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 4 and 16 are objected to because of the following informalities:
Claim 4, line 3: “imputing” should read –inputting--;
Claim 16, line 2: “the historical” should read –clean the historical--; and
Claim 16, line 3: “imputing” should read –inputting--.
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 1-2, 4-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite “obtaining historical CRFA data associated with a plurality of patients previously treated using the CRFA system, … training an ensemble machine learning model to predict success of CRFA procedures based on the historical CRFA data; determining a first set of operating parameters for the CRFA system using the trained ensemble machine learning model, wherein the first set of operating parameters comprise settings of the CRFA system that are determined to affect the success of CRFA procedures; and determining a value or a range of values for each of the first set of operating parameters for use in CRFA treatment procedures using a decision tree-based model” in claim 1, and “obtain historical CRFA data associated with a plurality of patients previously treated using a CRFA system, … train an ensemble machine learning model to predict success of CRFA procedures based on the historical CRFA data; determine a first set of operating parameters for the CRFA system using the trained ensemble machine learning model, wherein the first set of operating parameters comprise settings of the CRFA system that are determined to affect the success of CRFA procedures; and determine a value or a range of values for each of the first set of operating parameters for use in CRFA treatment procedures using a decision tree-based model” in claims 13 and 20. This judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
Claims 1, 13, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). For example, the claim language “obtaining historical RFA data associated with a plurality of patients” is merely data gathering, which is an insignificant extra solution activity. The claim language “training an ensemble machine learning model to predict success of RFA procedures” is broadly recited and also qualifies as an insignificant extra solution activity that’s recited at a high level of generality and falls under the WURC consideration since training a model is considered common currently. Also, there is no claim language that describes an improved way of training a machine learning model or improvements to a computer component or system performance based upon adjustments to parameters of a machine learning model. Additionally, the claim language “for use in RFA treatment procedures” isn’t sufficient for practical integration because it doesn’t actually require the RFA treatments to be performed as a part of the claimed method. Finally, the claim language “using a decision tree-based model” is merely a mental process with no additional elements to amount to significantly more than the judicial exception.
Claim 2 recites the claim language “presenting the first set of operating parameters, and each corresponding value or range of values, via a user interface”. This additional element is extra-solution activity (e.g., displaying data) and thus does not provide significantly more.
Claim 3 recites the claim language “operating the CRFA ablation system based on the first set of operating parameters, and each corresponding value or range of values”, which is an abstract idea based on dependencies. This is an additional element that may provide a practical application or significantly more (e.g., an improvement) and thus is patent eligible under 35 U.S.C. 101.
Claim 4 recites the claim language “cleaning and/or annotating the historical CRFA data prior to training the ensemble machine learning model, wherein cleaning comprises removing or imputing missing data”. Cleaning or annotating data is considered a mental process and thus does not provide significantly more.
Claim 5 recites the claim language “collecting additional CRFA data from a plurality of second patients that are treated based on the first set of operating parameters, and each corresponding value or range of values”. This additional element is extra-solution activity (e.g., further gathering data) and thus does not provide significantly more. Claim 5 also recites the claim language “retraining the ensemble machine learning model using hyperparameter tuning based on the additional CRFA data”. This is considered evaluation or judgment, which is a mental process and thus does not provide significantly more.
Claim 14 recites the claim language “present the first set of operating parameters, and each corresponding value or range of values, via a user interface”. This additional element is extra-solution activity (e.g., displaying data) and thus does not provide significantly more.
Claim 15 recites the claim language “control the CRFA ablation system based on the first set of operating parameters, and each corresponding value or range of values”, which is an abstract idea based on dependencies. This is an additional element that may provide a practical application or significantly more (e.g., an improvement) and thus is patent eligible under 35 U.S.C. 101.
Claim 16 recites the claim language “the historical CRFA data prior to training the ensemble machine learning model, wherein cleaning comprises removing or imputing missing data”. Cleaning or annotating data is considered a mental process and thus does not provide significantly more.
