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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has complied with all conditions for receiving the benefit of earlier filing dates of 18 Jul 2018 and 12 Mar 2019 under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) for subject matter disclosed in provisional applications 62/699,974 and 62/816,954, respectively.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 18 Dec 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the Examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include “classification voxels 20” mentioned in the description yet they include the reference number “25” not mentioned in the description. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
“Glucose” should read “glucose” ([0026]);
“(MRIs) similar to radiologists,” should read “similar to radiologists” ([0044]); and
“conventional structural magnetic resonance imaging (MRI)” in [0045] and [0048] should read “conventional structural MRI” since MRI is initially spelled out in [0044].
Appropriate correction is required.
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: independent claim 21 recites “uniform volumetric regions”, which lacks proper antecedent basis in the original specification.
The abstract of the disclosure is objected to because “Glucose” and “Insulin” should read “glucose” and “insulin”, respectively. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Additionally, Applicant is reminded that the abstract of the instant application describes originally presented claims directed to glucose measurements, insulin treatment history, and onset of hypoglycemic symptoms, not the preliminarily amended claims of 08 Sep 2025 that are currently under examination.
Claim Objections
Claims 21-40 are objected to because of the following informalities:
Repeated recitations of “one of neurological or cardiopulmonary analysis” in claim 21, except for the first recitation in line 2 of claim 21, should read “the one of neurological or cardiopulmonary analysis”;
“i) defining classification voxels which are representative of uniform volumetric regions wherein each classification voxel is made up of one or more resolution voxels,” should read “i) defining classification voxels which are representative of uniform volumetric regions, wherein each classification voxel is made up of one or more resolution voxels,” (claim 21);
“B) Implementing the validated multilayer convolutional network” should read “B) implementing the validated multilayer convolutional network” (claim 21);
Numeral “ii” is repeated in claim 21, lines 26-27;
“identifying initiation time for T- cell therapy or track ongoing T-cell therapy” should read “identifying initiation time for T- cell therapy or tracking ongoing T-cell therapy” (claim 21);
“implementing an artificial intelligence based imaging platform for one of neurological or cardiopulmonary analysis” should read “implementing the artificial intelligence based imaging plat form for the one of neurological or cardiopulmonary analysis” (claims 22-40);
“mapping neurologic tissue” should read “mapping the neurologic tissue” (claim 26);
“mapping of a neurological tumor of a patient” should read “mapping of the neurological tumor of the patient” (claim 27);
“identifying initiation time for T-cell therapy or track ongoing T-cell therapy of a patient” should read “identifying the initiation time for the T-cell therapy or tracking the ongoing T-cell therapy of the patient” (claim 30);
“predicting cardiopulmonary implant results for a patient” should read “predicting the cardiopulmonary implant results for the patient” (claim 33);
“mapping blood vessels of a patient” should read “mapping the blood vessels of the patient” (claim 36);
“identifying one of venothromboembolism or pulmonary embolism of a patient” should read “identifying the one of venothromboembolism or pulmonary embolism of the patient” (claim 39); and
“identifying penetrating atherosclerotic ulcer of a patient” should read “identifying the penetrating atherosclerotic ulcer of the patient” (claim 40).
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-40 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 21 recites the preamble “implementing an artificial intelligence based imaging platform for one of neurological or cardiopulmonary analysis” and the limitation “providing a validated multilayer convolutional network for one of neurological or cardiopulmonary analysis configured for segmenting data sets of one of neurological or cardiopulmonary scans into resolution voxels”. A review of the original specification of the instant application, however, does not disclose in sufficient detail, such as working examples, of segmenting data sets of cardiopulmonary scans using a validated multilayer convolutional network. In particular, the original specification discloses in sufficient detail of segmenting only the neurological scans (see [0055]-[0075] and Fig. 2-8). Claims 22-40 inherit the deficiency by the nature of their dependency on claim 21. Amending the claim to remove the recited cardiopulmonary analysis and cardiopulmonary scans would overcome this 35 U.S.C. 112(a) rejection.
