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 .
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.
Election/Restrictions
Applicant’s election without traverse of Group I, Claims 1-12, in the reply filed on 23 June 2026 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1, the limitation "labeling the received data" recited in the eleventh line of the claim lacks proper antecedent basis. This limitation is interpreted to mean "labeling the captured data". The same indefiniteness issue and interpretation also apply to claim 2. It is noted that the phrase “the received data” throughout the claims is being interpreted as “the captured data” (and, for Claim 11, “wherein capturing the data includes”).
Regarding Claim 2, the phrase “the occluded breathing airway” lacks proper antecedent basis. This limitation is interpreted to mean “the occluded target portion of the lung”.
Regarding Claim 8, it is unclear how the machine learning model, which is trained via an indication of whether collateral ventilation is present in a particular patient target lung portion, would also provide an output of the indication used to train it. In combination with Claim 8, Claim 1 is being interpreted such that the machine learning model is trained to provide an output of an indication of whether collateral ventilation is present in a particular patient target lung portion. The same indefiniteness issue and interpretation issue apply to Claim 9.
Claims not explicitly rejected above are rejected due to their dependence on the above claims.
Claim Rejections - 35 USC § 103
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.
Claims 1-3, 5-7, and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Krimsky'693 (U.S. Patent Publication 20180049693) in view of Rapaka et. al.'464 (U.S. Patent Publication 20200323464).
Regarding Claim 1, Krimsky’693 discloses a collateral ventilation quantification system for training a machine learning model for use in a computer-based clinical decision support system to assist in predicting patient outcome for endoluminal valve placement (Paragraph [0060] - Continuing with FIGS. 1-4, it is contemplated that a predictive approach may be utilized. Such an approach may better enable a clinician to determine whether the planned procedure will have the desired outcome or will result in deleterious effects), the collateral ventilation quantification system comprising:
at least one sensor to capture data based on at least one of pressure or airflow at a target portion of a lung of a patient that is occluded by a device from receiving air via a breathing airway of the lung (Paragraph [0056] - In embodiments, each of the one way valve 100a, balloon catheter 100b, and tool 100c may include a pressure sensor 100e (FIGS. 2A, 3A, and 3C) or other suitable device disposed thereon capable of measuring the pressure within the isolated portion of the lung);
processing circuitry; and memory, including instructions, which when executed by the processing circuitry, cause the processing circuitry to perform operations (Paragraph [0044] - The memory may include any non-transitory computer-readable storage media for storing data and/or software that is executable by a processor) comprising:
labeling the received data based on a corresponding patient breathing outcome to generate training data (Paragraph [0042] - Over time, a database may be built using the data obtained during each reversible isolation procedure. This database may be indexed such that clinicians may review data obtained from similar patients to better predict the outcome of the procedure).
Krimsky’693 further discloses training a database, based at least in part on the training data (Paragraph [0060] - Over time, a database may be built using the data obtained during each procedure), but fails to disclose training a machine learning model, based at least in part on the training data, to predict one or more patient breathing outcomes via an indication of whether collateral ventilation is present in a particular patient target lung portion. Rapaka et. al.'464 teaches training a machine learning model based on trained data from databases to predict a presence pertaining to ventilation issues (Paragraph [0051] - The GOLD scores may be used. The GOLD score for each sample is thresholded to find a binary classification of COPD or not (e.g., 0=no COPD and 1-4=COPD). Alternatively, the COPD score is used as the ground truth to classify into one of five classes (0-5 GOLD score); Paragraph [0052] - For training the model, a computer, workstation, server, or other processor accesses a database of hundreds or thousands of example inputs with known COPD information (e.g., yes/no or GOLD score). The machine learns the values of variables of the architecture (e.g., the convolutional neural network) from the example inputs and ground truth of the database. Using a piecewise-differentiable function or other deep learning function, the machine trains the model to learn to relate input values to output COPD information). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Krimsky’693 to include a machine learning model to predict clinical outcomes in order to increase understanding/predictability and as a result provide higher quality of life (Paragraph [0023] of Rapaka et al.’464 - This increased understanding and predictability may provide patients with higher quality of life (better preliminary identification of disease risk, diagnosis, management, etc.)) as well as assist in guiding clinicians' decisions regarding foreseeable condition output (Paragraph [0059] of Rapaka et al.’464 - For example, based on the learned correlations from the big-data machine learning analysis, new biomarkers may be identified to guide clinician's decision to schedule more, or less, frequent assessments and appointments with patients based on their current and foreseeable condition output as COPD information).
