DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The Preliminary Amendment filed 25 September 2023 has been entered and considered. Claims 1-20 have been canceled, and claims 21-40 have been added. Claims 21-40 are all the claims pending in the application, of which claims 35-39 are withdrawn. Claims 21-34 and 40 are rejected.
Election/Restriction
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 21-34 and 40, drawn to determining a color of a sampling point based on an interpolation result of normalized image values of one or more neighboring points, classified in CPC symbol G06V10/23.
II. Claims 35-39, drawn to determining a color of a sampling point based on the tissue that the sample point belongs to, classified in CPC symbol G06T7/187.
The inventions are independent or distinct, each from the other because:
Inventions I and II are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable. In the instant case, subcombination I has separate utility such as normalizing image values of neighboring points and interpolating based on the normalized image values, and subcombination II has separate utility such as determining color based on the tissue that a sample point belongs to. See MPEP § 806.05(d).
The examiner has required restriction between subcombinations usable together. Where applicant elects a subcombination and claims thereto are subsequently found allowable, any claim(s) depending from or otherwise requiring all the limitations of the allowable subcombination will be examined for patentability in accordance with 37 CFR 1.104. See MPEP § 821.04(a). Applicant is advised that if any claim presented in a divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
(a) the inventions have acquired a separate status in the art in view of their different classification;
(b) the inventions have acquired a separate status in the art due to their recognized divergent subject matter;
(c) the inventions require a different field of search (for example, searching different classes/subclasses or electronic resources, or employing different search queries);
(d) the prior art applicable to one invention would not likely be applicable to another invention;
(e) the inventions are likely to raise different non-prior art issues under 35 U.S.C. 101 and/or 35 U.S.C. 112, first paragraph.
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
During a telephone conversation with Applicant’s representative, Yangzhou Du, on 6 January 2026 a provisional election was made without traverse to prosecute the invention of Group I, claims 21-34 and 40. Affirmation of this election must be made by applicant in replying to this Office action. Claims 35-39 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
Applicant is reminded that upon the cancelation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-23, 28-34, and 40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 4-9 and 13 of U.S. Patent No. 11,769,249 (hereinafter “the ‘249 patent”). Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claims anticipate the claims in the subject application.
As to independent claim 21, claim 4 of the ‘249 patent discloses a method implemented on at least one machine each of which has at least one processor and at least one storage device, the method comprising: obtaining an image relating to volume data of a plurality of tissues organized in a tissue set; selecting a sample point based on the volume data; obtaining one or more neighboring points of the sample point (claim 1 of the ‘249 patent, from which claim 4 depends, recites “An image processing method implemented on at least one machine each of which has at least one processor and at least one storage device, the method comprising: obtaining an image relating to volume data of a plurality of tissues, wherein tissue labels of the plurality of tissues are organized in a tissue set; selecting a sample point based on the volume data; obtaining one or more neighboring points of the sample point”); obtaining normalized image values of the one or more neighboring points by normalizing image values of the one or more neighboring points; obtaining an interpolation result of the sample point based on an interpolation of the normalized image values of the one or more neighboring points; and determining a color of the sampling point based on the interpolation result (claim 4 of the ‘249 patent recites “ normalizing image values of the plurality of neighboring points based on the selected tissue label; obtaining an interpolation result of the sample point based on an interpolation of the normalized image values of the plurality of neighboring points; and determining the color of the sampling point based on the interpolation result”).
As to claim 22, claim 4 of the ‘249 patent further discloses wherein the method further includes: obtaining a volume rendering result of the plurality of tissues based on the color of the sample point (claim 1 of the ‘249 patent, from which claim 4 depends, recites “obtaining a volume rendering result of the plurality of tissues based on the color of the sample point”).
