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 Objections
Claims 1-2, 7, 9, 11, and 14 are objected to because of the following informalities:
Claim 1, lines 20-21 recites “at least one predefined disease conditions.” This should read “at least one predefined disease condition
Claim 2, line 2 recites “performed using a using a machine learning system.” This should read “performed
Claim 7, line 3 recites “phenotype model based data collected.” This should read “phenotype model based on data collected.”
Claim 9, line 5 recites “analysing the updated training data set using the neural network.” This should read “analysing the updated training dataset
Claim 11, lines 17-18 recites “with one or more disease state predict a probable disease state.” This should read “with one or more disease state; predict a probable disease state.”
Claim 14, lines 23-24 recites “at least one predefined disease conditions in a known.” This should read “at least one predefined disease condition
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 1-14 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1 is illustrative.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. MPEP 2106.03. The claim recites a method. Thus, the claim is a process, which is a statutory category of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claim 1, line 1 recites “[a] method for processing a sparsely populated data source.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed processing encompasses a trained clinician observing an acquired echocardiography dataset and performing an evaluation, identifying missing data and substituting this missing data with the knowledge of the trained clinician. See MPEP 2106.04 subsections I. and II.A.
Claim 1, lines 7-11 recites “dividing the base dataset into two portions: a first portion comprising a training dataset being a defined percentage, X%, of the base dataset; and a second portion comprising a validation dataset being a defined percentage (100% - X%) of the base dataset” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed processing encompasses a trained clinician observing an acquired echocardiography dataset and performing an evaluation, allocating a percentage of the acquired echocardiography dataset as a training dataset and the remaining percentage thereof as a validation dataset. See MPEP 2106.04 subsections I. and II.A.
Claim 1, lines 12-15 recites “analysing the training dataset to jointly model variable relationships using a non-linear function approximation algorithm applied iteratively to the records of the training dataset to obtain a trained model and measurement prediction protocols for population unpopulated fields in the training dataset.” Under its BRI these limitations require a mathematical calculation. Namely, a non-linear function approximation algorithm is required to obtain a trained model and measurement prediction protocols. These limitations hence recites a “mathematical calculation” and so falls in to the “mathematical concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2), subsection I.C.
These limitations also fall into the “mental process” grouping because under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the limitations encompass the observation of the echocardiography data of the training dataset by a clinician to train the clinician on the features of echocardiography data, analogous to a trained model. As another example, the limitations encompass the observation of the echocardiography data of the training dataset by a clinician to train the clinician to predict/substitute missing data with the knowledge of the clinician, analogous to the measurement prediction protocol.
Claim 1, lines 16-17 recites “using the measurement prediction protocols, computing predicted measurement values for each of the unpopulated data fields.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed computing encompasses a trained clinician observing an acquired echocardiography dataset and performing an evaluation, identifying missing measurement data and substituting/predicting this missing measurement data using the knowledge of the trained clinician. See MPEP 2106.04 subsections I. and II.A.
Claim 1, lines 18-19 recites “imputing the predicted measurement values in the patient records of the training dataset.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed imputing encompasses a trained clinician observing the acquired training echocardiography dataset and performing an evaluation, identifying missing measurement data and placing/imputing the missing measurement data substituted/predicted using the knowledge of the trained clinician for the missing measurement data in the training dataset.
Claim 1, lines 20-23 recites “analysing the training dataset based on at least one predefined disease conditions in a known portion of the patient records of the base dataset to form a phenotype model configured to associate patient phenotype data to a probability of a disease condition in the patient records of a trained dataset.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the limitations encompass the observation of the echocardiography data of the training dataset and a corresponding/associated disease condition for each of the echocardiography data of the training dataset by a clinician to train the clinician on the features of the echocardiography data that increase or decrease the probability/likelihood that given echocardiography data of a patient is associated with a disease condition, i.e., training a clinician to diagnose a disease condition analogous to a trained phenotype model.
Claim 1, line 21 recites “imputing the predicted measurement vales in the records of the validation dataset.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed imputing encompasses a trained clinician observing the acquired training echocardiography dataset and performing an evaluation, identifying missing measurement data and placing/imputing the missing measurement data substituted/predicted using the knowledge of the trained clinician for the missing measurement data in the validation dataset.
Claim 1, lines 25-29 recites “validating the phenotype model, where the validating comprises analysing the validation dataset using the phenotype model, wherein the records of the validation dataset comprise phenotype data associated with patient data, and determining a validation error comprising a probability of correctly predicting the patient phenotype associated with a disease state probability in the records of the validation dataset.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed validating encompasses a trained clinician observing the new set of echocardiography data of the validation dataset, performing an evaluation, predicting/diagnosing a disease condition of each patient in the validation dataset, observing the true/known corresponding/associated disease condition for each of the echocardiography data of the validation dataset, and performing an evaluation/judgement of whether the clinician’s prediction/diagnosis of each of the patient’s disease condition was correct to determine the clinician’s probability of error.
