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 Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-11 and 14-20 are rejected under 35 U.S.C. 102(a1, a2) as being anticipated by Hipsley US 20180000339 A1.
Re claim 1, Hipsley teaches a method comprising:locating one or more target structures comprised in an eye of a patient based on processing image data of the eye of the patient, wherein processing the image data comprises (see paragraphs 0022 and 0235-0239): providing at least a portion of the image data to one or more machine learning models (see paragraph 0241); and receiving an output from the one or more machine learning models in response to the one or more machine learning models processing at least the portion of the image data, wherein the output comprises location data of the one or more target structures (see paragraph 0241);
determining one or more measurements associated with an anterior portion of the eye, based on the location data and one or more characteristics associated with the one or more target structures (see paragraphs 0255 and 0247-0248); and determining a presence, an absence, a progression, or a stage of a disease of the eye based on the one or more measurements (see paragraphs 0255 and 0247-0248).
Re claim 2, Hipsley teaches wherein determining the presence, the absence, the progression, or the stage is based on a correlation between the one or more measurements and the disease (see paragraph 0255).
Re claim 3, Hipsley teaches further comprising: providing the one or more measurements to the one or more machine learning models (see paragraphs 0248); and receiving a second output in response to the one or more machine learning models processing the one or more measurements (see paragraph 0248), wherein: the second output comprises a probability of the disease of the eye (see paragraph 0255); and determining the presence, the absence, the progression, or the stage is based on the probability (see paragraph 0255).
Re claim 4, Hipsley teaches wherein: the output from the one or more machine learning models comprises one or more predicted masks (see paragraph 0494); and determining the location data, the one or more measurements, or both is based at least in part on the one or more predicted masks (see paragraph 0494).
Re claim 5, Hipsley teaches wherein the one or more measurements comprise at least one of: a measurement with respect to at least one axis of a set of axes associated with the eye (see paragraph 0298-0299), an angle between two or more axes of the set of axes (see paragraph 0298-0299); and a second measurement associated with an implant comprised in the eye (see paragraph 0255).
Re claim 6, Hipsley teaches wherein the one or more target structures comprise at least one of: tissue comprised in the eye; surgically modified tissue comprised in the eye; pharmacologically modified tissue comprised in the eye; and an implant comprised in the eye (see paragraph 0255).
Re claim 7, Hipsley teaches further comprising: determining a change in intraocular pressure in the eye based on the one or more measurements, wherein determining the presence, the absence, the progression, or the stage of the disease is based on the intraocular pressure (see paragraph 0256, 0341, and 0491).
Re claim 8, Hipsley teaches wherein: the one or more measurements are associated with a first region posterior to an iris of the eye, a second region anterior to the iris, or both (see paragraph 0247).
Re claim 9, Hipsley teaches wherein: the image data comprises one or more images generated based on one or more imaging signals, the one or more imaging signals comprising ultrasound pulses (see paragraph 0748); and the image data comprises a B-scan of the eye of the patient (see paragraph 0748).
Re claim 10, Hipsley teaches wherein: the image data comprises one or more images generated based on one or more imaging signals, the one or more imaging signals comprising infrared laser light (see paragraph 0748); and the image data comprises a B-scan of the eye of the patient (see paragraph 0748).
Re claim 11, Hipsley teaches wherein the one or more measurements comprise at least one of: anterior chamber depth; iris thickness; iris-to-lens contact distance; iris zonule distance; trabecular ciliary process distance; and trabecular iris space area; and a measurement associated with an implant comprised in the eye (see paragraph 0256).
Re claim 14, Hipsley teaches an apparatus comprising: a processor (see figures 25 A-C); and memory in electronic communication with the processor (see figures 25 A-C), wherein instructions stored in the memory are executable by the processor (see figures 25 A-C) to:locate one or more target structures comprised in an eye of a patient based on processing image data of the eye of the patient, wherein processing the image data comprises (see paragraph 0022 and 0233-0237): providing at least a portion of the image data to one or more machine learning models (see paragraphs 0241, 0255, 0247-0248); and receiving an output from the one or more machine learning models in response to the one or more machine learning models processing at least the portion of the image data, wherein the output comprises location data of the one or more target structures (see paragraphs 0241, 0255, 0247-0248);determine one or more measurements associated with an anterior portion of the eye, based on the location data and one or more characteristics associated with the one or more target structures (see paragraphs 0241, 0255, 0247-0248); and determine a presence, an absence, a progression, or a stage of a disease of the eye based on the one or more measurements (see paragraphs 0241, 0255, 0247-0248).
