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 .
Status of Claims
This action is in reply to the RCE filed on 05/01/25.
Claims 1, 2, 3, 5, 8, 9, 12, 17, 19 have been amended and are hereby entered.
Claims 4, 6, 7, 13, 14 were previously canceled.
Claims 1-3, 5, 8-12, 15-19 are currently pending and have been examined.
This action is made final.
Continuity
Acknowledge is made to Applicant’s claim to the benefit of Provisional Application 63/051,919, filed on 07/15/20. Accordingly, a priority date of 07/15/20 has been given to this application.
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 1-3, 5, 8-12, 15-19 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1
Claims 1-3, 5, 8-11, 16-17 are drawn to a method, Claims 12, 15, 18 are drawn to a non-transitory computer readable medium, and Claim 19 is drawn to a system, each of which are within the four statutory categories. Claims 1-3, 5, 8-12, 15-19 are further directed to an abstract idea on the grounds set out in detail below.
Step 2A Prong 1
Claim 1 and Claim 12 recite implementing the following steps:
receiving medical information about the patient comprising one or more patient demographics, one or more physiological measurements, or a patient diagnosis;
generating, based on a plurality of decision trees each comprising a plurality of decision points, a dataset comprising information about a condition of the patient relative to telemetry monitoring guidelines, the information about the condition of the patient comprising one or more of presence of specific elements of one or more of the plurality of decision points, a percentage of one or more decisions paths of one or more of the decisions trees having a positive indication, a number of the decision trees that are implicated by the received medical information, or a maximum level among the decision trees that has a positive indicator, wherein each of the plurality of decision trees respectively comprises a plurality of decision points derived from the telemetry guidelines;
generating the dataset by comparing the received medical information to the decision points;
determining, using the generated dataset, a telemetry indication score for the patient comprising a probability of whether the patient is likely to meet the telemetry guidelines
These steps of Claims 1 and 12 amount to managing personal behavior or relationships or interactions between people and therefore recite certain methods of organizing human activity. Collecting and analyzing patient’s medical information to determine the probability of the patient meeting telemetry guidelines and to determine a telemetry indication score is a personal behavior that may be performed by a healthcare provider.
Claim 19 recites implementing the following steps:
generating, based on a plurality of decision trees, wherein each of the plurality of decision trees respectively comprises a plurality of decision points derived from the telemetry monitoring guidelines, a dataset comprising information about a condition of the patient relative to the telemetry monitoring guidelines of the decision support tool, the information about the condition of the patient comprising one more of: (i) presence of specific elements of one or more of the plurality of decision points, a percentage of one or more decisions paths of one or more of the decisions trees having a positive indication, (ii) a number of the decision trees that are implicated by the received medical information, or (iii) a maximum level among the decision trees that has a positive indicator, and wherein the dataset is generated by comparing the received medical information to the decision points;
determining, using the generated dataset, a telemetry indication score for the patient comprising a probability of whether the patient is likely to meet the telemetry guideline
generating a telemetry indication report comprising the determined telemetry indication score and evidence supporting the telemetry indication score
providing the telemetry indication report for the patient
These steps of Claim 19 amount to managing personal behavior or relationships or interactions between people and therefore recite certain methods of organizing human activity. Using a patient’s medical information to determine the probability of the patient meeting telemetry guidelines and subsequently providing a telemetry indication report including a determined telemetry indication score is a personal behavior that may be performed by a healthcare provider.
