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 .
Claim Objections
Claim 13 is objected to because of the following informalities:
Claim 13, line 1, “machine-learned” should read “machine-learning”. 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.
Claims 1-20 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.
Claims 1, 9, and 15 recite the limitation “each entry of the training dataset comprising historical ultrasound signals detected for a joint and labeled with an identifier of the joint”, which is considered indefinite. It is not clear to the examiner whether the joint in the training dataset is the same as or different from the joint previously disclosed in the claims. Further it is unclear whether the labeled joint is the joint from the dataset or the joint previously discussed in the claims. Further clarification is requested. For the purpose of examination and this office action it is being interpreted that the joint in the training dataset and the labeled joint are the same but do not have to be the same joint previously discussed in the claims.
Claims dependent upon the rejected claims above, but not directly addressed, are also rejected because they inherit the indefiniteness of the claim(s) they respectively depend upon.
Examiners Note
Claims 1, 9, and 15 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph and claim objections, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: the prior art of record fails to reasonably teach or in combination render obvious the following limitations when the claims taken as a whole to include, “extracting physiological properties of bones connected at the joint and tissue surrounding the joint from features of the detected echoes of ultrasound signals, wherein the extracted physiological properties comprise one or more of: elasticity, density, strength, and vascularity; generating a bone displacement measurement of the bones connected at the joint, wherein generating the bone displacement measurement comprises: inputting the physiological properties of the joint and properties of the detected echoes of ultrasound signals to a machine-learning displacement model trained to generate a virtual representation of a space surrounding the joint, wherein the displacement model is trained using a training dataset of historical ultrasound signals collected from a population of users, each entry of the training dataset comprising historical ultrasound signals detected for a joint and labeled with an identifier of the joint and a known bone displacement measurement at the labeled joint; and determining the bone displacement measurement based on the virtual representation generated by the displacement model”.
The closest prior art:
Mohabir (“Knee cartilage segmentation of ultrasound images using convolutional neural networks and local phase enhancement”) discloses extracting physiological properties of the joint from the detected ultrasound signals and inputting the physiological properties of the joint and the detected ultrasound signals into a machine learning displacement model to generate a bone displacement measurement at the joint (pgs. 8-10 and 18).
Mahfouz et al. (US20120029345) discloses a set of sensor position around the joint of a user which are used for detecting ultrasound signals ([0093] and [0099]-[0102]).
However,
The closest prior art fails to teach “extracting physiological properties of bones connected at the joint and tissue surrounding the joint from features of the detected echoes of ultrasound signals, wherein the extracted physiological properties comprise one or more of: elasticity, density, strength, and vascularity; generating a bone displacement measurement of the bones connected at the joint, wherein generating the bone displacement measurement comprises: inputting the physiological properties of the joint and properties of the detected echoes of ultrasound signals to a machine-learning displacement model trained to generate a virtual representation of a space surrounding the joint, wherein the displacement model is trained using a training dataset of historical ultrasound signals collected from a population of users, each entry of the training dataset comprising historical ultrasound signals detected for a joint and labeled with an identifier of the joint and a known bone displacement measurement at the labeled joint; and determining the bone displacement measurement based on the virtual representation generated by the displacement model”.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW BEGEMAN whose telephone number is (571)272-4744. The examiner can normally be reached Monday-Thursday 8:30-5:00.
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/ANDREW W BEGEMAN/Primary Examiner, Art Unit 3798
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