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 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 1-19 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.
Claim 1 recites the limitation “predict at least one trajectory of the interventional device in the current image by providing the image data and the at least one untriggered control input to a model, wherein the model is configured to predict trajectories of the interventional device based on first input of image data of the interventional device in the anatomical structure and on second input of corresponding control inputs to the robot for guiding movement of the interventional device as shown in the image data of the first input”; it is unclear and indefinite whether the model is to predict the trajectory or whether the trajectories are inputted into the model. In other words, since the model is configured to predict the trajectories, it is unclear and confusing what the second input includes.
Claim 1 recites “trigger command for triggering at least one untriggered control input” in line 16. It is unclear whether this is the same “untriggered control input” previously recited or another. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-19 are rejected for depending on rejected claim 1.
Claim 7 recites “processor to: provide at least one new untriggered control input for controlling”; Claim 1 recites that the processor receives the untriggered and triggered inputs. It is unclear how the processor provides the input. The specification further does not provide any details. Clarification is required.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-6, 8-16, 19 and 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pat Pub No. 20190216553 A1 to Bono et al. (hereinafter “Bono”) in view of WO2020173814A1 to Toporek et al. (hereinafter “Toporek” – 2020/09/03)
Regarding claims 1 and 33. (Original) Bono discloses a system (para 0005) for estimating and visualizing trajectories of an interventional device guided by a robot and configured for insertion into an anatomical structure of a subject (para 0005, 0027), the system comprising: at least one processor (para 0027, “The computer system 11 includes a computer 82 having storage 83 and a processor 84”, fig 15); and a non-transitory memory for storing machine executable instructions that, when executed by the at least one processor, cause the at least one processor (para 0005 “controls and software coding”, 0027, “The computer system 11 includes a computer 82 having storage 83 and a processor 84”, fig 15) to: receive image data from a current image of the anatomical structure, the current image showing a current position of the interventional device with respect to the anatomical structure (claims 1, 10 “receive one or more image files of one or more pictures of a surgical site”); receive at least one untriggered control input for controlling the robot to guide future movement of the interventional device from the current position (para 0028, “input”; para 0034 “A proposed surgical tool path 60 is input by the operator”; para 0034 “user to select the type of tool”); predict at least one trajectory of the interventional device in the current image by providing the image data and the at least one untriggered control (para 0034 “surgical tool path”), to predict trajectories of the interventional device based on first input of image data of the interventional device in the anatomical structure and on second input of corresponding control inputs to the robot for guiding movement of the interventional device as shown in the image data of the first input (para 0034 “path” will be different depending on the input of the selection of tool or surgical site. Para 0036 “preliminary run through of the surgery”); and provide a trigger command for triggering at least one untriggered control input to control the robot to guide movement of the interventional device according to the at least one triggered control input (para 0036, performing the operation via the robot 12).
Bono discloses having path generation capabilities but fails to explicitly disclose using a model as claimed.
Toporek, from a similar field of endeavor teaches using a forward predictive model having been trained, employing a neural network base including an input layer, hidden layer and an output layer to predict navigated pose of the probe and further include confidence ratio to a display (“forward predictive model 60b, fig. 6D, see p. 22, l 30-p 25, l 26). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Bono with the teachings of Toporek, to use a machine learning model, to provide the predictable result of improving accuracy, speed and providing confidence ratio to the user.
Regarding claim 2. (Original) Bono as modified by Toporek renders obvious the system of claim 1, wherein the executed instructions further cause the at least one processor to: automatically determine whether a predicted trajectory is acceptable, wherein the trigger command is provided when the predicted trajectory is determined to be acceptable (see rejection of claim 1, Toporek “automated navigated embodiment”). The court has held that broadly providing an automatic to replace a manual activity which accomplishes the same result is not sufficient to distinguish over the prior art. See In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Regarding claim 3. (Original) Bono as modified by Toporek renders obvious the system of claim 2, wherein automatically determining whether the predicted trajectory is acceptable comprises: capturing a shape of the predicted trajectory to provide a segmented predicted trajectory; comparing the segmented predicted trajectory to a predetermined desired trajectory; and determining that the predicted trajectory is acceptable when the segmented predicted trajectory substantially matches the predetermined desired trajectory (Toporek, p 14, ln 15-22).
Regarding claim 4. (Original) Bono as modified by Toporek renders obvious the system of claim 2, wherein automatically determining whether the predicted trajectory is acceptable comprises: capturing a shape of the predicted trajectory to provide a segmented predicted trajectory; comparing the segmented predicted trajectory to a predetermined set of rules for navigating the interventional device through the anatomical structure; and determining that the predicted trajectory is acceptable when the segmented predicted trajectory substantially matches the predetermined set of rules (Toporek, p 14, ln 15-22).
Regarding claim 5. (Currently amended) Bono as modified by Toporek renders obvious the system of claim 1, wherein the executed instructions further cause the at least one processor to: provide a user interface configured for allowing a user to send said trigger command (Bono, para 0028, “input”; para 0034 “A proposed surgical tool path 60 is input by the operator”; para 0034 “user to select the type of tool”).
Regarding claim 6. (Original) Bono as modified by Toporek renders obvious the system of claim 5, wherein the user interface is further configured to provide to the user information regarding the at least one predicted trajectory to assist the user in deciding whether a predicted trajectory is acceptable, and thereby assisting the user to provide the trigger command (para 0034 “path” will be different depending on the input of the selection of tool or surgical site. Para 0036 “preliminary run through of the surgery”).
Regarding claim 8. (Currently amended) Bono as modified by Toporek renders obvious the system of claim 1,wherein said model is a trained model configured to predict trajectories of the interventional device based on training data from previous images of the interventional device in the anatomical structure and corresponding control inputs to the robot for guiding movement of the interventional device as shown in the previous images (Toporke, “training dataset D is a collection of expert data with reasonable coverage of different navigations of interventional device”).