Claims 1, 12 and 19 are therefore directed to an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application because the additional
elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
The independent claims additionally recite the following additional elements as implementing various aspects of the abstract idea:
a telemetry analysis system as implementing the step of receiving medical information about the patient (Claim 1)
a data transformer as implementing the step of generating a dataset (Claims 1, 12, 19)
a trained classifier as implementing the step of determining a telemetry indication score (Claims 1, 12)
a trained classifier, wherein the trained classifier is a random forest or XGBoost classifier as implementing the step of determining a telemetry indication score (Claim 19)
a decision support tool / a decision support tool comprising telemetry monitoring guidelines as implementing the step of providing telemetry monitoring guidelines (Claims 1, 12 / 19)
a user interface as implementing the step of providing a telemetry indication score for the patient (Claims 1, 12, 19)
a non-transitory computer readable medium storing instructions executed by one or more processors as implementing the steps of the abstract idea (Claim 12)
a processor as implementing the step of generating a telemetry indication report comprising the determined telemetry indication score and evidence supporting the telemetry indication score (Claim 19)
The broad recitation of these general purpose computing elements at a high level of generality only amounts to mere instructions to implement the abstract idea using computing components as tools:
Regarding the telemetry analysis system, per paras. [0069]-[0077], this is understood to be a general purpose computing device to perform the steps of the abstract idea ([0071], “According to an embodiment, system 1100 comprises a processor 1120 capable of executing instructions stored in memory 1130 or storage 1160 or otherwise processing data to, for example, perform one or more steps of the method”), and as such, amounts to mere instructions to implement the abstract idea using computing elements as tools.
Regarding the data transformer, this is understood to be an element implemented on a general purpose computing device ([0071]) to perform data processing and analysis steps (paras. [0044], [0050], [0061]), and as such, amounts to mere instructions to implement the abstract idea using computing elements as tools.
Regarding the trained classifier / a trained classifier wherein the trained classifier is a random forest or XGBoost this is understood to be an element implemented on a general purpose computing device ([0071]) to generate a telemetry indication score ([0016], [0048], [0051]). The specification, at para. [0062], discloses “According to an embodiment, a machine learning classifier such as random forest, XGBoost, and/or any other classifier can be used to learn the patient clinical phenotypes and monitoring needs of the patient. Many other classifiers are possible. The classifier can be any machine learning classifier sufficient to utilize the type of input data provided”. No specifics of the classifier are provided; as such, recitation amounts to mere instructions to implement the abstract idea using computing elements as tools.
Regarding the user interface, this is understood to be a graphical user interface functioning in its normal operating capacity ([0055], “The user interface may include one or more devices for enabling communication with a user. The user interface can be any device or system that allows information to be conveyed and/or received, and may include a display, a mouse, and/or a keyboard for receiving user commands”), and as such, amounts to mere instructions to implement the abstract idea using computing elements as tools.
Regarding the decision support tool, para. [0042] discloses that the decision support tool “may be of any configuration, design, or format that enables comparison of telemetry input data to elements of the received telemetry guidelines. For example, according to just one embodiment, the decision support tool may be or comprise one or multiple decision trees in which elements of the received telemetry guidelines are formatted into nodes of the decision trees” and that “Other examples of decision support tools are checklists, among many others”; para. [0079] discloses that processor 1120 may comprise one decision support tool 1164. No particulars of the decision support tool are provided. Therefore, it is given its broadest reasonable interpretation as a computer-implemented element to implement decision-making steps, and amounts to mere instructions to implement the abstract idea using computing elements as tools.
Regarding the non-transitory computer readable medium storing instructions executed by one or more processors, this is understood to be a general purpose computing device ([0071], “According to an embodiment, system 1100 comprises a processor 1120 capable of executing instructions stored in memory 1130 or storage 1160 or otherwise processing data to, for example, perform one or more steps of the method”), and as such, amounts to instructions to mere implement the abstract idea using computing elements as tools.
Regarding the processor, this is understood to be a general purpose computing element per para. [0071] (“According to an embodiment, system 1100 comprises a processor 1120 capable of executing instructions stored in memory 1130 or storage 1160 or otherwise processing data to, for example, perform one or more steps of the method. Processor 1120 may be formed of one or multiple modules. Processor 1120 may take any suitable form, including but not limited to a microprocessor, microcontroller, multiple microcontrollers, circuitry, field programmable gate array (FPGA), application-specific integrated circuit (ASIC), a single processor, or plural processors”). As such, amounts to mere instructions to implement the abstract idea using computing elements as tools.
