DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code – the specific text in question is “http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.7415&rep=rep1&type= pdf”. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
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 21-24 and 26-27 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 21, 23-24, and 27-28 of copending Application No. 16/712,219 in view of Duggirala et al. (US 2004/0147840 A1, Jul. 29, 2004) (hereinafter “Duggirala”).
Regarding claims 21-22: conflicting claim 21 discloses a method of diagnosing a condition of bodily tissue using a computer, the method comprising: performing an ultrasound scan of a patient bodily tissue to acquire raw RF signals; extracting tissue contours from the raw RF signals; using the tissue contours to create a patient-specific 3D tissue model; comparing, using a computer, a-the patient-specific 3D tissue model with at least one 3D tissue model having common tissue with the bodily tissue; diagnosing a condition of the bodily tissue responsive to comparing the 3D tissue models; wherein comparing the 3D tissue models includes comparing the patient-specific 3D tissue model with a plurality of 3D tissue models each having common tissue with the bodily tissue.
Conflicting claim 21 is silent on displaying a visual output responsive to diagnosing the condition.
Duggirala, in the same field of endeavor, teaches a computer-aided diagnostic method comprising diagnosing a condition by comparing a patient ultrasound-based model to a database containing a plurality of reference ultrasound models ([0027], [0041], [0060], [0065]), and displaying a visual output responsive to diagnosing the condition ([0041], [0067]). Duggirala further teaches that disclosed CAD system and method, including the visual display(s) improves diagnosis, and work flow and effectiveness may be improved while reducing subjectivity and variability ([0041]).
It would have been prima facie obvious for one having ordinary skill in the art at the time
of invention to implement the method of conflicting claim 21by displaying a visual output as taught by Duggirala in order to improve diagnostic workflow and effectiveness.
Regarding claims 23, 24, 26, and 27: conflicting claims 23, 24, 27 and 28 (respectively) disclose every limitation of instant claims 23, 24, 26, and 27.
This is a provisional nonstatutory double patenting rejection.
Claims 32-35 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 30-34 of copending Application No. 16/712,219 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because conflicting claims 30-34 disclose every limitation of instant claims 32-35.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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 21-27 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Tadross, Rimon, and Mohamed Mahfouz. "A Novel Imaging System for Patient-Specific 3D Knee Model Reconstruction Using Ultrasound.” 57th Annual Meeting of the Orthopaedic Research Society, Long Beach, CA, Jan. 13-16, 2011 (hereinafter “Tadross”) in view of Liew et al. (US 2004/0106868 A1, Jun. 3, 2004) (hereinafter “Liew”) and Duggirala et al. (US 2004/0147840 A1, Jul. 29, 2004) (hereinafter “Duggirala”).
Regarding claim 21: Tadross discloses performing an ultrasound scan of a patient bodily tissue to acquire raw RF signals; extracting tissue contours from the raw RF signals (see whole document); and using the tissue contours to create a patient-specific 3D tissue model (see whole document). However, Tadross does not disclose comparing, using a computer, the patient-specific 3D tissue model with at least one 3D tissue model having common tissue with the bodily tissue; and diagnosing a condition of the bodily tissue responsive to comparing the 3D tissue models.