Krimsky’693 further discloses storing the database (Paragraph [0044] - The memory may include any non-transitory computer-readable storage media for storing data and/or software that is executable by a processor; Paragraph [0063] - The data of these procedures is recorded and used to further the database for future clinicians), but fails to disclose storing the machine learning model. Rapaka et. al.'464 teaches storing machine learned models (Paragraph [0055] - The machine-learned model is stored in the memory with the training data or other memory). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Krimsky’693 to include storing machine learned models in order to provide diagnostic assistance even in remote locations (Paragraph [0055] - copies of the machine-learned model are distributed to or on different medical scanners for use in hospitals or medical practices. As another example, copies are stored in a memory of one or more servers for COPD diagnosis assistance as a service or for remote COPD diagnosis assistance) as well as output information pertaining to various inputs without needing to retrain the machine learned model (Paragraph [0056] - Once trained, the machine-learned model is applied. The input values for a particular patient are input. The machine-learned model outputs the COPD information for that patient in response to the input) as seen in Rapaka et. al.’464.
Regarding Claim 2, the sections of Krimsky’693 in view of Rapaka et. al.’464 cited above disclose an apparatus configured to perform the method set forth in the claim.
Regarding Claim 3, Krimsky’693 in view of Rapaka et. al.’464 discloses the method outlined in Claim 2 above. Krimsky’693 further discloses wherein the occluded breathing airway is occluded by a balloon to block an outflow airway, and wherein the received data is pressure data based on an applied positive pressure to an inflow airway (Paragraph [0049] - As a result of the inflation, an exterior surface 304 (FIG. 3A) of the balloon 302 expands and compresses against the inner walls of the airway; Paragraph [0050] - It is contemplated that the balloon catheter 100b may be utilized to inject a fluid such as a liquid or gas or other substance (e.g., saline or the like) within the isolated portion of the lungs to displace the air from the isolated portion of the lung and induce pulmonary consolidation (FIG. 3B). In this manner, the balloon catheter 100b may be in fluid communication with a fluid reservoir (not shown) and fluid pump (not shown) or other suitable device capable of injecting fluid through the cannula 306 (FIG. 3A) of the balloon catheter 100b and into the isolated portion of the lungs).
Regarding Claim 5, Krimsky’693 in view of Rapaka et. al.’464 discloses the method outlined in Claim 2 above. Krimsky’693 further discloses wherein training the machine learning model includes using at least one of volume data of a lung portion, a medical image of the patient, a fissure integrity score, a disease state of the patient, a patient age, or a comorbidity of the patient as additional input data (Paragraph [0044] - Patient characteristics may include, but are not limited to, age, sex, race, lung volume, disease type, respiration rate, observed overinflation, smoking history, oxygen saturation, or the like).
Regarding Claim 6, Krimsky’693 in view of Rapaka et. al.’464 discloses the method outlined in Claim 2 above. Krimsky’693 further discloses wherein the corresponding patient breathing outcome includes a clinician determination of whether the patient has collateral ventilation at the target portion of the lung based on the received data (Paragraph [0042] - Over time, a database may be built using the data obtained during each reversible isolation procedure. This database may be indexed such that clinicians may review data obtained from similar patients to better predict the outcome of the procedure).
Regarding Claim 7, Krimsky’693 in view of Rapaka et. al.’464 discloses the method outlined in Claim 2 above. Krimsky’693 further discloses wherein the corresponding patient breathing outcome includes an objective outcome of breathing of the patient or a patient reported breathing assessment obtained after a procedure to insert an endoluminal valve in the patient (Paragraph [0042] - The present disclosure is directed to devices and systems for irreversibly or reversibly inducing atelectasis or pulmonary consolidation in a patient as part of a process of evaluating the effects of a permanent treatment. As described herein, one or more airway closure or instillation devices may be navigated to a location within the airways and deployed to isolate a particular portion of the lungs).