These elements are therefore not sufficient to integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claim 19 additionally recites:
a database comprising medical information about the patient comprising one or
more of patient demographics, one or more physiological measurements, or a patient diagnosis
The element of “a database comprising medical information about the patient comprising one or more of patient demographics, one or more physiological measurements, or a patient diagnosis” only amounts to insignificant extra-solution activity. As stated in MPEP 2106.05(g), "[t]he term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim." In the present claim, the element of “a database comprising medical information about the patient comprising one or more of patient demographics, one or more physiological measurements, or a patient diagnosis” is only nominally or tangentially related to the process of using a patient’s medical information to determine the probability of a patient meeting telemetry guidelines and provide a telemetry indication score in a report, and accordingly constitutes insignificant extra-solution activity.
These elements are therefore not sufficient to integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Claims 1, 12, and 19, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to
more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 1, 12, 19 only recite the aforementioned computing elements as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f).
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Likewise, as explained above, the element of a database comprising medical information about the patient comprising one or more of patient demographics, one or more physiological measurements, or a patient diagnosis only amounts to insignificant extra-solution activity.
C. Well-Understood, Routine and Conventional Activities. MPEP 2106.05(d)
In addition to amounting to insignificant extra-solution activity the elements in Section B above constitute well-understood, routine and conventional activity. The element of a database comprising medical information about the patient comprising one or more of patient demographics, one or more physiological measurements only amounts to storing/retrieving information in memory, which has been previously held to be well-understood, routine and conventional when claimed at a high level of generality or as insignificant extra-solution activity. See MPEP 2106.05(d)(II).
Thus, taken alone, the additional elements do not amount to significantly more than the
above-identified judicial exception. Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. Their
collective functions merely provide conventional computer implementation.
Dependent Claims
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims or also recites certain methods of organizing human activity. For example, Claims 8, 9, 11 recite limitations which further narrow the scope of the independent claims. Claim 2, 3, 5, 10, 15, 17 further recites limitations that are certain methods of organizing human activity but for recitation of general purpose computing elements.
Claim 3 recites (i) receiving a dataset of historical patient data, the dataset comprising for one or more of a plurality of patient medical information about the patient and telemetry monitoring data for the patient; (ii) extracting, using the decision support tool, a plurality of features from the patient medical information and telemetry monitoring data; (iii) training the classifier using the extracted features. Regarding limitations (i) and (ii), these are also certain methods of organizing human activity as a healthcare provider may receive historical patient data and extract (e.g., identify or pull out) features from the patient medical information and telemetry data; recitation of “using the decision support tool” only amounts to mere instructions to implement the abstract idea using a computer as a tool, as discussed above with respect to this element in the independent claims. Regarding limitation (iii) and recitation of “training the classifier using the extracted features”, this only amounts to applying the abstract idea using a computer as a tool. Claim 3 only claims the outcome of training the classifier without details of how the training is accomplished. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claims 16 and 18 recite “wherein the trained classifier is a random forest or XGBoost classifier” which amounts to mere instructions to implement the abstract idea using a computer. Paras. [0046] and [0062] recite “a machine learning classifier such as random forest, XGBoost, and/or any other classifier can be used to learn the patient clinical phenotypes and monitoring needs of the patient. Many other classifiers are possible. The classifier can be any machine learning classifier sufficient to utilize the type of input data provided”. Random forest and XGBoost are known machine learning algorithms and are understood to be operating in their normal capacities. As such, this amounts to applying the abstract idea using a computer. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101 as they include all of the limitations of claim 1 or claim 12 respectively. The additional recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea. Beyond the limitations which recite the abstract idea, the claims recite additional elements consistent with those identified above with respect to the independent claims (e.g., decision support tool, classifier, etc.) which encompass adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The limitations of the dependent claims fail to integrate an abstract idea into a practical application because the dependent claims do not introduce additional elements to integrate the judicial exception into a practical application. Performing the further narrowed abstract ideas of the dependent claims, individually or in combination, does not impose any meaningful limits on practicing the abstract ideas and does not provide improvements to the functioning of computing systems or to another technology or technical field; therefore, the claims amount to merely using computing elements, in their ordinary capacity, as a tool to perform the abstract idea. Similarly, the additional recited limitations of the dependent claims fail to establish that the claims provide an inventive concept because claims that merely use computing elements, in their ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, when considered individually and in combination, amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). The claims are not patent eligible.
