DETAILED ACTION
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 .
Claim Status
Claims 1-29 are currently pending and under examination herein.
Claims 1-29 are rejected.
Priority
The application claims benefit to priority under 35 U.S.C. §119 to foreign application patent application number, Germany/DE10 2020 210 998.2, filing date 09/01/2020, and is acknowledged. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. In this action, all claims 1-29 are examined for an effective filing date of 09/01/2020. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The Information Disclosure Statements filed on 08/27/2021 and 08/04/2025, are in compliance with the provisions of 37 CFR 1.97 and has been considered. Signed copies of the list of references cited from the IDS are included with this Office Action.
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Drawings
The Drawings submitted 08/26/2021 are accepted.
Objection: Claim
The disclosure is objected to because of the following informalities:
Claim 15 recites “a computing unit, located outside the first facility; and an interface” . The punctuation after facility; is more clearly recited if the semicolon replaced with a comma, as in facility[;], .
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 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture, or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
With respect to step (1): yes, the claims are directed to a method for making synthetic versions of real medical datasets and moving/using them between local units and remote central server, therefore the answer is "yes".
With respect to step (2A)(1), the claims recite abstract ideas. To determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon, MPEP at 2106.03 teaches abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships, and mathematical calculations), certain methods of organizing human activity, and mental processes (including procedures for collecting, observing, evaluating, and organizing information (see MPEP 2106.04(a)(2)). In the instant application, the claims recite the following limitations that equate to an abstract idea with mental steps and mathematical concepts.
With respect to the instant claims, under the step (2A)(1) evaluation, the claims are found to direct to abstract ideas that fall into the grouping of mental processes (in particular steps for analyzing medical datasets with ranked variables) and mathematical concepts (in particular mathematical relationships between synthetic medical data).
The claims directing to abstract ideas are as follows:
Mental processes:
Claim 1: number of original individual datasets assigned to real existing patients and including original values for one or more higher-ranking variables…creating a synthetic dataset based on the medical dataset, each synthetic individual dataset of the number of synthetic individual datasets including synthetic values for same higher-ranking variables as the one or more higher-ranking variables medical dataset, not relating back to a real existing patient, wherein the creating is undertaken within the first facility by application of a sampling function to the medical data.
Claims 5 and 20: wherein: a number of data classes are defined in the medical dataset and wherein each respective original individual dataset, of the number of original individual datasets assigned to real existing patients, is assigned to a respective data class of the number of data classes; and wherein in the creating, the sampling function is applied to each of the number of data classes separately, so that each respective data class synthetic datasets are created during the creating, based on only a respective original individual dataset assigned to the respective data class.
Claims 6 and 21: wherein the synthetic dataset is utilizable within the central unit for at least one of: training of a trainable classifier to predict a clinical outcome based on the synthetic dataset; validation of a trainable classifier to predict a clinical outcome based on the synthetic dataset;
Claims 7 and 22: provisioning of a sampling function in the first facility, the sampling function being embodied for creating the synthetic dataset.
Claims 10 and 25: wherein at least one parameter of the sampling function is optimized by optimizing the quality functional for the medical dataset.
Claims 11 and 26: wherein the optimizing comprises: defining a number of selection values for the parameter; creating a respective synthetic dataset for each respective selection value of the number of selection values, wherein the respective selection value is used as the value for the parameter of sampling function to be optimized; computing the quality functional for each respective synthetic dataset created; comparing the computed quality functionals; and
selecting an optimal selection value for the parameter to be optimized based on the comparing.
Claims 12 and 27: selecting variables to be sampled from the higher-ranking variables, wherein in the creating, the sampling function is only applied to the original values of the medical dataset belonging to the variables to be sampled, so that the synthetic dataset includes synthetic values for the variables to be sampled.
