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 § 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 1-12 and 28-41 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.
The specification does not demonstrate that Applicant had possession of the full scope of “image of biological tissue” (emphasis added), which is a very broad genus comprised of many widely varying species. The broadest reasonable interpretation (BRI) of limitations “image of biological tissue” include visual or spatial representation of biological tissue regardless of how it is required including but not limited to optical microscopy images, scanning electron microscopy images, MRI images, CT images, ultrasound images, PET images, SPECT images, and macroscopic photographic images. However, the specification only discloses optical microscopy images. Specifically, the specification discloses H&E stained images, IHC images, ISH images, FISH images, and smFISH images as types of optical microscopy. The specification does not disclose scanning electron microscopy images, MRI images, CT images, ultrasound images, PET images, SPECT images, and macroscopic photographic images, so it does not demonstrate that Applicant had possession of a claim scope including these types of images. To remedy this deficiency, Applicant can amend the claims to require an interpretation of optical microscopy images. Depending claims do not remedy this deficiency.
Also, the specification does not demonstrate that Applicant had possession of the full scope of “machine-learning alignment model” (emphasis added) , which is a very broad genus comprised of many widely varying species. The broadest reasonable interpretation (BRI) of limitations “machine-learning alignment model” includes any model that learns from data to perform an alignment, including but not limited to CNN, Transformers, GANs, graph neural networks, random forests, SVM, and regression, etc. However, the specification only discloses the use of a CNN as the “machine-learning alignment model”. That is the specification discloses no other type of machine learning model other than CNN. To remedy this deficiency, Applicant can amend the claims to require an interpretation of CNN. Depending claims 11 and 38 remedy this deficiency. No other dependent claims remedy this deficiency.
Claims 1-12 and 28-41 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling for optical microscopy image of biological tissue, does not reasonably provide enablement for the broad genus of “image of biological tissue” including widely varying species of optical microscopy images, scanning electron microscopy images, MRI images, CT images, ultrasound images, PET images, SPECT images, and macroscopic photographic images. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use (i.e. species within “image of biological tissue” other than microscopy images) the invention commensurate in scope with these claims. The claim scope convers any biological image regardless of acquisition modality. The specification enables a person of ordinary skill in the art (POSITA) to make and use the invention for optical microscopy images. POSITA could not practice the full scope of the claimed invention without undue experimentation in consideration of the following Wands factors:
Quantity of experimentation: Adapting the invention for scanning electron microscopy images, MRI images, CT images, ultrasound images, PET images, SPECT images, or macroscopic photographic images requires substantial independent experimentation for each modality.
Guidance in specification: No guidance is provided for any other modality other than optical microscopy.
Working examples: All working examples in the specification are limited to the modality of optical microscopy.
Unpredictability of the art: implementation of the disclosed invention is not predictable for other modalities such as MRI images, CT images, ultrasound images, PET images, SPECT images, or macroscopic photographic images.
Breadth of the claims: “Image of biological tissue” is vastly broader than the disclosed optical microscopy image of biological tissue.
Depending claims do not remedy these deficiencies. To remedy this deficiency, Applicant can amend the claims to require an interpretation of optical microscopy images.
Also, while being enabling for CNN, the specification does not reasonably provide enablement for the broad genus of “machine-learning alignment model”, which includes many widely varying species including but not limited to CNN, Transformers, GANs, graph neural networks, random forests, SVM, and regression. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the invention commensurate in scope with these claims. The claim scope covers the entire genus of machine-learning alignment models. The specification only enables a single species of CNN. A POSITA could not make and use the full claimed scope without undue experimentation in consideration of the following Wands factors:
Quantity of experimentation: Designing, training, and validating non-CNN machine learning architectures such as Transformers, GANs, graph neural networks, random forests, SVM, and regression constitutes substantial independent experimentation for each architecture.
Guidance in the specification: The specification provides no guidance for selecting a suitable architecture of the many species falling within the broad genus of machine learning. It also provides no guidance on selecting parameters for different machine learning architectures.
Working examples: Working examples are only provided for CNNs.
Unpredictability of the art: Different model architectures have different characteristics and tradeoffs, which require experimentation where optimal parameters are empirically determined.
Breadth of claims: “machine-learning alignment model” is vastly broader than the disclosed CNN.
To remedy this deficiency, Applicant can amend the claims to require an interpretation of CNN. Depending claims 11 and 38 remedy this deficiency. No other dependent claims remedy this deficiency.
Allowable Subject Matter
Claims 1-12 and 28-41 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), 1st paragraph, set forth in this Office action. The following is a statement of reasons for the indication of allowable subject matter: Limitations pertaining to “performing a first registration of the normalized displaced image relative to the normalized reference image, resulting in a group of parameters defining a coarse transformation and further resulting in a second displaced image; supplying the normalized reference image to a machine-learning alignment model; supplying the second displaced image to the machine-learning alignment model; performing a second registration of the second displaced image relative to the reference image by applying the machine-learning alignment model to the reference image and the second displaced image, wherein the applying yields a deformation vector field representative of a registration transformation between the reference image and the second displaced image” (emphasis added), in conjunction with other limitations present in the independent claim(s), distinguish over the prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Atiba Fitzpatrick whose telephone number is (571) 270-5255. The examiner can normally be reached on M-F 10:00am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on (571) 270-5183. The fax phone number for Atiba Fitzpatrick is (571) 270-6255.
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Atiba Fitzpatrick
/ATIBA O FITZPATRICK/
Primary Examiner, Art Unit 2677