Claim 17 recites the claim language “collect additional CRFA data from a plurality of second patients that are treated based on the first set of operating parameters, and each corresponding value or range of values”. This additional element is extra-solution activity (e.g., further gathering data) and thus does not provide significantly more. Claim 5 also recites the claim language “retrain the ensemble machine learning model using hyperparameter tuning based on the additional CRFA data”. This is considered evaluation or judgment, which is a mental process and thus does not provide significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-11 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Templeton et al., US 20250336541, herein referred to as “Templeton”, in view of Wang et al., US 20200188054, herein referred to as “Wang”.
Regarding claim 1, Templeton discloses a method for determining settings for radiofrequency ablation (RFA) system (Figure 7 and Figure 22 and [0062]: “For example, energy generation device 1054 may include an RDN generator configured to generate radiofrequency energy for an ablation catheter (e.g., medical instrument 1030) to deliver to ablate tissue in renal arteries to treat hypertension.”), the method comprising: obtaining historical RFA data associated with a plurality of patients previously treated (This claim language is in the past tense, meaning the claim doesn’t require the treatments as part of the claimed method.) using the RFA system ([0138]: “For example, machine learning model(s) 7022 may be trained using data collected from past therapeutic medical procedures, such as imaging data, tracked motion of medical instruments, generator data, lesion classification or the like.”), wherein the historical RFA data includes patient characteristics ([0138] and [0246]), operating parameters of the RFA system ([0138] and [0246]), and treatment outcomes associated with each of the plurality of patients ([0138] and [0246]); training an ensemble machine learning model to predict success of RFA procedures based on the historical RFA data (Figure 22 and [0246]-[0247]); determining a first set of operating parameters for the RFA system using the trained ensemble machine learning model (Figure 8: 8004 and Figure 22: target output 22078), wherein the first set of operating parameters comprise settings of the RFA system that are determined to affect the success of RFA procedures ([0246]-[0247] and Figure 22: target output 22078); and determining a value or a range of values for each of the first set of operating parameters for use in RFA treatment procedures using a decision tree-based model ([0246]). Templeton does not explicitly disclose a method wherein the RFA system is specifically a cooled radiofrequency ablation (CRFA) system.
However, Wang teaches a method wherein the RFA system is specifically a cooled radiofrequency ablation (CRFA) system ([0003]: “Cooled radiofrequency ablation is achieved by delivering cooling fluid (e.g., sterile water) via a peristaltic pump through an active electrode on a radiofrequency ablation probe in a closed-loop circulation.”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the method disclosed by Templeton so that the RFA system is specifically a cooled radiofrequency ablation (CRFA) system as taught by Wang to allow the electrode-tissue interface temperature to be maintained at a level so as to not increase the temperature of the tissue close to the boiling point of water resulting in charring or dehydration or significant desiccation of the surrounding tissue, thus meaning more energy can be delivered to the lesion site, resulting in the creation of a larger lesion volume compared to conventional radiofrequency ablation methods that do not include such cooling (Wang [0003]).
Regarding claim 2, Templeton in view of Wang discloses the method of claim 1, and Templeton further discloses a method further comprising presenting the first set of operating parameters, and each corresponding value or range of values, via a user interface ([0070]: “For example, computing device 1050, guidance workstation 1052, and/or server 1060 executing the machine learning model may output for display the most likely to be successful treatment strategies for the particular type of lesion.”).
Regarding claim 3, Templeton in view of Wang discloses the method of claim 1, and Templeton further discloses a method further comprising operating the RFA ablation system based on the first set of operating parameters, and each corresponding value or range of values ([0017]-[0018]). In combination with Wang, the RFA ablation system of Templeton is a CRFA ablation system.