Claims 13 and 28: wherein one of the higher-ranking variables refers to an absolute point in time, in which the original values of an original individual dataset were recorded; and the computer-implemented method further comprising: converting the absolute points in time into relative time intervals, wherein the relative time intervals are each defined within groups of the original individual datasets defined by assignment of the original individual datasets to the same patient, and a relatively earliest absolute point in time within a group is used as a reference time for computing the relative time intervals.
Claims 14 and 29: wherein in the creating, for creation of a respective synthetic individual dataset, only original individual datasets belonging to the same patient are sampled.
Mathematical concepts:
Claim 2: wherein the sampling function is embodied to create the synthetic dataset by sampling the entire medical dataset while replacing all the original values.
Claims 3 and 18: wherein the sampling function includes a trained function.
Claims 4 and 19: wherein the sampling function includes a k-nearest neighbors algorithm.
Claims 6 and 21: a statistical evaluation of the synthetic dataset;
Claims 8 and 23: wherein the number of the synthetic individual datasets in the synthetic dataset is greater than the number of the original individual datasets in the medical dataset.
Claims 9 and 24: computing a quality functional, the quality functional being a measure for the match between the statistical characteristics of the synthetic dataset and the statistical characteristics of the original dataset.
Hence, the claims explicitly recite elements that, individually and in combination, constitute abstract ideas.
With respect to step (2A), under the broadest reasonable interpretation (BRI), the instant claims a method for making synthetic versions of real medical datasets and moving/using them between local units and remote central server. Instant claims are therefore directed to the judicial exceptions of abstract groupings, both mathematical (sampling function … trained function… k-nearest neighbors algorithm, statistical evaluation… synthetic dataset is greater than the number of the original…computing a quality functional… statistical characteristics) which can be performed with the human mind with pen and paper.
Because the claims do recite judicial exceptions, direction under step (2A)(2) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (MPEP 2106.04(d). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exception, the claim is said to fail to integrate into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, the claims recite the following additional elements considered for practical application:
Claim 1: storing the medical dataset within a first facility, the medical dataset including a number of original individual datasets…transferring the synthetic dataset from the first facility to a central unit outside the first facility, the synthetic dataset being utilizable within the central unit.
Claims 6 and 21: archiving of the synthetic dataset in the central unit.
Claim 1-29: computer-implemented, first facility, computing unit, central unit, system, interface, non-transitory computer readable medium including a program, directly loadable into a memory of a programmable computing unit of a processing unit, the program including program segments, processing unit, at least one of a determination system and training system.
Said steps that are “in addition” to the recited judicial exception in the instant claims represent those of mere data handling instructions or field of use limitations (original/medical/synthetic datasets, central unit, first facility ) to implement in the recited judicial exception and do not impart meaning to said recited judicial exception, such that is applied in a practical manner. Further with respect to the additional elements in the instant claims, these steps direct to mere data gathering and handling (storing…transferring…archiving…synthetic datasets) to carry out the abstract idea without imposing any meaningful limitation on the abstract idea. Thereby these steps are insignificant extra-solutions activity steps and are insufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
Further steps herein directed to additional non-abstract elements of computer components in FIG 1 (system, computing unit, interface, non-transitory computer program product, including a program, directly loadable into a memory of a programmable computing unit of a processing unit, the program including program segments) do not describe any specific computational steps by which the “computer parts” perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than generic computer elements used as a tool to perform the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application. The courts have weighed in and consistently maintained that when, for example, non-transitory computer readable medium… are recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer. (see MPEP 2106.05(f)). None of the recited dependent claims recite additional elements which would integrate a judicial exception into a practical application.
As such, the claims are lastly evaluated using the step (2B) analysis, wherein it is determined that because the claims recite abstract ideas, and do not integrate that abstract ideas into a practical application, the claims also lack a specific inventive concept. The judicial exception alone cannot provide the inventive concept or the practical application and that the identification of whether the additional elements amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements of data gathering, instructions, and field of use limitations described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant recitations, the claims recite the following additional elements considered for inventive concepts:
Claim 1: storing the medical dataset within a first facility, the medical dataset including a number of original individual datasets…transferring the synthetic dataset from the first facility to a central unit outside the first facility, the synthetic dataset being utilizable within the central unit.