Claim 21 recites the limitations “wherein the resolution voxels are representative of uniform volumetric regions within which the platform can segment data sets” and “defining classification voxels which are representative of uniform volumetric regions”. A review of the original specification, however, does not disclose these limitations. In particular, there is no antecedent basis for “uniform volumetric regions” in the original specification, thus introducing new matter. A review of the original specification discloses in [0046] that the resolution voxel “references smallest volumetric region that the platform 10 can segment the data from a scan 12 into for classification, which in this case can be a cube of 1 mm per side” and [0054] that classification voxels are assigned with classification labels instead. Claims 22-40 inherit the deficiency by the nature of their dependency on claim 21. Amending the claim in light of the original specification, as noted above, would overcome this 35 U.S.C. 112(a) rejection.
Claim 21 recites the limitation “Implementing the validated multilayer convolutional network by classification of one of neurological or cardiopulmonary tissue within classification voxels of at least one patient data set by the validated multilayer convolutional network for one of neurological or cardiopulmonary analysis with each classification voxel of the at least one patient data set assigned a label, wherein the implementing the validated multilayer convolutional network includes at least one of i) mapping neurologic tissue; ii) mapping of a neurological tumor of a patient, ii) mapping cardiopulmonary tissue, iii) identifying initiation time for T-cell therapy or track ongoing T-cell therapy of a patient, iv) predicting cardiopulmonary implant results for a patient, v) identifying one of venothromboembolism or pulmonary embolism of a patient, vi) mapping blood vessels of a patient, and vii) identifying penetrating atherosclerotic ulcer of a patient”. A review of the original specification of the instant application, however, does not disclose in sufficient detail, such as working examples, of implementing the claimed convolutional network for cardiopulmonary analysis or any one of mapping cardiopulmonary tissue, identifying initiation time for T-cell therapy or tracking ongoing T-cell therapy of a patient, predicting cardiopulmonary implant results for a patient, identifying one of venothromboembolism or pulmonary embolism of a patient, mapping blood vessels of a patient, or identifying penetrating atherosclerotic ulcer of a patient. In particular, the original specification discloses in sufficient detail of implementing the claimed convolutional network in only mapping a neurologic tissue and mapping a tumor of a patient (see [0055]-[0075] and Fig. 2-8). Therefore, implementing the claimed convolutional network in a cardiopulmonary analysis or any one of mapping cardiopulmonary tissue, identifying initiation time for T-cell therapy or tracking ongoing T-cell therapy of a patient, predicting cardiopulmonary implant results for a patient, identifying one of venothromboembolism or pulmonary embolism of a patient, mapping blood vessels of a patient, or identifying penetrating atherosclerotic ulcer of a patient is not described in the original specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor had a possession at the time the application was filed. Claims 22-40 inherit the deficiency by the nature of their dependency on claim 21, and claims 30-40 specifically are directed to any one of mapping cardiopulmonary tissue, identifying initiation time for T-cell therapy or tracking ongoing T-cell therapy of a patient, predicting cardiopulmonary implant results for a patient, identifying one of venothromboembolism or pulmonary embolism of a patient, mapping blood vessels of a patient, or identifying penetrating atherosclerotic ulcer of a patient. Amending the claim to remove the recited “ii) mapping cardiopulmonary tissue, iii) identifying initiation time for T-cell therapy or track ongoing T-cell therapy of a patient, iv) predicting cardiopulmonary implant results for a patient, v) identifying one of venothromboembolism or pulmonary embolism of a patient, vi) mapping blood vessels of a patient, and vii) identifying penetrating atherosclerotic ulcer of a patient” would overcome this 35 U.S.C. 112(a) rejection.
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 21-40 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 21 recites in the preamble “implementing an artificial intelligence based imaging platform” and the limitation “implementing the validated multilayer convolutional network”. A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 21 recites the broad recitation “artificial intelligence”, and the claim also recites “convolutional network”, which is the narrower statement of the range/limitation. The claims are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims. Claims 22-40 inherit the deficiency by the nature of their dependency on claim 21. For the purposes of the examination, the preamble is being given a broadest reasonable interpretation as “implementing a validated multilayer convolutional network based imaging platform”.
