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 .
This is a reply to Applicant's Arguments/Remarks filed on 06/04/2026 for U.S. Application 18/694293 where:
a. Claims 1-19 and 26 are pending in the application.
b. Claims 1, 9, 18, and 26 are currently amended.
c. Claims 20-25 and 27-29 are currently cancelled.
Response to Arguments
Applicant's arguments with respect to claims 1-19 and 26 have been considered but are moot because the arguments are based on the amendments and a new ground of rejection has been entered.
Allowable Subject Matter
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening 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 1-8 and 10-19 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Robinson et al. (US 2021/0137384, hereinafter Robinson) in view of Hyvonen et al. (US 12119102, hereinafter Hyvonen).
Regarding claim 1, Robinson teaches: A system (Abstract, disclosed herein are systems and method. See fig. 4, system 400) comprising:
a database ([0079], These scores can be stored in a database, see fig. 4, storage device 430); and
a computing device communicatively coupled to the database (see fig. 4, input device 445 connection to storage device 430) and configured to:
receive image data for an organ of a patient ([0073-0074], In some examples, the one or more input mappings can be one or more images from a singular patient. At block 105, at least one processor can receive one or more input mappings (e.g., of a corresponding ventricle, a corresponding atria, etc.).);
determine a recommended target area of the organ for treatment based on the image data ([0076, 0081, 0083, 0116], and see FIGS. 6A, 6B, and 6C are example outputs of the decision support module.);
generate recommended target data characterizing the recommended target area of the organ ([0085, 0087], see FIGS. 6A, 6B, and 6C, The at least one processor can determine one or more targets for ablation, for example, the target for ablation may include one or more cardiac arrhythmia target segments.); and
store the recommended target data in the database (fig. 2, steps 215 and 230, [0121], The data and/or ablation plan for each patient may be stored on a database to inform future patient treatment.).
Robinson does not explicitly teach: determine a segment of a segment model based on the recommended target area and generate the segment of the segment model.
However, Hyvonen teaches: determine a segment of a segment model based on the recommended target area (col. 6, For instance, in a non-limiting example, the analytics server 110a may query and retrieve medical images from the database 120d and combine the medical images with segment data received from a medical professional operating the medical professional device 120b and/or medical device 160 to perform preprocessing on the medical image (e.g., segment the medical image).) and generate the segment of the segment model (Col. 9, For instance, the analytics server may segment the training dataset into three groups (i.e., training, validation, and test). The analytics server may train the AI model based on the first group (training). The analytics server may then execute the (at least partially) trained AI model to predict results for the second group of data (validation). The analytics server then verifies whether the prediction is correct. Using the above-described method, the analytics server may evaluate whether the AI model is properly trained.).
Therefore, the Applicant's claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Robinson to include determine a segment of a segment model based on the recommended target area and generate the segment of the segment model as taught by Hyvonen. The motivation/suggestion would have been to further enhance/improve the system since doing so will allow for automate the pixel level isolation of organs, lesion and tumors therefore dramatically speeds up analysis, reduces human error, and yields precise volumetric measurements for clinical treatment planning.
Regarding claim 2, Robinson and Hyvonen teach: The system of claim 1, wherein the computing device is configured to: receive report data characterizing medical findings of the patient (Robinson, [0093-0094], As additional patients are treated, the previous treatments can inform future treatments); and
determine the recommended target area based on the report data (Robinson, [0093-0094], As additional patients are treated, the previous treatments can inform future treatments and can be used to adjust the target of the current patient (for example, by training the neural network). In another example, the target may be adjusted based on a weighting of input mappings or the confidence score of the combined mappings.).
Regarding claim 3, Robinson and Hyvonen teach: The system of claim 2, wherein the computing device is configured to determine the recommended target area by applying a text extracting process to the report data to identify text within the report data (Robinson, [0093-0094], see fig. 6A. In an example, the decision support module may be displayed, printed, or provided to the physician in any form capable of providing the information. FIGS. 6A, 6B, and 6C are example outputs of the decision support module.).
Regarding claim 4, Robinson and Hyvonen teach: The system of claim 3, wherein the computing device is configured to determine the recommended target area based on applying a rule to the text (Robinson, [0081-0082]. The labeled data can be the actual target probabilities.).
Regarding claim 5, Robinson and Hyvonen teach: The system of claim 1, wherein the computing device is configured to determine the recommended target area based on applying one or more machine learning models to the image data (Robinson, [0124], In some examples, method 200 can be used in a machine-learning environment (for example, as shown in FIG. 5). The target can be adjusted automatically using machine learning. Future ablation plans and decision support modules may be automatically adjusted using machine learning. For example, blocks 215, 220, 225, and/or 230 may be adjusted automatically using machine learning. Machine learning tools and predictive analytics can be integrated within method 200 to create a clinical decision support infrastructure such as the decision support module.).
Regarding claim 6, Robinson and Hyvonen teach: The system of claim 5, wherein the computing device is configured to: generate features based on historical image scans; and train the one or more machine learning models based on the generated features (Robinson, [0124], The target can be adjusted automatically using machine learning. Future ablation plans and decision support modules may be automatically adjusted using machine learning. For example, blocks 215, 220, 225, and/or 230 may be adjusted automatically using machine learning).