As to claim 23, claim 4 of the ‘249 patent further discloses wherein the method further includes: determining whether the sample point belongs to a target tissue of the plurality of tissues; and in response to determining that the sample point belongs to a target tissue of the plurality of tissues, obtaining normalized image values of the one or more neighboring points by normalizing image values of the one or more neighboring points based on a selected tissue among the plurality of tissues (claim 4 of the ‘249 patent recites “in response to determining that the target neighboring point belongs to the tissue set, selecting a tissue label in the tissue set and normalizing image values of the plurality of neighboring points based on the selected tissue label”).
As to claim 28, claim 4 of the ‘249 patent further discloses wherein tissue labels of the plurality of tissues are organized in a tissue set, one or more neighboring labels corresponding to the one or more neighboring points respectively are organized in a neighboring point set (claim 1 of the ‘249 patent, from which claim 4 depends, recites “wherein tissue labels of the plurality of tissues are organized in a tissue set…wherein one or more neighboring labels corresponding to the one or more neighboring points respectively are organized in a neighboring point set”), the determining whether the sample point belongs to the target tissue of the plurality of tissues includes: determining whether a label of the sample point belongs to the one or more neighboring labels (claim 2 of the ‘249 patent, from which claim 4 depends, recites “the determining whether the one or more neighboring labels belong to the tissue set comprising: determining whether the target neighboring label is the same as one of the tissue labels in the tissue set”).
Claims 5-9 of the ‘249 patent recite identical subject matter as claims 29-33 of the subject application, respectively.
As to claim 34, claim 4 of the ‘249 patent further teaches in response to determining that the sample point does not belong to the plurality of tissues, obtaining a first color list based on the target neighboring point, the first color list including preset color attributes corresponding to image values respectively; and determining the color of the sample point based on an image value of the sample point and the first color list (claim 1 of the ‘249 patent, from which claim 4 depends, recites “in response to determining that a target neighboring label of the one or more neighboring labels does not belong to the tissue set, obtaining a first color list based on the target neighboring label, the first color list including preset color attributes corresponding to image values respectively; and determining the color of the sample point based on an image value of the sample point and the first color list”).
As to independent claim 40, claim 13 of the ‘249 patent discloses a system for image processing, comprising: at least one storage device storing a set of instructions; and at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions, the at least one processor is directed to cause the system to: obtaining an image relating to volume data of a plurality of tissues organized in a tissue set; selecting a sample point based on the volume data; obtaining one or more neighboring points of the sample point (claim 10 of the ‘249 patent, from which claim 13 depends, recites “A system for image processing, comprising: at least one storage device storing a set of instructions; and at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions, the at least one processor is directed to cause the system to: obtain an image relating to volume data of a plurality of tissues, wherein tissue labels of the plurality of tissues are organized in a tissue set; select a sample point based on the volume data; obtain one or more neighboring points of the sample point”); obtaining normalized image values of the one or more neighboring points by normalizing image values of the one or more neighboring points based on the selected tissue; obtaining an interpolation result of the sample point based on an interpolation of the normalized image values of the one or more neighboring points; and determining a color of the sampling point based on the interpolation result (claim 13 of the ‘249 patent recites “normalize image values of the plurality of neighboring points based on the selected tissue label; obtain an interpolation result of the sample point based on an interpolation of the normalized image values of the plurality of neighboring points; and determine the color of the sampling point based on the interpolation result”.
Claim Objections
Claims 23-27 are objected to because of the following informalities:
Claim 23 recites “the method further includes: the method further includes”. One of these phrases should be deleted for clarity.
Claims 24-27 inherit this deficiency by virtue of their dependency on claim 23.
Additionally, claim 27 recites “the trained machine learning model”. Although claim 26 recites “a trained machine learning model”, none of the claims from which claim 27 depends (claims 21, 23, and 24) recites such a limitation. Accordingly, this limitation lacks antecedent basis in the claims.
Appropriate correction is required.
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.