Claim 21 recites the limitation “providing a validated multilayer convolutional network for one of neurological or cardiopulmonary analysis configured for segmenting data sets of one of neurological or cardiopulmonary scans into resolution voxels, wherein the resolution voxels are representative of uniform volumetric regions within which the platform can segment data sets”. First, it is unclear whether “segment(ing) data sets” recited twice in the limitation are the same or different. Second, it is unclear what “wherein the resolution voxels are representative of uniform volumetric regions within which the platform can segment data sets” means. See the 35 U.S.C. 112(a) rejection above for new matter. Thus, the metes and bounds of the claim are unclear in view of the limitation. Claims 22-40 inherit the deficiency by the nature of their dependency on claim 21. For purposes of the examination, the limitation is being given a broadest reasonable interpretation as “providing a validated multilayer convolutional network for neurological analysis configured for segmenting data sets of neurological scans into resolution voxels, wherein the resolution voxels are smallest volumetric region that the platform segments the data sets” in light of the original specification of the instant application.
Claim 21 recites the limitation “wherein the validation of the multilayer convolutional network includes …” The antecedent basis for “the validation of the multilayer convolutional network” is unclear. In particular, the claim merely recites “providing a validated multilayer convolutional network” and does not require the step of validating the multilayer convolutional network. Claims 22-40 inherit the deficiency by the nature of their dependency on claim 21. For the purposes of the examination, the limitation is being given a broadest reasonable interpretation as “wherein the validated multilayer convolutional network is validated by …”
Claim 21 recites the limitations “defining classification voxels which are representative of uniform volumetric regions wherein each classification voxel is made up of one or more resolution voxels”. First, it is unclear whether the “classification voxels” that are being defined are of a training data set, a specific given validation data set, or otherwise. In particular, claim 21 also recites “said classification voxels of a training data set” and “said classification voxels of a specific given validation data set”. Second, it is unclear what “uniform volumetric regions” are. See the 35 U.S.C. 112(a) rejection above for new matter. Thus, the metes and bounds of the claim are unclear in view of the limitation. Claims 22-40 inherit the deficiency by the nature of their dependency on claim 21. For purposes of the examination, the limitation is being given a broadest reasonable interpretation as “defining classification voxels of a training data set and a specific given validation data set, wherein each classification voxel is assigned with a classification label, wherein each classification voxel is made up of one or more resolution voxels” in light of the original specification of the instant application.
Claim 21 recites the limitation “supervised learning of the platform by classification of tissue within said classification voxels of a training data set by the multilayer convolutional network”. The antecedent basis for “the multilayer convolutional network” is unclear. In particular, “the multilayer convolutional network” recited in the limitation appears a multilayer convolutational network prior to being validated. Claims 22-40 inherit the deficiency by the nature of their dependency on claim 21. For purposes of the examination, the limitation is being given a broadest reasonable interpretation as “supervised learning of the platform by classification of tissue within said classification voxels of a training data set by a multilayer convolutional network”.
Claim 22 recites the limitation “performing weighted imaging on at least one patient data set”. It is unclear whether “at least one patient data set” recited in the limitation is the same or different from “at least one patient data set” recited in claim 21, to which claim 22 depends. Claims 23-25 inherit the deficiency by the nature of their dependency on claim 22. For purposes of the examination, the limitation is being given a broadest reasonable interpretation as “performing weighted imaging on the at least one patient data set”.
Claim 24 recites the limitation “wherein the classification voxels are at least 5 times larger than the resolution voxels and wherein the resolution voxels are cubes of 1 mm”. First, the antecedent bases for “the classification voxels” and “the resolution voxels” are unclear among various “classification voxels” and “resolution voxels” recited in claim 21, to which claim 24 depends. Second, cubes are well known in the art to be volumetric and are expressed in a three-dimensional unit. However, “cubes of 1 mm” recites only one dimension. For purposes of the examination, the limitation is being given a broadest reasonable interpretation as “wherein the classification voxels of the training data set are at least 5 times larger than the one or more resolution voxels of the training data set, and wherein the one or more resolution voxels of the training data set are cubes of 1 mm per side”.