Claim 1, lines 30-31 recites “repeating Steps (c) to (h) to minimise the validation error and computing a prediction of a probable disease state phenotype for each patient record in the base dataset.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed repeating of Steps (c) to (h) encompasses the mental processes and mathematical concepts as detailed above for claim 1, lines 12-29. Further, the claimed minimising of the validation error encompasses the trained clinician repeating the observation and evaluation steps to train the clinician such that the clinician’s knowledge is improved wherein the trained clinician diagnoses/predicts the disease condition with a lower probability of error. Furthermore, the claimed computing encompasses the trained clinician observing the acquired echocardiography data for each patient in the base dataset and performing an evaluation, diagnosing/predicting the probable disease state phenotype for each patient.
As there are no bright lines between the types of judicial exceptions, and many of the concepts identified by the courts as exceptions can fall under several exceptions, MPEP 2106.04, subsection I instructs examiners to “identify . . . the claimed concept (the specific claim limitation(s) that the examiner believes may recite an exception) [that] aligns with at least one judicial exception.” While the limitations of claim 1, lines 12-15 can be categorized under several exceptions (a mathematical concept-type abstract idea and a mental process-type abstract idea), it is adequate for an examiner to identify the limitation as falling under at least one judicial exception and to base further analysis on that identification. The remainder of this discussion is premised on the recited exception as an abstract idea. See MPEP 2106.04, subsection II.B. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. MPEP 2106.04(d).
Claim 1, lines 2-6 recites “retrieving data, from a sparsely populated data source, to form a base dataset, the data source comprising a plurality of patient records including patient mortality data wherein each patient record comprises at least one unpopulated data field corresponding to a medical measurement.” Under the BRI these limitations encompass insignificant extra-solution activity that amounts to mere data gathering incidental to the limitations of claim 1, lines 7-31. All uses of the judicial exception require retrieving/receiving a base dataset of patients. As all uses of the recited judicial exceptions require such data gathering, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering. See MPEP 2106.05(g).
Further, the limitations “the data source comprising a plurality of patient records including patient mortality data wherein each patient record comprises at least one unpopulated data field corresponding to a medical measurement” is understood as no more than an attempt to generally link the judicial exception to a field of use. The limitations merely specify that the gathered data are patient records including mortality data and having an unpopulated data field corresponding to a medical measurement. This is a mere indication of the field of use or technological environment in which the abstract idea is performed, the abstract idea is performed for data pertaining to patient mortality and medical measurements. See MPEP 2106.05(h).
Furthermore, the recitations of “a trained model,” “measurement prediction protocols,” and “phenotype model” in claim 1, under the BRI, merely references automation of actions that are manually performable (e.g., actions taken to train a clinician or by the trained clinician applying the trained clinician’s knowledge). However, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource Corp. v. Retail Decision, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”). There is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of extracting features and determining the severity of aortic stenosis rather than to any technology. See MPEP 2106.05(a).
Thus, even when considering the elements in combination, the claim as a whole does not integrate the recited exception into a practical application. (Step 2A, Prong Two: NO). Thus, claim 1 is directed to a judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05.
The additional elements identified in Step 2A, Prong Two should be re-evaluated in Step 2B, in which the extra-solution activity consideration takes into account whether or not an extra-solution activity is well-known.
As discussed above in Step 2A, Prong Two above, the data gathering activities of claim 1, lines 2-6 is described as retrieving data including patient mortality data and an unpopulated data field corresponding to a medical instrument. The elements amount to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform the observations and evaluations. The information elements do not impose any other meaningful limits on the claim. Therefore, the additional limitations are insignificant extra-solution activity. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. See also MPEP 2106.05(g).
Further, as discussed above in Step 2A, Prong Two above, limiting the retrieved data to patient mortality data and medical measurement data merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (medical diagnostic systems) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
As discussed in Step 2A, Prong Two above, performing the observation, evaluation, judgment, and opinion of a clinician using “a trained model,” “measurement prediction protocols,” and “phenotype model” merely references an automation of actions that are manually performable (e.g., actions of observing, evaluating, judging, and opinion in training a clinician, testing the knowledge of the trained clinician, and using the knowledge of the trained clinician to substitute for missing medical measurements and to determine a patient phenotype/disease state). However, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource Corp. v. Retail Decision, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”). There is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of substituting for missing medical measurements and to determine a patient phenotype/disease state rather than to any technology. See MPEP 2106.05(a).
