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 .
DETAILED ACTION
1. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/08/2025 has been entered.
2. The amendment filed 09/17/2025 has been received and considered. Claims 1, 5, 7-9 and 11-15 are presented for examination.
Claim Objections
3. Claims 1 is objected to because of the following informalities:
In particular, claim 1 recites the limitation “wherein the user is a patient and the subject comprises at least part of a patient” in line 23 which is unclear what “the user” refers. The claim previously recites “one or more user” in line 5 and “a user” in line 22. Which “user” is it referring to?
Appropriate correction is required.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claims 1, 5, 7-9 and 11-15are rejected under 35 U.S.C. 103 as being unpatentable over ABEYRATNE et al. (US 20200027558 A1), and further in view of Sahu et al. (US 20210124465 A1).
As per Claim 1, 11, and 14, ABEYRATNE et al. teaches a method/ implemented using one or more processors (Abstract, [0071], Fig. 1 and 11A), the method comprising:
providing a plurality of trained models configured to be implemented by a digital twin ([0071], [0099]-[0100] “a plurality of diagnostic models 20”), wherein each of the plurality of trained models is trained to generate a different digital twin output based on one or more user needs of a subject and one or more situational needs of a subject when implemented by the digital twin ([0075], “the device 1 collects patient metadata, diagnosis and treatment/referral information”; [0078] “in diagnosing a disease state”; [0099]-[0100] “The various models are arranged to classify a patient as “diseased” or “non-diseased” based upon the diagnostic parameters for the patient that are entered by the clinician.”, [0102]), wherein each of the plurality of trained models is associated with an indicator of the respective trained models' performance ([0071] “a model lookup table 22 which the microprocessor 3 uses to determine the most appropriate diagnostic model based on available diagnostic parameters for a patient 2. ”, [0102] “the cough features to diagnostic models 20 that form part of the diagnostic application 6a and which are configured to take into account other diagnostic parameter values that are input by the clinician 4. ”);
identifying the one or more user needs of a subject seeking to utilize the digital twin (Fig. 2 & 8, [0087] “based on the patient parameters that have been submitted. To select the best of models 20 for the information that the clinician has entered,”; [0095] –[0096] “Using the data for tables A1-A3 or B1-B3 (depending on age), the clinician's inputted preference for optimization for sensitivity, specificity or accuracy, and the inputted patient parameters,”, “the patient values that were entered for each of the diagnostic parameters that were selected are applied by microprocessor 3 to the selected optimal diagnostic model. ”);
identifying the one or more situational needs of a subject to be simulated by the digital twin, wherein the one or more situational needs are identified based on health data obtained from the subject ([0031]. “diagnostic parameters comprise breathing rate, temperature, heart rate and cough sound analysis.”; [0082] “patient diagnostic parameters”; [0096] “the patient values that were entered for each of the diagnostic parameters that were selected are applied”; [0075], “the device 1 collects patient metadata, diagnosis and treatment/referral information”; [0102] “cough features”: inputting “diagnostic parameters” and/or ““the cough features”);
selecting (Fig. 8, [0095] “Using the data for tables A1-A3 or B1-B3 (depending on age), the clinician's inputted preference for optimization for sensitivity, specificity or accuracy, and the inputted patient parameters, the device 1 automatically selects a particular numbered diagnostic model using the procedure set out in FIG. 9.”), based on the identified one or more user needs of the subject and the identified one or more of the situational needs of the subject, one of the plurality of trained models to be implemented by the digital twin ([0087], “the microprocessor 3 selects the optimal diagnostic model, amongst models 20 which are stored in memory 5, based on the patient parameters that have been submitted.”), wherein the selecting is further based on a comparison of the identified one or more of the user needs and the identified one or more of the situational needs of the subject with one or more lookup tables ([0087] “To select the best of models 20 for the information that the clinician has entered, the microprocessor 3, as configured by diagnostic program 6, queries diagnostic model lookup tables 22 which are stored in memory 5 and which the Inventors have produced.”, [0091] “the clinician picks “accuracy” optimization…”), and further wherein selecting is based on the indicators associated with the trained models' performance ([0088], [0091] “ the clinician picks “accuracy” optimization then the best model will be the top row of Table A3”);
applying, by the digital twin, the selected one of the plurality of trained or more models
to generate digital twin output ([0051] “operating the electronic device to apply the values of the disease diagnostic parameters to the selected diagnostic model”; [0096] “the patient values that were entered for each of the diagnostic parameters that were selected are applied by microprocessor 3 to the selected optimal diagnostic model.”), wherein the digital twin output simulates one or more aspects of the subject ([0040] “operating the input/output interface device in accordance with the diagnosis output to indicate presence or absence of pneumonia in the patient to the carer, for use by the carer in providing care to the patient;”; [0097] “the diagnostic results to the carer as shown in the screen of FIG. 11”); and
based on the digital twin output, providing visual or audible output to a user about the subject ([0097] “the diagnostic results to the carer as shown in the screen of FIG. 11”), wherein … and the subject comprises at least part of a patient ([0075], “the device 1 collects patient metadata, diagnosis and treatment/referral information”; [0079] “respiratory disease state”).