Regarding claim 4, Templeton in view of Wang discloses the method of claim 1, and Templeton further discloses a method further comprising cleaning and/or annotating the historical RFA data prior to training the ensemble machine learning model, wherein cleaning comprises removing or imputing missing data ([0168] and [0009]: “The medical system may be configured to redact, remove, obfuscate, or otherwise render illegible text information such as a patient name, birthdate, or other personal health information. In some examples, imaging data from one or more image sensors may include PHI, and the medical system may be configured to scan the imaging data, identify a text overlay, and redact, remove, obfuscate, or otherwise render illegible the text overlay.”). In combination with Wang, the historical RFA data of Templeton is historical CRFA data.
Regarding claim 5, Templeton in view of Wang discloses the method of claim 1, and Templeton further discloses a method further comprising: collecting additional RFA data from a plurality of second patients that are treated based on the first set of operating parameters, and each corresponding value or range of values ([0138]); and retraining the ensemble machine learning model using hyperparameter tuning based on the additional RFA data ([0079] and [0247]). In combination with Wang, the additional RFA data of Templeton is additional CRFA data.
Regarding claim 6, Templeton in view of Wang discloses the method of claim 1, and Templeton further discloses a method further comprising: receiving new patient characteristics for a new patient that is set to undergo a RFA treatment procedure ([0138]: “For example, machine learning model(s) 7022 may be trained using data collected from past therapeutic medical procedures, such as imaging data, tracked motion of medical instruments, generator data, lesion classification or the like.”); and predicting a value or range of values for each of the first set of operating parameters for the RFA treatment procedure based on the new patient characteristics (Figure 22 and [0246]-[0247]). In combination with Wang, the RFA treatment procedure of Templeton is a CRFA treatment procedure.
Regarding claim 7, Templeton in view of Wang discloses the method of claim 1, and Templeton further discloses a method wherein the ensemble machine learning model comprises one or more bagged and gradient boosted decision trees and/or one or more artificial neural networks ([0093] and [0246]).
Regarding claim 8, Templeton in view of Wang discloses the method of claim 1, and Templeton further discloses a method further comprising collecting the historical RFA data by storing the patient characteristics, the operating parameters, and the treatment outcomes associated with the plurality of patients over a period of time ([0242] and [0107]). In combination with Wang, the historical RFA data of Templeton is historical CRFA data.
Regarding claim 9, Templeton in view of Wang discloses the method of claim 1, and Templeton further discloses a method wherein the first set of operating parameters comprise one or more of: a current, a voltage, an impedance, a power, a temperature, a treatment duration, a total treatment time, a temperature ramp rate, or a ramp time ([0144]-[0145] wherein a simulation must have a treatment time).
Regarding claim 10, Templeton in view of Wang discloses the method of claim 1, and Templeton further discloses a method wherein the operating parameters included in the historical RFA data comprise one or more of: a current, a voltage, an impedance, a power, a temperature, a treatment duration, a total treatment time, a temperature ramp rate, or a ramp time ([0246]: “Training data 22072 may include, for example, data collected from past medical procedures, imaging data 2014, generator data 2020, motion data 2028, medical instrument data 2026, and/or any other training data described herein.” And [0242]: generator data). In combination with Wang, the historical RFA data of Templeton is historical CRFA data.
Regarding claim 11, Templeton in view of Wang discloses the method of claim 1, and Templeton further discloses a method wherein the patient characteristics comprise one or more of age, gender, or body mass index (BMI) ([0208]: “In some examples wherein the patient characteristic includes at least one of a patient age, height, weight, sex, disease, or diagnosis.”).