Claim 32 is 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 32 recites “is less than the threshold” in line 4. Although claim 31 recites a threshold, none of the claims from which claim 32 depends (claims 21, 23, 28, 29, and 30) recites a threshold. Thus the term lacks antecedent basis. Additionally, claim 32 recites “a threshold” in lines 10-11 and later recites “the threshold” in lines 13 and 18. It is unclear whether these recitations are intended to refer back to the threshold of line 4 or the threshold of lines 10-11. If the thresholds are different, the Examiner recommends amending the claim language to include distinguishing modifiers (e.g., “first threshold” and “second threshold”).
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.
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 21-22, 33, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over “Virtual Endoscopy for Preoperative Planning and Training of Endonasal Transsphenoidal Pituitary Surgery” by Neubauer et al. (cited in the IDS filed 24 October 2023; hereinafter “Neubauer”) in view of U.S. Patent Application Publication No. 2016/0364840 to Zhang (hereinafter “Zhang”).
As to independent claim 21, Neubauer discloses a method implemented on at least one machine each of which has at least one processor and at least one storage device (Abstract discloses that Neubauer is directed to “STEPS, a virtual endoscopy system” which is “CPU-based”; p. 36 discloses that the system include “memory”), the method comprising: obtaining an image relating to volume data of a plurality of tissues organized in a tissue set (pp. 28, 32, 84 discloses using one of a variety of imaging modalities to obtain “volume data” which is used for volume rendering the “different tissue types” in the volume data, wherein the tissue types are distinguished by “color coding” (e.g., red for soft tissue and white for bone)); selecting a sample point based on the volume data; obtaining one or more neighboring points of the sample point; obtaining an interpolation result of the sample point based on an interpolation of the image values of the one or more neighboring points; and determining a color of the sampling point based on the interpolation result (pp. 56-37, 96-102 disclose that the final color of a voxel in the rendered volume may be determined through interpolation between colors of its neighbors, for example, between the colors of 8 neighboring voxels which form a cube that encloses the voxel).
Neubauer does not expressly disclose obtaining normalized image values of the one or more neighboring points by normalizing image values of the one or more neighboring points or that the interpolation result is based on interpolation of the normalized image values.
Zhang, like Neubauer, is directed to interpolation in images (Abstract). In particular, Zhang discloses performing “normalization on the displacement” of a sample pixel dot in multiple directions with respect to the “four pixel dots adjacent to” and surrounding the pixel dot, setting weighting factors for the four adjacent pixel dots according to the normalized displacements, and interpolating the sample pixel dot according to the weighting factors for the four adjacent pixel dots ([0086-0104] and Fig. 5a).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Neubauer to perform the interpolation based on normalized values for the neighboring points, as taught by Zhang, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have created a smooth color representation of the rendered volume since each sample point would reflect a properly weighted color value of its neighboring points.
As to claim 22, Neubauer as modified above further teaches obtaining a volume rendering result of the plurality of tissues based on the color of the sample point (pp. 35-37 of Neubauer discloses volume rendering the final colors of the voxels).
As to claim 33, Neubauer as modified above further teaches that the interpolation includes at least one of a linear interpolation, a nonlinear interpolation, an interpolation based on a regularization function, or a diffusion interpolation based on a partial differential equation (p. 96 of Neubauer discloses acquiring the color of the sample point through “linear interpolation”).
Independent claim 40 recites system for image processing, comprising: at least one storage device storing a set of instructions; and at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions, the at least one processor is directed to (Abstract discloses that Neubauer is directed to “STEPS, a virtual endoscopy system” which is “CPU-based”; p. 36 discloses that the system include “memory”; p. 131-132 discloses that the algorithm is implemented in “software”, the instructions of which are necessarily stored) cause the system to perform the method recited in independent claim 21. Accordingly, claim 40 is rejected for the above reasons and for reasons analogous to those discussed above in conjunction with claim 21 (including the reasons for combining Neubauer and Zhang).
Claims 23 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Neubauer, in view of Zhang and further in view of U.S. Patent Application Publication No. 2014/0334705 to Ishii et al. (hereinafter “Ishii”).