ABEYRATNE et al. fails to teach explicitly wherein the user is a patient.
Sahu et al. teaches wherein the user is a patient ([0077], [0079] “When a user (e.g., the patient 210, patient family member (e.g., parent, spouse, sibling, child, etc.),”; “a plurality of systems such as workflow, communications, collaboration, etc., can impact access to care 420 by the patient 210.”).
ABEYRATNE et al. and Sahu et al. are analogous art because they are both related to a machine learning approach for a health system.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate Sahu et al. into ABEYRATNE et al.’s invention for automatic disease state diagnosis of a patient to provide a system with interaction between the digital twin and the patient which can help improve diagnosis, treatment, health maintenance, etc., for the patient benefiting from a real-time or substantially real-time (Sahu et al.: [0065]).
As per Claim 5 and 15, ABEYRATNE et al. teaches further comprising applying data indicative of the one or more of the user needs and the one or more of the situational needs of the subject as inputs across a machine learning model to generate model selection output ([0095], [0099] “the Inventors developed the diagnostic models from quantitative test data derived from a population of subjects including patients suffering from respiratory disease. The various models are arranged to classify a patient as “diseased” or “non-diseased” based upon the diagnostic parameters for the patient that are entered by the clinician. A preferred approach to developing the models is by use of a logistic regression machine (LRM). Other types of classification decision engines may also be used, for example support vector machines (SVMs).”), wherein the selecting is based on the model selection output (Fig. 8, [0095], [0099]).
As per Claim 7, ABEYRATNE et al. teaches further comprising refining the one or more situational needs of the subject based on the digital twin output ( [0098] “if the diagnostic results indicate a disease state such as pneumonia then the carer may use that information, as indicated in box 52, to apply therapy to the patient 2, for example by organising hospitalisation and/or providing antibiotics or antiviral medication”).
As per Claim 8, ABEYRATNE et al. teaches wherein the one or more user needs and the one or more situational needs are selected from an enumerated list of needs (Fig. 3 & 6-7; [0084] “wherein microprocessor 3 displays screen 67 (FIG. 7) on interface 11. In the presently described preferred embodiment of the invention screen 67 includes selection buttons 69, 71 and 73 for clinician 4 to indicate a preference for the diagnostic model that will be used to optimize for any one of diagnostic “sensitivity”, “specificity” or “accuracy”. These terms have the following meanings: “; [0086] “screen 63 (FIG. 6) to display on LCD touchscreen 11 to prompt for patient diagnostic parameters from the clinician 4.”; [0097] “FIG. 3 shows the diagnostic device 1 displaying a data entry screen 57 on LCD touch screen interface 11 for the clinician 4 to enter the patient parameters.”), and the one or more user needs are prioritized over the one or more situational needs ([0091] “If the clinician picks “accuracy” optimization then the best model will be the top row of Table A3 (if the patient is 2 to 11 months of age) and top Row of Table B3 (if the patient is 12 to 60 months of age). However, if the parameters that were entered by the clinician do not allow for the top row #1 to be used then the microprocessor will check if row #2 can be used and so on.”).
As per Claim 9, ABEYRATNE et al. teaches further comprising: prompting the user to reconsider one or more of the user needs (Fig. 7, [0058] “FIG. 7 depicts a screen generated by the diagnostic device to confirm the entered diagnostic information to the clinician”) .
As per Claim 12, ABEYRATNE et al. teaches wherein the subject comprises a patient ([0075], “the device 1 collects patient metadata, diagnosis and treatment/referral information”; [0079] “respiratory disease state”).
Response to Arguments
5. Applicant's arguments filed 09/17/2025 have been fully considered but they are not persuasive.
Applicant’s arguments with respect to claim(s) 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 – in view of Sahu et al.
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571)272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/ Primary Examiner, Art Unit 2188