idea above such as:
further limiting the time period to 24 hours (Claim 2, 12);
further limiting the prediction features (Claim 3, 11);
further limiting the type of patient (Claim 5);
those that recite additional abstract ideas such as:
predicting when not required features are missing (Claim 6);
time-to-event analysis with competing risks (Claim 9, 15);
obtaining, extracting and curating health features to identify prediction features (Claim 16, 18);
binning health features (Claim 8);
those that recite well-understood, routine and conventional activity or computer functions such as:
the prediction system is a component of another system (Claim 4, 13);
presenting the predicted ICU LOS (Claim 17, 19);
known techniques for training the model (Claim 16, 18);
storing the trained model (Claim 16, 18)
those that recite insignificant extra-solution activities; or those that are an ancillary part of the abstract idea.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. As such, the additional element do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention.
The apparatus claims are no different from the method claims in substance. “The equivalence of the method, system and media claims is readily apparent.” “The only difference between the claims is the form in which they were drafted.” (Bancorp). The method claims recite the abstract idea implemented on a generic computer, while the apparatus claims recite generic computer components configured to implement the same idea. Specifically, Claims 10 – 13, 15 and 18 - 20 merely add the generic hardware noted above that nearly every computer will include. The apparatus claim’s requirement that the same method be performed with a programmed computer does not alter the method’s patentability under U.S.C. 101 (In re Grams). Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 – 6, 10 – 13, 17, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Saeed: (US 9,569,723 B2).
CLAIMS 1, 10 and 20
Saeed discloses a method and system for the continuous prediction of patient length of stay that includes the following limitations:
A method for predicting a length of stay (LOS) for a patient in an intensive care unit (ICU), (Saeed col. 1 line 15 – 25); the method comprising:
obtaining, from an electronic medical records database, a plurality of records for the patient in the ICU covering at least a first time period; (Saeed col. 2 line 4 – 12, col. 3 line 6 – 17, col. 4 line 15 – 21 & 34 – 52);
extracting, from the obtained plurality of records, a plurality of different defined ICU LOS prediction features for the patient; (Saeed col. 5 line 42 – 52, col. 6 line, col 7 line 67 to col. 8 line 7);
analyzing the extracted plurality of different defined ICU LOS prediction features using a trained ICU LOS prediction model; and predicting, by the trained ICU LOS prediction model, an ICU LOS for the patient; (Saeed col. 5 line 4 – 10, 24 – 42 & 53 – 63);
wherein the predicted ICU LOS comprises a predicted probability of the patient leaving the ICU at a plurality of different timepoints; (Saeed col.6 line 35 – 41 & 52 – 54).
Saeed discloses a system and method for predicting outcome variables, such as a patient length of stay (LOS), applicable to an ICU Unit – i.e. ICU LOS. Saeed discloses generating an outcome variable estimation algorithm (i.e. a trained ICU LOS prediction model) to predict an ICU LOS using a plurality of past patient data, the corresponding time of stay indicative of a time that the current patient has been under care, and associated outcome variable, accessed from a database. The database includes records of past patient cases that include specified input data fields that are correlated with the outcome variables (i.e. prediction features).
A controller receives and stores patient information for a current patient in a clinical database (i.e. an electronic medical records database), including physiological or laboratory data and a time of stay. The patient information corresponding to the specified input data fields is accessed (i.e. extracting), and analyzed by the algorithm to predict an ICU LOS for the patient. Saeed discloses that the prediction may be performed in real-time throughout the stay (col. 2 line 25 - 28); and maybe developed for each day after admission (t) where t = 0, 1, 2, 3 etc. days (i.e. at a plurality of different timepoints).
With respect to Claims 10 and 20, Saeed further discloses the following system components:
an intensive care unit (ICU) length of stay (LOS) prediction system for predicting an LOS for a patient in an ICU, (Saeed col. 1 line 56 – 57); the system comprising:
an electronic medical records database comprising a plurality of records for a plurality of patients; (Saeed Fig. 1 # 52, col. 4 line 29 – 36); and
a processor; (Saeed col. 4 line 57 – 60)
non-transitory computer-readable storage medium having stored a computer program comprising instructions, which, when executed by a processor; (Saeed col. 5 line 11 – 28).
CLAIMS 2 and 12
Saeed discloses the limitations above relative to Claims 1 and 10. Additionally, Saeed discloses the following limitations:
wherein the first time period is at least 24 hours; (Saeed col. 6 line 52 – 54).
