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 .
Response to Arguments
Applicant’s amendment filed on 08/17/2026 has been entered and made of record. Claims 1-2, 11-12 and 21-22 are amended. No New Claim was added. No Claims were cancelled. Claims 1-30 are pending.
Applicant’s remarks in view of the newly presented amendments have been considered but are not found to be persuasive for at least the following reasons:
The applicant argues on page 8-9 of the remarks filed the previous cited prior art of Dickmann et al. US PG-Pub(US 20250166195 A1) in view of Jordan et al US PG-Pub(US 20220028551 A1) do not explicitly disclose the newly amended limitation of wherein segmenting the single CT scan further comprises identifying blood vessels of the patient based on the single CT scan, generating a vessel segmentation representing the blood vessels of the patient, and including the vessel segmentation in the one or more VS masks. The Examiner agrees as the previously cited prior art fails to disclose this newly amended limitation. However, after further search and consideration, the newly discovered art of Siemionow et al. US PG-Pub(US 20200320751 A1) would disclose wherein segmenting the single CT scan further comprises identifying blood vessels of the patient based on the single CT scan(See Fig. 1, Step 104 discloses extracting the region of interest in the CT Scan in which blood vessels are identified.), generating a vessel segmentation representing the blood vessels of the patient(See ¶[0073], discloses blood vessels are segmented in the CT slices obtained of the patient.) , and including the vessel segmentation in the one or more VS masks(¶[0078]-¶[0079] discloses the vessel segmentation contains binary masks of the vessel.). Please see the updated claim rejections under 35 USC § 103 below.
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 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.
Claims 1-6, 8-16, 17-26 and 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Dickmann et al. US PG-Pub(US 20250166195 A1) in view of Jordan et al US PG-Pub(US 20220028551 A1) in view of Siemionow et al. US PG-Pub(US 20200320751 A1).
Regarding Claim 1, Dickmann teaches a method, comprising: acquiring a single computed tomography (CT) scan of one or more regions of a patient(¶[0037], “a series of CT scans taken at various phases of a patient's breathing cycle. Each 3D-tomographic image data within this dataset represents the same anatomical region but at different time points”, ¶[0037] discloses acquiring CT scans of a patient and the tomographic image represents an anatomical region of the patient.)segmenting the single CT scan to generate one or more volumetric segmentation (VS) masks([¶[0026], “[0026] The input data for the segmentation algorithm is the 3D-tomographic image data containing voxel values that represent anatomical structures. Especially, for each time point 3D-tomographic image data related to this time point are input data for the segmentation algorithm. The output can be a binary mask or labeled map that delineates the organ of interest within the original image” [0040] Preferably, the segmentation algorithm is configured to generate a 3D-contour and/or 3D-Volume of the segmented organ, wherein the step applying a segmentation algorithm comprises providing the generated 3D-contour and/or 3D-Volume to the step applying the scoring function. A 3D-contour, short for three-dimensional contour, refers especially to a three-dimensional representation of the outer boundary or surface of a segmented object.”, [0026] discloses segmenting the CT image and generating a volumetric mask of the organ of interest. ); combining the single CT scan and the one or more VS masks to generate a 4D image(¶[0015],”4D-tomographic image data refers preferably to a dynamic dataset generated by combining 3D-tomographic images taken at various time points.” ¶[0015] discloses that the 4D image data is generated by combining 3d tomographic images and ¶[0038] “the segmentation algorithm is applied to N 3D-tomographic image data, wherein N is an integer number. The number N is preferably between 5 and 100 and especially between 10 and 25. The segmentation algorithm is used to identify and delineate the regions of interest (e.g., organs or structures) within each of the N 3D-tomographic images”, discloses the 3d tomographic images are segmented to mask the region of interest and combined to form a 4d dataset.);
Dickmann does not explicitly teach providing the 4D image to one or more predictive models trained to predict therapeutic agent responses based on the 4D image; and generating, by a processing device, a predicted treatment response score to a treatment plan for the patient based on the 4D image and the one or more predictive models.
