DETAILED ACTION
Notices to Applicant
This communication is a Final Office Action on the merits. Claims 1-20 as filed 01/05/2026, are currently pending and have been considered below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994)
The disclosure of the prior-filed application, Application No. 16/668,423 as filed 10/30/2019, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. In particular, the above prior-filed application fails to provide adequate support for the present claim limitation of “select a prognosis” as recited in claim 1 and similarly recited in claim 11. Accordingly, the present invention as currently claims does not have priority benefit of Application No. 16/668,423 as filed 10/30/2019 and only has the benefit of the presently filed Application Specification filed 08/31/2020.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
Claim 1, lines 56-59 recite “wherein the cooperation ranking comprising a numerical value used to update at least one of the classification machine-learning model, the statistical machine-learning process, or the simulation-machine-learning process,” – the limitation should read: “wherein the cooperation ranking comprising a numerical value is used to update at least one of the classification machine-learning model, the statistical machine-learning process, or the simulation-machine-learning process”. Claim 11 recites substantially the same informality.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Claims 1-10 are drawn to a system for prioritizing comprehensive prognoses and generating an associated treatment instruction set, which is within the four statutory categories (i.e. system).
Independent Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites (additional elements bolded):
A system for prioritizing comprehensive prognoses and generating a treatment instruction set, the system comprising: a computing device comprising at least a processor communicatively connected to a memory, wherein the computing device is designed and configured to:
receive, using the at least a processor, at least a user biological marker, wherein the user biological marker comprises a plurality of structured biological marker values encoded as machine-readable numerical data elements and stored in the memory;
generate, using the at least a processor, a classification machine-learning model wherein generating the classification machine-learning model which further comprises:
receiving classification training data set, wherein the classification training data set comprises outputs correlated to inputs represented as feature vectors, wherein the inputs comprise a plurality of biological marker data inputs and comprise a plurality of associated diagnostics outputs stored as labeled classification data;
training, iteratively, the classification machine-learning model using the classification data training data set, wherein iterative training comprises updating model parameters stored in memory until a classification convergence threshold is satisfied;
determine, using the at least a processor, a diagnostic as a function of the trained classification machine-learning model, wherein the biological marker data is provided to the trained classification machine-learning model as an input to output the associated diagnostic, the diagnostic being a machine-generated output value produced by execution of the trained classification machine-learning model;
rank the diagnostic, using the at least a processor, wherein ranking further comprises using a statistical machine-learning process to determine a figure of merit of the diagnostic matching the user biological marker, which further comprises:
receiving, using the at least a processor, a training data set, wherein the training data set comprises outputs correlated to inputs, wherein the inputs comprise a plurality of diagnostics and a plurality of figure of merits and the outputs comprise a plurality of a series of values that correspond to a likelihood of the plurality of diagnostics to match the user biological marker which are ranked as a function of their associated figure of merit;
training, iteratively, using the at least a processor, the statistical machine learning process using the training data set, wherein the statistical machine-learning process generates numerical likelihood scores stored in the memory for each diagnostic; and
select, using the at least a processor, a prognosis as a function of the figure of merit, wherein selecting the prognosis further comprises: generating at least a treatment, wherein the treatment is selected based on the numerical likelihood scores and encoded as a structured treatment data object stored in memory;
performing, using the at least a processor, a simulation machine-learning process, wherein the simulation machine-learning process generates an output containing a prognosis using the at least a treatment as an input;
determining, using the at least a processor, a rank for the prognosis, wherein the computing device is configured to assign a numerical ranking of each prognosis based on treatment options; and
providing, using the at least a processor, a treatment instruction set that results in an optimal prognosis;
receiving, using the at least a processor, updated user data after a treatment instruction set is provided, wherein the updated user data comprises user feedback on an efficacy of the treatment instruction set, and
generating, using the at least a processor, a cooperation ranking as a function of the received updated user data, wherein the cooperation ranking increases as a function of the user completion of an instruction of the treatment instruction set, wherein the cooperation ranking comprising a numerical value used to update at least one of the classification machine-learning model, the statistical machine-learning process, or the simulation-machine-learning process;
display, using a graphical user interface, the treatment instruction set.