Regarding claim 13, Templeton discloses a system for determining settings for use in radiofrequency ablation (RFA) (Figure 7 and Figure 22 and [0062]: “For example, energy generation device 1054 may include an RDN generator configured to generate radiofrequency energy for an ablation catheter (e.g., medical instrument 1030) to deliver to ablate tissue in renal arteries to treat hypertension.”), the system comprising: one or more processors (Figure 7: processing circuitry 7004); and memory having instructions stored thereon (Abstract and [0017]) that, when executed by the one or more processors, cause the system to ([0248]): obtain historical RFA data associated with a plurality of patients previously treated (This claim language is in the past tense, meaning the claim doesn’t require the treatments as part of the claimed method.) using a RFA system ([0138]: “For example, machine learning model(s) 7022 may be trained using data collected from past therapeutic medical procedures, such as imaging data, tracked motion of medical instruments, generator data, lesion classification or the like.”), wherein the historical RFA data includes patient characteristics ([0138] and [0246]), operating parameters of the RFA system ([0138] and [0246]), and treatment outcomes associated with each of the plurality of patients ([0138] and [0246]); train an ensemble machine learning model to predict success of RFA procedures based on the historical RFA data (Figure 22 and [0246]-[0247]); determine a first set of operating parameters for the RFA system using the trained ensemble machine learning model (Figure 8: 8004 and Figure 22: target output 22078), wherein the first set of operating parameters comprise settings of the RFA system that are determined to affect the success of RFA procedures ([0246]-[0247] and Figure 22: target output 22078); and determine a value or a range of values for each of the first set of operating parameters for use in RFA treatment procedures using a decision tree-based model ([0246]). Templeton does not explicitly disclose a system wherein the RFA system is specifically a cooled radiofrequency ablation (CRFA) system.
However, Wang teaches a system wherein the RFA system is specifically a cooled radiofrequency ablation (CRFA) system ([0003]: “Cooled radiofrequency ablation is achieved by delivering cooling fluid (e.g., sterile water) via a peristaltic pump through an active electrode on a radiofrequency ablation probe in a closed-loop circulation.”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the system disclosed by Templeton so that the RFA system is specifically a cooled radiofrequency ablation (CRFA) system as taught by Wang to allow the electrode-tissue interface temperature to be maintained at a level so as to not increase the temperature of the tissue close to the boiling point of water resulting in charring or dehydration or significant desiccation of the surrounding tissue, thus meaning more energy can be delivered to the lesion site, resulting in the creation of a larger lesion volume compared to conventional radiofrequency ablation methods that do not include such cooling (Wang [0003]).
Regarding claim 14, Templeton in view of Wang discloses the system of claim 13, and Templeton further discloses a system wherein the instructions further cause the system to: present the first set of operating parameters, and each corresponding value or range of values, via a user interface ([0070]: “For example, computing device 1050, guidance workstation 1052, and/or server 1060 executing the machine learning model may output for display the most likely to be successful treatment strategies for the particular type of lesion.”).
Regarding claim 15, Templeton in view of Wang discloses the system of claim 13, and Templeton further discloses a system wherein the instructions further cause the system to: control the RFA ablation system based on the first set of operating parameters, and each corresponding value or range of values ([0017]-[0018]). In combination with Wang, the RFA ablation system of Templeton is a CRFA ablation system.
Regarding claim 16, Templeton in view of Wang discloses the system of claim 13, and Templeton further discloses a system wherein the instructions further cause the system to: clean the historical RFA data prior to training the ensemble machine learning model, wherein cleaning comprises removing or imputing missing data ([0168] and [0009]: “The medical system may be configured to redact, remove, obfuscate, or otherwise render illegible text information such as a patient name, birthdate, or other personal health information. In some examples, imaging data from one or more image sensors may include PHI, and the medical system may be configured to scan the imaging data, identify a text overlay, and redact, remove, obfuscate, or otherwise render illegible the text overlay.”). In combination with Wang, the historical RFA data of Templeton is historical CRFA data.
Regarding claim 17, Templeton in view of Wang discloses the system of claim 13, and Templeton further discloses a system wherein the instructions further cause the system to: collect additional RFA data from a plurality of second patients that are treated based on the first set of operating parameters, and each corresponding value or range of values ([0138]); and retrain the ensemble machine learning model using hyperparameter tuning based on the additional RFA data ([0079] and [0247]). In combination with Wang, the additional RFA data of Templeton is additional CRFA data.
