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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 02/03/2026 has been entered.
Response to Arguments
Applicant’s arguments have been fully considered but are respectfully not persuasive. Regarding the independent claims, the claims have been amended to include subject matter from dependent claims 6/12. However, these claims were previously rejected over Hal 2019/0298253. It is unclear how these amendments overcome the rejection and therefore it is maintained.
Applicant argues regarding claims 2 and 8 that the references do not teach the limitations. Examiner respectfully disagrees. The rejection has been updated to show how the Hal reference teaches this.
Regarding claims 4 and 10, it is not clear how PFPS, a common disease of the knee does not read on the body part being a knee.
Regarding claim 13, Applicant’s arguments are respectfully unpersuasive. While arthroscopy is cited, the previous section includes MRIs which inherently include composite imaging along with the CT scans of the previous references.
Regarding claim 14, Examiner respectfully disagrees with Applicant’s arguments, the cited techniques inherently include repots that would be analyzed.
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.
Claim(s) 1-2, 4-5 and 7-8, 10-11, 13-15, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi, Wuxiang, et al. "Diagnosis of patellofemoral pain syndrome based on a multi-input convolutional neural network with data augmentation." in view of Hou, Ruichao, et al. "Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model." further in view of Articular Cartilage Lesions of the Knee September 2020 [herein ACLK] and Hal US 2019/0298253.
Regarding claims 1 and 7, Shi teaches “a medical diagnosis system based on a multi-input convolutional neural network (MI-CNN) for diagnosing a disorder and/or severity of the disorder related to a bone and muscle and recommending a treatment, the medical diagnosis system comprising: a processor and a memory configured to implement the steps of” (abstract “this paper proposes a multi-input convolutional neural network (MI-CNN) method that uses two input channels to mine the information of lower limb biomechanical data from two mainstream data preprocessing methods (standardization and normalization) to diagnose PFPS” and pg. 5 table 1):
While Shi generally teaches medical imaging and various techniques for it, Hou more specifically teaches “receiving two or more types of medical imaging data relating to a body part of a patient as an input, the two more types of medical imaging data are selected from a group consisting of Electromyography (EMG), X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Ultrasonography, and Arthroscopy” (Hou abstract “The aim of medical image fusion is to improve the clinical diagnosis accuracy, so the fused image is generated by preserving salient features and details of the source images. This paper designs a novel fusion scheme for CT and MRI medical images based on convolutional neural networks (CNNs)” and pg. 2 left col. ¶3 “The objective auxiliary diagnosis methods of PFPS include X-ray, magnetic resonance imaging, computed tomography, and arthroscopy”);
It would have been obvious to one having ordinary skill in the art at the time that the invention was filed to combine the teachings of Shi with that of Hou since a combination of known methods would yield predictable results. Both references discuss various imaging techniques and it is known in the art that CNNs are very applicable to image data. Therefore, when combined, the references would operate in a known and predictable manner that allow the CNNs of each reference to operate on imaging data also described in both references.
Shi further teaches “pre-processing the input and the patient’s data by using normalization and standardization” (pg. 3 left col. “The level of each index differs greatly if the original data is directly used for analysis, highlighting the role of the index with a high numerical value in the comprehensive analysis and relatively weakening the role of the index with a low numerical level. Therefore, the original data must be preprocessed to ensure the reliability of the results” original dat including patient’s data and right col.
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“implement a multi-input convolutional neural network (MI-CNN), the MI-CNN comprising a plurality of processing layers arranged in an order, each layer of the plurality of processing layers comprises at least one node, wherein at least one node of each layer of the plurality of processing layers is connected to at least one node of a following layer or a preceding layer in the order, the plurality of processing layers comprises” (pg. 5
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“a convolution layer for feature extraction from an image” (CONV1D as shown above),
“a rectified linear activation function to scale data into a linear scale of 0 and 1” (RELU as shown above),
“a pooling layer for dimension reduction extracting dominant features, suppressing noise, increasing computational power, and analysis accuracy” (maxpooling as shown above),
“a flatten layer to convert the pooling layer to a single column vector” (flatten as shown above), and
“a full connection layer to compute class scores and classify features for meaningful output deviation” (full connection layer as shown above); and
“applying the MI-CNN to the pre-processed input for grading the severity of the disorder to obtain a grade [from a plurality of predefined grades]” (output layer as shown above and abstract “MI-CNN was used to automatically extract features to classify patients with PFPS and pain-free controls”)
While the references do not explicitly teach predefined grades, ACLK teaches it (ACLK
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It would have been obvious to one having ordinary skill in the art at the time that the invention was filed to combine the teachings of Shi and Hou with that of ACLK since a combination of known methods would yield predictable results that is, grading scales are common in the medical field and classifying data to a predefined grade would operate in a known and predictable manner as it is just another form of classifying.
While the references mention numerous medical data, Hal more specifically teaches “wherein the input further comprises a patient's data selected from a group consisting of weight, age, body mass index, or a combination thereof” (Hal [0047] “The initial data may comprise patient height, weight, sex, age, body mass index (BMI), body fat percentage, appendage circumference, and the like.”)
It would have been obvious to one having ordinary skill in the art at the time that the invention was filed to combine the teachings of Shi, Hou, and ACLK with that of Hal since a combination of known methods would yield predictable results. Hal shows very well known and understood medical data i.e. age, weight, etc. This common medical data would operate in a known and predictable manner with the medical processing systems described above.
Note that independent claim 7 recites the same substantial subject matter as independent claim 1, only differing in embodiment. The difference in embodiment, a system as opposed to a method is an obvious variation of the other and therefore the claim is subject to the same rejection.