Claims 6 and 21: archiving of the synthetic dataset in the central unit.
Claim 1-29: computer-implemented, first facility, computing unit, central unit, system, interface, non-transitory computer readable medium including a program, directly loadable into a memory of a programmable computing unit of a processing unit, the program including program segments, processing unit, at least one of a determination system and training system.
These additional elements do not contribute significantly more to well-known and conventional steps to obtain “original individual datasets assigned to real existing patients”, performed with local and remote server equipment, and analyzed by one with ordinary skill in the art as of the effective filing date. The instant claims and references cited in the specification recite steps (storing…transferring…archiving…synthetic datasets) known in the art by bioinformaticists. These limitations equate to well-understood, routine and conventional activities as evidenced by:
Summer US 7024399 teaches a computer system/CRM for predicting a clinical outcome for a subject virtual patients, adding back data from virtual prediction and actual outcome of patient added back to virtual simulation, taken over time, and including various agents and patient states. These patients can evolve with additional information. The virtual patients are then used to predict a clinical outcome, so that an accurate treatment can be selected and applied. The actual outcome of the patient is then added back to the virtual patient simulation, to further evolve the simulations. The various clinical parameters and outcomes are represented as event rate distributions which represent a likelihood of a particular event. The prevalence of certain traits or events is set forth at col 38. Actions to be taken, such as time, agent, exercise are disclosed as well as patient states of weight gain, and lifestyle choices. Specific disease information is provided for asthma, myocardial infarction, diabetes, etc.
Paterson US 2002019397 [PTO 892 cited] teaches virtual patients, used to predict a response or outcome and using virtual patient models to be validated with a series of stimulus-response tests [0029]. Virtual patients are created using patient data such as physical conditions, symptoms, genotype information and disease information. Groups of virtual patients can be created, so that the prevalence of any one parameter can be identified. These groups can then be subjected to virtual tests to identify a predicted outcome.
Bangs US 20050131663 discloses using virtual patient models to apply experimental protocols to generate sets of outputs wherein the outputs project or predict an outcome for the subject . Virtual patients are created using patient data such as physical conditions, symptoms, genotype information and disease information. Groups of virtual patients can be created, so that the prevalence of any one parameter can be identified. These groups can then be subjected to experimental protocols to identify a predicted outcome. Variances and prevalences of various traits or elements in a population can be determined. Stimulus – response tests can be performed to predict clinical outcomes. Biomarkers, including genetic markers can be used as such an element.
There is no active step of making synthetic versions of real medical datasets, which is unconventional. Data (original individual datasets, original values for one or more higher-ranking variables, synthetic values for same higher-ranking variables as the one or more higher-ranking variables medical dataset, not relating back to a real existing patient) are merely manipulated data to be used in the judicial exception. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination, as evidenced by the cited references teaching the combination of elements as well as the individual elements themselves, that transforms the claimed judicial exception into a patent-eligible application of the judicial exception.
With respect to the instant claims, the steps (analyzing statistical characteristics of the synthetic dataset for one or more higher-ranking variables, then comparing to the original dataset) and additional elements (system, non-transitory computer-readable medium, storing the medical dataset within a first facility/central unit) involving mathematical relationships and automated mental steps do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-29 are not patent eligible.
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.
Claims 1-29 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 12-13, 15, and 27-28 recite the term “higher” in “higher-ranking variables,” is a relative term which renders the claim indefinite. The term “higher” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree of rank for variables and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claims 1, and 15-16 recites “assigned to real existing patients” is indefinite because it is unclear if the assign[ing] step occurred within the metes and bounds of the instant application. The claims fails to set forth positive, active method steps which set forth how to achieve the intended result of obtaining medical datasets “assigned to real existing patient”. The minimally sufficient set of limitations required to achieve the invention is not present in the independent claims.