Regarding Claim 10, Krimsky’693 in view of Rapaka et. al.’464 discloses the method outlined in Claim 2 above. Krimsky’693 further discloses further comprising occluding, using the device, the breathing airway of the target portion of the lung (Paragraph [0049] - As a result of the inflation, an exterior surface 304 (FIG. 3A) of the balloon 302 expands and compresses against the inner walls of the airway. In this manner, the portion of the lungs containing the area of interest is sealed off from the remaining portions of the lung).
Regarding Claim 11, Krimsky’693 in view of Rapaka et. al.’464 discloses the method outlined in Claim 2 above. Krimsky’693 further discloses wherein receiving the data includes recurrently or periodically obtaining measurement data of the airflow or the pressure at the target portion of the lung (Paragraph [0056] - In embodiments, each of the one way valve 100a, balloon catheter 100b, and tool 100c may include a pressure sensor 100e (FIGS. 2A, 3A, and 3C) or other suitable device disposed thereon capable of measuring the pressure within the isolated portion of the lung, such as a digital or analog sensor (piezoelectric, capacitive, electromagnetic, optical, piezoresistive, resonant, thermal, or the like)).
Regarding Claim 12, Krimsky’693 in view of Rapaka et. al.’464 discloses the method outlined in Claim 2 above. Krimsky’693 further discloses wherein the occluded breathing airway is occluded by a valve to block an inflow airway while allowing outflow air, and wherein the received data is outflow air data (Paragraph [0048] - Endobronchial valve 202 is temporarily secured within the airway such that air may escape those portions of the lungs distal the valve (i.e., the area of the lung including the area of interest), but no new air may be drawn in those portions of the lung).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Krimsky'693 (U.S. Patent Publication 20180049693) in view of Rapaka et. al.'464 (U.S. Patent Publication 20200323464), as applied to claim 3 above, and further in view of Freitag et. al.'455 (U.S. Patent Publication 20140142455).
Regarding Claim 4, Krimsky’693 in view of Rapaka et. al.’464 discloses the method outlined in Claim 3 above. Krimsky’693 further discloses supplying a gas to the targeted location (Paragraph [0042] - a fluid such as a liquid, gas, or other substance may be injected within the lungs to displace the air within the isolated portion of the lungs, thereby inducing pulmonary consolidation in the isolated portion of the lung), but fails to disclose wherein the applied positive pressure includes a constant applied pressure. Freitag et. al.'455 teaches supplying constant positive pressure to observe changes at a targeted area (Paragraph [0107] - In some embodiments, detection of collateral ventilation is assisted with the application of medically safe continuous positive airway pressure (CPAP)…The isolated target lung compartment is not subjected directly to CPAP, however if collateral channels are present, the detection of these channels is facilitated because the CPAP amplifies the degree of airflow across the channels due to simple pressure gradient laws). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Krimsky’693 in view of Rapaka et. al.’464 to include continuous positive air pressure as a source for applying gas in order to avoid the need of supplying gas directly to the target area while still observing collateral ventilation as seen in Freitag et. al.’455.
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Krimsky'693 (U.S. Patent Publication 20180049693) in view of Rapaka et. al.'464 (U.S. Patent Publication 20200323464), as applied to claim 2 above, and further in view of Raffy et. al.'270 (U.S. Patent Publication 20150238270).
