DETAILED ACTION
Claims 1-2, 4-6, 9-11, 13-15, and 18-20 are presented for examination.
Claims 1, 2, 9-11, and 18-19 have been amended.
Claims 3, 7, 8, 12, and 16-17 have been cancelled.
This office action is in response to the amendment submitted on 24-Nov-2025.
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 .
Response to Arguments – 35 USC 101
On pgs. 1-2 of the Applicant/Arguments Remarks dated 24/11/2025 (hereinafter ‘Remarks’), Applicant argues the amended claims have overcome the rejection under 35 USC 101.
Applicant’s arguments have been fully considered. The rejection has been withdrawn.
Response to Arguments – 35 USC 103
On pgs. 3-4 of the Applicant/Arguments Remarks dated 24/11/2025 (hereinafter ‘Remarks’), Applicant argues the amended claims overcome the rejection under 35 USC 103.
Applicant argues “the symptoms … necessitating in-patient care” and “the patient being flagged for previous wounds” are trigger conditions for providing the input data to the trained ML model. Applicant further argues the combination of Anderson, Drakos and Tschannen doesn’t teach or suggest trigger conditions, let alone the specific trigger conditions claimed.
The examiner respectfully disagrees. Drakos teaches using ML to predict requiring in-patient care given various patient data, including wounds, Col 45 Line 22-28, “When patients encountered by EMS meet certain criteria (i.e. there is some relatively high level of diagnostic certainty relative to the EMS medical providers' expertise) for the conditions mentioned above, those patients are transported directly to the relevant specialty center even if it means bypassing a closer medical facility”.
Additionally, Anderson uses the EHR patient database to train the machine learning. The EHR database includes pre-conditions as part of the patient data, for example, “Presence of comorbidities (e.g. cardiac, pulmonary, vascular, infection, paralysis, trauma, presence of PI, neurological, cancer) – Number of chronic diseases – Presence of multiple-organ failure – Self-reported health status” as indicated in Pg. 7 of “The pressure injury predictive model” on which Anderson builds their data model.
It would have been obvious for a POSITA to combine Anderson and Drakos to use symptoms necessitating in-patient care with wound flagging in training the ML and predicting likelihoods of developing more complications and other conditions.
Furthermore, the ‘input data’ in the limitation: “providing input data to a machine learning model that is trained” is not claimed to be ‘triggered’ or conditioned by any condition.
Both Anderson as explained above and Drakos cover training of the ML models used for prediction purposes. See Drakos, Fig. 3A
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Applicant’s arguments have been fully considered and are not persuasive. The rejection over 35 USC 103 is maintained.
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.
Claims 1-2, 4-6, 9-11, 13-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al. (Modeling and prediction of pressure injury) in view of Drakos (US11978558B1) and further in view of Tschannen et al (The pressure injury predictive model).
Regarding Claim 1, Anderson teaches collecting data relating to a patient’s health comprising symptoms experienced by the patient (Page 2, PI risk prediction using EHR “Widespread use of the electronic health record (EHR) has led to the collection of vast amounts of clinical data”).
wherein the data indicates that the patient is flagged for previous wounds
sustained by the patient (Page 2, Risk prediction complexity and PIPM “the Pressure Injury Prediction Model (PIPM) was developed representing 6 constructs: pressure, tissue tolerance, friction and shear, as well as three new constructs—patient characteristics, environment, and episode of care.” The PIPM model defined by Tschannen at al. and incorporated into Anderson includes, within the patient characteristics pg. 7
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and episode of care pg. 14-16, previous injuries/wounds as defined by Tschannen et al.).
in response to the patient being flagged for previous wounds, providing input data to a machine learning model that is trained to generate output data indicating a likelihood that the patient will develop a wound at one or more healthcare facilities, wherein the input data is based, at least in part, on the data relating to the patient’s health (Pg. 1, Methods, “The AI training was based on large, incongruent, incomplete, heterogeneous, and time-varying data of hospitalized patients. Both model-based statistical methods and model-free AI strategies were used to forecast PI outcomes and determine the salient features that are highly predictive of the outcomes,” Pg. 1, Conclusions, “Our PI (Pressure Injury) prediction model provide a first generation of AI guidance to prescreen patients at risk for developing PIs,” and Pg. 11, Conclusion: “The app allows interactive forecasting of the probability of acquiring PI in different hospitalization settings”).
communicating, to a first healthcare facility of the one or more healthcare facilities, a message indicating an action that should be taken to reduce the first likelihood that the patient will sustain the first wound while the patient is at the first healthcare facility, facility (Pg. 11, Conclusion: “Findings from this study have identified risk profiles for various surgical services that must be considered when determining prevention intervention strategies to employ. The importance of getting this type of discrete information to the bedside nurse cannot be overstated. To meet this need, we are developing an interactive webapp that implements the RF model to predict PI within specific surgical services or globally for an entire hospital.” The prevention strategies reduce the risk profile).