Dependent claims 2-3, 5, 8-11, 15-18 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
For the reasons stated, Claims 1-3, 5, 8-12, 15-19 fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Response to Applicant’s Remarks/Arguments
Please note: When referencing page numbers of Applicant’s response, references are to page numbers as printed.
Claim Objections
The objections to Claims 1, 12, and 19 for minor informalities are withdrawn in view of Applicant’s amendments to these claims.
101 Rejections
Applicant’s remarks have been fully considered but are not persuasive. At page 8, Applicant asserts that the claims are not directed to a judicial exception and asserts that the claims are “more accurately characterized as directed to generating features from patient data to be ingested by a trained classifier for the output of an indication of need for telemetry monitoring”. Examiner submits that as drafted, the use of “features” and “trained classifier” only amount to implementation of the abstract idea (analyzing patient data to determine if the patient meets telemetry guidelines) using computing elements as tools. Therefore, this argument is not persuasive.
Regarding remarks at page 9 that “a feature engineering and machine learning pipeline cannot be said to be directed to a human behavior”, Examiner respectfully submits that the claimed invention is not directed to machine learning itself, e.g., Applicant has not invented a new method of machine learning nor is a new method of machine learning claimed in the instant claim set. Rather, Applicant is using known methods of machine learning as a tool to arrive at a telemetry indication score (see instant specification [0046], “At step 118 of the method, the system trains the machine learning algorithm, which will be the classifier utilized to analyze medical information from a patient as described or otherwise envisioned herein. The machine learning algorithm is trained using the extracted features according to known methods for training a machine learning algorithm. According to an embodiment, a machine learning classifier such as random forest, XGBoost, and/or any other classifier can be used to learn the patient clinical phenotypes and monitoring needs of the patient. Many other classifiers are possible”; see also, [0062], “The classifier is trained using the extracted features according to known methods for training a machine learning algorithm. According to an embodiment, a machine learning classifier such as random forest, XGBoost, and/or any other classifier can be used to learn the patient clinical phenotypes and monitoring needs of the patient. Many other classifiers are possible. The classifier can be any machine learning classifier sufficient to utilize the type of input data provided”). Therefore, this argument is not persuasive.
Regarding Step 2A Prong 1, an improvement to the abstract idea of collecting and analyzing a patient’s medical information to determine and provide a telemetry indication score does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to the abstract idea itself in determining a telemetry score. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient.
Examiner notes that, in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claims recite certain methods of organizing human activity, and are not subject matter eligible. The use of a computer to train a model, including a random forest model, XG Boost, and/or “any other classifier”, utilizing the training embodiments offered in the instant specification (see at least [0046] and [0064]) amount to applying data to an algorithm and reporting the results (MPEP § 2106.05(f)(2), see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014) consistent with Example 47 claim 2. The techniques outlined, and Examiner notes the known methods of training to one of ordinary skill in the art, are mathematical algorithms or certain methods of organizing human activity of labeling and fitting data to a particular model representation.
Regarding remarks at page 10 with respect to bolded limitation “determining…”, Applicant asserts that the “alleged abstract idea…is an implementation step, implementing the trained classifier using the dataset”. Examiner respectfully disagrees with Applicant’s statement regarding an “improved input” to a trained classifier. The abstract idea, as outlined in the 101 analysis section above, shows that “determining, using the dataset, a telemetry indication score for the patient comprising a probability of whether the patient is likely to meet the telemetry guideline” is within the scope of the abstract idea. Recitation of “by the classifier” only amounts to mere instructions to implement the abstract idea using computers as a tool. Therefore, this argument is not persuasive.
Regarding remarks pertaining to “using domain-specific feature engineering/pre-processing that optimizes inputs for the trained classifier”, Applicant has not cited to relevant support ins specification, nor can Examiner find evidence of, any discussion of how the input is being optimized or how the claimed combination of elements provides an improvement over prior art systems. Therefore, this argument is not persuasive.