Consequently, for the reasons discussed above, the additional elements individually or in combination with the judicial exception do not provide an inventive concept; so, the claim as a whole does not amount to significantly more than a generic instruction to “apply” the judicial exception. (Step 2B: NO). Therefore, claim 1 is not eligible.
Turning to independent claim 11:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. MPEP 2106.03. The claim recites a method. Thus, the claim is an apparatus, which is a statutory category of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claim 11, lines 11-12 recites “associate the measurement data and phenotype data to determine a patient phenotype associated with one or more disease states.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the limitations encompass a trained clinician observing echocardiography measurement data and phenotype data and performing an evaluation, matching/associating each patient in the measurement data and phenotype data with a diagnosis of a disease state.
Claim 11, lines 13-17 recites “generate at least one of: a measurement prediction protocol configured to predict measurement data for unpopulated measurement fields; a phenotype model configured to associate the patient data with a phenotype associated with one or more disease state.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the limitations encompass the observation of the echocardiography data of the training dataset by a clinician to train the clinician on the features of echocardiography data, analogous to a trained model. As another example, the limitations encompass the observation of the echocardiography data of the training dataset and a corresponding/associated disease condition for each of the echocardiography data of the training dataset by a clinician to train the clinician on the features of the echocardiography data that increase or decrease the probability/likelihood that given echocardiography data of a patient is associated with a disease condition, i.e., training a clinician to diagnose a disease condition analogous to a trained phenotype model.
Claim 11, lines 18-19 recites “predict a probable disease state for the patient undergoing the measurement procedure.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the limitations encompass a trained clinician observing the measurement data for a patient undergoing an echocardiography and performing an evaluation, predicting/diagnosing the patient with a probable disease state.
Claim 11, lines 22-24 recites “provide a set of directions to the measurement operator to collect a dataset of relevant measurement data to be collected based on the predicted probable disease state.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the limitations encompass a trained clinician observing the measurement data for a patient undergoing an echocardiography and performing an evaluation, predicting/diagnosing the patient with a probable disease state, and then a subsequent evaluation/judgement in which the trained clinician adapts/modifies the measurement protocol to collect a dataset of echocardiography measurement data that is relevant to the probable disease state that the clinician predicted/suspected/diagnosed for the patient. A trained clinician is knowledgeable of what types of echocardiography measurement data are most relevant for a particular disease state/condition of a patient and takes steps to ensure such data is collected during the echocardiography procedure.
The remainder of this discussion is premised on the recited exception as an abstract idea. See MPEP 2106.04, subsection II.B. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. MPEP 2106.04(d).
Claim 11, lines 1-2 recites “a system for conducting a measurement procedure on a patient.” This limitation is understood as no more than an attempt to generally link the judicial exception to a field of use. The limitation merely specify that the apparatus is in the field of measurement procedures for patients. This is a mere indication of the field of use or technological environment in which the abstract idea is performed, the abstract idea is performed to conduct a measurement procedure on a patient. See MPEP 2106.05(h).
Claim 11, lines 3-4 recites “at least one measurement tool configured to perform the measurement procedure.” Under the BRI these limitations encompass insignificant extra-solution activity that amounts to mere data gathering incidental to the limitations of claim 11, lines 11-19 and 22-24. All uses of the judicial exception require performing the measurement procedure using a measurement tool. As all uses of the recited judicial exceptions require such data gathering, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering. See MPEP 2106.05(g).
Claim 11, lines 5-6 recites “at least one recording device configured to record measurement data from the patient during the measurement procedure.” Under the BRI these limitations encompass insignificant extra-solution activity that amounts to mere data gathering incidental to the limitations of claim 11, lines 11-19 and 22-24. All uses of the judicial exception require recording of the measurement data. As all uses of the recited judicial exceptions require such data gathering, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering. See MPEP 2106.05(g).
Claim 11, lines 7-8 recites “at least one transmitter configured to transmit the measurement data to at least one processor, wherein the processor is configured to:” perform the process of claim 11, lines 10-24. Under the BRI, the transmitter, encompasses insignificant extra-solution activity that amounts to mere data gathering incidental to the limitations of claim 11, lines 11-19 and 22-24. All uses of the judicial exception require recording and transmission of the measurement data. As all uses of the recited judicial exceptions require such data gathering, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering. See MPEP 2106.05(g). Further, the processor is recited at a high level of generality. The processor is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The processor is further used to perform the abstract ideas, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Claim 11, line 10 recites “receive the measurement data and phenotype data.” Under the BRI these limitations encompass insignificant extra-solution activity that amounts to mere data gathering incidental to the limitations of claim 11, lines 11-19 and 22-24. All uses of the judicial exception require receiving of the measurement data and phenotype data. As all uses of the recited judicial exceptions require such data gathering, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering. See MPEP 2106.05(g).