However, Anderson does not appear to teach the symptoms experienced by the patient necessitating in-patient care
wherein the action is tailored according to the first healthcare facility and the patient
Drakos teaches the symptoms experienced by the patient necessitating in-patient care (Col 45 Line 22-28 “When patients encountered by EMS meet certain criteria (i.e. there is some relatively high level of diagnostic certainty relative to the EMS medical providers' expertise) for the conditions mentioned above, those patients are transported directly to the relevant specialty center even if it means bypassing a closer medical facility”).
wherein the action is tailored according to the first healthcare facility and the patient (Col 19 line 41-46 “The PreDICT system determines that a patient at Hospital A with chest pain is experiencing an ST elevation myocardial infarction (STEMI) and requires a cardiac catheterization to relieve the coronary artery obstruction. Hospital A does not have this capability but Hospital B does”).
Anderson, Drakos, and Tschannen are analogous art because they are from the same field of endeavor in machine learning and healthcare. Anderson strictly applies machine learning to learn and predict probabilities of wounds, while Drakos creates a sophisticated diagnosis and routing application to minimize adverse effects and optimize outcomes for patients. Anderson incorporates the model and database defined by Tschannen. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Anderson, Drakos, and Tschannen to arrive at utilizing the predictive data models of wounds and injuries to optimally route the patient to the most equipped facility to minimize any adverse outcomes. “In this manner, users including expert and nonexpert users can provide information regarding a condition of a subject and receive timely and accurate information regarding risk stratification, treatment options and other medical evaluation information” (Drakos Col 6 line 41-45).
Regarding Claim 2, Anderson in view of Drakos and further in view of Tschannen teaches the method of claim 1. Drakos further teaches the output data comprises at least: first data indicating the likelihood that the patient will develop the wound at the first healthcare facility; and second data score indicating the likelihood that the patient will develop the wound at a second health care facility of the one or more healthcare facilities, (Drakos Col 30 line 18-20 “For any possibility-set, there are multiple branches with different levels of probability that are often in dynamic interplay” and FIG. 6B. Showcases the comparison of probabilities and using the comparison in the time domain to determine the best course of action. Col 30 Line 47-56 “The underlying risk is progressing with time and the probability of realizing the terminal outcome (death) if appropriate intervention (to mitigate or avert the underlying risk, hemorrhage) is taken at time t is less than if appropriate intervention is taken at time (t+x), when the patient is experiencing more physiologic dysfunction. Stated differently, the patient has a higher probability of benefit (surviving) if intervention is accomplished at time t than at time (t+x)” Here there explicit probability branches being compared to each other. In the last example a (first) probability at time t is directly compared to a (second) probability at time (t+x).).
Regarding Claim 4, Anderson in view of Drakos and further in view of Tschannen teaches the method of claim 1. Anderson further teaches collecting a dataset indicating (i) a plurality of past physical wounds sustained by different patients while the different patients were in the first healthcare facility (Page 4, Methods Data “Electronic health record (EHR) data for over 23,000 patient encounters discharged between June 1st, 2014 and June 26th, 2016 were obtained from the study site.” The data includes past wounds while at the first as well as other subsequent healthcare facilities).
(ii) a plurality of wound types of the plurality of past physical wounds (Page 2, Paragraph 1 “The authors note that likely contributors to their heterogeneous findings were that the studies varied on the PI stages that were included (e.g., Stage I, I & II, III & IV).” Each stage of PI is a different type of wound).
dividing the dataset into a training dataset and a validation dataset (Page 5, AI Modeling and analytics “The data was split into training: testing and a sequential network model was fit using the following parameter settings: units = 500, activation = relu or sigmoid, layer dropout rate in the range [0.3, 0.4], layer unit density between 2 and 128, loss function = binary cross-entropy, ADAM optimizer, accuracy metric, epochs = 100, batch size = 10, and validation split = 0.3”).
training the machine learning model using the training dataset (Page 3, Col 2 “The model built using random forest was the strongest, with precision of 0.998, and the average AUC of 1.00, in the training set”).
validating the machine learning model using the validation dataset after the machine learning model is trained (Page 3, Col 2 “however the AUC in the validation set the AUC was 0.864 with random forest still providing the best results”).