Liew discloses creating a patient-specific 3D tissue model ([0068], [0071], [0072]) using ultrasound data ([0036]); comparing, using a computer, the patient-specific 3D tissue model with at least one 3D tissue model having common tissue with the bodily tissue ([0046], [0068], [0071]-[0072] - 3D images, patterns or models; table 1 – at least “2D or 3D location of cartilage defect/diseased cartilage in articular surface”, “2D or 3D location of cartilage in relationship to weight-bearing area”, “3D surface contour information of subchondral bone”; tables 2-3); and diagnosing a condition of the bodily tissue responsive to comparing the 3D tissue models (([0011] – “…diagnosing a disease, determining disease staging, monitoring disease progression, managing a disease, disease prognostication, predicting a disease, monitoring therapy…”, “…comparing the at least one of quantitative and qualitative data in step b to at least one of: a database of at least one of quantitative and qualitative data obtained from a group of subjects; at least one of quantitative and qualitative data obtained from the subject; and at least one of a quantitative and qualitative data obtained from the subject at time Tn.”; [0018] – “Any data obtained, extracted or generated under any of the methods can be compared to a database, a subset of a database, or data previously obtained, extracted or generated from the subject.”; [0021] – “The images or data can then be compared to a database of images or data (e.g., "normal" images or data) and/or compared to one or more images or data taken from the same subject…”; [0078] – “As will be appreciated by those of skill in the art, the fully automated measurement is, for example, possible with image processing techniques such as segmentation and registration. This process can include, for example, seed growing, thresholding, atlas and model based segmentation methods, live wire approaches, active and/or deformable contour approaches, contour tracking, texture based segmentation methods, rigid and non-rigid surface or volume registration, for example based on mutual information or other similarity measures. One skilled in the art will readily recognize other techniques and methods for fully automated assessment of the parameters and measurements specified in Table 1, Table 2 and Table 3”, [0096] – “A system is provided that includes (a) a device for electronically transferring a degeneration pattern or a pattern of normal, diseased or abnormal tissue for the bone or the joint to a receiving device located distant from the transferring device; (b) a device for receiving said pattern at the remote location; (c) a database accessible at the remote location for generating additional patterns or measurements for the bone or the joint of the human wherein the database includes a collection of subject patterns or data, for example of human bones or joints, which patterns or data are organized and can be accessed by reference to characteristics such as type of joint, gender, age, height, weight, bone size, type of movement, and distance of movement; (d) optionally a device for transmitting the correlated pattern back to the source of the degeneration pattern or pattern of normal, diseased or abnormal tissue.”, [0101]-[0102] – “Thus, data (e.g., bone structural information or bone mineral density information or articular information) is obtained from normal control subjects using the methods described herein. …The comparison of the subject's x-ray information to the reference database can be used to determine if the subject's bone information falls outside the normal range found in the reference database or is statistically significantly different from a normal control.”). Liew further discloses that conditions such as osteoporosis and osteoarthritis have a significant impact on society, but diagnostic and monitoring methods remain limited and that improved diagnostics would be a benefit for patient monitoring and management ([0003]-[0005]).
It would have been prima facie obvious for one having ordinary skill at the time of invention to perform diagnosis as taught by Liew using the models of Tadross in order to provide more accurate bone models for improved diagnostics for bone conditions in view of the teachings of Liew that improved diagnostic methods are beneficial.
Further regarding claim 21: Tadross and Liew are silent on displaying a visual output responsive to diagnosing the condition.
Duggirala, in the same field of endeavor, teaches a computer-aided diagnostic method comprising diagnosing a condition by comparing a patient ultrasound-based model to a database containing a plurality of reference ultrasound models ([0027], [0041], [0060], [0065]), and displaying a visual output responsive to diagnosing the condition ([0041], [0067]). Duggirala further teaches that disclosed CAD system and method, including the visual display(s) improves diagnosis, and work flow and effectiveness may be improved while reducing subjectivity and variability ([0041]).
It would have been prima facie obvious for one having ordinary skill in the art at the time
of invention to implement the method of Tadross and Liew by displaying a visual output as taught by Duggirala in order to improve diagnostic workflow and effectiveness.
Regarding claim 22: Tadross, Liew and Duggirala disclose the method of claim 21. Liew further discloses wherein comparing the 3D tissue models includes comparing the 3D tissue model derived from the ultrasound scan of the bodily tissue with a plurality of 3D tissue models each having common tissue with the bodily tissue ([0011], [0018], [0021], [0078], [0096], [0101]-[0102]).
Regarding claim 23: Tadross, Liew and Duggirala disclose the method of claim 21. Liew further discloses wherein: the at least one 3D tissue model comprises a plurality of 3D tissue models each having common tissue with the bodily tissue ([0011], [0018], [0021], [0078], [0096], [0101]-[0102]); and including that a neural network may be used to perform the analyses ([0108]), but Liew does not explicitly state that the neural network is used to perform the comparison.
Duggirala further discloses teaches a computer-aided diagnostic method where a neural network is used to diagnose a condition by comparing a patient ultrasound- based model to a database containing a plurality of reference ultrasound models ([0027], [0041], [0060], [0065]).
Duggirala further teaches that conventional methods provide limited options for diagnostic assistance, particularly for ultrasound imaging ([0005]) and that the disclosed CAD system improves diagnosis, and work flow and effectiveness may be improved while reducing subjectivity and variability ([0041]).