Saeed discloses time periods including a first day.
CLAIMS 3 and 11
Saeed discloses the limitations above relative to Claims 1 and 10. Additionally, Saeed discloses the following limitations:
wherein the extracted plurality of different defined ICU LOS prediction features for the patient comprises one or more of ICU length of stay, ICU discharge status deceased, age, sex, pre-ICU admission lead time, elective surgery, ventilated, artificial airway, BMI, mean blood pressure, diastolic blood pressure, systolic blood pressure, heart rate, oxygen saturation, respiratory rate, PaCO2, glucose, lactate, pH, while blood cell count, hemoglobin, albumin, sodium, potassium, creatinine, GCS total, ICU type, ICU admission source, or diagnostic group; (Saeed col. 7 Table of patient input variables).
Saeed includes at least the variables recited in the claim that are underlined above.
CLAIMS 4 and 13
Saeed discloses the limitations above relative to Claims 1 and 10. Additionally, Saeed discloses the following limitations:
wherein the predicting is performed by an ICU LOS prediction system that is a component of a patient data management systems (PDMS), a patient flow management system, or a patient monitoring system; (Saeed Figure 1).
Saeed discloses that the prediction system (Figure 1 # 60) is part of a Patient information Server (#18) – i.e. a patient data management system.
CLAIM 5
Saeed discloses the limitations above relative to Claim 1. Additionally, Saeed discloses the following limitations:
wherein the patient is a historical patient; (Saeed col. 5 line 4 – 10 & 42 – 52)
Saeed discloses historical patient information in the patient database.
CLAIMS 17 and 19
Saeed discloses the limitations above relative to Claims 1 and 10. Additionally, Saeed discloses the following limitations:
presenting, via a user interface, the predicted ICU LOS for the patient; (Saeed col. 5 line 41 – 42).
Saeed displays results of the analysis on a user interface.
CLAIM 6
Saeed discloses the limitations above relative to Claim 1. Additionally, Saeed discloses the following limitations:
wherein the trained ICU LOS prediction model is configured to analyze the extracted plurality of different defined ICU LOS prediction features and predict the ICU LOS for the patient; (Saeed col. 5 line 4 – 10, 24 – 42 & 53 – 63).
Saeed discloses a system and method for predicting outcome variables, such as a patient length of stay (LOS), applicable to an ICU Unit – i.e. ICU LOS using prediction features for the patient. With respect to the following limitation:
when one or more not required ICU LOS prediction features are missing from the obtained plurality of records.
Saeed discloses finding correlations to input data fields and associated outputs (@ col. 4 line 52 – 57). This includes optimizing which clinical data items, represented as a vector, that minimizes the difference between estimated and observed outcomes in the training data. (@ col. 5 line 53 to col. 6 line 41) Examiner construes this disclosure as “feature selection”, which is a process known to those of skill in the art. As such, Saeed determines the optimum set of input data elements – that is, Saeed determines which data elements (X) are “chosen” from N different patients and K clinical data items. Data elements that are “chosen” in Saeed are construed as being “required” in the present application.
Conversely, some of the data elements (K) are “not chosen” because they do not optimize the outcome prediction – i.e. the data elements are “not required”. These “not chosen” data elements are not included in the vector representation – i.e. they are missing.
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 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Saeed: (US 9,569,723 B2) in view of Yu et al.: (US PGPUB 2022/0327418 A1)
CLAIMS 16 and 18
Saeed discloses the limitations above relative to Claims 1 and 10. Additionally, Saeed discloses the following limitations:
wherein the ICU LOS prediction model is trained by: obtaining, from an electronic medical records database, a plurality of historical records for each of a plurality of historical patients in an ICU; extracting, from the obtained plurality of records, a plurality of different health features for each of the plurality of historical patients; training the ICU LOS prediction model using the plurality of different historical ICU LOS prediction features; and storing the trained ICU LOS prediction model; (Saeed col. 1 line 60 to col. 2 line 3; col. 4 line 34 – 60; col. 5 line 4 – 14 & col. 5 line 42 to col. 6 line 41).
Saeed discloses training and storing a prediction algorithm, including neural networks, using an optimized data input vector from historical records of historical ICU patients extracted from a patient database.
With respect to the following limitations:
curating the extracted plurality of different health features to identify a plurality of different historical ICU LOS prediction features, wherein curating comprise identifying which of the different health features are required ICU LOS prediction features and which of the different health features are not required ICU LOS prediction features; (Yu 0001, 0004, 0006, 0030, 0031).