Claims 1, and 15-16 recites “training of a trainable classifier to predict a clinical outcome based on the synthetic dataset”. It is indefinite whether the real/synthetic dataset has any clinical outcome being sought for prediction and not limitations on the original dataset source, the real existing patient. The metes and bounds when there is no particular condition/outcome sought, no particular healthy or diseased patient, so one of ordinary skill in the related art would be unable to assess scope or infringement.
Clarification is requested by amendment of claim language.
Claim Interpretation - 35 U.S.C. 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claims 1 and 2 limitations of “sampling function” as in “sampling function is embodied to create the synthetic dataset by sampling the entire medical dataset while replacing all the original values” is being interpreted under 35 U.S.C. 112(f) with “function” as generic placeholders for means, and “sampling” for the corresponding functional language. The limitation “sampling function “ is being interpreted to cover the corresponding structure (create the synthetic dataset by sampling the entire medical dataset while replacing all the original values), as performing the claimed function, and equivalents therefore. Furthermore, the term “sampling function” does not have a sufficiently definite meaning for a person of ordinary skill in the art to glean the scope of structures included in “sampling the entire medical dataset while replacing all the original values” (see MPEP 2181.I.A).
Claims 9 and 24 limitations of “quality functional” in “computing a quality functional, the quality functional being a measure for the match between the statistical characteristics of the synthetic dataset and the statistical characteristics of the original dataset” is being interpreted under 35 U.S.C. 112(f) with “functional” as generic placeholders for means, and “quality” for the corresponding functional language. The limitation “quality functional“ is being interpreted to cover the corresponding structure (a measure for the match between the statistical characteristics of the synthetic dataset and the statistical characteristics of the original dataset), as performing the claimed function, and equivalents therefore. Furthermore, the term “quality functional” does not have a sufficiently definite meaning for a person of ordinary skill in the art to glean the scope of structures (see MPEP 2181.I.A).
If Applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f), Applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f).
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 § 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.
Instant claims 1-29 are rejected under 35 U.S.C. 103 as being unpatentable over Summer US 7024399 [2008: Simulating Patient-Specific Outcomes; PTO 892 cited)
in view of Rasheed A. et al. [2020: Digital twin: Values, challenges and enablers from a modeling perspective. IEEE access, 8, 21980-22012; PTO 892 cited].
Note: citations from the instant application are italicized in the following section.
Regarding instant claims 1, 15, and 16, instant application recite:
storing the medical dataset within a first facility, the medical dataset including a number of original individual datasets assigned to real existing patients and including original values for one or more higher-ranking variables;
creating a synthetic dataset based on the medical dataset, each synthetic individual dataset of the number of synthetic individual datasets including synthetic values for same higher-ranking variables as the one or more higher-ranking variables medical dataset, not relating back to a real existing patient, wherein the creating is undertaken within the first facility by application of a sampling function to the medical data; and
transferring the synthetic dataset from the first facility to a central unit outside the first facility, the synthetic dataset being utilizable within the central unit.
The prior art to Summer discloses a method of:
Claim 9 recites a computer system/readable storage medium/user interface providing a virtual population wherein each virtual patient of the virtual population (creating a synthetic dataset based on the medical dataset) has an associated prevalence (including a number of original individual datasets assigned to real existing patients and including original values for one or more higher-ranking variables). A user interface receives input data about a subject (original individual datasets assigned to real existing patients); then reports a set of outputs to a user (central unit). A processor select one or more virtual patients from the virtual population based on a similarity between each of the selected virtual patients and the input data (creating a synthetic dataset based on the medical dataset, each synthetic individual dataset of the number of synthetic individual datasets including synthetic values for same higher-ranking variables as the one or more higher-ranking variables medical dataset, not relating back to a real existing patient, wherein the creating is undertaken within the first facility by application of a sampling function to the medical data).
However, Summer does not teach transferring the synthetic dataset from the first facility to a central unit outside the first facility.
