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
In the response filed on 07 July 2026, the following has occurred: claims 1, 5, 8 and 10-11 have been amended; claim 12 has been canceled; claims 14-20 are newly added.
Now claims 1-11 and 13-20 are pending.
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 1-11 and 13-20 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) without significantly more.
Claims 1 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite system and methods systems and methods for assisting a user in evaluating and responding to a health status of a human user. The limitations of:
Claim 1, which is representative of claim 11
comprising: [… obtain …]: at least one user data, measured and/or obtained from a user, and at least one reference data, wherein both said at least one user data and said at least one reference data are related to an abnormality; […]: generate a training dataset using said at least one user data and said at least one reference data; obtain a […] model by feeding said training dataset to a first model, wherein said […] model is configured to [… obtain …] as input at least one current user data and provide as output at least one risk score of the user for developing said abnormality, and calculate at least one risk score of the user for developing the abnormality using the [… ] model, wherein the calculated at least one risk score is used to update an avatar of the user and the avatar is [… organized for …] preventive scenarios and potential outcomes of the preventive scenarios; […] provide said […] model and an interactive [… figure …] for each risk factor.
as drafted, is a system, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with a device comprising at least one input, processor and output, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, but for a device comprising at least one input, processor and output, the claim encompasses collection and organization of data using model to update an avatar and provide a output to a human user to use in their treatment of a patient (i.e., human activity). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a device comprising at least one input, processor and output, which implements the abstract idea. The device comprising at least one input, processor and output are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 1, paragraph [0054], [0078]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “receive…”, “obtain a trained model by feeding said training dataset to a first model”, “a virtual twin configured to simulate” and a “radar graph”. The “receive…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. “obtain a trained model by feeding said training dataset to a first model” is recited at a high-level of generality (i.e., training and using a generic off the shelf machine learning algorithm to make predictions) and amounts to merely linking of the abstract idea to particular technological environment. The “a virtual twin configured to simulate” is recited at a high-level of generality (i.e., generic data simulations) and amounts to merely linking of the abstract idea to particular technological environment. The “radar graph” is recited at a high-level of generality (i.e., a organization of data for a user) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of device comprising at least one input, processor and output to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using generic hardware components cannot provide an inventive concept ("significantly more").
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “receive…”, “obtain a trained model by feeding said training dataset to a first model” were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The “receive…” steps have been re-evaluated under the "significantly more" analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.0S(d)(II)(i) "Receiving or transmitting data over a network" is well-understood, routine, and conventional. The “obtain a trained model by feeding said training dataset to a first model” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Varadan (20230181121): see below but at least paragraphs [0037]-[0039]; Lefkofsky (20210118559): paragraph [0094]; Kumar (20230307115): paragraph [0047]; training and use of a machine learning model to make predictions is well-understood, routine and conventional. The “a virtual twin configured to simulate” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Bostic (20210005324): paragraph [0007]; Changanti (20250299828): see below but at least paragraph [0023]; simulation of data using a virtual twin is well-understood, routine and conventional. The “radar graph” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Aoyama (20240169671): paragraph [0024]; Barbosa (20200065739): paragraph [0020]; Shiga (20110201902): see below but at least paragraph [0098]; display of data in a radar graph is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide "significantly more." As such the claim is not patent eligible.
Claims 2-10 and 13-20 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2, 10 and 13 recites the additional elements of using a wearable sensor, a diagnostic device, a peripheral device, a receiving interface and a storage device, however these various components are recited at a high-level of generality (i.e., generic computer components to implement generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a wearable sensor, a diagnostic device, a peripheral device, a receiving interface and storage device to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using generic hardware components cannot provide an inventive concept ("significantly more").
Claim 3 further describes the database, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claim 4 recites the additional elements of a ANN or CNN, however these neural networks are recited at a high-level of generality (i.e., training and using a generic off the shelf machine learning algorithm to make predictions) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the neural networks were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. These neural networks have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Varadan (20230181121): see below but at least paragraphs [0037]-[0039]; Lefkofsky (20210118559): paragraph [0094]; Kumar (20230307115): paragraph [0047]; training and use of a neural network to make predictions is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide "significantly more." As such the claim is not patent eligible.