Saeed discloses optimizing input data elements using one of several optimization techniques (@ col. 6 line 9 – 13), but does not disclose curating, as claimed. Yu discloses a system and method for optimizing feature importance for a classifier that includes curating input training data, by a subject matter expert, and labeling each piece of training data with either a “YES” indicating the input data represents the output category of interest – i.e. required prediction features; or with a “NO” indicating the input data does not represent the output category of interest – i.e. not required prediction features. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the ICU LOS prediction system of Saeed so as to have included curation of machine learning model input features, including manual curation, in accordance with the teaching of Yu, in order to reduce computer processing time.
Claims 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Saeed: (US 9,569,723 B2) in view of Wen et al.: “Time-to-event modeling for hospital length of stay prediction for COVID-19 patients”; 18 June, 2022.
CLAIMS 9 and 15
Saeed discloses the limitations above relative to Claims 1 and 10. With respect to the following limitations:
wherein ICU LOS prediction model comprises time-to-event analysis with competing risks, the time-to-event comprising time elapsed from the admission of the patient to the ICU until a time the patient leaves the ICU, and the competing risks comprising an exit status of the patient either alive or deceased; (Wen – entire article; in particular: 1. Introduction, 2. Time-to-event models for LOS prediction; 2E. DeepHit; 3. Experiments).
Saeed does not expressly disclose time-to-event analysis with competing risks. The specification discloses that this function may be provided using “DeepHit” to model time-to-event with competing risks. DeepHit is known in the art. In particular, Wen discloses DeepHit for analyzing patient length of stay with competing risks including discharge or death. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the ICU LOS prediction system of Saeed so as to have included time-to-event analysis with competing risks, in accordance with the teaching of Wen, in order to account for censored patient data.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Saeed: (US 9,569,723 B2) in view of Yu et al.: (US PGPUB 2022/0327418 A1) in view of Rahman et al.: 2023/0005603 A1).
CLAIM 8
Saeed/Yu discloses the limitations above relative to Claim 16. With respect to the following limitations:
wherein at least some of the extracted plurality of different health features are binned in bins prior to training the ICU LOS prediction model; (Rahman 0041 – 0048, 0067) and
wherein the bins comprise a bin comprising missing data; (Rahman 0068).
Saeed/Yu does not expressly disclose “binning” including for missing data. Rahman discloses an emergency triage system including a patient acuity level predictor that outputs a patient’s acuity level based on categorical input features extracted from health care records. Rahman discloses data preparations steps including mapping and binning data into a plurality of categories, including for missing data. (Examiner notes that binning is an old and well known data preparation technique.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the ICU LOS prediction system of Saeed/Yu so as to have included binning data, including missing data, in accordance with the teaching of Rahman, in order to reduce data complexity.
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PGPUB 2011/0077968 A1 to Kelly et al. discloses a scoring system and method for patient’s staying in an ICU, including predicting a timeframe of length of stay.
US PGPUB 2011/0161857 A1 to Kramer discloses a system and method for determining patient length of stay in an ICU.
US PGPUB 2021/0391085 A1 to Fusaro discloses predicting a patient ICO length of stay that includes feature engineering to improve accuracy.
“DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks”; Lee et al.; 2018.
“Competing risk between in-hospital mortality and recovery: An application of DeepHit on COVID-19 clinical data”; Lintu et al.; 8 February, 2021.
“Prediction of intensive care units length of stay: a concise review”; Peres et al.; 2021.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to John A. Pauls whose telephone number is (571) 270-5557. The Examiner can normally be reached on Mon. - Fri. 8:00 - 5:00 Eastern. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773.
Official replies to this Office action may now be submitted electronically by registered users of the EFS-Web system. Information on EFS-Web tools is available on the Internet at: http://www.uspto.gov/patents/process/file/efs/guidance/index.jsp. An EFS-Web Quick-Start Guide is available at: http://www.uspto.gov/ebc/portal/efs/quick-start.pdf.
Alternatively, official replies to this Office action may still be submitted by any one of fax, mail, or hand delivery. Faxed replies should be directed to the central fax at (571) 273-8300. Mailed replies should be addressed to “Commissioner for Patents, PO Box 1450, Alexandria, VA 22313-1450.” Hand delivered replies should be delivered to the “Customer Service Window, Randolph Building, 401 Dulany Street, Alexandria, VA 22314.”
/JOHN A PAULS/Primary Examiner, Art Unit 3683
Date: 26 March, 2026
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