Claim 11, lines 20-21 recites “alert a measurement operator of the predicted measurement data and the probable disease state.” Under the BRI, the alert encompasses insignificant post-solution activity that amounts to mere data output incidental to the limitations of claim 11, lines 11-19 and 22-24. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data outputting. See MPEP 2106.05.
Claim 11, lines 22-24 recites “provide a set of directions to the measurement operator to collect a dataset of relevant measurement data to be collected based on the predicted probable disease state.” Under the BRI, the providing of the set of directions encompasses insignificant post-solution activity that amounts to mere data output incidental to the limitations of claim 11, lines 11-19 and 22-24. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data outputting. See MPEP 2106.05. Further, the collection of a dataset of relevant measurement data is further insignificant extra-solution activity that amounts to mere data gathering. See MPEP 2106.05(g).
Furthermore, the recitations of “a measurement prediction protocol” and “a phenotype model” in claim 1, under the BRI, merely references automation of actions that are manually performable (e.g., actions taken to train a clinician or by the trained clinician applying the trained clinician’s knowledge). However, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource Corp. v. Retail Decision, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”). There is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of extracting features and determining the severity of aortic stenosis rather than to any technology. See MPEP 2106.05(a).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05.
The additional elements identified in Step 2A, Prong Two should be re-evaluated in Step 2B, in which the extra-solution activity consideration takes into account whether or not an extra-solution activity is well-known.
A conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed above in Step 2A, Prong Two above, the recitations of a measurement tool to perform measurement, a recording device to record the measurement data, transmitting the measurement data, receiving the measurement data, and outputting an alert and a set of directions of the measurement data and the output of the analysis of the abstract ideas, the probable disease state, are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
As discussed above in Step 2A, Prong Two above, the recitation of a processor to perform the limitations of the abstract ideas amounts to no more than mere instructions to apply the exception using a generic computer component.
As discussed in Step 2A, Prong Two above, performing the observation, evaluation, judgment, and opinion of a clinician using “a measurement prediction protocol,” and “a phenotype model” merely references an automation of actions that are manually performable (e.g., actions of observing, evaluating, judging, and opinion in training a clinician, testing the knowledge of the trained clinician, and using the knowledge of the trained clinician to substitute for missing medical measurements and to determine a patient phenotype/disease state). However, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource Corp. v. Retail Decision, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”). There is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of substituting for missing medical measurements and to determine a patient phenotype/disease state rather than to any technology. See MPEP 2106.05(a).
Consequently, for the reasons discussed above, the additional elements individually or in combination with the judicial exception do not provide an inventive concept; so, the claim as a whole does not amount to significantly more than a generic instruction to “apply” the judicial exception. (Step 2B: NO). Therefore, claim 11 is not eligible.
Turning to independent claim 14, the additional element in claim 14, lines 2-5 recites “a non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform operations for generating an inspection tool sampling plan, the operations comprising:” process steps similar to claim 1, lines 3-31. The non-transitory CRM, processor, and computing device are merely objects on which the method operates wherein the generic computer disclosed at a high level of generality is merely a tool to perform the process that does not amount to significantly more than a judicial exception. To add significantly more the computer must play a significant part in permitting the claimed method to be performed, rather than function solely as a mechanism for permitting the abstract idea to be achieved more quickly, i.e., automating a manual process. See MPEP 2106.05(b). Therefore, it fails to meaningfully limit the claim because it does not require any particular application of the abstract idea and therefore amounts only to a generic instruction to “apply” the exception or to a mere indication of the field of use or technological environment in which the abstract idea is performed. Furthermore, merely adding generic computer components to perform the method is not sufficient. See MPEP 2106.05(f). Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP 2106.05(a). Further, the limitation “an inspection tool sampling plan” merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (a sampling plan for an inspection tool, e.g., a measurement procedure for echocardiography) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). The limitations of claim 14, lines 6-34 are addressed as above for the similar limitations in claim 1, lines 3-31.
Therefore, claim 14 is not eligible.
Turning to the dependent claims:
Claim 2 recites “analysing the training data set is performed using a using a machine learning system.” Performing the observation, evaluation, judgment, and opinion of a clinician using “a trained model,” and “measurement prediction protocols,” using a machine learning system merely references an automation of actions that are manually performable (e.g., actions of observing, evaluating, judging, and opinion in training a clinician, testing the knowledge of the trained clinician, and using the knowledge of the trained clinician to substitute for missing medical measurements and to determine a patient phenotype/disease state). However, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource Corp. v. Retail Decision, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”). There is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of substituting for missing medical measurements and to determine a patient phenotype/disease state rather than to any technology. See MPEP 2106.05(a). Further, the machine learning system is recited at a high level of generality. The system is used to perform the abstract ideas, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Claim 3 recites that “the disease state is aortic stenosis.” This limitation is understood as no more than an attempt to generally link the judicial exception to a field of use. The limitations merely specify that the disease state is aortic stenosis. This is a mere indication of the field of use or technological environment in which the abstract idea is performed, the abstract idea is performed for data pertaining to aortic stenosis. See MPEP 2106.05(h).