Claim 5 further describes use and training of additional ML models, however training and use of ML models was already considered above and is incorporated herein.
Claims 6-7 further describe the health-related instruction, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 8-10 recite the additional element of a graphical interface to display, however this graphical interface is recited at a high-level of generality (i.e., a generic GUI presenting data) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the graphical interface was considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The graphical interface displaying have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Lefkofsky (20210118559): paragraph [0080]; Kumar (20230307115): paragraph [0155]; Manganaro (20240028964): paragraph [0125]; display of data on a GUI is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide "significantly more." As such the claim is not patent eligible.
Claims 14 and 15 further describe math and probability associated with the risk score, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 16 and 17 describe comparison of data for output to a human user, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 18-20 further describe the collection and organization of data and the timing associated with the collection and updating of outputs, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
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.
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.
Claim(s) 1-2, 4, 11 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20230181121 (hereafter “Varadan”), in view of U.S. Patent Pub. No. 20250299828 (hereafter “Changanti”), in further view of U.S. Patent Pub. No. 20110201902 (hereafter “Shiga”).
Regarding (Currently Amended) claim 1, Varadan teaches a device (Varadan: paragraph [0009], “systems and methods to manage and predict post-surgical recovery”) comprising:
at least one input configured to receive: at least one user data, measured and/or obtained from a user, and at least one reference data, wherein both said at least one user data and said at least one reference data are related to an abnormality (Varadan: paragraphs [0018]-[0020], “analyzing and treatment of abnormal physiological and chemical parameters… input data using an engineering system”, paragraph [0037], “the population is a large population comprising at least 50 patients each for a chosen surgical procedure”, paragraphs [0050]-[0052], “the input data is selected from past diagnoses, test results for blood biomarkers, proteins, metabolites, and/or cholesterol, biomedical vital signs collected from non-invasive medical devices… the assessment provides an overall metric that is reflective of the patient's state of recovery, a risk stratification score or number that is reflective of a probability or likelihood of a patient developing symptoms of a complication or risk of developing a condition that requires emergency treatment following a surgical procedure or combinations thereof”, paragraph [0139], “after the input data [100] is obtained, it is processed for anomaly detection… additional data obtained from an historic library of recovery patterns or templates for the procedure and the associated demographics… representation of a specific patient population defined by their demographic, their medical history, and type of procedure… patterns of occurrence of anomalies”);
at least one processor (Varadan: paragraph [0043], “a control module”, paragraph [0090], “computer”) configured to:
generate a training dataset using said at least one user data and said at least one reference data (Varadan: paragraph [0039], “generates the input data that is part of the training data set. This component generates the input data and is trained to generate the input data… generates a set of features that can be used to train another neural network or machine learning model to predict post-surgical recovery or risk of complication”, paragraphs [0061]-[0062], “a model is pre-trained on a population… adding further input data obtained from the patient… generates a set of outputs that are used as features that can be used to train another neural network or machine learning model”, paragraph [0085], “Such a pretrained neural network, can then be provided with the training data prepared for patient status assessments and can be trained or the internal terms or constants referred to as weights can be changed to better estimate patient status assessments from the chosen inputs. The idea is that the pre-trained neural network will already possess a mechanism to infer attributes from any input data. By re-training the networks, the transfer function implemented by the neural network is fine-tuned so that it will predict patient status assessments, instead of what it was originally trained to predict”, paragraph [0111], “The computational method used for the perioperative assessment can be developed using a set of data collected from a large patient population… Once the features of Table 1 are extracted, they are combined with the simultaneously acquired measurements from a reference device such as a sphygmomanometer to create the training data set. After the creation of the trained data set, model training and selection is performed. Model training is the process of improving the accuracy of a model represented mathematically as a mathematical function or operation that associates a set of chosen inputs with the desired outcome that is a predicted assessment”);