Regarding Claim 5, Anderson in view of Drakos and further in view of Tschannen teaches the method of claim 1. Anderson further teaches removing portions of the dataset that were generated prior to a time threshold before training the machine learning model (Page 6, Results “After removal of cases that didn’t meet the study inclusion criteria (e.g. LOS ≥2 days, and undergoing a surgical procedure, and having staffing data).” This teaches removing data that doesn’t meet the cut off criteria).
Regarding Claim 6, Anderson in view of Drakos and further in view of Tschannen teaches the method of claim 1. Anderson further teaches the first healthcare facility is flagged for previous wounds sustained at the first healthcare facility (Page 2, Risk prediction complexity and PIPM “the Pressure Injury Prediction Model (PIPM) was developed representing 6 constructs: pressure, tissue tolerance, friction and shear, as well as three new constructs—patient characteristics, environment, and episode of care.” The environment of care includes hospital flags such as hospital setting, type, certification status, teaching status and bed size as specified in the referenced study: “the sheer number of possible predictors and predictor combinations contributes to the complexity of PI risk assessment and prevention [4].” The PIPM is defined fully by Tschannen et al.
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the environment variables are defined on pg. 12).
Drakos further teaches providing the input data to the machine learning model in response to determining that the first healthcare facility is within a distance threshold of the patient (Col 19 Line 58-64 “The PreDICT system can evaluate historical transport data and real-time air and ground traffic data to determine that transport by helicopter will place the patient in the cardiac catheterization lab at Hospital B twelve minutes faster than transport by ground ambulance at this time of day due to heavy traffic volumes.” The training input was covered in Claim 1 above).
Regarding Claim 8, Anderson in view of Drakos and further in view of Tschannen teaches the method of claim 1. Drakos further teaches providing the input data to the machine learning model based on the in-patient care predicted to last for a duration that exceeds a threshold (Fig. 8 illustrates the time calculations including thresholds, additionally, Col 40 Line 26-30 “the PreDICT system not only recommends (or autonomously applies) the risk optimal intervention at the optimal point in time, it also recommends (or performs) the optimal sequencing and logistics to maximize the efficiency, relative to both time and interventional risk, of the intervention”).
Regarding Claim 9, Anderson in view of Drakos and further in view of Tschannen teaches the method of claim 1. Drakos further teaches communicating the message is in response to determining that the action reduces the likelihood that the patient will sustain the wound while the patient is at the first healthcare facility (Col 33 Line 37-47 Drakos teaches a sophisticated system for determining the probability of an incident and how to optimize/minimize its impact through a multi factor equation: “Considering tOR and its components yields the more specific function:
RB(tOR)=[DC(tDA)×PB(tOR)]−IR(tI) Equation 5:
Time of operational risk (tOR) components, or some of the components, will often be in dynamic interplay. For example, there may be several loops between tDX and tDA before a clear intervention and/or pathway to performing that intervention is identified. The components of tOR can be conceived of as a more comprehensive and detailed OODA (Observe, Orient, Decide, Act) Loop process that is not complete until the “act” is resulted”).
Regarding Claim 10, Drakos teaches An apparatus comprising: a memory; and a hardware processor communicatively coupled to the memory, the hardware processor configured to (Col 8 Line 27-28 “The illustrated user device 102 may include, for example, a smart phone, tablet computer or similar device”). The remaining limitations are similar to Claim 1 and are rejected for the same reason.
Regarding Claims 11, 13-15, and 17-18, Anderson in view of Drakos and further in view of Tschannen teaches the apparatus of claim 10. Claims 11, 13-15, and 17-18 recite limitations similar to claims 2, 4-6, and 8-9 respectively and are rejected under the same rationale.
Claim 19 recites a non-transitory computer readable medium storing instructions: that, when executed by a processor, cause the processor to (Col 8 Line 28-29 “The user device 102 includes one or more sensors 110, a processor 112”).
The remaining limitations are similar to claim 1 and are rejected under the same rationale.
Claim 20, Anderson in view of Drakos and further in view of Tschannen teaches the apparatus of claim 19. The remaining limitations are similar to claim 6 and are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US11410756B2: Discloses predicting health events from medical records.
US20200273581A1: Discloses post discharge risk prediction.
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/A.E.D./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187