Claim 4 recites “said sparsely populated data source comprises a plurality of medical records comprising measurement data obtained from a study procedure.” This limitation is understood as no more than an attempt to generally link the judicial exception to a field of use. The limitations merely specify that the sparsely populated data source comprises medical records comprising measurement data from a study. This is a mere indication of the field of use or technological environment in which the abstract idea is performed, the abstract idea is performed for data pertaining to medical records of study measurement data. See MPEP 2106.05(h).
Claim 5 recites “the medical study procedure comprises an echocardiography procedure.” This limitation is understood as no more than an attempt to generally link the judicial exception to a field of use. The limitations merely specify that the sparsely populated data source comprises medical records comprising measurement data from an echocardiography procedure. This is a mere indication of the field of use or technological environment in which the abstract idea is performed, the abstract idea is performed for data pertaining to medical records of echocardiography measurement data. See MPEP 2106.05(h).
Claim 6 recites “during a measurement procedure, unpopulated measurement data is predicted using the measurement prediction protocols on the basis of data collected by a procedure operator.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed prediction encompasses a trained clinician observing an acquired echocardiography dataset and performing an evaluation, identifying missing measurement data and substituting/predicting this missing measurement data using the knowledge of the trained clinician. See MPEP 2106.04 subsections I. and II.A. In so far as the limitations encompass additional data gathering, these limitations encompass insignificant extra-solution activity that amounts to mere data gathering incidental to the limitations of claim 6. All uses of the judicial exception require gathering of the measurement data. As all uses of the recited judicial exceptions require such data gathering, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering. See MPEP 2106.05(g).
Claim 7 recites “during a measurement procedure, a patient phenotype is determined by the phenotype model based data collected by at least one of: a procedure operator and on measurement data predicted using the measurement prediction protocols.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the claimed determination encompasses a trained clinician observing an acquired echocardiography dataset and performing an evaluation, determining/diagnosing a patient phenotype for the patient based on the measured echocardiography data or the substituted/predicted missing measurement data using the knowledge of the trained clinician. See MPEP 2106.04 subsections I. and II.A. In so far as the limitations encompass additional data gathering, these limitations encompass insignificant extra-solution activity that amounts to mere data gathering incidental to the limitations of claim 7. All uses of the judicial exception require gathering of the measurement data. As all uses of the recited judicial exceptions require such data gathering, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering. See MPEP 2106.05(g).
Claim 8 recites “the unpopulated measurement data and the patient phenotype is computed in real-time during the measurement procedure.” Performing the observation, evaluation, judgment, and opinion of a clinician using “a measurement prediction protocol,” and “a phenotype model” merely references an automation of actions that are manually performable (e.g., actions of observing, evaluating, judging, and opinion in training a clinician, testing the knowledge of the trained clinician, and using the knowledge of the trained clinician to substitute for missing medical measurements and to determine a patient phenotype/disease state). However, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource Corp. v. Retail Decision, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”). There is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of substituting for missing medical measurements and to determine a patient phenotype/disease state rather than to any technology. See MPEP 2106.05(a).
Claim 9, lines 2-3 recites “the machine learning system comprises: a neural network.” Performing the observation, evaluation, judgment, and opinion of a clinician using “a trained model,” and “measurement prediction protocols,” using a neural network merely references an automation of actions that are manually performable (e.g., actions of observing, evaluating, judging, and opinion in training a clinician, testing the knowledge of the trained clinician, and using the knowledge of the trained clinician to substitute for missing medical measurements and to determine a patient phenotype/disease state). However, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource Corp. v. Retail Decision, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”). There is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of substituting for missing medical measurements and to determine a patient phenotype/disease state rather than to any technology. See MPEP 2106.05(a). Further, the neural network is recited at a high level of generality. The system is used to perform the abstract ideas, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Claim 9, lines 4-7 recites “during a measurement procedure, measurements obtained by a procedure operator are incorporated into the training data set to form an updated training dataset and analysing the updated training data set using the neural network to compute at least one of: updated measurement prediction protocols and an updated phenotype model.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the limitations encompass an evaluation, the placement of obtained echocardiography measurements obtained by the clinician into the training dataset to update the training dataset, the observation of the echocardiography data of the updated training dataset by a clinician to train the clinician to predict/substitute missing data with the knowledge of the clinician, analogous to the updated measurement prediction protocols. As another example, the limitations encompass the observation of the echocardiography data of the updated training dataset by the clinician to train the clinician on the features of the updated echocardiography data, analogous to a trained updated phenotype model.