Jordan teaches providing the 4D image to one or more predictive models(¶[0053], “Training a 4D CNN prediction model with input ROI shape being [N.sub.x, N.sub.y, N.sub.z, 2], where Nx, Ny, Nz are the number of voxels along each axis and 2 corresponds to two (or more) imaging time points, each represented with a single 3D volume within the 4D input volume)”, ¶[0053] discloses training a CNN by inputting a 4d image ) trained to predict therapeutic agent responses based on the 4D image(¶[0051], “ a treatment response model is trained to predict patient's likelihood of disease progression, pseudo-progression, or hyper-progression using baseline and first intra-treatment follow-up scan. In this clinical scenario, the model prediction may be used to significantly reduce the timeline to make treatment decision or adjustment, such as moving patient to a different therapeutic agent, adding a secondary therapeutic agent, or discontinuing therapy.”, ¶[0051] discloses the model is trained to predict a treatment response); and generating, by a processing device, a predicted treatment response score to a treatment plan for the patient based on the 4D image and the one or more predictive models. ([0033] “At block 203, processing logic generates (e.g., by a processing device) a predicted treatment response score (e.g., on a scale representing least likely to have a positive of negative effect to most likely to have a positive or negative effect) to an immunotherapy treatment based on the deep learning models. In some embodiments, the predicted treatment response score may be a numerical value. In one embodiment, processing logic generates the predicted treatment response score based on the single pre-treatment image and the at least one deep learning model.”, ¶[0033] discloses generating a predicted response score to a treatment plan by using a predictive model.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Dickmann with Jordan in order to train a predictive model to predict therapeutic agent responses. One skilled in the art would have been motivated to modify Dickmann in this manner in order to predict immunotherapy treatment response using deep learning analysis. (Jordan, ¶[0002])
However, Dickmann and Jordan do not explicitly teach wherein segmenting the single CT scan further comprises identifying blood vessels of the patient based on the single CT scan, generating a vessel segmentation representing the blood vessels of the patient, and including the vessel segmentation in the one or more VS masks;
Siemionow teaches wherein segmenting the single CT scan further comprises identifying blood vessels of the patient based on the single CT scan(Fig. 1, Step 104 discloses extracting the region of interest in the CT Scan in which blood vessels are identified.), generating a vessel segmentation representing the blood vessels of the patient(¶[0073], “Next, in step 107, the coronary vessel segmentation is performed, preferably individually for each plane, by segmentation CNNs in a similar way as for pericardium, for the two-dimensional slices obtained in the previous step. Therefore, preferably (2N+1)*3 networks are used.”, discloses blood vessels are segmented in the CT slices obtained of the patient.) , and including the vessel segmentation in the one or more VS masks([0078] “The segmentation CNN in accordance with one embodiment has a structure as discussed with reference to FIG. 4, with the following differences. The input data is a masked raw volume and (optionally) the corresponding masked volumetric Jerman filter response. If both types of inputs are used, then the segmentation CNNs should include an additional channel for processing that data. The output is a binary coronary vessels mask image containing the location of the coronary vessels.
[0079] Next, the coronary vessels masks output for the masked slices for the different planes can be combined to a segmented 3D data set representing the shape, location and size of the coronary vessels.”, ¶[0078]-¶[0079] discloses that the segmented blood vessel is masked and used to generate a segmented 3d dataset representing shape, location and size of the vessel.);
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Dickmann and Jordan with Siemionow in order to perform vessel segmentation to generate masks of blood vessels. One skilled in the art would have been motivated to modify Dickmann and Jordan in this manner in order to perform autonomous segmentation of contrast filled coronary artery vessels on computed tomography images which is useful in particular for the field of computer assisted diagnosis, treatment, and monitoring of coronary artery diseases. (Siemionow, ¶[0001])
Regarding Claim 2, the combination of Dickmann, Jordan and Siemionow teach the method of claim 1, Dickmann further teaches wherein the one or more VS masks are indicative of one or more of an anatomical structure, a body composition segmentation or a lesion segmentation. (¶[0026], “The output can be a binary mask or labeled map that delineates the organ of interest within the original image”, ¶[0026] discloses masking the organ of interest in the CT scan.)