As to claim 23, Neubauer as modified above further teaches obtaining normalized image values of the one or more neighboring points by normalizing image values of the one or more neighboring points ([0086-0104] and Fig. 5a of Zhang discloses performing “normalization on the displacement” of a sample pixel dot in multiple directions with respect to the “four pixel dots adjacent to” and surrounding the pixel dot, setting weighting factors for the four adjacent pixel dots according to the normalized displacements, and interpolating the sample pixel dot according to the weighting factors for the four adjacent pixel dots; the reasons for combining the references are the same as those discussed above in conjunction with claim 21).
Neubauer as modified by Zhang does not expressly disclose determining whether the sample point belongs to a target tissue of the plurality of tissues or that performing the normalizing/interpolating is in response to determining that the sample point belongs to a target tissue of the plurality of tissues and based on a selected tissue among the plurality of tissues.
Ishii, like Neubauer, is directed to image interpolation using medical images (Abstract and [0159]). Ishii discloses determining “if neighboring pixels are in the same tissue class” prior to calculating a “spatial interpolation” in the image ([0157-0159]).
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 proposed combination of Neubauer and Zhang to determine whether the neighboring image elements are in the same tissue class (target tissue) prior to performing the normalized interpolation based on that selected tissue class, as taught by Ishii, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have “control[ed] a level of smoothness the pixel value x should acquire if neighboring pixels are in the same tissue class” ([0157] of Ishii).
As to claim 28, the proposed combination of Neubauer, Zhang and Ishii further teaches that tissue labels of the plurality of tissues are organized in a tissue set (p. 84 of Neubauer discloses that the tissue types are distinguished by “color coding” (e.g., red for soft tissue and white for bone), one or more neighboring labels corresponding to the one or more neighboring points respectively are organized in a neighboring point set ([0152] of Ishii discloses a vector z indicating “a tissue class of each pixel” including the neighboring pixels), the determining whether the sample point belongs to the target tissue of the plurality of tissues includes: determining whether a label of the sample point belongs to the one or more neighboring labels ([0152] of Ishii discloses that the element of the vector is “1 only if it is applied to the tissue class c to which the pixel j in the k-th frame belongs whereas the other elements become zero”; the reasons for combining the references are the same as those discussed above in conjunction with claim 23).
Claims 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Neubauer, in view of Zhang and Ishii and further in view of U.S. Patent Application Publication No. 2017/0003366 to Jafari-Lhouzani et al. (hereinafter “Jafari-Lhouzani”).
As to claim 24, Ishii discloses a binary determination (0 or 1) – rather than a probability – of whether the neighboring pixels belong to the same tissue class as the pixel of interest ([0152]). Thus, Neubauer as modified above does not expressly disclose that the determining whether the sample point belongs to a target tissue of the plurality of tissues includes: obtaining a probability of the sample point belongs to the target tissue of the plurality of tissues; and determining whether the sample point belongs to the target tissue of the plurality of tissues based on the probability.
Jafari-Lhouzani, like Neubauer, is directed to image interpolation using medical images (Abstract). Similar to Zhang, Jafari-Lhouzani discloses that weighting factors for points surrounding a sample point to be interpolated are set based on a normalization ([0051-0052]). Jafari-Lhouzani notes the problem that “inaccurate interpolation” can occur when “neighboring voxels, from which an unknown voxel is estimated, may not have the same tissue type as the unknown voxel” ([0028]). To address this problem, Jafari-Lhouzani discloses that “the probability of neighbors having similar tissue type” is calculated, “weights are generated” using “the probability that voxels v and k have similar tissue types”, and the weights are “used in the interpolation” ([0050-0054]).
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 proposed combination of Neubauer, Zhang and Ishii to determine a probability of whether an unknown voxel belongs to a same tissue type as a neighboring voxel, as taught by Jafari-Lhouzani, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have addressed the problem that “inaccurate interpolation” can occur when “neighboring voxels, from which an unknown voxel is estimated, may not have the same tissue type as the unknown voxel” ([0028] of Jafari-Lhouzani). It is also predictable that Jafari-Lhouzani’s probability is a more nuanced metric than Ishii’s binary determination.