Regarding remarks beginning at page 11, MPEP 2106.04(a)(2) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Examiner submits that the identified claim elements represent a series of steps that comprise the personal behavior a healthcare provider could take to analyze a patient’s medical data to determine whether the patient is likely to meet telemetry guidelines. Furthermore, Examiner submits that Applicant’s specification, at paras. [0002]-[0003] discuss telemetry technicians performing monitoring duties of non-critically ill cardiac patients; in particular, [0003] discloses “Constant re-evaluation is required to determine when a patient can be taken off telemetry, which is very time consuming. Additionally, the evaluation of telemetry usage requires communication among multiple roles and cannot be accomplished by a clinician alone, e.g., a doctor needs to communicate with a nurse and telemetry technician to determine if the patient has been asymptomatic and alarm free, respectively” – e.g., managing personal behaviors or interactions of clinicians, nurses, and/or technicians. Because the claim elements fall under managing the personal behavior of a healthcare provider to analyze a patient’s data and determine if the patient is likely to meet telemetry guidelines, the claimed invention is directed to an abstract idea.
At page 11, Applicant asserts “Applicant has indeed invented a new method of machine
learning, as the present claims recite a novel and non-obvious method and system in which input data to the classifier is generated using domain-specific feature engineering/pre-processing, which optimizes that input”. Examiner respectfully disagrees; as previously stated, paras. [0046] and [0062] disclose that “the training data set is utilized to train the classifier. The classifier is trained using the extracted features according to known methods for training a machine learning algorithm. According to an embodiment, a machine learning classifier such as random forest, XGBoost, and/or any other classifier can be used to learn the patient clinical phenotypes and monitoring needs of the patient. Many other classifiers are possible. The classifier can be any machine learning classifier sufficient to utilize the type of input data provided (Emphasis Examiner). Applicant further cites to [0088], which discloses “Implementing the novel telemetry analysis system significantly improves the sources of data thereby used to make improved analyses regarding telemetry requirements, and avoids utilizing too much time by healthcare professionals, thereby significantly improving care. Additionally, by reducing unnecessary telemetry monitoring, the novel telemetry analysis system can reduce the significant problem of alarm fatigue”. Examiner submits that using improved “sources of data” to make “improved analyses” may be an improvement to the abstract idea itself, but is not a technological improvement per MPEP 2106.05(a), which states “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” This argument is not persuasive.
Regarding remarks page 12, Applicant states, “Indeed, the claims have been found to be novel and non-obvious due to the novelty and non-obviousness of generating the input data used by the trained classifier (i.e., the claimed generating step)! The Patent Office itself asserts that Applicant has indeed invented a new method of machine learning, as the present claims recite a novel and non-obvious method and system in which input data to the classifier is generated using domain-specific feature engineering/pre-processing”. Examiner strongly disagrees with this statement, and submits that it has never been stated in any prior office actions or other communications that Applicant has invented “a new method of machine learning” as stated by Applicant. Examiner cites to the explanation of the withdrawal of the 103 prior art rejections below, taken from Final Office Action dated 08/29/24 (pages 13-14):
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Examiner submits that the withdrawal of 103 prior art rejections states that the claimed combination of elements distinguishes the claimed invention over a search of publicly available prior art. There is no mention of “a new method of machine learning” in the reasons for 103 rejection withdrawal. Furthermore, Examiner directs Applicant to MPEP 2106.05(I), which states, “Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Therefore, these arguments are not persuasive.
Regarding remarks at bottom of page 13 in which Applicant remarks, “The claims are not directed to managing personal behavior or relationships or interactions between people”, this argument has previously been addressed; please see above. Regarding remarks page 13, Examiner maintains that a healthcare provider could generate a dataset comprising information about a condition relative to telemetry monitoring guidelines; although the decision support tool has been identified as an additional element in 101 analysis section above, Examiner notes that per para. [0042], “Other examples of decision support tools are checklists, among many others”, and asserts that a healthcare provider could generate (create) a dataset comprising information about a condition relative to telemetry monitoring guidelines using a checklist. Therefore this argument is not persuasive.