Further, under the BRI, the measurements obtained by the clinician performing a measurement procedure encompass insignificant extra-solution activity that amounts to mere data gathering incidental to the limitations of claim 9, lines 4-11. All uses of the judicial exception require obtaining measurements. As all uses of the recited judicial exceptions require such data gathering, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering. See MPEP 2106.05(g).
Claim 9, lines 8-11 recites “the measurements obtained during the measurement procedure are analysed using at least one of: the update measurement prediction protocols and the updated phenotype model, to predict a probable disease state for a patient undergoing the measurement procedure.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the limitations encompass observation of the echocardiography data acquired by the trained clinician, performing an evaluation, identifying missing measurement data and substituting/predicting this missing measurement data using the knowledge of the trained clinician, and performing an evaluation, predicting/diagnosing the patient with a probable disease state. As another example, the limitations encompass observation of the echocardiography data acquired by the trained clinician, performing an evaluation, identify the probability/likelihood that the measured echocardiography data is associated with a phenotype of the patient disease state, and performing an evaluation, predicting/diagnosing the patient with a probable disease state.
Claim 10 recites “as a result of the updated phenotype model associated with the probable disease state, directing a measurement operator to record relevant measurement data to increase the confidence of the patient phenotype and prediction of an associated disease state.” Under its BRI these limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, the limitations encompass a trained clinician observing the measurement data for a patient undergoing an echocardiography and performing an evaluation, predicting/diagnosing the patient with a probable disease state, and then a subsequent evaluation/judgement in which the trained clinician adapts/modifies the measurement protocol to collect a dataset of echocardiography measurement data that is relevant to the probable disease state that the clinician predicted/suspected/diagnosed for the patient. A trained clinician is knowledgeable of what types of echocardiography measurement data are most relevant for a particular disease state/condition of a patient and takes steps to ensure such data is collected during the echocardiography procedure.
Furthermore, under the BRI, the directing a measurement operator to record relevant measurement data encompasses insignificant post-solution activity that amounts to mere data output incidental to the limitations of claim 11, lines 11-19 and 22-24. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data outputting. See MPEP 2106.05. Further, the collection of a dataset of relevant measurement data is further insignificant extra-solution activity that amounts to mere data gathering. See MPEP 2106.05(g).
Claim 12 recites that “the disease state is aortic stenosis.” This limitation is understood as no more than an attempt to generally link the judicial exception to a field of use. The limitations merely specify that the disease state is aortic stenosis. This is a mere indication of the field of use or technological environment in which the abstract idea is performed, the abstract idea is performed for data pertaining to aortic stenosis. See MPEP 2106.05(h).
Claim 13 recites that “a display surface configured to display a notification to the measurement operator comprising the predicted measurement data or a probable disease state.” Displaying a notification encompasses insignificant post-solution activity that amounts to mere data output incidental to the limitations of claim 11, lines 11-19 and 22-24. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data outputting. See MPEP 2106.05. Further, the display is recited at a high level of generality. The display is used as a tool to perform the generic computer function of displaying data. See MPEP 2106.05(f).
Claim 17 recites that “the disease state is aortic stenosis.” This limitation is understood as no more than an attempt to generally link the judicial exception to a field of use. The limitations merely specify that the disease state is aortic stenosis. This is a mere indication of the field of use or technological environment in which the abstract idea is performed, the abstract idea is performed for data pertaining to aortic stenosis. See MPEP 2106.05(h).
Therefore, claims 2-10, 12-13, and 17 are not 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bordin et al. (WO2019/8153039A1), hereinafter “Bordin.”