It would have been prima facie obvious for one having ordinary skill in the art at the time
of invention to implement the method of Tadross, Liew and Duggirala using a neural network as further taught by Duggirala in order to improve diagnostic workflow and effectiveness, and in view of the suggestion of Liew that neural networks may be used to perform the disclosed analyses.
Regarding claim 24: Liew in view of Duggirala discloses the method of claim 23, wherein the diagnosing includes an indication that the bodily tissue is at least one of normal, dislocated, and fractured (table 1, [0076] – “evaluating the amount or the degree of normal, diseased or abnormal tissue”, emphasis added).
Regarding claim 25: Tadross, Liew and Duggirala disclose the method of claim 23. Duggirala further discloses wherein the plurality of tissue models of the neural network include a training data set comprising a plurality of tissue models each having a known diagnosis ([0041], [0066]) where the training data is the same type of data as the diagnosis data, which in the case of Tadross, Liew and Duggirala as combined above, would be the 3D tissue models (Duggirala – [0041]; Liew - [0046], [0068],[0071]-[0072]).
Regarding claim 26: Tadross, Liew and Duggirala discloses the method of claim 21. Liew further wherein: the 3D tissue model derived from the ultrasound scan of the bodily tissue includes both bone and cartilage; and the at least one 3D tissue model having common tissue with the bodily tissue includes both bone and cartilage ([0019], table 1, [0076], [0089], [0107], claims 6-7). It would have been prima facie obvious for one having ordinary skill in the art at the time of invention to implement the models of Tadross, Liew and Duggirala to include both bone and cartilage, as disclosed by Liew, in order to provide diagnostic information relating to conditions involving cartilage such as osteoarthritis.
Regarding claim 27: Tadross, Liew and Duggirala disclose the method of claim 21. Liew further discloses wherein: the at least one 3D tissue model having common tissue with the bodily tissue comprises a 3D baseline tissue model ([0076] - initial time T1 is "baseline"); the comparing includes identifying portions of the patient-specific 3D tissue model that exceed a predetermined statistical variance limit with respect to the 3D baseline tissue model ([0101] – “statistically significantly different”).
Claim 28 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Tadross, Liew and Duggirala as applied to claim 21 above, and further in view of Hinz, Antje, and Andrew T. Fischer Jr. "Comparison of the accuracy of radiography and ultrasonography for detection of articular lesions in horses." Veterinary surgery 40.7 (2011): 881-885 (hereinafter “Hinz”).
Regarding claim 28: Tadross, Liew and Duggirala disclose the method of claim 21. Liew further discloses wherein the patient-specific 3D tissue model includes a fluid collection ([0064], table 1, claim 7 – subchondral cysts) and diagnosing the condition includes using a location of the fluid collection ([0068] – locating the ROI, i.e. “using a location of the fluid collection” is the first step of the diagnostic process; [0071]-[0072], [0078], table 1, claim 7).
While Liew further discloses that the image data and 3D patterns (tissue models) can be made using ultrasound ([0036]), Liew does not disclose that the data on cysts is acquired via ultrasound.
Hinz, in the same field of endeavor, discloses identifying and diagnosing subchondral cysts using ultrasound, and that ultrasound is at least as accurate as other radiography methods as well as being advantageous in situations where radiography may be difficult to perform (Results, Discussion).
It would have been prima facie obvious for one having ordinary skill in the art at the time of invention to acquire the cyst data of Liew using ultrasound as taught by Hinz in order to provide accurate diagnosis in situations where other imaging methods such as radiography are too difficult to perform.
Claims 29-31 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Liew et al. (US 2004/0106868 A1, Jun. 3, 2004) (hereinafter “Liew”) in view of Hinz, Antje, and Andrew T. Fischer Jr. "Comparison of the accuracy of radiography and ultrasonography for detection of articular lesions in horses." Veterinary surgery 40.7 (2011): 881-885 (hereinafter “Hinz”).