Regarding Claim 3, the combination of Dickmann, Jordan and Siemionow teach the method of claim 1, where Jordan further teaches wherein generating the predicted treatment response score is further based on at least one of a pre-treatment 4D image or non-imaging features. (¶[0033], “the predicted treatment response score based on the single pre-treatment image and the at least one deep learning model. For example, in one embodiment, results from the different models may be combined (e.g., averaged, or combined in any other way) to generate a single response score…. one or more non-imaging features (e.g., genomic tests, electronic medical record information, PD-L1 immunohistochemistry assays, etc.) may be used to generate the predicted response score.”, ¶[0033] discloses the treatment score is based on non-imaging features)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Dickmann and Siemionow with Jordan in order to determine a treatment score based on non-imaging features. One skilled in the art would have been motivated to modify Dickmann and Siemionow in this manner in order to predict immunotherapy treatment response using deep learning analysis. (Jordan, ¶[0002])
Regarding Claim 4, the combination of Dickmann, Jordan and Siemionow teach the method of claim 3, where Jordan further teaches wherein the non-imaging features comprises at least one of: a change in blood lab values, a change in urine lab values, or a change in imaging features. ([0052] “Calculating the difference in imaging features between scan #1 and scan #2, which are subsequently used to create a prediction model. In one embodiment, sets of imaging features may be calculated independently for scan #1 and scan #1. The feature weights or values calculated from scan #1 may be subtracted from the features or values calculated from scan #2. The difference or changes in the individual features may constitute a set of new “delta features” that corresponds to temporal variations in typical image features (e.g., change in shape, intensity, texture, etc. as a function of time).”, ¶[0052] discloses determining image changes between two scans. )
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Dickmann and Siemionow with Jordan in order to determine a treatment plan based on changes between images. One skilled in the art would have been motivated to modify Dickmann and Siemionow in this manner in order to predict immunotherapy treatment response using deep learning analysis. (Jordan, ¶[0002])
Regarding Claim 5, the combination of Dickmann, Jordan and Siemionow teach the method of claim 1, where Jordan further teaches wherein the one or more predictive models are further trained to predict the therapeutic agent responses based on a change in lesion volume. (¶[0027], “Examples of lesion response may include, numerical assessment (e.g. change in lesion volume, change in one or more primary dimensions of the lesion, change in image intensity within the lesions), tumor growth rate (TGR), or categorical assessment (e.g. responding lesion, stable lesion, progressing lesion, new lesion). Predicting treatment response at patient level is performed by aggregating one or more lesion-level model predictions” , ¶[0027] discloses predicting a treatment response based on a change in lesion volume.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Dickmann and Siemionow with Jordan in order to predict a treatment response based on lesion volume change. One skilled in the art would have been motivated to modify Dickmann and Siemionow in this manner in order to predict immunotherapy treatment response using deep learning analysis. (Jordan, ¶[0002])
Regarding Claim 6, the combination of Dickmann, Jordan and Siemionow teach the method of claim 1, where Jordan further teaches wherein the single CT scan is acquired prior to administering the treatment plan to the patient. ([0030] “The treatment image may be taken at the time of diagnosis (prior to start of treatment) or during any other suitable time. The treatment image may be, but is not limited to, a computed tomography (CT) scan, a positron emission tomography (PET) scan, or a magnetic resonance imaging (MM) scan.”, ¶[0030] discloses acquiring a CT scan before the start of the treatment.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Dickmann and Siemionow with Jordan in order acquire CT scan before giving the treatment plan. One skilled in the art would have been motivated to modify Dickmann and Siemionow in this manner in order to predict immunotherapy treatment response using deep learning analysis. (Jordan, ¶[0002])
Regarding Claim 8, the combination of Dickmann, Jordan and Siemionow teach the method of claim 1, where Dickmann further teaches wherein segmenting the single CT scan to generate the one or more volumetric segmentation (VS) masks further comprises: generating labeling information describing one or more structural components of the patient captured in the single CT scan. (¶[0025], “the segmentation algorithm is trained on labeled medical images and can efficiently perform segmentation tasks by learning patterns and features within the data.[0026] The input data for the segmentation algorithm is the 3D-tomographic image data containing voxel values that represent anatomical structures. Especially, for each time point 3D-tomographic image data related to this time point are input data for the segmentation algorithm. The output can be a binary mask or labeled map that delineates the organ of interest within the original image.”, ¶[0025] discloses generating the mask or a labelled map of the organ of interest based on labeled medical images)
Regarding Claim 9, the combination of Dickmann, Jordan and Siemionow teach the method of claim 8, where Dickmann further teaches wherein the 4D image comprises the labeling information describing the one or more structural components of the patient. (¶[0038], “The segmentation algorithm is used to identify and delineate the regions of interest (e.g., organs or structures) within each of the N 3D-tomographic images. This process results in segmentations specific to each individual image within the dataset. For example in the context of lung imaging, the segmentation algorithm is applied to each of the 100 3D CT scans within the 4D dataset to outline the lung over a breathing cycle”, ¶[0038] discloses the segmentation algorithm identifies organs or structures within the 4d dataset.)