Regarding remarks on page 13 that “a healthcare provider does not use a data transformer and a plurality of decision trees to generate input data for a trained classifier”, Examiner submits that the data transformer is an additional element as explained above in the 101 analysis section, and is not included in the abstract idea. Regarding “plurality of decision trees”, the broadest reasonable interpretation is a visual representation that maps out potential outcomes of a decision, which may be presented on paper. A healthcare provider could use a plurality of decision trees, each comprising a plurality of decision points, to generate a dataset comprising information about a condition of a patient relative to telemetry monitoring guidelines. Regarding the classifier, this element has been addressed above and is outside of the scope of the abstract idea. It is only recited as mere instructions to implement the abstract idea using a computer. These arguments are not persuasive.
Regarding remarks at bottom of page 14, the independent claims have been reviewed as a whole as shown in detail in 101 analysis section above and shown that the claims are directed to a judicial exception. Examiner has previously addressed remarks at top of page regarding why the claims include certain methods of organizing human activity. These arguments are not persuasive.
Regarding remarks directed to claim 19, Examiner respectfully disagrees. Please see above remarks regarding why the claims are directed to an abstract idea, specifically, certain methods of organizing human activity. Claim 19 has been analyzed in detail in 101 section above. Regarding the elements cited by Applicant at top of page 15 (data transformer, processor, etc.), Examiner has provided citations and explanations in 101 section above showing how these have been classified and why they do not integrate the judicial exception into a practical application or amount to significantly more than the abstract idea. Recitation of the additional elements shown at page 15 only amounts to mere instructions to implement the abstract idea. Examiner submits that “plurality of decision trees” has been included within the scope of the abstract idea, as this element is not inherently limited to computers and the broadest reasonable interpretation of “decision tree” is a visual representation that maps out potential outcomes of a decision, which may be presented on paper. Therefore, this argument is not persuasive.
Regarding remarks at page 16-17, Examiner respectfully disagrees with Applicant’s position for the reasons explained above. Regarding citation to para. [0088], Examiner reiterates that the claimed invention may provide an improvement to the abstract idea itself (improved sources of data to make improved analyses regarding telemetry requirements, per [0088]) but do not provide a technological improvement per MPEP as the improvement does not come from the additional elements. Therefore, this argument is not persuasive.
Regarding remarks pages 16-17, Examiner respectfully disagrees with Applicant’s position. Applicant has not cited to evidence in specification, nor can Examiner find evidence, of how the claimed system provides domain-specific feature engineering/pre-processing that optimizes inputs for corresponding classifiers and enables lighter-weight and more accurate models. Furthermore, and regardless of specification support Examiner respectfully submits that “optimizing inputs” falls within the scope of the abstract idea which. Improvements to the abstract idea itself are not sufficient to integrate the judicial exception into a practical application (MPEP 2106.05(a)). Therefore, this argument is not persuasive.
Regarding remarks at page 17 pertaining to “the claims are directed” an abstract idea: MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent managing personal behaviors of a healthcare provider to determine a telemetry indication score for a patient. The Examiner notes that Applicant’s Background describes telemetry monitoring (see Spec. Paras. [0002]-[0003]) as a human task. Furthermore, the Examiner submits that healthcare itself is inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization. Recitation of the various computing elements only amounts to mere instructions to apply the abstract idea. Because the claim elements fall under a series of personal behaviors that a person or persons would follow to determine a telemetry indication score for a patient, the claimed invention is directed to an abstract idea.
Examiner submits that Applicant’s previous arguments and “evidence” have been addressed. As previously stated, regarding claims which novel and non-obvious under 35 USC 102/103, this has been previously addressed; however, to reiterate, please see MPEP 2106.05(I), which states, “Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Therefore, these arguments are not persuasive.
Regarding citation to [0088] at page 17, Examiner respectfully submits that improving the sources of data used to make an improved analysis regarding telemetry requirements is an improvement to the abstract idea itself. As discussed above, this is not sufficient to integrate the judicial exception into a practical application. See MPEP 2106.05(a). Applicant asserts “The claims are directed to domain-specific feature engineering/pre-processing that optimizes inputs…”. This remarks has already been addressed above; “optimizing inputs” falls within the scope of the abstract idea and is not sufficient to render the claims subject-matter eligible.