Regarding claim 29: Liew discloses a method of diagnosing, using a computer, a condition of a bodily tissue associated with an internal hemorrhage causing an abnormal fluid collection the method comprising: evaluating, using a computer, a 3D tissue model ([0068], [0071]-[0072], [0078]; 3D pattern 132) derived from a scan of the bodily tissue associated to identify an abnormal fluid collection ([0064] - subchondral cyst, see table 1 and claim 7) and, diagnosing a condition of the bodily tissue responsive to evaluating the 3D tissue model using a location of the fluid collection ([0068] – locating the ROI, i.e. “using a location of the fluid collection” is the first step of the diagnostic process; [0071]-[0072], [0078], table 1, claim 7).
While Liew further discloses that the image data and 3D patterns (tissue models) can be made using ultrasound ([0036]), Liew does not disclose that the data on cysts is acquired via ultrasound.
Hinz, in the same field of endeavor, discloses identifying and diagnosing subchondral cysts using ultrasound, and that ultrasound is at least as accurate as other radiography methods as well as being advantageous in situations where radiography may be difficult to perform (Results, Discussion).
It would have been prima facie obvious for one having ordinary skill in the art at the time of invention to acquire the cyst data of Liew using ultrasound as taught by Hinz in order to provide accurate diagnosis in situations where other imaging methods such as radiography are too difficult to perform.
Regarding claim 30: Liew and Hinz disclose the method of claim 29. Liew further discloses wherein evaluating the 3D tissue model includes correlating a location of the fluid collection with at least one of an associated organ (bones are organs) or vascular injury ([0022] – locating the ROI can be performed based on image analysis results; fig. 1B, [0036]-[0037, [0066] – locating a part of the body for study and then identifying an ROI is “correlating a location of the fluid collection with at least one of an associated organ or vascular injury” where the bone is an organ).
Regarding claim 31: Liew and Hinz disclose the method of claim 29, wherein the 3D tissue model derived from the ultrasound scan of the bodily tissue visualizes the fluid collection using a volume imaging mode ([0036] – ultrasound C-scan).
Claims 32-35 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Liew et al. (US 2004/0106868 A1, Jun. 3, 2004) (hereinafter “Liew”) in view of Tadross, Rimon, and Mohamed Mahfouz. "A Novel Imaging System for Patient-Specific 3D Knee Model Reconstruction Using Ultrasound.” 57th Annual Meeting of the Orthopaedic Research Society, Long Beach, CA, Jan. 13-16, 2011 (hereinafter “Tadross”) and Duggirala et al. (US 2004/0147840 A1, Jul. 29, 2004) (hereinafter “Duggirala”).
Regarding claim 32: Liew discloses a method of diagnosing a condition of bodily tissue using a computer, the method comprising: using a model created from an ultrasound scan of the bodily tissue to at least one of classify a new case concerning the bodily tissue and categorize an injury to the bodily issue; and, diagnosing a condition of the bodily tissue responsive to at least one of classifying the new case and categorizing the injury ([0036] - ultrasound, [0046], [0068], [0071]-[0072] - 3D images, patterns or models, see also figs. 2A-3J; [0011], [0018], [0021], [0078], [0096], [0101]-[0102]).
Liew is silent on the model being created using raw ultrasound data.
Tadross, in the same field of endeavor, teaches generating a point cloud model of a
bone directly from raw ultrasound data, which bypasses the image reconstruction process (see
whole document).
It would have been prima facie obvious for one having ordinary skill in the art at the time
of invention to modify the method of Liew by generating the tissue (bone) models directly from
the raw ultrasound data as taught by Tadross in order to increase efficiency and lower
computational cost by bypassing image reconstruction.
Further regarding claim 32: Liew discloses that a neural network may be used to perform
the analyses ([0108]), but does not explicitly state that the neural network is used to perform the
comparison.
Duggirala, in the same field of endeavor, teaches a computer-aided diagnostic system
where a neural network is used to diagnose a condition by comparing a patient ultrasound-
based model to a database containing a plurality of reference ultrasound models ([0027], [0041],
[0060], [0065]), the diagnosing the condition of the bodily tissue includes automatically
outputting a diagnosis by the neural network responsive to the comparing ([0067]). Duggirala
teaches that conventional methods provide limited options for diagnostic assistance, particularly
for ultrasound imaging ([0005]) and that the disclosed CAD system improves diagnosis, and
work flow and effectiveness may be improved while reducing subjectivity and variability ([0041]).