The prior art to Rasheed teaches a digital twin can be continuously updated with sensor data (unit in first facility) in near real-time. The sensor data can be augmented with synthetic data generated from simulators which bring physical realism at high spatiotemporal resolutions. The digital twin does not only give real-time information for more informed decision making but can also make predictions about how the offshore asset (first facility) will evolve or behave in the future… also gives the possibility for humans to interact physically with the asset using an avatar for real-time remote monitoring and control [ Rasheed p21981 Col 2-21982Col 1: Real-time remote monitoring and control: Generally, it is almost impossible to gain an in-depth view of a very large system physically in real-time. A digital twin owing to its very nature can be accessible anywhere. The performance of the system can not only be monitored but also controlled remotely using feedback mechanisms] (transferring the synthetic dataset from the first facility to a central unit outside the first facility, the synthetic dataset being utilizable within the central unit).
Therefore, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Summer’s virtual patient analysis for clinical outcome prediction to incorporate Rasheed’s real-time, remote control digital twin features. Combining these prior art elements would have been obvious because virtual representation of an asset enabled through data and simulators for real-time prediction, provides optimization, monitoring, controlling, and improved decision making [Rasheed at Abstract]. One of ordinary skill in the art would predict a reasonable expectation of success, as both said prior art are analogously applicable to optimizing virtual representations in outcome prediction The invention is therefore prima facie obvious.
Regarding instant claim 2, instant application recites:
wherein the sampling function is embodied to create the synthetic dataset by sampling the entire medical dataset while replacing all the original values.
The prior art to Summer teaches applying one or more virtual protocols (sampling function) to selected virtual patients to generate a set of outputs (create the synthetic dataset) projecting a clinical outcome for the subject, wherein a set of outputs is generated for each selected virtual patient (sampling the entire medical dataset while replacing all the original values) [Summer claim 9].
Regarding instant claims 3 and 18, instant application recites:
wherein the sampling function includes a trained function.
The prior art to Summer teaches applying one or more virtual protocols (a trained function) to the one or more selected virtual patients comprises calculating a likelihood of each clinical outcome based upon the prevalence of the one or more virtual patients. [Summer claims 11-12].
Regarding instant claims 4 and 19, instant application recites:
wherein the sampling function includes a k-nearest neighbors algorithm.
The prior art to Summer teaches applying one or more virtual protocols (a trained function) to the one or more selected virtual patients comprises calculating a likelihood of each clinical outcome based upon the prevalence of the one or more virtual patients. [Summer claims 11-12].
However, Summer does not teach k-nearest neighbors algorithm.
The prior art to Rasheed teaches a digital twin based algorithms like
clustering analysis like k-mean having better utility when the need of dependent variables (labeled data) might not always be available as in the case of anomaly [Rasheed p21989 Col 1].
Regarding instant claims 5 and 20, instant application recites:
wherein: a number of data classes are defined in the medical dataset and wherein each respective original individual dataset, of the number of original individual datasets assigned to real existing patients, is assigned to a respective data class of the number of data classes; and wherein in the creating, the sampling function is applied to each of the number of data classes separately, so that each respective data class synthetic datasets are created during the creating, based on only a respective original individual dataset assigned to the respective data class.
The prior art to Summer teaches:
applying one or more virtual protocols (sampling function) to selected virtual patients to generate a set of outputs (create the synthetic dataset) projecting a clinical outcome for the subject, wherein a set of outputs is generated for each selected virtual patient (sampling the entire medical dataset while replacing all the original values) [Summer claim 9]. Further, claim 9 recites a computer system/readable storage medium/user interface providing a virtual population wherein each virtual patient of the virtual population (creating a synthetic dataset based on the medical dataset) has an associated prevalence (including a number of original individual datasets assigned to real existing patients and including original values for one or more higher-ranking variables). A user interface receives input data about a subject (original individual datasets assigned to real existing patients); then reports a set of outputs to a user (central unit). A processor select one or more virtual patients from the virtual population based on a similarity between each of the selected virtual patients and the input data (creating a synthetic dataset based on the medical dataset, each synthetic individual dataset of the number of synthetic individual datasets including synthetic values for same higher-ranking variables as the one or more higher-ranking variables medical dataset, not relating back to a real existing patient, wherein the creating is undertaken within the first facility by application of a sampling function to the medical data).