obtain a trained model by feeding said training dataset to a first model, wherein said trained model is configured to receive as input at least one current user data and provide as output at least one risk score of the user for developing said abnormality (Varadan: paragraph [0052], “the assessment provides an overall metric that is reflective of the patient's state of recovery, a risk stratification score or number that is reflective of a probability or likelihood of a patient developing symptoms of a complication or risk of developing a condition that requires emergency treatment following a surgical procedure or combinations thereof”, paragraph [0085], “Such a pretrained neural network, can then be provided with the training data prepared for patient status assessments and can be trained or the internal terms or constants referred to as weights can be changed to better estimate patient status assessments from the chosen inputs. The idea is that the pre-trained neural network will already possess a mechanism to infer attributes from any input data. By re-training the networks, the transfer function implemented by the neural network is fine-tuned so that it will predict patient status assessments, instead of what it was originally trained to predict”, paragraph [0111], “The computational method used for the perioperative assessment can be developed using a set of data collected from a large patient population… Once the features of Table 1 are extracted, they are combined with the simultaneously acquired measurements from a reference device such as a sphygmomanometer to create the training data set. After the creation of the trained data set, model training and selection is performed. Model training is the process of improving the accuracy of a model represented mathematically as a mathematical function or operation that associates a set of chosen inputs with the desired outcome that is a predicted assessment”);
calculate at least one risk score of the user for developing the abnormality using the trained model (Varadan: paragraph [0052], “the assessment provides an overall metric that is reflective of the patient's state of recovery, a risk stratification score or number that is reflective of a probability or likelihood of a patient developing symptoms of a complication or risk of developing a condition that requires emergency treatment following a surgical procedure or combinations thereof”, paragraph [0145], “using supervised machine learning methods or combinations of supervised and unsupervised machine learning methods.”. Also see, mapping for claim 1, incorporated herein), […];
at least one output configured to provide said trained model […] for each risk factor (Varadan: Fig. 4, paragraph [0040], “using assessment predictions generated during perioperative care, wherein the assessment predictions are further configured as inputs to develop time series forecasting model”, paragraphs [0058]-[0060], “e) performing one or more normalization, combination and/or transformation methods or processes for the signal and model assessment to provide inputs for the patient status assessment model for improvements, conditioning, and correction… f) using the output of step e to obtain an assessment”, paragraph [0070], “dashboard also displaying”, paragraph [0110], “the output is the best performing model that includes all steps that led to the definition of that model inclusive of choice of input data, conditioning and preparation of data, transformation and/or decomposition of data, and finally the machine learning algorithm or combinations of machine learning algorithms that were used in the model”, paragraph [0129], “In FIG. 4, an exemplary clinician dashboard for a patient being monitored remotely is shown which displays vital signs that are important for remote monitoring such as heart rate, blood pressure, and respiration along with several other vital signs”).
Varadan may not explicitly teach (Underlined below for clarity):
wherein the calculated at least one risk score is used to update an avatar of the user and the avatar is a virtual twin configured to simulate preventive scenarios and potential outcomes of the preventive scenarios;
Changanti teaches wherein the calculated at least one risk score is used to update an avatar of the user and the avatar is a virtual twin configured to simulate preventive scenarios and potential outcomes of the preventive scenarios (Changanti: paragraphs [0054]-[0055], “the avatar changes to reflect a current health score or another biomarker for the patient, such as a color… The virtual avatar or the digital twin of the patient is a digital representation of the patient's physical health and wellness. The avatar will show animations to indicate health status and/or physical activity of the patient”, paragraph [0064], “the data from the patient model is used to predict risk scores for most common chronic conditions based on changes in daily activity”);
One of ordinary skill in the art before the effective filing date would have found it obvious to include using an avatar and virtual twin as taught by Changanti within the risk score determination as taught by Varadan with the motivation of “improving patient outcomes and empowering patients to take charge of their health” (Changanti: paragraph [0001]).
Varadan and Changanti may not explicitly teach (Underlined below for clarity):
at least one output configured to provide said trained model and an interactive radar graph for each risk factor.
Shiga teaches at least one output configured to provide said trained model and an interactive radar graph for each risk factor (Shiga: Fig. 7, paragraph [0098]-[0099], “the risk distribution 210 of three illness risks or the basis of calculation of the health age is displayed using the radar chart… the respective influence intensity of the four biomarkers… commonly used in the calculation of each illness risk are shown using the radar chart”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a radar graph for features as taught by Shia within the display of various outputs for each feature as taught by Varadan and Changanti with the motivation of “improve [… a patient’s …] condition shown by the evaluation” (Shiga: paragraph [0005]).