The above limitations, as drafted, is a system that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for but the recitation of generic computer components. That is, other than reciting the above bolded claim limitations, nothing in the claim precludes the steps from practically being performed in the mind. For example, but for the above bolded language, receiving at least a user biological marker, determining a diagnostic as a function of the biological marker data as input to output the associated diagnostic, ranking the diagnostic by determining a figure of merit of the diagnostic matching the user biological marker, selecting a prognosis by generating at least a treatment and determining a rank for the prognosis based on treatment options, providing a treatment instruction set that results in an optimal prognosis, receiving user feedback after a treatment instruction on the efficacy of the treatment instruction set and generating a cooperation rank in the context of this claim encompasses the user manually collecting and analyzing data for prioritizing comprehensive prognoses and generating an associated treatment instruction set through observation, evaluation, judgment, and opinion. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, the above language amounts to rules or instructions for managing personal behavior or interactions between people such that the claim is also directed to the abstract idea of “Certain Methods of Organizing Human Activity.” Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above bolded additional elements to perform the claim limitations. The additional elements in each step are recited at a high-level of generality (i.e., a computing device, at least a processor communicatively connected to a memory for storing and executing software instructions, a graphical user interface as they relate to general purpose computers, and training and executing machine learning models (e.g. mathematical expression and/or regression/loss function iterated to gradually converge towards a minimum by optimizing weights) implemented as any hardware and/or software (Application Specification [0008], [0009], [0013], [0046]-[0047], [0053], [0095], [0127]-[0129])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer or other machinery in its ordinary capacity, or merely uses a computer or other machinery in its ordinary capacity as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). The additional elements of the claimed machine learning models/process do not recite a technical solution to a technical problem, but rather, recite the training and execution of machine learning models/processes at a high level of generality as regression/loss function algorithms for analysis optimization of the claimed input data to generate a determined output as data analysis. Further, the “display, using a graphical user interface, the treatment instruction set,” amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP 2105.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do 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 above bolded additional elements are used to perform the collecting and analyzing data limitations. The elements in each of these steps are recited at a high-level of generality (i.e., a computing device, at least a processor communicatively connected to a memory for storing and executing software instructions, a graphical user interface as they relate to general purpose computers, and training and executing machine learning models (e.g. mathematical expression and/or regression/loss function iterated to gradually converge towards a minimum by optimizing weights) implemented as any hardware and/or software (Application Specification [0008], [0009], [0013], [0046]-[0047], [0053], [0095], [0127]-[0129])). Further, the “display, using a graphical user interface, the treatment instruction set,” amounts to adding insignificant extra-solution activity to the judicial exception and is well-understood, routine, and conventional activity of displaying data. See MPEP 2105.05(g), Application Specification at [0113]) (Persons skilled in the art, upon review of this disclosure in its entirety, will be aware of the various ways in which a graphical user interface may be implemented for the purposes described herein, and the various devices which may be used as user devices)). Mere instructions to apply an exception using generic computer components or other machinery in tis ordinary capacity and adding insignificant extra-solution activity to the judicial exception cannot provide an inventive concept. See MPEP 2106.05(f)(2),(g). The claim is not patent eligible.
Dependent claims 2-10 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond collecting, analyzing, and displaying data. Claim 2 recites the additional element of “a wearable device,” claim 3 recites “a database,” and claim 9 recites “a user device,” however, these additional elements are recited at a high-level of generality such that they amount to no more than generic computer components (i.e. a wearable device such as a sensor to detect heart rate, blood oxygen, pulse, blood sugar, etc.; a database such as a relational database or any other format or structure, and a user device such as any smartphone, laptop, tablet, computing device, or the like. (See Application Specification [0015], [0019], [0113])). See MPEP 2106.05(f). Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Claims 11-20 are drawn to a method for prioritizing comprehensive prognoses and generating an associated treatment instruction set, which is within the four statutory categories (i.e. method).