Regarding instant claims 6 and 21, instant application recites:
wherein the synthetic dataset is utilizable within the central unit for at least one of: training of a trainable classifier to predict a clinical outcome based on the synthetic dataset; validation of a trainable classifier to predict a clinical outcome based on the synthetic dataset;
The prior art to Summer teaches applying one or more virtual protocols (sampling function) to selected virtual patients to generate a set of outputs (create the synthetic dataset) projecting a clinical outcome for the subject, [trainable classifier to predict a clinical outcome based on the synthetic dataset] wherein a set of outputs is generated for each selected virtual patient (sampling the entire medical dataset while replacing all the original values) [Summer claim 9].
Regarding instant claims 7 and 22, instant application recites:
provisioning of a sampling function in the first facility, the sampling function being embodied for creating the synthetic dataset.
The prior art to Summer teaches applying one or more virtual protocols (sampling function) to selected virtual patients to generate a set of outputs (create the synthetic dataset) projecting a clinical outcome for the subject, [trainable classifier to predict a clinical outcome based on the synthetic dataset] wherein a set of outputs is generated for each selected virtual patient (sampling the entire medical dataset while replacing all the original values). A user interface receives input data about a subject (original individual datasets assigned to real existing patients); then reports a set of outputs to a user (central unit). A processor select one or more virtual patients from the virtual population based on a similarity (sampling function, a k-nearest neighbors algorithm) between each of the selected virtual patients and the input data [Summer claim 9].
Regarding instant claims 8 and 23, instant application recites:
wherein the number of the synthetic individual datasets in the synthetic dataset is greater than the number of the original individual datasets in the medical dataset.
The prior art to Summer teaches applying one or more virtual protocols (sampling function) to selected virtual patients to generate a set of outputs (create the synthetic dataset) projecting a clinical outcome for the subject, [trainable classifier to predict a clinical outcome based on the synthetic dataset] wherein a set of outputs (synthetic dataset is greater than the number of the original individual datasets in the medical dataset) is generated for each selected virtual patient (sampling the entire medical dataset while replacing all the original values) [Summer claim 9].
Regarding instant claims 9 and 24, instant application recites:
computing a quality functional, the quality functional being a measure for the match between the statistical characteristics of the synthetic dataset and the statistical characteristics of the original dataset.
The prior art to Summer teaches a processor selects virtual patients from the virtual population based on a similarity (quality functional) between each of the selected virtual patients and the input data [Summer claim 9] and wherein applying one or more virtual protocols to the one or more selected virtual patients comprises calculating a likelihood of each clinical outcome based upon the prevalence of the one or more virtual patients [Summer at claim 11-12] (quality functional being a measure for the match between the statistical characteristics of the synthetic dataset and the statistical characteristics of the original dataset).
Regarding instant claims 10 and 25, instant application recites:
wherein at least one parameter of the sampling function is optimized by optimizing the quality functional for the medical dataset.
The prior art to Summer teaches wherein the virtual population is a prevalence-weighted (quality functional) virtual population, wherein each virtual patient of the virtual population has an associated prevalence weight [Summer claim 13] (optimizing the quality functional for the medical dataset).
Regarding instant claims 11 and 26, instant application recites:
wherein the optimizing comprises: defining a number of selection values for the parameter; creating a respective synthetic dataset for each respective selection value of the number of selection values, wherein the respective selection value is used as the value for the parameter of sampling function to be optimized; computing the quality functional for each respective synthetic dataset created; comparing the computed quality functionals; and selecting an optimal selection value for the parameter to be optimized based on the comparing.