Regarding claim 7, Robinson and Hyvonen teach: The system of claim 1, wherein the image data is at least one of magnetic resonance image data (Robinson, [0005], the anatomic mapping may be at least one of a computer tomography image or a magnetic resonance image) and computed tomography image data (Robinson, [0005], the anatomic mapping may be at least one of a computer tomography image or a magnetic resonance image).
Regarding claim 8, Robinson and Hyvonen teach: The system of claim 1, wherein the computing device is configured to transmit the recommended target data to a second computing device to treat the patient (Robinson, [0146, 0150] and see figs. 4 and 5, The output from the trained neural network can be provided to a treatment unit 514 for treating a patient. In some examples, the output from the trained neural network can be inputted directly into a treatment unit to perform a procedure on a patient.).
Regarding claim 10, Robinson and Hyvonen teach: The system of claim 9, wherein the computing device is configured to determine a segment of the segment model corresponding to the recommended target area of the organ (Robinson, [0077, 0084], For example, the input mappings can be combined by overlapping segmentation models, combining a segmentation model and 3D geometries, or combinations thereof.).
Regarding claim 11, Robinson and Hyvonen teach: The system of claim 10, wherein the computing device is configured to determine the segment based on a relative location of the recommended target area to a portion of the organ (Robinson, [0081] and fig. 6A, Any modeling approach can be used to learn the image features that predict the location of an abnormality (e.g., VT) on each input mapping.).
Regarding claim 12, Robinson and Hyvonen teach: The system of claim 11, wherein the computing device is configured to: determine a distance and a direction from the portion of the organ to the recommended target area; and based on the distance and the direction, determine the segment (Robinson, [0081] and fig. 6A, Any modeling approach can be used to learn the image features that predict the location of an abnormality (e.g., VT) on each input mapping.).
Regarding claim 13, Robinson and Hyvonen teach: The system of claim 9, wherein the computing device is configured to generate the 3d structure image based on an interactive model (Robinson, [0085, 0107], In some examples, the non-image data, such as the 12-lead mappings can be displayed to the user and the user can interact with or click on segments of the cardiac arrhythmia target. The cardiac arrhythmia target can be a segment, multiple segments, or a 3D volume).
Regarding claim 14, Robinson and Hyvonen teach: The system of claim 13, wherein the computing device is configured to: receive an input selecting one or more segments of the interactive model; and update the displayed segment model to indicate the selected one or more segments (Robinson, [0085, 0107], In some examples, the non-image data, such as the 12-lead mappings can be displayed to the user and the user can interact with or click on segments of the cardiac arrhythmia target. The cardiac arrhythmia target can be a segment, multiple segments, or a 3D volume).
Regarding claim 15, Robinson and Hyvonen teach: The system of claim 1, wherein the computing device is configured to: obtain electrocardiogram (EKG) data for the patient; and determine the recommended target area based on the EKG data (Robinson, [0076,0079,0090], In one example, an input mapping (e.g. a 17-lead ECG) can be divided into 17 segments. The segments can be the same or similar size, different sizes, or combinations thereof. After an input mapping has been segmented, the segments that include the abnormality can be determined.)
Regarding claim 16, Robinson and Hyvonen teach: The system of claim 1, wherein the computing device is configured to: determine a scar location of an organ based on the image data; determine healthy portions of the organ based on the scar location; and display a model of the organ identifying the scar location and the healthy portions (Robinson, [0076 and [0093], and see fig. 6A, scar size, number of VT's, type of cardiomyopathy (e.g., ischemic or non-ischemic), transmurality (e.g., thick or thin scar), location of abnormality (e.g., certain segments may be more or less successful), age, gender, size of heart, ejection fraction, thickness of heart (e.g., weak or healthy heart)).
Regarding claim 17, Robinson and Hyvonen teach: The system of claim 1, wherein receiving the image data for the organ of the patient comprises receiving image data for each of a plurality of imaging technologies, wherein the computing device is configured to determine a segment of a model based on the image data received for each of the plurality of imaging technologies (Robinson, [0005], the anatomic mapping may be at least one of a computer tomography image or a magnetic resonance image. [0049, 0051]). As used herein, “magnetic resonance imaging” (MRI) refers to the use of use magnetic fields and radio waves to form images of the body. Typically, when used in cardiac situations, cardiovascular magnetic resonance imaging (CMR) involves ECG gating which combats the artifacts created by the beating of the heart.).
Claims 18 and 26 are rejected for reasons similar to claim 1 above.
Regarding claim 19, Robinson and Hyvonen teach: The computer-implemented method of claim 18 comprising: receive report data characterizing medical findings of the patient (Robinson, [0093-0094], As additional patients are treated, the previous treatments can inform future treatments); and
determine the recommended target area based on the report data (Robinson, [0093-0094], As additional patients are treated, the previous treatments can inform future treatments and can be used to adjust the target of the current patient (for example, by training the neural network). In another example, the target may be adjusted based on a weighting of input mappings or the confidence score of the combined mappings.).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 ANDREW H LAM whose telephone number is (571)270-7969 and fax number is 571-270-8969. The examiner can normally be reached on 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benny Tieu can be reached on 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANDREW H LAM/
Primary Examiner, Art Unit 2682
Prosecution Insights