Regarding (Original) claim 2, Varadan, Changanti and Shiga teach the limitations of claim 1, and further teaches wherein said at least one user data is obtained from at least one of a medical database, using a wearable sensor, during a clinical examination, and with a diagnostic device (Varadan: paragraphs [0006]-[0009], “Multiparametric wearable device data that simultaneously captures heart sounds, ECG, thoracic impedance, activity, and posture”, paragraph [0050], “the input data is selected from past diagnoses, test results for blood biomarkers, proteins, metabolites, and/or cholesterol, biomedical vital signs collected from non-invasive medical devices”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 4, Varadan, Changanti and Shiga teach the limitations of claim 1, and further teaches wherein said first model is an Artificial Neural Network (ANN) or a Convolutional Neural Network (CNN) (Varadan: paragraph [0053], “a convolutional neural network”, paragraph [0084], “Additionally, for the purpose of transforming the input data, neural networks may be used to apply transformations”).
The motivation to combine is the same as in claim 1, incorporated herein.
REGARDING CLAIM(S) 11
Claim(s) 11 is/are analogous to Claim(s) 1, thus Claim(s) 11 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
Regarding (Original) claim 13, Varadan, Changanti and Shiga teach a non-transitory program storage device comprising instructions which, when executed by a computer (Varadan: paragraph [0043], “a control module”, paragraph [0090], “computer”, paragraph [0139], “computational hardware and the available memory”), cause the computer to carry out the method of claim 11 (See mappings for claim 1, and 11 incorporated herein).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (New) claim 14, Varadan, Changanti and Shiga teach the limitations of claim 1, and further teach wherein the at least one risk score further comprises a probability value indicating a likelihood of an event occurring within a specified time period (Varadan: paragraph [0005], “after a set recovery period”, paragraph [0052], “a risk stratification score or number that is reflective of a probability or likelihood of a patient developing symptoms of a complication or risk of developing a condition that requires emergency treatment following a surgical procedure or combinations thereof”, paragraph [0100], “time-series data is a type of data that has a unique value associated with a series periodic or a periodic instance of time”, paragraph [0122], “over a specified period of time”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (New) claim 15, Varadan, Changanti and Shiga teach the limitations of claim 1, and further teach wherein the at least one risk score further comprises a number of standard deviations away from a mean of a reference population (Varadan: paragraph [0038], “historical data belonging to a particular population”, paragraph [0161], “Statistical measures of the variability of these aggregated vital signs in terms of mean, median, standard deviation could be further transformations”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (New) claim 16, Varadan, Changanti and Shiga teach the limitations of claim 1, and further teach wherein the at least one risk score further comprises a comparison of physiological age to chronological age (Shiga: Fig. 7, paragraph [0097], “FIG. 7 shows one example of the screen display. The left side in the upper level of the screen is the display example of the "health age"… The user can then intuitively understand whether good or bad his/her health condition is through comparison of the health age and his/her actual age. The health age may be preferably displayed”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (New) claim 17, Varadan, Changanti and Shiga teach the limitations of claim 16, and further teach wherein a spider chart is configured to compare the physiological age to the chronological age (Shiga: Fig. 7, paragraph [0097], “FIG. 7 shows one example of the screen display. The left side in the upper level of the screen is the display example of the "health age"… The user can then intuitively understand whether good or bad his/her health condition is through comparison of the health age and his/her actual age. The health age may be preferably displayed”, paragraph [0098]-[0099], “the risk distribution 210 of three illness risks or the basis of calculation of the health age is displayed using the radar chart… the respective influence intensity of the four biomarkers… commonly used in the calculation of each illness risk are shown using the radar chart”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (New) claim 18, Varadan, Changanti and Shiga teach the limitations of claim 1, and further teach wherein updating the avatar involves continuous data assimilation and model recalibration (Varadan: paragraph [0005], “tracking progress over time”, paragraph [0085], ‘By re-training the networks, the transfer function implemented by the neural network is fine-tuned”; Changanti: paragraph [0064], “updated with the addition of new information that integrates daily PGHD from wearables and self-reported to surveys with the medical data of the patient obtained from EHR systems”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (New) claim 19, Varadan, Changanti and Shiga teach the limitations of claim 1, and further teach wherein detection of an anomaly triggers an immediate update of the avatar (Varadan: paragraph [0107], “provide an instantaneous assessment of the holistic state of the patient.”; Changanti: paragraphs [0054]-[0055], “the avatar changes to reflect a current health score or another biomarker for the patient, such as a color”, paragraph [0079], “real-time”).