Regarding claim 1, Bordin discloses a method for processing a sparsely populated data source ([0021]) comprising:
retrieving data, from a sparsely populated data source, to form a base dataset, the data source comprising a plurality of patient records including patient mortality data wherein each patient record comprises at least one unpopulated data field corresponding to a medical measurement ([0021]; patient records including patient mortality data, [0008]-[0010], [0204], [0229]-[0230], [0239], [0241]-[0242]);
dividing the base dataset into two portions ([0022]):
a first portion comprising a training dataset being a defined percentage, X%, of the base dataset ([0022]); and
a second portion comprising a validation dataset being a defined percentage (100% - X%) of the base dataset ([0022]);
analysing the training dataset to jointly model variable relationships using a non-linear function approximation algorithm applied iteratively to the records of the training dataset to obtain a trained model and measurement prediction protocols for population unpopulated fields in the training dataset ([0023]);
using the measurement prediction protocols, computing predicted measurement values for each of the unpopulated data fields ([0024]);
imputing the predicted measurement values in the patient records of the training dataset ([0025]);
analysing the training dataset based on at least one predefined disease conditions in a known portion of the patient records of the base dataset to form a phenotype model configured to associate patient phenotype data to a probability of a disease condition in the patient records of a trained dataset ([0026]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]);
imputing the predicted measurement vales in the records of the validation dataset ([0027]);
validating the phenotype model, where the validating comprises analysing the validation dataset using the phenotype model, wherein the records of the validation dataset comprise phenotype data associated with patient data, and determining a validation error comprising a probability of correctly predicting the patient phenotype associated with a disease state probability in the records of the validation dataset ([0028]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]);
repeating Steps (c) to (h) to minimise the validation error and computing a prediction of a probable disease state phenotype for each patient record in the base dataset ([0029]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]).
Regarding claim 2, Bordin discloses analysing the training data set is performed using a using a machine learning system (systems and methods for artificial intelligence, [0001]-[0002], [0017], [0074], [0103]-[0110], [0205]-[0206], [0242]; neural network, [0035], [0141]-[0188], claim 7).
Regarding claim 9, Bordin discloses the machine learning system (systems and methods for artificial intelligence, [0001]-[0002], [0017], [0074], [0103]-[0110], [0205]-[0206], [0242]; neural network, [0035], [0141]-[0188], claim 7) comprises:
a neural network ([0035]);
during a measurement procedure, measurements obtained by a procedure operator are incorporated into the training data set to form an updated training dataset and analysing the updated training data set using the neural network to compute at least one of: updated measurement prediction protocols and an updated phenotype model ([0035]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]); and
the measurements obtained during the measurement procedure are analysed using at least one of: the update measurement prediction protocols and the updated phenotype model, to predict a probable disease state for a patient undergoing the measurement procedure ([0035]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]).
Regarding claim 10, Bordin discloses as a result of the updated phenotype model associated with the probable disease state, directing a measurement operator to record relevant measurement data to increase the confidence of the patient phenotype and prediction of an associated disease state ([0036]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]).
Regarding claim 3, Bordin discloses the disease state is aortic stenosis ([0036]).
Regarding claim 4, Bordin discloses said sparsely populated data source comprises a plurality of medical records comprising measurement data obtained from a study procedure ([0031]).
Regarding claim 5, Bordin discloses the medical study procedure comprises an echocardiography procedure ([0031]).
Regarding claim 6, Bordin discloses during a measurement procedure, unpopulated measurement data is predicted using the measurement prediction protocols on the basis of data collected by a procedure operator ([0032]).
Regarding claim 8, Bordin discloses the unpopulated measurement data and the patient phenotype is computed in real-time during the measurement procedure ([0034]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]).
Regarding claim 7, Bordin discloses during a measurement procedure, a patient phenotype is determined by the phenotype model based data collected by at least one of: a procedure operator and on measurement data predicted using the measurement prediction protocols ([0033]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]).
Claims 11-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bordin.
Regarding claim 11, Bordin discloses a system for conducting a measurement procedure on a patient (apparatus for conducting a measurement procedure on a patient, [0037]; systems and methods for artificial intelligence, [0001]-[0002], [0017], [0074], [0103]-[0110], [0205]-[0206], [0242]), the system comprising:
at least one measurement tool configured to perform the measurement procedure ([0037]; echocardiography, [0002], [0017], [0031], [0074], [0092], [0093], [0096], [0099], [0104], [0193], [0195], [0271]-[0272]),);
at least one recording device configured to record measurement data from the patient during the measurement procedure (means for recording measurement data configured to record measurement data from the patient during the measurement procedure, [0037]); and
at least one transmitter configured to transmit the measurement data to at least one processor (means for transmitting measurement data configured to transmit the measurement data to an analysis means, [0037]; transmitting/communication means, [0198], [0202]-[0203], [0243]-[0249]; analysis means is one or more processors, [0019], [0062]-[0066], [0197], [0250]-[0258]), wherein the processor is configured to:
receive the measurement data and phenotype data (analysis means receives the measurement data transmitted by the means for transmitting measurement data, [0037]; analysis means is configured to receive a measurement prediction protocol and sparsely populated data, [0037]; analysis means transmitting/communication means, [0198], [0202]-[0203], [0243]-[0249]; sparsely populated data is phenotype data, [0021]-[0025], [0031]-[0035], [0191], [0230], [0236]-[0238], [0240]-[0242]);
associate the measurement data and phenotype data to determine a patient phenotype associated with one or more disease states (analysis means analyses the measurement data and sparsely populated data to determine a patient probable disease state, [0037]; obtain a trained disease model and measurement prediction protocols, [0021]-[0029]; measurement data and sparsely populated data are analyzed to determine a patient disease state, [0031]-[0035]; sparsely populated data is phenotype data, [0021]-[0025], [0031]-[0035], [0191], [0230], [0236]-[0238], [0240]-[0242]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]);
generate at least one of:
a measurement prediction protocol configured to predict measurement data for unpopulated measurement fields (analysis means comprises a measurement prediction protocol for predicting measurements data for unpopulated measurement fields, [0037]; obtain measurement prediction protocols for populating unpopulated fields, [0021]-[0029], [0031]-[0036]);
a phenotype model configured to associate the patient data with a phenotype associated with one or more disease state (analysis means comprises a disease model for predicting a probable disease state, [0037]; obtain a trained disease model, [0021]-[0029], [0031]-[0036]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242])
predict a probable disease state for the patient undergoing the measurement procedure ([0037]); and
alert a measurement operator of the predicted measurement data and the probable disease state ([0037]); and
provide a set of directions to the measurement operator to collect a dataset of relevant measurement data to be collected based on the predicted probable disease state ([0037]).