Claims 25, 28, 31, 34, and 37 each recites the limitation “wherein the clinician can set the size of the classification voxel”. The antecedent basis for “the classification voxel” is unclear among various “classification voxels” recited in claim 21, to which claims 25, 28, 31, 34, and 37 all depend. For purposes of the examination, the limitation in each of the claims is being given a broadest reasonable interpretation as “wherein the clinician can set a size of the classification voxels of the training data set”.
Claims 29, 32, 35, and 38 each recites the limitation “wherein the classification voxels are at least 5 times larger than the resolution voxels”. The antecedent bases for “the classification voxels” and “the resolution voxels” are unclear among various “classification voxels” and “resolution voxels” recited in claim 21, to which claims 29, 32, 35, and 38 depend. For purposes of the examination, the limitation is being given a broadest reasonable interpretation as “wherein the classification voxels of the training data set are at least 5 times larger than the one or more resolution voxels of the training data set”.
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 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 and 19 of U.S. Patent No. 11521742 – hereinafter referred to as ‘742.
Regarding claim 21 of the instant application, patented claim 1 of ‘742 recites a method of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification comprising the steps of: Providing a multilayer convolutional network for neurological tumor identification configured for segmenting data sets of full neurologic scans into resolution voxels, wherein the resolution voxels are representative of uniform volumetric regions … within which the platform can segment data sets; Defining classification voxels which are representative of uniform volumetric regions wherein each classification voxel is made up of one or more resolution voxels; Supervised learning of the platform by classification of tissue within said classification voxels of a specific given training data set by the multilayer convolutional network for neurological tumor identification with each classification voxel of the training data set having a predetermined ground truth; Validating the classification of tissue within said classification voxels of a specific given validation data set by the multilayer convolutional network for neurological tumor identification with each classification voxel of the validation data set having a predetermined ground truth; Implementing the platform by classification of tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for neurological tumor identification (or “mapping a neurological tumor of a patient” in claim 21 of the instant application) with each classification voxel of each data set assigned a label. Therefore, claim 21 of the instant application is anticipated by the patented claim 1 of ‘742.
Also regarding claim 21 of the instant application, patented claim 19 of ‘742 recites a method of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification comprising the steps of: Providing a multilayer convolutional network for neurological tumor identification configured for segmenting data sets of full neurologic scans into resolution voxels, wherein the resolution voxels are representative of uniform volumetric regions … within which the platform can segment data sets; Defining classification voxels which are representative of uniform volumetric regions wherein each classification voxel is made up of one or more resolution voxels; Supervised learning of the platform by classification of tissue within said classification voxels of a specific given training data set by the multilayer convolutional network for neurological tumor identification with each classification voxel of the training data set having a predetermined ground truth; Validating the classification of tissue within said classification voxels of a specific given validation data set by the multilayer convolutional network for neurological tumor identification with each classification voxel of the validation data set having a predetermined ground truth; Implementing the platform by classification of tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of each data set assigned a label, … wherein the platform identifies false markers of tumor growth as an indication to introduce T-cell therapy (or “identifying imitation time for T-cell therapy” in claim 21 of the instant application). Therefore, claim 21 of the instant application is anticipated by the patented claim 19 of ‘742.
Claims 21 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11848105 – hereinafter referred to as ‘105.