Regarding claims 2 and 8, the Shi, Hou, ACLK, and Hal references have been addressed above. Hal further teaches “wherein the processor and memory are further configured to: presenting through the screen of an interface, the grade and a recommendation for treatment based on the grade” (Hal [0038] “Further, real-time diagnostic information can be provided and displayed on graphical user interface (GUI) 131 or a or similar display in a standalone application or via a web based system using a web server. The display can incorporate without limitation 3D medical anatomical displays, biomechanical data interpretation, and interactive imaging that is needed for the diagnosis and/or treatment of patient 104” which can display the combination of data as shown above)
Regarding claims 4 and 10, the Shi, Hou, ACLK, and Hal references have been addressed above. Shi further teaches “wherein the body part is a knee” (abstract “Patellofemoral pain syndrome (PFPS) is a common disease of the knee”)
Regarding claims 5 and 11, the Shi, Hou, ACLK, and Hal references have been addressed above. Shi further teaches “wherein the knee disorder is patellofemoral pain syndrome” (abstract “Patellofemoral pain syndrome (PFPS) is a common disease of the knee”)
Regarding claim 13, the Shi, Hou, ACLK, and Hal references have been addressed above. ACLK further teaches “wherein the medical imaging data is in the form of a video, wherein selected frames in the video are combined to form a composite image” (ACLK
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Regarding claim 14, the Shi, Hou, ACLK, and Hal references have been addressed above. Shi further teaches “wherein the medical imaging data is in the form of a set of images, patient medical reports, and text reports, wherein the medical imaging data, when are images, is captured by a medical imaging device and are representative of parts of a body showing internal structures for medical diagnosis” (pg. 2 left col. ¶3 “The objective auxiliary diagnosis methods of PFPS include X-ray, magnetic resonance imaging, computed tomography, and arthroscopy” which include image and associated reports)
Regarding claims 15 and 19, the Shi, Hou, ACLK, and Hal references have been addressed above. ACLK further teaches “wherein the disorder is a bone disorder, wherein the predefined grades comprise: mild grade characterized by softening of the cartilage in the knee area; moderate grade characterized by softening of the cartilage along with abnormal surface characteristics; severe grade characterized thinning of cartilage with active deterioration of the tissue; and critical grade characterized by exposure of the bone with a significant portion of cartilage deteriorated” (ACLK
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which is an analogous grading scale)
Regarding claim 18, the Shi, Hou, ACLK, and Hal references have been addressed above. ACLK further teaches “wherein the disorder is a bone disorder, wherein the predefined grades comprise: mild grade characterized by softening of the cartilage in the knee area; moderate grade characterized by softening of the cartilage along with abnormal surface characteristics; severe grade characterized thinning of cartilage with active deterioration of the tissue; and critical grade characterized by exposure of the bone with a significant portion of cartilage deteriorated” (ACLK
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which is an analogous grading scale)
Claim(s) 16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi, Hou, ACLK, and Hal further in view of Touliopolous, Steven, and Elliott B. Hershman. "Lower leg pain: diagnosis and treatment of compartment syndromes and other pain syndromes of the leg.".
Regarding claims 16 and 20, the references have been addressed above. They do not explicitly teach the claim limitations. Touliopolous however teaches “wherein: the recommendation for the mild grade comprises reducing pressure on the kneecap & joint, resting, stabilizing, icing the knee joint, over-the-counter pain medicines, and wearing a right shoe and shoe inserts; the recommendation for the moderate grade comprises physical therapy focusing on strengthening quadriceps, hamstrings, adductors, and abductors to improve muscle strength and balance;the recommendation for the severe grade comprises weight-bearing exercises; andthe recommendation for the critical grade comprises surgery” (Touliopolous describes numerous methods for treatment including resting, therapy, etc.)
It would have been obvious to one having ordinary skill in the art at the time that the invention was filed to combine the teachings Shi, Hou, ACLK, and Hal with that of Touliopolous since a combination of known methods would yield predictable results. Tou shows numerous generic ways to treat leg injuries which would be obvious to one having leg injuries or trying to treat them.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi, Hou, ACLK, and Hal further in view of Patel, Niketa, Lavina Rajesh Khatri, and Lata Parmar. "Assessment of Functional End Ranges of Lower Limb Joints in Positions Commonly Used for ADLs in India." and Farina, Dario, et al. "Effect of joint angle on EMG variables in leg and thigh muscles."
Regarding claims 17, the references have been addressed above. They do not explicitly teach the claim limitations. Both Patel and Farina however teach “wherein the medical image data from three joint angles including hip flexion angle (HF), knee flexion angle (KF), and ankle dorsiflexion angle (ADF) of the lower limbs and” (Patel abstract “: The purpose of the study was to assess the functional end-ranges of the hip, knee and ankle joints in healthy Indian subjects in positions commonly used for ADLs in India which includes squatting and cross-legged sitting” and §1.1 “It is floor sitting posture which requires full ankles dorsiflexion, knees and hips flexion” i.e. these are known angles that are examined when taking medical images) “EMG signals of seven muscles including semimembranosus (SEB), rectus femoris (REF), vastus lateralis (VL), vastus medialis (VM), biceps femoris (BIF), medial gastrocnemius (MG), and lateral gastrocnemius (LG) are used” (Farina pg. 2
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which shows all the above muscles. Semitendinosus is analogous to semimembranosus as functionally these are just different muscles being analyzed)
The motivation to combine these references with the previous references is that these are known measurements taken when doing imaging and therefore they are important for determining illness. Therefore, they would operate in a known and predictable manner with the systems above.
Conclusion
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KEVIN W FIGUEROA
Primary Examiner
Art Unit 2124
/Kevin W Figueroa/Primary Examiner, Art Unit 2124 /Kevin W Figueroa/Primary Examiner, Art Unit 2124