The prior art to Summer teaches
applying one or more virtual protocols (sampling function) to selected virtual patients to generate a set of outputs (create the synthetic dataset) projecting a clinical outcome for the subject, [trainable classifier to predict a clinical outcome based on the synthetic dataset] wherein a set of outputs is generated for each selected virtual patient (defining a number of selection values for the parameter; creating a respective synthetic dataset for each respective selection value of the number of selection values) is generated for each selected virtual patient (sampling the entire medical dataset while replacing all the original values) [Summer claim 9].
wherein the virtual population is a prevalence-weighted (quality functional variable) virtual population, wherein each virtual patient of the virtual population has an associated prevalence weight [Summer claim 13] (the value for the parameter of sampling function to be optimized; computing the quality functional for each respective synthetic dataset created; comparing the computed quality functionals; and selecting an optimal selection value for the parameter to be optimized based on the comparing).
Regarding instant claims 12 and 27, instant application recites:
selecting variables to be sampled from the higher-ranking variables, wherein in the creating, the sampling function is only applied to the original values of the medical dataset belonging to the variables to be sampled, so that the synthetic dataset includes synthetic values for the variables to be sampled..
The prior art to Summer teaches:
virtual protocol is selected from the group (variables) consisting of a therapeutic regimen, passage of time, exercise, weight gain, diet, a lifestyle choice and a combination of two or more of the same [ Summer at claim 14] (selecting variables to be sampled from the higher-ranking variables).
applying one or more virtual protocols (sampling function) to selected virtual patients to generate a set of outputs (create the synthetic dataset) projecting a clinical outcome for the subject, wherein a set of outputs is generated for each selected virtual patient (sampling the entire medical dataset while replacing all the original values) [Summer claim 9] and wherein the virtual population is a prevalence-weighted (quality functional) virtual population, wherein each virtual patient of the virtual population has an associated prevalence weight (higher ranking variable) [Summer claim 13]
Regarding instant claims 13 and 28, instant application recites:
wherein one of the higher-ranking variables refers to an absolute point in time, in which the original values of an original individual dataset were recorded; and the computer-implemented method further comprising: converting the absolute points in time into relative time intervals, wherein the relative time intervals are each defined within groups of the original individual datasets defined by assignment of the original individual datasets to the same patient, and a relatively earliest absolute point in time within a group is used as a reference time for computing the relative time intervals.
The prior art to Summer teaches:
virtual protocol is selected from the group consisting of passage of time (relative time intervals variable), exercise, weight gain, diet, a lifestyle choice and a combination of two or more of the same [Summer at claim 14].
applying one or more virtual protocols (sampling function) to selected virtual patients to generate a set of outputs (create the synthetic dataset) projecting a clinical outcome for the subject, wherein a set of outputs is generated for each selected virtual patient (sampling the entire medical dataset while replacing all the original values) [Summer claim 9] and wherein the virtual population is a prevalence-weighted (quality functional) virtual population, wherein each virtual patient of the virtual population has an associated prevalence weight (higher ranking variable) [Summer claim 13]. It would have been prima facie obvious, to one of ordinary skill in the related art, at the time of filing, absent evidence to the contrary to choose to weigh the variable of passage of time to evaluate virtual populations in order to predict an outcome.
Regarding instant claims 14 and 29, instant application recites:
wherein in the creating, for creation of a respective synthetic individual dataset, only original individual datasets belonging to the same patient are sampled.
The prior art to Summer teaches applying one or more virtual protocols (sampling function) to selected virtual patients to generate a set of outputs (create the synthetic dataset) projecting a clinical outcome for the subject, wherein a set of outputs is generated for each selected virtual patient (original individual datasets belonging to the same patient are sampled) [Summer claim 9]
Regarding instant claim 15, 16, and 17, instant application recites:
a computing unit, located outside the first facility; and an interface for communication between the computing unit and the first facility, wherein the computing unit is embodied: to induce a local creation of a synthetic dataset in the first facility via the interface, the synthetic dataset including a number of synthetic individual datasets, each synthetic individual dataset of the number of synthetic individual datasets including synthetic values for same higher-ranking variables as the one or more higher-ranking variables medical dataset, not relating back to a real existing patient; to receive the synthetic dataset from the first facility via the interface; and to utilize the synthetic dataset outside the first facility.