Regarding Claim 10, the combination of Dickmann, Jordan and Siemionow teach the method of claim 1, where Jordan further teaches further comprising: improving a prediction accuracy of the one or more predictive models by training the one or more predictive models with sets of 4D images. (¶[0053],”Training a 4D CNN prediction model with input ROI shape being [N.sub.x, N.sub.y, N.sub.z, 2], where Nx, Ny, Nz are the number of voxels along each axis and 2 corresponds to two (or more) imaging time points, each represented with a single 3D volume within the 4D input volume)”, ¶[0053] discloses training a prediction model using 4d images.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Dickmann and Siemionow with Jordan in order to train a prediction model using 4d images. One skilled in the art would have been motivated to modify Dickmann and Siemionow in this manner in order to predict immunotherapy treatment response using deep learning analysis. (Jordan, ¶[0002])
Regarding Claim 11, claim 11 is considered an apparatus claim substantially corresponding to claim 1. Please see the discussion of claim 1 above for a discussion of similar limitations. Furthermore, Dickmann teaches a treatment analysis system(See Figure 1 and ¶[0003] discussing 4d imaging for radiotherapy treatment planning) comprising: a memory to store a pre-treatment image of a target subject(Figure 2 shows a scanner device 1 that is connected to a tomographic scanner 2 in which images of the subject are acquired and transmitted to the scanner console 3 may also be linked to the cloud 4 for data storage or processing outsourcing, See ¶[0073])).; and a processing device(see ¶[0066] discloses a processor), operatively coupled to the memory(see ¶[0050] discloses a processor coupled to memory to execute the method of image segmentation.),
Regarding claim 12, it is substantially similar to claim 2 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 13, it is substantially similar to claim 3 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 14, it is substantially similar to claim 4 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 15, it is substantially similar to claim 5 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 16, it is substantially similar to claim 6 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 18, it is substantially similar to claim 8 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 19, it is substantially similar to claim 9 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 20, it is substantially similar to claim 10 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 21, claim 21 is considered an apparatus claim substantially corresponding to claim 1. Please see the discussion of claim 1 above for a discussion of similar limitations. Furthermore, Dickmann teaches a non-transitory computer-readable storage medium comprising instructions(¶[0050] discloses a non-transitory computer readable medium) which when executed by a processing device, cause the processing device to:¶[0050] discloses a processor coupled to memory to execute the method of image segmentation.)
Regarding claim 22, it is substantially similar to claim 2 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 23, it is substantially similar to claim 3 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 24, it is substantially similar to claim 4 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 25, it is substantially similar to claim 5 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 26, it is substantially similar to claim 6 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 28, it is substantially similar to claim 8 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 29, it is substantially similar to claim 9 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 30, it is substantially similar to claim 10 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 7, 17 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Dickmann et al. US PG-Pub(US 20250166195 A1) in view of Jordan et al US PG-Pub(US 20220028551 A1) in view of Siemionow et al. US PG-Pub(US 20200320751 A1) in view of Lachaine et al. US PG-Pub(US 20200129780 A1).
Regarding Claim 7, the combination of Dickmann, Jordan and Siemionow teach the method of claim 6, where Jordan teaches further comprising: wherein generating the predicted treatment response score is further based on a change between the single CT scan and the second CT scan. ([0036] At block 207, the processing logic may receive an intra-treatment follow-up image.
[0037] At block 209, the processing logic may provide the intra-treatment follow-up image to the machine learning model.
[0038] At block 211, the processing logic may generate an updated predicted treatment response score.
,¶[0036]-¶[0038] disclose receiving a follow up image and generating a treatment response score based on the follow up image.
[0052] 1. Approach #1: Calculating the difference in imaging features between scan #1 and scan #2, which are subsequently used to create a prediction model.
¶[0052] discloses determining difference between a first and second scan to create a predication model to generate a treatment response score.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Dickmann and Siemionow with Jordan in order to generate a prediction score based on changes between two scans. One skilled in the art would have been motivated to modify Dickmann and Siemionow in this manner in order to predict immunotherapy treatment response using deep learning analysis. (Jordan, ¶[0002])
However, Dickmann, Jordan and Siemionow do not explicitly teach generating a second 4D image based on a second CT scan that is generated prior to administering the treatment plan to the patient;
Lachaine teaches generating a second 4D image based on a second CT scan that is generated prior to administering the treatment plan to the patient (¶[0068], “4D CT acquired during a planning phase, which is used to generate a treatment plan; a 4D CBCT acquired immediately prior to each treatment session, with the patient in treatment position, generated for example by rotating a kV imager around the patient on a conventional linac; a 4D MR acquired prior to each treatment session on an MR-linac, or the like.”, ¶[0068] discloses a pre-treatment 4D CT image is acquired prior to each treatment session.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Dickmann, Jordan and Siemionow with Lachaine in order to generate a pre-treatment 4d image before the treatment plan. One skilled in the art would have been motivated to modify Dickmann, Jordan and Siemionow in this manner in order for the data to be an excellent representation of the patient's respiratory degrees of freedom since it was acquired immediately prior to treatment. (Lachaine, ¶[0074])
Regarding claim 17, it is substantially similar to claim 7 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 27, it is substantially similar to claim 7 respectively, and is rejected in the same manner, the same art, and reasoning applying.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAN D HOANG whose telephone number is (571)272-4344. The examiner can normally be reached Monday-Friday 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JOHN M VILLECCO can be reached at 571-272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAN HOANG/Primary Examiner, Art Unit 2661
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