Regarding claim 12, Bordin discloses the disease state is aortic stenosis ([0036]).
Regarding claim 13, Bordin discloses a display surface configured to display a notification to the measurement operator comprising the predicted measurement data or a probable disease state ([0037]).
Claims 14 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bordin.
Regarding claim 14, Bordin discloses a non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device ([0039]; non-transitory CRM that stores a set of instructions executable by at least one processor of a computing device, [0019], [0062]-[0066], [0196]-[0203], [0250]-[0258]) to cause the computing device to perform operations for generating an inspection tool sampling plan (measurement tool, [0037], [0038]; echocardiography, [0002], [0017], [0031], [0074], [0092], [0093], [0096], [0099], [0104], [0193], [0195], [0271]-[0272]; collecting relevant measurement data, i.e., sampling plan, [0031]-[0036], [0037], [0100], [0205]-[0206]), the operations comprising:
retrieving data, from a sparsely populated data source, to form a base dataset, the data source comprising a plurality of patient records including patient mortality data, each patient record comprises at least one unpopulated data field corresponding to a medical measurement ([0021], [0039]; patient records including patient mortality data, [0008]-[0010], [0204], [0229]-[0230], [0239], [0241]-[0242]);
dividing the base dataset into two portions ([0022], [0040]):
a first portion comprising a training dataset being a defined percentage, X% of the base dataset ([0022], [0040]); and
a second portion comprising a validation dataset being a defined percentage (100% - X%) of the base dataset ([0022], [0040]);
analysing the training dataset to jointly model variable relationships using a non-linear function approximation algorithm applied iteratively to the records of the training dataset to obtain a trained model and measurement prediction protocols for populating unpopulated fields in the training dataset ([0023], [0041]);
using the measurement prediction protocols, computing predicted measurement values for each of the unpopulated data fields ([0024], [0042]);
imputing the predicted measurement values in the patient records of the training dataset ([0025], [0043]);
analysing the training dataset based on at least one predefined disease condition in a known portion of the patient records of the base dataset to form a phenotype model configured to associate patient phenotype data to a probability of a disease condition in the patient records of a trained dataset ([0026], [0044]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]);
imputing the predicted measurement values in the records of the validation dataset ([0027], [0045]);
validating the phenotype model, where the validating comprises analysing the validation dataset using the phenotype model, wherein the records of the validation dataset comprise phenotype data associated with patient data, and determining a validation error comprising a probability of correctly predicting the patient phenotype associated with a disease state probability in the records of the validation dataset ([0028], [0046]; disease model is a phenotype model, [0191], [0230], [0236]-[0238], [0240]-[0242]);
repeating Steps (c) to (h) to minimise the validation error and computing a prediction of a probable disease state phenotype for each patient record in the base dataset ([0029]).
Regarding claim 17, Bordin discloses the disease state is aortic stenosis ([0036]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Beymer et al. (U.S. Pub. No. 2015/0170055) discloses a method, system, and CRM for generating a measurement prediction protocol for predicting values for unpopulated data fields in patient medical records, a trained phenotype model for diagnosing a patient with a disease condition based on patient medical records, and a validation minimization process.
Abolmaesumi et al. (U.S. Pub. No. 2019/0125298) discloses a method, system, and CRM for predicting a probable patient disease state during a measurement procedure and presenting on a display the measurement data, probable disease state, and directions for obtaining relevant measurement data to the user.
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/Johnathan Maynard/Examiner, Art Unit 3798
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