Independent Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 11 recites:
A method of prioritizing comprehensive prognoses and generating a treatment instruction set, the method comprising:
receiving, by a computing device comprising at least a processor communicatively connected to a memory, at least a user biological marker wherein the user biological marker comprises a plurality of structured biological marker values encoded as machine-readable numerical data elements and stored in the memory;
generating, by the at least a processor, a classification machine-learning model which further comprises:
receiving, by the at least a processor, a classification training data set, wherein the classification training data set comprises outputs correlated to inputs represented as feature vectors, wherein the inputs comprise a plurality of biological marker data corresponding to associated diagnostics as outputs stored as labeled classification data;
training, iteratively, by the at least a processor, the classification machine-learning model using the classification training data set, wherein iterative training comprises updating model parameters stored in memory until a classification convergence threshold is satisfied;
determining, by the at least a processor, a diagnostic as a function of the trained classification machine-learning model, wherein the plurality of biological marker data is provided to the trained classification machine-learning model as an input to output the associated diagnostic, the diagnostic being a machine-generated output value produced by execution of the trained classification machine-learning model;
ranking, by the at least a processor, the diagnostic, wherein ranking further comprises using a statistical machine-learning process to determine a figure of merit of the diagnostic matching the user biological marker which further comprises:
receiving a training data set, wherein the training data set comprises outputs correlated to inputs, wherein the inputs comprise a plurality of diagnostics and a plurality of figure of merits and the outputs comprise a plurality of a series of values that correspond to a likelihood of the plurality of diagnostics to match the user biological marker which are ranked as a function of their associated figure of merit;
training, iteratively, the statistical machine-learning process using the training data set, wherein the statistical machine-learning process generates numerical likelihood scores stored in the memory for each diagnostic; and
selecting, by the at least a processor, a prognosis as a function of the figure of merit, wherein selecting the prognosis further comprises:
generating at least a treatment, wherein the treatment is selected based on the numerical likelihood scores and encoded as a structured treatment data object stored in memory;
performing, by the at least a processor, a simulation machine-learning process, wherein the simulation machine-learning process generates an output containing a prognosis using the at least a treatment as an input;
determining, by the at least a processor, a rank for the prognosis, wherein the computing device uses the ranking machine-learning process, assigning a numerical ranking of each prognosis based on treatment options and
providing, by the at least a processor, a treatment instruction set that results in an optimal prognosis;
displaying, by the computing device, using a graphical user interface, the instruction set;
receiving, by the at least a processor, updated user data after a treatment instruction set is provided, wherein the updated user data comprises user feedback on an efficacy of the treatment instruction set, and
generating, by the at least a processor, a cooperation ranking as a function of the received updated user data, wherein the cooperation ranking increases as a function of the user completion an instruction of the treatment instruction set, wherein the cooperation ranking comprising a numerical value used to update at least one of the classification machine-learning model, the statistical machine-learning process, or the simulation machine-learning process.
The above limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for but the recitation of generic computer components. That is, other than reciting the above bolded claim limitations, nothing in the claim precludes the steps from practically being performed in the mind. For example, but for the above bolded language, receiving at least a user biological marker, determining a diagnostic as a function of the biological marker data as input to output the associated diagnostic, ranking the diagnostic by determining a figure of merit of the diagnostic matching the user biological marker, selecting a prognosis by generating at least a treatment and determining a rank for the prognosis based on treatment options, providing a treatment instruction set that results in an optimal prognosis, receiving user feedback after a treatment instruction on the efficacy of the treatment instruction set and generating a cooperation rank in the context of this claim encompasses the user manually collecting and analyzing data for prioritizing comprehensive prognoses and generating an associated treatment instruction set through observation, evaluation, judgment, and opinion. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, the above language amounts to rules or instructions for managing personal behavior or interactions between people such that the claim is also directed to the abstract idea of “Certain Methods of Organizing Human Activity.” Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above bolded additional elements to perform the collecting and analyzing data limitations. The additional elements in each step are recited at a high-level of generality (i.e., a computing device, at least a processor communicatively connected to a memory for storing and executing software instructions, a graphical user interface as they relate to general purpose computers, and training and using machine learning models (e.g. mathematical expression and/or loss regression/function iterated to gradually converge towards a minimum by optimizing weights)implemented as any hardware and/or software (Application Specification [0008], [0009], [0013], [0046]-[0047], [0053], [0095], [0127]-[0129])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer or other machinery in its ordinary capacity, or merely uses a computer or other machinery in its ordinary capacity as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). The additional elements of the claimed machine learning models/process do not recite a technical solution to a technical problem, but rather, recite the training and execution of machine learning models/processes at a high level of generality as regression/loss function algorithms for analysis optimization of the claimed input data to generate a determined output as data analysis. Further, the “display, using a graphical user interface, the treatment instruction set,” amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP 2105.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do 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 above bolded additional elements are used to perform the claim limitations. The elements in each of these steps are recited at a high-level of generality (i.e., a computing device, at least a processor communicatively connected to a memory for storing and executing software instructions, a graphical user interface as they relate to general purpose computers, and training and executing machine learning models (e.g. mathematical expression and/or regression/loss function iterated to gradually converge towards a minimum by optimizing weights) implemented as any hardware and/or software (Application Specification [0008], [0009], [0013], [0046]-[0047], [0053], [0095], [0127]-[0129])). Further, the “displaying, by the computing device, using a graphical user interface, the instruction set,” amounts to adding insignificant extra-solution activity to the judicial exception and is well-understood, routine, and conventional activity of displaying data. See MPEP 2105.05(g), Application Specification at [0113]) (Persons skilled in the art, upon review of this disclosure in its entirety, will be aware of the various ways in which a graphical user interface may be implemented for the purposes described herein, and the various devices which may be used as user devices)). Mere instructions to apply an exception using generic computer components or other machinery in tis ordinary capacity and adding insignificant extra-solution activity to the judicial exception cannot provide an inventive concept. See MPEP 2106.05(f)(2),(g). The claim is not patent eligible.
Dependent claims 12-20 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond collecting, analyzing, and displaying data. Claim 12 recites the additional element of “a wearable device,” claim 13 recites “a database,” and claim 19 recites “a user device,” however, these additional elements are recited at a high-level of generality such that they amount to no more than generic computer components (i.e. a wearable device such as a sensor to detect heart rate, blood oxygen, pulse, blood sugar, etc.; a database such as a relational database or any other format or structure, and a user device such as any smartphone, laptop, tablet, computing device, or the like. (See Application Specification [0015], [0019], [0113])). See MPEP 2106.05(f). Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Examiner Statement - 35 USC § 102/103
The closest prior art of record – U.S. Patent Application Pub. No. 2021/0407672 A1 (hereinafter “Zumbrun et al.”), U.S. Patent Application Pub. No. 2020/0303047 A1 (hereinafter “Bostic et al.”), U.S. Patent Application Pub. No. 2017/0372029 A1 (hereinafter “Saliman et al.”), U.S. Patent Application Pub. No. 2017/0116379 A1 (hereinafter “Scott et al.”), and U.S. Patent Application Pub. No. 2015/0031064 A1 (hereinafter “Bilello et al.”) – do not teach each limitation of independent claims 1 and 11 in the particular order as currently recites, and therefore, fail to anticipate or otherwise render the claimed invention obvious. As a result, independent claims 1 and 11, along with dependent claims 2-10 and 12-20 are free of prior art in the particular ordered combination as currently recited.
Response to Arguments
Applicant's arguments filed 01/05/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed on 01/05/2026.
In the remarks, Applicant argues in substance that:
Regarding the Priority status of claims 1-20, Applicant argues that the limitation “select a prognosis” is supported by the present Application Specification.
Regarding the 101 rejection of claims 1-20, Applicant argues that the claims are allowable under Step 2A and/or Step 2B; and
Regarding the 103 rejection of claims 1-20, Applicant argues that the previously cited references fail to teach or disclose the newly amended limitations of independent claims 1 and 1
In response to Applicant’s argument (a) regarding the Priority benefit of the instant claims, Examiner submits that the present Application fails to provide adequate support for the present claim limitation of “select a prognosis” as recited in claim 1 and similarly recited in claim 11. Accordingly, the present invention as currently claims does not have priority benefit of Application No. 16/668,423 as filed 10/30/2019 and only has the benefit of the presently filed Application Specification filed 08/31/2020.
In response to Applicant’s argument that (b) regarding the 101 rejection of claims 1-20, Examiner respectfully disagrees.