Regarding remarks pertaining to March 5, 2025 action beginning at page 17:
Regarding a. Claims are directed to an subtract idea, this argument has already been addressed above. Applicant argues that the claims are providing “improved input”; as stated above, generating improved data may be an improvement to the abstract idea itself. Furthermore, as paraphrased by Applicant, “generating the dataset comprising information about a condition of the patient relative to telemetry monitoring guidelines” would also fall within the scope of certain methods of organizing human activities, as a physician or telemetry technician could generate a dataset using patient condition information relative to telemetry monitoring guidelines”. This argument is not persuasive.
Regarding b, “feature engineering or machine learning pipeline”, this argument has previously been addressed. Applicant’s representative appears to be rehashing arguments that were previously found to be unpersuasive and were previously fully addressed. Applying known machine learning algorithms (e.g., random forest) to new sets of data or new field of use does not constitute “inventing a new method of machine learning”. Examiner submits that Applicant has not invented a new method of machine learning, but rather, is applying known methods (e.g., [0046] and [0062], as cited in prior response) to a particular type of data. As discussed above, absence of a 102/103 prior art rejection does not automatically confer subject matter eligibility. Furthermore, abstract ideas do not become nonabstract by limiting them to a particular field of use (e.g., telemetry). This argument is not persuasive.
Regarding remarks “c. improvement to the abstract idea”, at page 20, Examiner maintains the position that as provided in the 101 analysis section above, the trained classifier is only used as a tool for arriving at an solution. Regarding “improved input”, Examiner submits that this would further be an improvement to the abstract idea rather than an improvement coming from an additional element. Per [0046], the classifier can be “random forest, XGBoost, and/or any other classifier can be used to learn the patient clinical phenotypes and monitoring needs of the patient. Many other classifiers are possible”. The classifier is a known element being used as a tool to arrive at a solution. Applicant again asserts that the claims are directed to generating a dataset comprising information about a condition of the patient relative to telemetry monitoring guidelines; as stated above, this falls within the scope of certain methods of organizing human activity. Recitation of the computing elements (trained classifier, etc.) only amount to mere instructions to implement the abstract idea. This argument is not persuasive.
Regarding “d. machine learning and 2024 Guidance” at page 21, Examiner submits that
limitations that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are not subject matter eligible. Applicant again asserts “the claims are clearly directed to generating the dataset comprising information…”; Examiner maintains the position that generating a dataset falls within the scope of certain methods of organizing human activities. This argument is not persuasive.
Regarding remarks e. the claims are “directed to”, this argument has been addressed multiple times above. Generating a dataset comprising information about a condition of a patient… falls under the scope of certain methods of organizing human activity, as such, generating “an improved input” is an improvement to the abstract idea itself, which is not sufficient to integrate the abstract idea into a practical application. This argument is not persuasive.
Regarding argument f. the “generating step”, please see above remarks pertaining to 102/103 rejections not automatically conferring subject matter eligibility under 35 USC 101. Applying known machine learning methods to a particular field of use (e.g., telemetry) does not render the claim subject matter eligible. This argument is not persuasive.
Regarding remark g. certain methods of organizing human activity at page 22-23, Examiner respectfully disagrees. The steps as presented by Applicant could be steps performed by a human such as a healthcare provider; as such, the claims have been classified as being directed to an abstract idea. This argument is not persuasive.
Regarding h. “certain methods of organizing human activity”, Examiner has already addressed this argument. Applying a known machine learning model to a particular type of data or particular field of use may be novel, e.g., able to overcome 102/103 prior art rejections. However, as previously discussed, limiting the scope of the abstract idea to a particular field of use or type of data does not make the abstract idea nonabstract. This argument is not persuasive.
Regarding remarks i. Novelty and non-obviousness, this argument has already been addressed. Please see above remarks pertaining to 102/103 rejections not conferring subject matter eligibility. Further, please see above remarks regarding the claims being directed to “generation of input data”; as previously stated, generating input data falls within the scope of certain methods of organizing human activities. This argument is not persuasive.