Regarding claim 9. (Currently amended) Bono as modified by Toporek renders obvious the system of claim 1,wherein the executed instructions further cause the at least one processor to: perform a training of said model with regard to predicting trajectories of the interventional device based on training data from at least one of previous images of the interventional device in the anatomical structure and the received image data and at least one of previous control inputs corresponding to the previous images and the at least one untriggered control input to the robot for guiding movement of the interventional device as shown in the previous images (Toporke, “training dataset D is a collection of expert data with reasonable coverage of different navigations of interventional device”).
Regarding claim 10. (Currently amended) Bono as modified by Toporek renders obvious the system of claim 1,wherein said model is a convolutional-long-short term memory (LSTM) neural network model (Toporke, “LSTM”).
Regarding claim 11. (Original) Bono as modified by Toporek renders obvious the system of claim 10, wherein the LSTM neural network model is configured with dimension preserving architecture to predict the at least one trajectory of the interventional device, wherein the first input of the image data and the second input of the corresponding control inputs are combined at an earliest layer of an encoder of the LSTM neural network model (Toporke “the neural architecture of the exemplary embodiment of inverse predictive model 70a as illustrates has a unique number of layers, depending on the complexity of the task”, it is noted that selecting the number of layers, parameters and inputting data into various layers are understood to be recited at a high level of abstraction and as merely reciting an abstract idea”. The mentioned limitations, without any support of criticality, are not considered to be technological improvements. This limitation is considered an obvious variation). See MPEP 2143 “(B) Simple substitution of one known element for another to obtain predictable results;” and (E) “Obvious to try” – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;”.
Regarding claim 12. (Original) Bono as modified by Toporek renders obvious the system of claim 10, wherein the LSTM neural network model is configured with dimension varying architecture to predict the at least one trajectory of the interventional device, wherein the first input of the image data is input at an earliest layer of an encoder of the LSTM neural network model, and the second input of the corresponding control inputs is input at or after a latent state between the encoder and a decoder of the LSTM neural network model (Toporke “the neural architecture of the exemplary embodiment of inverse predictive model 70a as illustrates has a unique number of layers, depending on the complexity of the task”; it is noted that selecting the number of layers, parameters and inputting data into various layers are understood to be recited at a high level of abstraction and as merely reciting an abstract idea”. The mentioned limitations, without any support of criticality, are not considered to be technological improvements. This limitation is considered an obvious variation). See MPEP 2143 “(B) Simple substitution of one known element for another to obtain predictable results;” and (E) “Obvious to try” – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;”.
Regarding claim 13. (Currently amended) Bono as modified by Toporek renders obvious the system of claim 1, wherein the model is further configured to predict trajectories of the interventional device further based on an input of shape data of the interventional device shown in the image data of the first input (para 0034 “Each tool selected has an associated tool offset length and diameter”).
Regarding claim 14. (Currently amended) Bono as modified by Toporek renders obvious the system of claim 1, wherein the model is further configured to process temporal sequences of imaging data such that the trajectories are progressively predicted over time, when the interventional device moves (Bono, para 0029, 0036, Toporek, fig. 11B, 12B).
Regarding claim 15. (Currently amended) Bono as modified by Toporek renders obvious the system of claim 1, wherein the executed instructions further cause the at least one processor to estimate an uncertainty of the at least one predicted trajectory using the model, and to display the estimated uncertainty with the at least one predicted trajectory overlaid on the current image of the anatomical structure (Bono, para 0036 “overlaid”; and Toporek “confidence ratio of the prediction is shown to the user”).
Regarding claim 16. (Currently amended) Bono as modified by Toporek renders obvious the system of claim 1,wherein the executed instructions further cause the at least one processor to: display the at least one predicted trajectory of the interventional device overlaid on the current image of the anatomical structure for a user to determine whether the at least one predicted trajectory is acceptable (Bono, para 0036 “overlaid”; and Toporek “confidence ratio of the prediction is shown to the user”).
Regarding claim 18. (Currently amended) Bono as modified by Toporek renders obvious the system ooperation, thus, the process is understood to be repeated (i.e., additional images being collected); Toporek “Feed-Forward (preferably Continuous) Positioning Control”); and display an actual trajectory of the interventional device overlaid on the current image, along with the predicted trajectory, after triggering the untriggered control inputs (para 0036 “Tools 92 and surgical tool paths 60 for each selected tool can be overlaid upon the DICOM data to show precisely where the tools will enter and traverse according to the tool paths and tools that have been selected”; Toporek “continuous”).
Regarding claim 19. (Currently amended) Bono as modified by Toporek renders obvious the system of claim 1, further comprising: a robot controller configured to enable control of the robot in accordance with the at least one triggered control inputs (fig. 15).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bono as modified by Toporek as applied to claims above, and further in view of US Pat Pub No. 20030208122 A1 to Melkent et al. (hereinafter “Melkent”).
Regarding claim 17. (Currently amended) Bono as modified by Toporek renders obvious the system of claim 15, wherein the executed instructions further cause the at least one processor to display the at least one predicted trajectory as a centerline, and to display the estimated uncertainty asbut fails to disclose color coded pixels defining margins along the centerline of the predicted trajectory.
Melkent, from a similar field of endeavor teaches using change in color between the look- ahead trajectory 725, 727 and the current position 715, 717 to show different lines (para 0060). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Bono with the teachings of Melkent to provide the predictable result of helping the surgeon view the different projections (paths).
Conclusion
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/SANA SAHAND/Examiner, Art Unit 3796