It would have been prima facie obvious for one having ordinary skill in the art at the time
of invention to implement the method of Liew and Tadross using a neural network and providing
output as taught by Duggirala in order to improve diagnostic workflow and effectiveness, and in
view of the suggestion of Liew that neural networks may be used to perform the disclosed
analyses.
Regarding claim 33: Liew in view of Tadross and Duggirala further discloses wherein classifying the new case includes classifying the new case as at least one of a trauma condition, a soft tissue damage condition, and a bone fracture condition (table 1, [0076]).
Regarding claim 34: Liew, Tadross and Duggirala teach the method of claim 32, wherein the neural network is trained with a training set of vectors (Duggirala - [0041], [0066]), where the training data vectors are the same type of data as the diagnosis data, which in the case of Liew, Tadross and Duggirala would be 3D ultrasound data (Duggirala – [0041]; Liew - [0046], [0068], [0071]-[0072]).
Regarding claim 35: Liew, Tadross and Duggirala teach the method of claim 32, wherein the neural network is trained to differentiate between normal tissue anatomy and abnormal
tissue anatomy (Duggirala – [0065]).
Response to Arguments
In light of the amendments to the claims, claim 24 is entitled to the priority date of 08/12/2011 while claims 28-35 are entitled to the priority date of 08/13/2012.
Rejection of claim 28 under U.S.C. §112(a) and (b) is withdrawn in light of the amendments to the claims.
With respect to the objection to the specification, Applicant “presumes this section was included by mistake.” Examiner notes that Applicant was made aware of the same issue in parent application 16/712,219 which has the same specification, so it is unclear why Applicant would conclude that there has been a mistake. Regardless, since Applicant wishes the objectionable text to be identified, the specific text in question is “http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.7415&rep=rep1&type= pdf.” The objection is maintained.
The objection to claim 36 is withdrawn in light of the cancelation of claim 36.
Applicant’s arguments with respect to the provisional non-statutory double patenting rejection have been fully considered but are not persuasive.
Applicant asserts that this rejection is premature.
Examiner respectfully disagrees. The rejection is a provisional rejection. The text of the rejection, which was provided in the last Office Action, reads as follows: “This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.” The rejection is appropriately timed. Additionally, under the principles of compact prosecution as outlined in MPEP 2103, Examiner is required to raise every applicable ground of rejection: “…examiners should state all reasons and bases for rejecting claims in the first Office action. Deficiencies should be explained clearly, particularly when they serve as a basis for a rejection.” It is noted that Applicant has not argued the merits of the rejection. The provisional double patenting rejection is maintained.
Applicant’s arguments with respect to prior art rejection of claims 29-31 have been fully considered but are moot in view of the updated grounds of rejection necessitated by amendment.
Applicant’s arguments with respect to prior art rejection of claims 21-27 have been fully considered but are moot in view of the updated grounds of rejection necessitated by amendment.
Applicant’s arguments with respect to prior art rejection of claim 28 have been fully considered but are moot in view of the updated grounds of rejection necessitated by amendment.
Applicant’s arguments with respect to prior art rejection of claims 32-36 have been fully considered but are not persuasive.
Applicant argues that Liew, Duggirala and Tadross do not disclose the limitations of claim 32 because “[c]omparing 3D models to one another, even if a model has a predetermined diagnosis, fails to satisfy the foregoing limitations.”
Examiner respectfully disagrees. The claim includes the open transitional phrase “comprising” in the preamble which does not preclude the presence of any additional, unrecited intermediate steps such as generating a model from the raw data. Examiner further notes that if Applicant is attempting to suggest that the neural network performs classification or categorization directly on the raw ultrasound data, there is nothing in the instant disclosure to support such an interpretation. The instant disclosure at paragraph [000134] describes training the neural network using 3-D ultrasound models, and comparing the training set to a database of ultrasound models. At paragraph [000135] the disclosure states that “[o]nce the neural network 302 is trained, the neural network 302 may be used to classify new cases and categorize an injury type using raw ultrasound data.” While the phrase “using raw ultrasound data” appears, it cannot reasonably be concluded that this neural network which has been trained only on 3D models would be capable of directly classifying raw data, nor is “using raw ultrasound data” limited to only directly operating on the raw data. Operating on a model made from raw data is “using” the raw data. The rejections are maintained.
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.
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/CAROLYN A PEHLKE/ Primary Examiner, Art Unit 3799