Regarding j. Generating a dataset: As previously discussed, generating a dataset falls within the scope of the abstract idea, e.g., a health care provider could generate a dataset. Recitation of a classifier and transformer, as previous discussed, this amounts to mere instructions to implement the abstract idea. This is not sufficient to overcome the outstanding 101 rejections.
Regarding k. data transformer, Examiner respectfully disagrees with Applicant’s position. Applicant argues “it is the most important element of the claim”. Examiner maintains the position that the data transformer is merely recited as a means for performing the steps of the abstract idea using a computer. This is not persuasive.
Regarding l. Claim 19 on page 27, Examiner respectfully disagrees. While the claim may recite “an entire system”, in Applicant’s words, the various elements identified by Applicant (represented by numbers 1-7) are additional elements which only amount to mere instructions to apply the abstract idea using computers/computing components. For example, the processor is an electronic means of performing the step of “generate a telemetry indication report comprising the determined telemetry indication score”; generating a telemetry report with a determined score is a personal behavior that could be performed by a healthcare provider. Examiner acknowledges Applicant’s remark that this cannot be directed to a mental process and submits that as shown above, it has not been classified as a mental process. These remarks are not persuasive.
Regarding m. Significant improvement, Applicant’s remarks have been considered but are not persuasive. As previously stated, generating “optimized inputs” is an improvement to the abstract idea itself. Regarding paragraph [0088] and its disclosure of “significantly improves the sources of data thereby used to make improved analyses regarding telemetry requirements”, Examiner reiterates the position that any purported improvements are only improvements to the abstract idea. As described, “constant re-evaluation is required to be determined when a patient can be taken off telemetry, which is very time-consuming” (para. [0088]). Providing “improved sources of data” to improve the process of the re-evaluation, or analysis of data, to make such a determination, is at best, an improvement to the abstract idea itself. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more. Regarding remark at page 28, “Applicant notes that Applicant is the expert on this subject…”, Examiner respectfully submits that this is an argument put forth by Applicant’s representative and is not evidence. Applicant’s representative is encouraged to file a R1.132 affidavit setting forth Applicant’s credentials so that evidence may be weighed appropriately. However, as previously stated, Examiner submits that generating “improved” data amounts to improvements to the abstract idea itself and does not constitute a technological improvement. Please see MPEP 2106.05(a) as previously cited above. This argument is not persuasive.
Regarding remarks at pages 28-29 pertaining to Step 2A Prong 2, please see all of the above remarks and 101 analysis section above. Any purported improvements may be improvements to the abstract idea, but are not technological improvements. The additional elements only amount to mere instructions to apply the abstract idea on a computer, which is not sufficient to integrate the abstract idea into a practical application.
Regarding remarks directed to Ex parte YINAN CHEN and FAN LI, Appeal 2023-003769 (U.S. Pat. App. No. 16/958,077, PTAB Decision 20 November 2024), Examiner submits that PTAB Decisions of other applications are not legally binding on the instant claims. PTAB decisions are particular to the facts of each individual case, and each case is evaluated individually on its own merits. These remarks are not persuasive.
For the above reasons, Applicant’s arguments are not persuasive and the rejection of Claims 1-3, 5, 8-12, 15-19 under 35 USC 101 is maintained.
Conclusion
In the interest of expediting prosecution, Examiner respectfully requests that Applicant provides citations to relevant paragraphs of specification for support for amendments in future correspondence.
The following relevant prior art not cited is made of record:
US Publication 20170312530 A1, directed to managing telemetry communications of a medical device
US Publication 20170216610 A1, directed to telemetry overuse reduction in an implantable device using artificial intelligence
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE-MARIE K ALDERSON whose telephone number is (571)272-3370. The examiner can normally be reached on Mon-Fri 9:00am-5:00pm EST, and generally schedules interviews in the timeframe of 2:00-5:00pm EST.
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/ANNE-MARIE K ALDERSON/Primary Examiner, Art Unit 3682
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