The motivation to combine is the same as in claim 1, incorporated herein.
Claim(s) 3 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20230181121 (hereafter “Varadan”), U.S. Patent Pub. No. 20250299828 (hereafter “Changanti”) and U.S. Patent Pub. No. 20110201902 (hereafter “Shiga”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 20210118559 (hereafter “Lefkofsky”).
Regarding (Original) claim 3, Varadan, Changanti and Shiga teach the limitations of claim 1, but may not explicitly teach wherein said at least one reference data is obtained from a knowledge-based database comprising established findings at least associated to said abnormality.
Lefkofsky teaches wherein said at least one reference data is obtained from a knowledge-based database comprising established findings at least associated to said abnormality (Lefkofsky: paragraph [0080], “wherein said at least one reference data is obtained from a knowledge-based database comprising established findings at least associated to said abnormality”, paragraph [0138], “A bioinformatics pipeline may implement variant characterization via processes including a variant characterization DNA process and a variant characterization RNA process”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a bioinformatic database as taught by Lefkofsky within the use of reference libraries that have associated abnormality as taught by Varadan, Changanti and Shiga with the motivation of “improve the performance of the respective module for the subject results being processed at the time of operation” (Lefkofsky: paragraph [0094]).
Regarding (New) claim 20, Varadan, Changanti and Shiga teach the limitations of claim 1, and further teach wherein the virtual twin is created based upon longitudinal data analysis […] (Varadan: paragraph [0005], “tracking progress over time”; Changanti: paragraph [0057], “Longitudinal data such as physical activity, lab values, and vital signs may be displayed as graphs that show the change in patient's health status over time”, paragraphs [0054]-[0055], “The virtual avatar or the digital twin of the patient is a digital representation of the patient's physical health and wellness”, paragraph [0064], “updated with the addition of new information that integrates daily PGHD from wearables and self-reported to surveys with the medical data of the patient obtained from EHR systems”).
Varadan, Changanti and Shiga may not explicitly teach (Underlined below for clarity):
wherein the virtual twin is created based upon longitudinal data analysis and continuously revalidated against updated clinical scores.
Lefkofsky teaches wherein the virtual twin is created based upon longitudinal data analysis and continuously revalidated against updated clinical scores (Lefkofsky: paragraph [0019], “analyze changes to the raw lab results over time in coordination with changes in the subject's clinomic profile over time”, paragraph [0177], “Validation may be performed”).
The motivation to combine is the same as in claim 3, incorporated herein.
Claim(s) 5-6 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20230181121 (hereafter “Varadan”), U.S. Patent Pub. No. 20250299828 (hereafter “Changanti”) and U.S. Patent Pub. No. 20110201902 (hereafter “Shiga”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 20230307115 (hereafter “Kumar”).
Regarding (Currently Amended) claim 5, Varadan, Changanti and Shiga teach the limitations of claim 1, and further teach wherein the at least one processor (Varadan: paragraph [0043], “a control module”, paragraph [0090], “computer”) is further configured to:
provide as input to a trained second model said […] to obtain as output said at least one health-related instruction, said trained second model being previously trained using a database comprising at least […] associated instructions (Varadan: paragraph [0041], “the assessment prediction also provides treatment recommendations based on input data obtained from the patient post-surgery combined with the assessment predictions”, paragraph [0062], “train another neural network or machine learning model.”, paragraph [0110], “to obtain the Output of the Process, several different types of models are trained on the training data”);
at least one output is further configured to provide said at least one health-related instruction (Varadan: paragraph [0005], “deliver personalized care instructions including wound care and physical therapy”, paragraph [0070], “dashboard also displaying”, paragraph [0041], “the assessment prediction also provides treatment recommendations based on input data obtained from the patient post-surgery combined with the assessment predictions”).
