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 .
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 24 November 2025, has been considered by the Examiner.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8 and 10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 8 recites “display a virtual model of the user, preferably obtained using a biostatistical model”, such “preferably obtained” language renders the claim indefinite because it is unclear whether the limitation(s) of the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 10 recites the limitation "the graphical interface" and “the command” in line 2 respectively. There is insufficient antecedent basis for this limitation in the claim.
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 1-13 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, 5 and 11-13 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 claims 11 and 13
obtaining a […] model for prediction of at least one risk score of a user for developing an abnormality with respect to a health status of the user, wherein […] comprises: [… obtain …] at least one user data, measured and/or obtained from said user, and at least one reference data, both said user data and said reference data being related to said abnormality; […]: generate a training dataset using said at least one user data and said at least one reference data; obtain said […] model for prediction by feeding said training dataset to a first model, wherein said […] model for prediction is configured to receive as input at least one current user data and provide as output said at least one risk score of the user for developing said abnormality; […] provide said […] model for prediction.
Claim 5, which is representative of claim 10
assisting the user by providing at least one health-related instruction for improving a health status of the user, wherein […] comprises: [… obtain …] at least one current user data; […]: calculate at least one risk score of the user for developing said abnormality using a […] model for prediction obtained […] according to claim 1; provide as input to a […] second model said at least one risk score to obtain as output said at least one health-related instruction, said […] second model being previously [… created …] using a database comprising at least risk scores and associated instructions; […] provide said at least one health-related instruction.
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 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…”, “a trained model… trained using a database…”. 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. “a trained model… trained using a database…” 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.
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…”, “a trained model… trained using a database…” 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 “a trained model… trained using a database…” 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. Well-understood, routine, and conventional elements/functions cannot provide "significantly more." As such the claim is not patent eligible.
Claims 2-4 and 6-10 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 and 10 recites the additional elements of using a wearable sensor, a diagnostic device, a peripheral device and a receiving interface, 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 and a receiving interface 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.
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.
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4, 11 and 13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Pub. No. 20230181121 (hereafter “Varadan”).
Regarding claim 1, Varadan teaches a device for obtaining a trained model for prediction of at least one risk score of a user for developing an abnormality with respect to a health status of the user (Varadan: paragraph [0009], “systems and methods to manage and predict post-surgical recovery”, paragraph [0037], “a pre-trained model (i.e., a pre-trained neural network) on a population is further trained with additional input data from an individual to generate a surgical recovery assessment model that is unique for that individual”, 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 [0110], “models are trained on the training data”), wherein the device comprises:
at least one input configured to receive: at least one user data, measured and/or obtained from said user, and at least one reference data, both said user data and said reference data being related to said 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”, 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,”, 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”);
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 said trained model for prediction by feeding said training dataset to a first model, wherein said trained model for prediction is configured to receive as input at least one current user data and provide as output said 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”);
at least one output configured to provide said trained model for prediction (Varadan: 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”).
Regarding claim 2, Varadan teaches 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”).
Regarding claim 4, Varadan teaches 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”).
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 claim 13, Varadan teaches 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).
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) 3 is 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. 20210118559 (hereafter “Lefkofsky”).
Regarding claim 3, Varadan teaches 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 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]).
Claim(s) 5-6, 8-10 and 12 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. 20230307115 (hereafter “Kumar”).
Regarding claim 5, Varadan teaches the limitations of claim 1, and further teaches a device for assisting the user by providing at least one health-related instruction for improving a health status of the user (Varadan: paragraph [0005], “deliver personalized care instructions including wound care and physical therapy”, paragraph [0009], “systems and methods to manage and predict post-surgical recovery”, paragraph [0041], “a the assessment prediction also provides treatment recommendations based on input data”. The Examiner notes that “for improving a health status of the user” is an intended use of the instruction that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the instruction), wherein the device comprises:
at least one input configured to receive at least one current user data (Varadan: paragraphs [0018]-[0020], “input data using an engineering system”, 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”, paragraph [0135], “the current observation i.e., an observation in time of the input data from a patient for whom the recovery score or risk score is being computed,”, paragraph [0139], “after the input data [100] is obtained, it is processed for anomaly detection”);
at least one processor (Varadan: paragraph [0043], “a control module”, paragraph [0090], “computer”) configured to:
calculate at least one risk score of the user for developing said abnormality using a trained model for prediction obtained by the device according to claim 1 (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);
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 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 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 with the motivation of “improve the ability of the system to evaluate” (Kumar: paragraph [0075]).
Regarding claim 6, Varadan 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 claim 8 Varadan and Kumar teach the limitations of claim 5, and further teach wherein the device further comprises at least one graphical interface to display a virtual model of the user, preferably 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 claim 9, Varadan 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 claim 10, Varadan and Kumar teach the limitations of claim 5, 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
wherein the 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.
REGARDING CLAIM(S) 12
Claim(s) 12 is/are analogous to Claim(s) 5, thus Claim(s) 12 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20230181121 (hereafter “Varadan”) 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 claim 7, Varadan 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 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]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Pub. No. 20240028964 (hereafter “Manganaro”) teaches clinical decision support (CDS) using a trained model and personalization of trained model for particular patients.
U.S. Patent Pub. No. 20250201409 (hereafter “Fraraccio”) teaches various models for classification of risk using a combination of the various models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew E Lee whose telephone number is (571)272-8323. The examiner can normally be reached M-Th 9-5:00 PM.
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/A.E.L./Examiner, Art Unit 3684
/RAJESH KHATTAR/Primary Examiner, Art Unit 3684