First, under Step 2A, Prong One, Applicant argues that each step is tied to a computing device and cannot be performed practically or feasibly in the human mind. See Remarks at pgs. 13-14. Examiner respectfully submits that, but for the recitation of generic computer components (e.g. at least a processor, memory, computing device, etc.) the claim limitations are directed to a Mental Process of observation (i.e. receiving data), evaluation, judgment, and/or opinion (analysis) of data. Further, the limitations are also directed to the abstract idea of Certain Methods of Organizing Human Activity through rules or instructions for managing personal behavior or interactions between people. See MPEP § 2106.04(a)(2)(II)(C) (citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982)). Accordingly, the claim recites an abstract idea. Examiner further submits that he limitations of machine learning were considered as additional elements of the claim, and that the claim still recites limitations directed to the above abstract ideas as discussed in the above Office Action (e.g. determining, ranking, selecting, etc.), such that in view of the August 2025 USPTO memorandum on reminders in view of all USPTO guidance, the claims as currently drafted, recite limitations directed to abstract ideas.
Second under Step 2A, Prong Two, Applicant argues the instant claims are analogous to Example 47 and 48 as the machine learning steps recite an improvement of the operation of a prognostic system rather than merely performing abstract analysis and by providing machine generated diagnostic outputs. Examiner disagrees and respectfully submits, however, that Example 47 is directed to a technical solution of a technical problem of network security (and Example 48 tied to a technical improvement of synthesizing speech waveforms to create new speech signal that no longer contains extraneous speech signals from unwanted sources), thereby improving the functioning of a computer or other technology. The instant claims, however, merely invoke machine learning models and processes to optimizing the abstract ideas (i.e. determine a diagnostic, ranking the diagnostic, select a prognosis) of a Mental Process/Certain Method of Organizing Human Activity through optimization functions of data analysis akin to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982)). The claims do not recite a technical improvement to the functioning of a computer or technical field (i.e. an improvement to a technical problem rooted in Machine Learning (See Ex Parte Desjardins directed to a solution for machine learning “catastrophic forgetting”). Accordingly, the limitations amount to no more than mere instructions to apply the abstract idea using a generic computer. See MPEP 2106.05(f)(2).
Lastly, under Step 2B, Applicant argues that the claims recite significantly more than the abstract idea through a non-conventional arrangement of machine learning. Examiner respectfully disagrees. That is, the additional elements are recited at a high-level of generality (i.e., a computing device, at least a processor communicatively connected to a memory for storing and executing software instructions, a graphical user interface as they relate to general purpose computers, and training and executing machine learning models (e.g. mathematical expression and/or regression/loss function iterated to gradually converge towards a minimum by optimizing weights) implemented as any hardware and/or software (Application Specification [0008], [0009], [0013], [0046]-[0047], [0053], [0095], [0127]-[0129])). Mere instructions to apply an exception using a generic computer component and adding insignificant extra-solution activity to the judicial exception cannot provide an inventive concept. See MPEP 2106.05(f)(2), (g). These additional elements are considered “apply it” and are not subject to a Berkheimer analysis of well-understood, routine, and conventional activity, and further, the additional elements of the claim, as discussed above under Step 2A, Prong Two, when viewed individually and as a whole, are not directed to an improvement to technology or technical field (i.e. Machine Learning), but rather, is directed to an improvement to the abstract ideas.
Examiner respectfully maintains the 101 rejection of claims 1-20 as applied in the above Office Action.
In response to Applicant’s argument that (c) regarding the 103 rejection of claims 1-20, Examiner is persuaded. That is, the closest prior art of record (as discussed in the above Office Action), does not teach or render obvious to particular ordered combination of limitations as currently amended. Accordingly, Examiner has withdrawn the 103 rejection of claims 1-20 as being free of prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Pub. No. 2021/0057107 teaches a ranked order of predicted effectiveness given the patient’s processed collected data, and predict the outcomes of each treatment (survival, length, and end state) ([0111]).
U.S. Patent Application Pub. No. 2021/0295992 A1 teaches a platform for providing a user with diagnosis and providing prescription plans (Abstract);
U.S. Patent Application Pub. No. 2021/0004714 A1 teaches a classification of prognosis labels based on a physiological input of a human subject (Abstract); and
U.S. Patent Application Pub. No. 2017/0323064 A1 teaches a machine learning module for diagnostic/treatment database and medical equipment ([0116]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTHONY BALAJ whose telephone number is (571)272-8181. The examiner can normally be reached 8:00 - 4:00 M-F.
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/A.M.B./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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