Varadan, Changanti and Shiga may not explicitly teach (Underlined below for clarity):
provide as input to a trained second model said at least one risk score to obtain as output said at least one health-related instruction, said trained second model being previously trained using a database comprising at least risk scores and associated instructions;
Kumar teaches provide as input to a trained second model said at least one risk score to obtain as output said at least one health-related instruction, said trained second model being previously trained using a database comprising at least risk scores and associated instructions (Kumar: paragraph [0094], “the machine learning system trains the machine learning model based on… risk score”, paragraph [0109], “the machine learning system (or another system, such as the intervention system 240 of FIG. 2) can optionally select one or more interventions based on the generated risk score”, paragraph [0145], “machine learning system… trend indicates that the risk score”);
One of ordinary skill in the art before the effective filing date would have found tit obvious to include use a risk score for training and input of a machine learning determination of instruction as taught by Kumar within the recommendations provided by the ML model as taught by Varadan, Changanti and Shiga with the motivation of “improve the ability of the system to evaluate” (Kumar: paragraph [0075]).
Regarding (Original) claim 6, Varadan, Changanti, Shiga and Kumar teach the limitations of claim 5, and further teach wherein said health-related instruction is at least one of the following: a proposition to undergo a medical examination, an invitation to schedule an appointment with a healthcare professional, an advice to modify a lifestyle habit, an instruction to trigger an alert, an instruction to control a peripheral device, and an instruction to establish a communication with another user (Varadan: paragraph [0005], “paging system alerts”, paragraph [0153], “perform anomaly detection and raise alerts or alarms”; Kumar: paragraph [0027], “the machine learning model enables real-time insights, including alerts that notify”).
The motivation to combine is the same as in claim 5, incorporated herein.
Regarding (Currently Amended) claim 8 Varadan, Changanti, Shiga and Kumar teach the limitations of claim 5, and further teach at least one graphical interface configured to display a virtual model of the user, obtained using a biostatistical model (Kumar: paragraph [0155], “the machine learning system may output the aggregate data on a GUI, enabling users to quickly review”).
The motivation to combine is the same as in claim 5, incorporated herein.
Regarding (Original) claim 9, Varadan, Changanti, Shiga and Kumar teach the limitations of claim 8, and further teach wherein said at least one risk score is used at least to display, in the at least one graphical interface, a health status of at least one organ of the user on the virtual model of the user (Varadan: paragraph [0052], “the assessment provides an overall metric that is reflective of the patient's state of recovery, a risk stratification score or number that is reflective of a probability or likelihood of a patient developing symptoms of a complication or risk of developing a condition that requires emergency treatment following a surgical procedure or combinations thereof”, paragraph [0137], “Essentially, this is a means to visualize a multidimensional data which is abstract in a more interpretable and displayable format such as on a flat surface or projection in 3D. Finally, the Output of Process”; Kumar: paragraph [0155], “the machine learning system may output the aggregate data on a GUI, enabling users to quickly review”).
The motivation to combine is the same as in claim 5, incorporated herein.
Regarding (Currently Amended) claim 10, Varadan, Changanti, Shiga and Kumar teach the limitations of claim 8, and further teach wherein said health-related instruction is displayed to the user on the graphical interface (Kumar: paragraph [0155], “the machine learning system may output the aggregate data on a GUI, enabling users to quickly review”), and
a command associated with the health-related instruction consists in at least one of the following: sending a notification to the user related to a health status of the user; and/or displaying a report to the user related to a health status of the user; and/or sending a control signal to at least one peripheral device; and/or sending a control signal to at least one receiving interface (Varadan: paragraph [0005], “paging system alerts”, paragraph [0153], “perform anomaly detection and raise alerts or alarms”, claim 56, “wherein a report is generated assessing the status of the patient”; Kumar: paragraph [0027], “the machine learning model enables real-time insights, including alerts that notify”).
The motivation to combine is the same as in claim 5, incorporated herein.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20230181121 (hereafter “Varadan”), U.S. Patent Pub. No. 20250299828 (hereafter “Changanti”), U.S. Patent Pub. No. 20110201902 (hereafter “Shiga”) and U.S. Patent Pub. No. 20230307115 (hereafter “Kumar”) as applied to claim 5 above, and further in view of U.S. Patent Pub. No. 20210118559 (hereafter “Lefkofsky”).