Regarding claim 21 of the instant application, patented claim 1 of ‘105 recites a method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis comprising the steps of: Providing a multilayer convolutional network for cardiopulmonary analysis configured for segmenting data sets of cardiopulmonary scans into resolution voxels, wherein the resolution voxels are representative of uniform volumetric regions … within which the platform can segment data sets; Defining classification voxels which are representative of uniform volumetric regions wherein each classification voxel is made up of one or more resolution voxels; Supervised learning of the platform by classification of tissue within said classification voxels of a specific given training data set by the multilayer convolutional network for cardiopulmonary analysis with each classification voxel of the training data set having a predetermined ground truth; Validating the classification of tissue within said classification voxels of a specific given validation data set by the multilayer convolutional network for cardiopulmonary analysis with each classification voxel of the validation data set having a predetermined ground truth; Implementing the platform by classification of cardiopulmonary tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for cardiopulmonary analysis with each classification voxel of each data set assigned a label, wherein the Implementing the platform by classification of cardiopulmonary tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for cardiopulmonary analysis further includes prediction of transcatheter aortic valve implantation results for the patient (or “predicting cardiopulmonary implant results for a patient” in claim 21 of the instant application). Therefore, claim 21 of the instant application is anticipated by the patented claim 1 of ‘105.
Allowable Subject Matter
Claims 21-29 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 112(a) and 112(b) as well as the Double Patenting Rejections set forth in this Office action (see the Examiner’s suggestions and interpretation noted above in the respective rejections as well as filing a Terminal Disclaimer).
When claims 21-29 are considered as a whole, incorporating Examiner’s suggestions and interpretation noted above, prior arts do not disclose providing a validated multilayer convolutional (neural) network that is validated by defining classification voxels that are representative of uniform volumetric regions, or smallest volumetric regions of cubes of 1 mm per side, supervised learning classification of tissue within classification voxels of training data set having a predetermined ground truth, and validating the classification of tissue with classification voxels of a validation data set having a predetermined ground truth; wherein the validated multilayer convolutional network is configured for segmenting data sets of neurological MRI scans into resolution voxels of uniform volumetric regions; and implementing the validated multilayer convolutional network to classify a neurological tissue within classification voxels of a patient data set for mapping a neurologic tissue or a neurological tumor of a patient.
In particular, Moeskops et al. (Moeskops et al. "Automatic Segmentation of MR Brain Images With a Convolutional Neural Network," in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1252-1261, May 2016, doi: 10.1109/TMI.2016.2548501.) discloses at least mapping a brain structure using a voxel-wise classifier using a trained convolutional (neural) network (see pg. 1253: II. Method; pg. 1255-1259: Fig. 4 and IV. Experiments and Results). Mehta et al. (Mehta et al. "BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures," Journal of Medical Imaging 4(2), 024003 (20 Apr 2017). Doi: 10.1117/1.JMI.4.2.024003.) also discloses mapping a brain structure using a voxel-wise classifier using a trained convolutional network (see pg. 2-9: Fig. 3; 2 Methodology; and 4 Experiments and Results). But neither Moeskops et al. nor Mehta et al. discloses at least validating the convolutional network by performing a supervised learning using classification voxels of a training data set with a predetermined ground truth for a tissue classification; validating the tissue classification against classification voxels of a validation data set with a predetermined ground truth; and implementing the convolutional network to map the brain structure. Additionally, Rao et al. (Rao et al. "Contusion segmentation from subjects with Traumatic Brain Injury: A random forest framework," 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, China, 2014, pp. 333-336, doi: 10.1109/ISBI.2014.6867876.) discloses at least segmenting a brain structure using a voxel-wise classifier comprising a multi-atlas label in mapping a neurologic tissue (see pg. 333-335: 2. Material and Methods), but does not disclose at least validating a multilayer convolutional (neural) network by performing a supervised learning using a training data set and validating a classification of tissue based on training data set against classification voxel of validation data set and implementing the convolutional network to map a neurologic tissue by classifying the neurologic tissue within classification voxels of a patient data set.
The technical advantage of the claimed invention “the platform 10, like the radiologist, uses more than merely pixel/voxel color differentiation (or edge and contrast values) in tumor identification … providing an effective and efficient precision artificial intelligence based neuroradiology platform 10 for neurological tumor identification” ([0055] of the specification of the instant application).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Younhee Choi whose telephone number is (571)272-7013. The examiner can normally be reached M-F 9AM-5PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anhtuan Nguyen can be reached at 571-272-4963. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Y.C./Examiner, Art Unit 3797
/ANHTUAN T NGUYEN/Supervisory Patent Examiner, Art Unit 3795
6/17/26
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