Regarding Claim 8, Krimsky’693 in view of Rapaka et. al.’464 discloses the method outlined in Claim 2 above. Krimsky’693 further discloses indication of whether collateral ventilation is present in a particular patient target lung portion (Paragraph [0042] - This database may be indexed such that clinicians may review data obtained from similar patients to better predict the outcome of the procedure; Paragraph [0048] - due to a condition known as collateral ventilation, it may be necessary to place multiple endobronchial valves 202 within the affected portion of the lung in order to isolate the desired portion of the lung. In this manner, a plurality of endobronchial valves 202 may be placed within one or more airways (FIG. 2B) in order to effectively evacuate air within the isolated portion of the lung; Paragraph [0061] - In this manner, the clinician may better predict the clinical consequences of the planned procedure, as will be described in further detail hereinbelow; Paragraph [0063] - By recording the above described information, the clinical consequences of the procedure are made accessible by clinicians performing similar procedures in the future, and may be used to predict the clinical effect of similar procedures in the future), but fails to disclose the indication of whether collateral ventilation is present in a particular patient target lung portion is output from the model as a binary display of either collateral ventilation being present or collateral ventilation not being present. Raffy et. al.'270 teaches indicating a presence of collateral ventilation via output from a binary model (Paragraph [0055] - The results that are presented to the clinician may be a prediction that one or more features are either present or absent, such as that collateral ventilation is present or absent in a particular lobe or sub-lobe). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Krimsky’693 in view of Rapaka et. al.’464 to include a binary output comprising “present” or “absent” as a method for presenting a clinician with a prediction pertaining to the status of collateral ventilation in order to assist with patient selection (triage) and treatment planning as seen in Raffy et. al.’270 (Paragraph [0054] - One or more of the predictions made by each of these models and the final treatment outcome prediction may be made automatically and presented to a clinician to assist with patient selection as part of a treatment planning and prediction system; Paragraph [0065] - the predicted features determined in this step may be used to triage patients into those who are eligible for particular procedures including endoscopic lung volume reduction procedures such as valve placement).
Regarding Claim 9, Krimsky’693 in view of Rapaka et. al.’464 discloses the method outlined in Claim 2 above. Krimsky’693 further discloses indication of whether collateral ventilation is present in a particular patient target lung portion (Paragraph [0042] - This database may be indexed such that clinicians may review data obtained from similar patients to better predict the outcome of the procedure; Paragraph [0048] - due to a condition known as collateral ventilation, it may be necessary to place multiple endobronchial valves 202 within the affected portion of the lung in order to isolate the desired portion of the lung. In this manner, a plurality of endobronchial valves 202 may be placed within one or more airways (FIG. 2B) in order to effectively evacuate air within the isolated portion of the lung; Paragraph [0061] - In this manner, the clinician may better predict the clinical consequences of the planned procedure, as will be described in further detail hereinbelow; Paragraph [0063] - By recording the above described information, the clinical consequences of the procedure are made accessible by clinicians performing similar procedures in the future, and may be used to predict the clinical effect of similar procedures in the future), but fails to disclose the indication is output from the model including a probability of the patient having collateral ventilation in the target portion. Raffy et. al.'270 teaches indicating a probability pertaining to a presence of collateral ventilation (Paragraph [0055] - Alternatively or additionally, the results may be presented as a predicted likelihood, such as a likelihood that a feature is present or that an outcome will occur, such as a likelihood that collateral ventilation is present (such as in an amount above a particular threshold), which may be presented as a percentage or other numerical value representing likelihood). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the method of Krimsky’693 in view of Rapaka et. al.’464 to include a prediction probability as a method for presenting a clinician with a prediction pertaining to the status of collateral ventilation in order to assist with patient selection (triage) and treatment planning as seen in Raffy et. al.’270 (Paragraph [0054] - One or more of the predictions made by each of these models and the final treatment outcome prediction may be made automatically and presented to a clinician to assist with patient selection as part of a treatment planning and prediction system; Paragraph [0065] - the predicted features determined in this step may be used to triage patients into those who are eligible for particular procedures including endoscopic lung volume reduction procedures such as valve placement).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mantri et. al.'027 (U.S. Patent Publication 20120150027) teaches an occlusion device comprising sensors to monitor air flow, temperature, and pressure. De Bruin et. al.’212 (U.S. Patent Publication 20110119212) teaches using machine learning to determine patient outcomes pertaining to a treatment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH ANN WESTFALL whose telephone number is (571) 272-3845. The examiner can normally be reached Monday-Friday 7:30am-4:30pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson can be reached at (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SARAH ANN WESTFALL/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791
Prosecution Insights