Regarding (Original) claim 7, Varadan, Changanti, Shiga and Kumar teach the limitations of claim 5, but may not explicitly teach wherein a command is associated with said health-related instruction.
Lefkofsky teaches wherein a command is associated with said health-related instruction (Lefkofsky: paragraphs [0041], “functions may be stored or transmitted as one or more instructions or code”, paragraph [0045], “control a computer or processor based device”, paragraph [0263], “instructions, for causing the machine to perform”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include providing a command as taught by Lefkofsky with the providing of an instruction as taught by Varadan, Changanti, Shiga and Kumar with the motivation of “improve the performance of the respective module for the subject results being processed at the time of operation” (Lefkofsky: paragraph [0094]).
Response to Arguments
Applicant's arguments filed on 07 July 2026 have been fully considered but they are not persuasive. Applicant's arguments will be addressed below in the order in which they appear in the response filed on 07 July 2026.
Rejections under 35 U.S.C. § 112
Regarding the rejections of claims 8-10, in view of the amendments to the claims the rejections have been removed.
Rejections under 35 U.S.C. § 101
Regarding the rejection of claims 1-13, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues
The recited subject matter is directed to detailed display on a computer network rather than merely managing personal behavior… The innovative aspect of using virtual twins lies in their application to replicate not just the patient's current health status but also to simulate preventive scenarios and their potential outcomes… Using an interactive radar chart that contrasts the physiological age with the actual age of the patient for various diseases and risk factors is advantageous because it allows complex medical information to be communicated in a simple and easily understandable way for both patients and doctors.
The Examiner respectfully disagrees.
It is respectfully submitted, that under the broadest reasonable interpretation the claims are directed toward collection and organization of data into risk scores to organize an output of collected data for a human user to use in their treatment of a health abnormality, which as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”.
The claimed additional elements do not recite a technical solution to a technical problem recited in Applicant’s specification, in particular the newly amended features may be additional elements however the additional elements are used to generally link the abstract idea to a particular technological environment and do not address technical problems recited in Applicant’s specification. The argued problems of simulation of preventive health scenarios and problems in communicating data for a human user to comprehend are not technical problems rooted in computer hardware technology, these are at best treatment decision and user comprehension problems, which are human activity problems that are not rooted in computer hardware technology, the claims may improve upon the abstract idea, nevertheless an improved abstract idea is still an abstract idea, as none of the claimed additional elements actually recite a technical solution to a technical problem recited in Applicant’s specification the claims do not provide a practical application and/or significantly more.
Rejections under 35 U.S.C. § 103
Regarding the rejection of claims 1-13, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues
The Office Action relied upon Varadan, citing (e.g., [0018]-[0020], [0037], [0050]) for collecting patient-specific input data (e.g., diagnoses, biomarkers, and vital signs) and comparing such data to historical patient populations or recovery templates. However, the reference data is characterized as historical data associated with particular procedures, demographics, or recovery patterns, not as data corresponding to the same abnormality as represented by the user data… The references to "risk stratification" and ML methods are presented at a conceptual outcome level rather than as a specific configured model-based inference architecture receiving current user data as claimed. Claim 1, as amended, recites: "the calculated at least one risk score is used to update an avatar of the user and the avatar is a virtual twin configured to simulate preventive scenarios and potential outcomes of the preventive scenarios" (emphasis added). Similar subject matter appears in claim 11. This is a modified version of subject matter from claim 5.
The Examiner respectfully disagrees.
It is respectfully submitted, that Varadan explicitly teaches that input data is obtained for anomaly detection and that additional historic data is obtained which is at least related to the abnormality under the broadest reasonable interpretation. The other argued features are taught by newly added Changanti and Shiga respectively, and would be prima facie obvious to combine with the motivations of “improving patient outcomes and empowering patients to take charge of their health” (Changanti: paragraph [0001]) and “improve [… a patient’s …] condition shown by the evaluation” (Shiga: paragraph [0005]). Therefore, in view of the new grounds of rejection as necessitated by amendment the argument is not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