Re claim 15, Hipsley teaches wherein determining the presence, the absence, the progression, or the stage is based on a correlation between the one or more measurements and the disease (see paragraph 0255).
Re claim 16, Hipsley teaches wherein the instructions are further executable by the processor to: provide the one or more measurements to the one or more machine learning models (see paragraph 0248); and receive a second output in response to the one or more machine learning models processing the one or more measurements (see paragraph 0248), wherein: the second output comprises a probability of the disease of the eye (see paragraph 0255); and determining the presence, the absence, the progression, or the stage is based on the probability (see paragraph 0255).
Re claim 17, Hipsley teaches wherein: the output from the one or more machine learning models comprises one or more predicted masks (see paragraph 0494); and determining the location data, the one or more measurements, or both is based at least in part on the one or more predicted masks (see paragraph 0494).
Re claim 18, Hipsley teaches wherein the one or more measurements comprise at least one of: a measurement with respect to at least one axis of a set of axes associated with the eye (see paragraph 0298-0299); an angle between two or more axes of the set of axes (see paragraph 0298-0299); and a second measurement associated with an implant comprised in the eye (see paragraph 0255).
Re claim 19, Hipsley teaches wherein the one or more target structures comprise at least one of: tissue comprised in the eye (see paragraph 0255); surgically modified tissue comprised in the eye (see paragraph 0255); pharmacologically modified tissue comprised in the eye; and an implant comprised in the eye (see paragraph 0255).
Re claim 20, Hipsley teaches a non-transitory computer readable medium comprising instructions, which when executed by a processor (see figures 25 A-C): generates image data of an eye of a patient based on one or more imaging signals (see paragraphs 0022 and 0235-0239); locates one or more target structures comprised in an eye of a patient based on processing image data of the eye of the patient (see paragraph 0241), wherein processing the image data comprises: providing at least a portion of the image data to one or more machine learning models (see paragraph 0241); and receiving an output from the one or more machine learning models in response to the one or more machine learning models processing at least the portion of the image data, wherein the output comprises location data of the one or more target structures (see paragraph 0241); determines one or more measurements associated with an anterior portion of the eye, based on the location data and one or more characteristics associated with the one or more target structures (see paragraphs 0255 and 0247-0248); and determines a presence, an absence, a progression, or a stage of a disease of the eye based on the one or more measurements (see paragraphs 0255 and 0247-0248).
Allowable Subject Matter
Claims 12-13 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.
The following is a statement of reasons for the indication of allowable subject matter: The prior art taken singularly or in combination fails to anticipate or fairly suggest the limitations of the independent claims, in such a manner that a rejection under 35 U.S.C. 102 or 103 would be proper.
In regard to dependent claim 12, the prior art taken either singly or in combination fails to anticipate or fairly suggest comprising training the one or more machine learning models based on a training data set, the training data set comprising at least one of: reference image data associated with at least one eye of one or more reference patients; label data associated with the one or more target structures; one or more reference masks for classifying pixels included in the reference image data in association with locating the one or more target structures; and image classification data corresponding to at least one image of a set of reference images, wherein the reference image data, the label data, the one or more reference masks, and the image classification data are associated with a pre-operative state, an intraoperative state, a post-operative state, a disease state, or a combination thereof; recited together in combination with the totality of particular features/limitations recited therein
In regard to dependent claim 13, the prior art taken either singly or in combination fails to anticipate or fairly suggest the image data comprises a set of pixels; and processing at least the portion of the image data by the one or more machine learning models comprises: generating encoded image data in response to processing at least the portion of the image data using a set of encoder filters; and generating a mask image in response to processing at least the portion of the encoded image data using a set of decoder filters, wherein the mask image comprises an indication of one or more pixels, included among the set of pixels comprised in the image data, that are associated with the one or more target structures; recited together in combination with the totality of particular features/limitations recited therein
Cited Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US-20200286208-A1 WO-2018183987-A1 JP-2020512917-A5
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES R GREECE whose telephone number is (571)272-3711. The examiner can normally be reached 8-4.
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/JAMES R GREECE/Supervisory Patent Examiner, Art Unit 2875