Regarding claim 18, Templeton in view of Wang discloses the system of claim 14, and Templeton further discloses a system wherein the instructions further cause the system to: receive new patient characteristics for a new patient that is set to undergo a RFA treatment procedure ([0138]: “For example, machine learning model(s) 7022 may be trained using data collected from past therapeutic medical procedures, such as imaging data, tracked motion of medical instruments, generator data, lesion classification or the like.”); and predict a value or range of values for each of the first set of operating parameters for the RFA treatment procedure based on the new patient characteristics (Figure 22 and [0246]-[0247]). In combination with Wang, the RFA treatment procedure of Templeton is a CRFA treatment procedure.
Regarding claim 19, Templeton in view of Wang discloses the system of claim 13, and Templeton further discloses a system wherein the ensemble machine learning model comprises one or more bagged and gradient boosted decision trees and/or one or more artificial neural networks ([0093] and [0246]).
Regarding claim 20, Templeton discloses a non-transitory computer readable medium having instructions stored thereon ([0019] and [0021]) that, when executed by one or more processors (Figure 7: processing circuitry 7004), cause a device to ([0248]): obtain historical RFA data associated with a plurality of patients previously treated (This claim language is in the past tense, meaning the claim doesn’t require the treatments as part of the claimed method.) using a RFA system ([0138]: “For example, machine learning model(s) 7022 may be trained using data collected from past therapeutic medical procedures, such as imaging data, tracked motion of medical instruments, generator data, lesion classification or the like.”), wherein the historical RFA data includes patient characteristics ([0138] and [0246]), operating parameters of the RFA system ([0138] and [0246]), and treatment outcomes associated with each of the plurality of patients ([0138] and [0246]); train an ensemble machine learning model to predict success of RFA procedures based on the historical RFA data (Figure 22 and [0246]-[0247]); determine a first set of operating parameters for the RFA system using the trained ensemble machine learning model (Figure 8: 8004 and Figure 22: target output 22078), wherein the first set of operating parameters comprise settings of the RFA system that are determined to affect the success of RFA procedures ([0246]-[0247] and Figure 22: target output 22078); and determine a value or a range of values for each of the first set of operating parameters for use in RFA treatment procedures using a decision tree-based model ([0246]). Templeton does not explicitly disclose a non-transitory computer readable medium wherein the RFA system is specifically a cooled radiofrequency ablation (CRFA) system.
However, Wang teaches a non-transitory computer readable medium wherein the RFA system is specifically a cooled radiofrequency ablation (CRFA) system ([0003]: “Cooled radiofrequency ablation is achieved by delivering cooling fluid (e.g., sterile water) via a peristaltic pump through an active electrode on a radiofrequency ablation probe in a closed-loop circulation.”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the non-transitory computer readable medium disclosed by Templeton so that the RFA system is specifically a cooled radiofrequency ablation (CRFA) system as taught by Wang to allow the electrode-tissue interface temperature to be maintained at a level so as to not increase the temperature of the tissue close to the boiling point of water resulting in charring or dehydration or significant desiccation of the surrounding tissue, thus meaning more energy can be delivered to the lesion site, resulting in the creation of a larger lesion volume compared to conventional radiofrequency ablation methods that do not include such cooling (Wang [0003]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Templeton in view of Wang, further in view of McKinnon et al., US 20200275976, herein referred to as “McKinnon”.
Regarding claim 12, Templeton in view of Wang discloses the method of claim 1, but does not explicitly disclose a method wherein the treatment outcomes included in the historical CRFA data are derived from patient-provided pain scores.
However, McKinnon teaches a method wherein the treatment outcomes included in the historical RFA data are derived from patient-provided pain scores ([0211]). In combination with Wang, the historical RFA data of McKinnon is historical CRFA data.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the method disclosed by Templeton so that the treatment outcomes included in the historical CRFA data are derived from patient-provided pain scores as taught by McKinnon so that the treatment can be tailored based on the unique and personal patient goals for surgery (McKinnon [0313]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nora W Rhodes whose telephone number is (571)272-8126. The examiner can normally be reached Monday-Friday 10am-6pm EST.
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/N.W.R./Examiner, Art Unit 3794
/SEAN W COLLINS/Primary Examiner, Art Unit 3794
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