As to claim 25, the proposed combination of Neubauer, Zhang, Ishii, and Jafari-Lhouzani further teaches that the probability of sample point belongs to the target tissue of the plurality of tissues is determined based on a filter corresponding to the tissue, the filter being determined based on an attribute of the tissue ([0051-0056] of Jafari-Lhouzani discloses that the “probability of neighbors having similar tissue type” is expressed in equation 5 which involves a feature vector F of “tissue propert[ies]” of neighboring voxels determined using “filters”; the reasons for combining the references are the same as those discussed above in conjunction with claim 24).
Claims 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Neubauer, in view of Zhang, Ishii and Jafari-Lhouzani and further in view of “ADNet++: A Few-Shot Learning Framework for Multi-Class Medical Image Volume Segmentation with Uncertainty-guided Feature Refinement” by Hansen et al. (hereinafter “Hansen”).
As to claim 26, Neubauer as modified above does not expressly disclose that the probability of the sample point belongs to the target tissue of the plurality of tissues is determined based on a trained machine learning model.
Hansen, like Neubauer, is directed to “multi-class segmentation” of a “medical image volume” (Abstract and Title). In particular, Hansen discloses a trained deep learning framework ADNet++ which inputs the medical image volume and outputs a softmax probability of tissue type class for each voxel therein, the tissue classes including “left kidney”, “right kidney”, “spleen” and “liver” (Sections 3-4).
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 proposed combination of Neubauer, Zhang, Ishii, and Jafari-Lhouzani to use a trained machine learning model to determine a probability of each voxel in the volume belonging to a tissue in the tissue set, as taught by Hansen, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have provided “more trustworthy and more accurate predictions” for each voxel, as deep learning frameworks such as Hansen’s outperform classic image-based techniques of classification (Section 1 of Hansen).
As to claim 27, Neubauer as modified above does not expressly disclose that the trained machine learning model is trained according to a training process including: obtaining a training set of data, the training set of data including inputs each of which has a known output, each of the inputs including sample volume data and a reference probability of a sample point belongs to a tissue in the plurality of tissues in the sample volume data; and performing, based on the training set of data, an iteration process including multiple iterations until a termination condition is satisfied.
Hansen, like Neubauer, is directed to “multi-class segmentation” of a “medical image volume” (Abstract and Title). In particular, Hansen discloses a trained deep learning framework ADNet++ which inputs the medical image volume and outputs a softmax probability of tissue type class for each voxel therein, the tissue classes including “left kidney”, “right kidney”, “spleen” and “liver” (Sections 3-4). Hansen discloses that the training process is performed using “a training dataset with base classes” including “ground-truth segmentations” which label each voxel with the expected output tissue class, wherein the training is performed “over 25k iterations” which is a stopping condition (Section 3 and 4.1).
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 proposed combination of Neubauer, Zhang, Ishii, and Jafari-Lhouzani to use a trained machine learning model to determine a probability of each voxel in the volume belonging to a tissue in the tissue set, wherein the machine learning model is trained using a training set of volume image data and corresponding ground-truth labels over 25K iterations of weight optimization, as taught by Hansen, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have provided “more trustworthy and more accurate predictions” for each voxel, as deep learning frameworks such as Hansen’s outperform classic image-based techniques of classification (Section 1 of Hansen).
Allowable Subject Matter
Claims 29-32 and 34 are 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 and if the relevant rejections under 35 USC 112 and double patenting rejections are overcome.
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Taguchi (U.S. Patent Application Publication No. 2015/0131883) is directed to estimating tissue types in medical images. Taguchi discloses determining whether two tissue types of neighboring image elements are the same tissue based on tissue labels of the respective image elements.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN M CONNER whose telephone number is (571)272-1486. The examiner can normally be reached 10 AM - 6 PM Monday through Friday, and some Saturday afternoons.
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/SEAN M CONNER/Primary Examiner, Art Unit 2663