A non-transitory computer program product, including a program, directly loadable into a memory of a programmable computing unit of a processing unit, the program including program segments for carrying out the method of claim 1 when the program is executed in the computing unit of the processing unit.
storing readable and executable program sections for carrying out the method of claim 1 when the program sections are executed by at least one of a determination system and training system
The prior art to Summer teaches:
a computer system/CRM for predicting a clinical outcome for a subject virtual patients, adding back data from virtual prediction and actual outcome of patient added back to virtual simulation, taken over time, and including various agents and patient states [Abstract].
applying one or more virtual protocols (sampling function) to selected virtual patients to generate a set of outputs (create the synthetic dataset) projecting a clinical outcome for the subject, wherein a set of outputs is generated for each selected virtual patient (original individual datasets belonging to the same patient are sampled) [Summer claim 9]
a computer readable storage medium comprising providing a virtual population comprising a plurality of virtual patients, wherein each virtual patient of the virtual population has an associated prevalence; (b) a user interface capable of receiving input data about a subject; and reporting a set of outputs to a user; and a programmable processor capable of L() selecting one or more virtual patients [Summer at claim 9].
However, Summer does not teach a computing unit, located outside the first facility; and an interface for communication between the computing unit and the first facility, wherein the computing unit is embodied: to induce a local creation of a synthetic dataset in the first facility via the interface, the synthetic dataset including a number of synthetic individual datasets… to receive the synthetic dataset from the first facility via the interface; and to utilize the synthetic dataset outside the first facility.
The prior art to Rasheed teaches a digital twin can be continuously updated with sensor data (unit in first facility) in near real-time. The sensor data can be augmented with synthetic data generated from simulators which bring physical realism at high spatiotemporal resolutions. The digital twin does not only give real-time information for more informed decision making but can also make predictions about how the offshore asset (first facility) will evolve or behave in the future… also gives the possibility for humans to interact physically with the asset using an avatar for real-time remote monitoring and control (central unit) [ Rasheed p21981 Col 2-21982Col 1: Real-time remote monitoring and control: Generally, it is almost impossible to gain an in-depth view of a very large system physically in real-time. A digital twin owing to its very nature can be accessible anywhere. The performance of the system can not only be monitored but also controlled remotely using feedback mechanisms] It would have been prima facie obvious, to one of ordinary skill in the related art, that the remote sensors on an asset (first facility) transferring output to a remote user/unit (central unit) provides an obvious improvement in real-time control of the asset through the virtual representation, for example, an ocean drilling platform with remote controlled avatars able to manipulate the asset (a computing unit, located outside the first facility; and an interface for communication between the computing unit and the first facility, wherein the computing unit is embodied: to induce a local creation of a synthetic dataset in the first facility via the interface, the synthetic dataset including a number of synthetic individual datasets… to receive the synthetic dataset from the first facility via the interface; and to utilize the synthetic dataset outside the first facility).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission.
Note: references to the instant application are italicized in the following Double Patent section.
Instant claims 1, 15, and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 12, and 20 of US Application No. US20210097439A1, VODENCAREVIC Asmir, 17/023,458 filed 09/17/2020. The reference claims of ‘439 are obvious variants/species (discloses a method for client-specific federated learning including a central server unit and a plurality of client units located at different local sites with local data under privacy regulations. The method includes provides a toolset of different machine learned models, receiving from client units and trained on respective local data by the client units and storing in the central server unit [Abstract]) of the claims of the generic instant application. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
No claims are allowed.
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/VR/
Examiner
Art Unit 1685
/MARY K ZEMAN/Primary Examiner, Art Unit 1686
Prosecution Insights