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 .
Election/Restrictions
Applicant’s election without traverse of group I in the reply filed on 12/15/2025 is acknowledged.
Claims 17-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 12/15/2025.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8, 9-14 and 16 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) :
Claim 1 : “accessing a plurality of input images, each input image including a portion of body of a patient selected from a plurality of patients; accessing a label for each input image that indicates whether the selected patient in the input image has a disease condition;” which is directed to collecting a set of images and associated labels.
“obtaining a plurality of training examples, each training example corresponding to a given input image and comprising: for each respective location in the given input image, an indication that the given input image contains an object of interest at the respective location, wherein the object of interest is indicative of a disease, and the label of the given input image;”, which is directed to mere data gathering. See MPEP 2106.05(g)
“for a diagnostic model, the diagnostic model comprising a machine learning model that is configured to output a diagnosis of a disease condition based on an input of indications of whether there is an object of interest at each location within a sample image:” This is directed to an additional element (machine learning model) which is a well-understood, routine, conventional activity recited at a high level of generality. See MPEP 2106.05(d)
“training the diagnostic model by repeatedly applying a training example from the plurality of training examples to the diagnostic model and updating parameters of the diagnostic model to improve an objective performance threshold thereof, and stopping the training after the objective performance threshold satisfies a condition.”, is directed to supervised training of a mathematical model on labeled data, which is a mathematical calculation and optimization process. See MPEP 2106.04(a)(2).
Additionally, the training step falls within Well-Understood, Routine and Conventional Activity .
Furthermore, the claims are a similar to a mental process. A person could review images and label them as having regions containing disease indicative findings, and could adjust a mathematical model parameters based on training performance and record value for later use.
This judicial exception is not integrated into a practical application.
Claim 9 is rejected under similar grounds as claim 1.
Claims 2-6, 8, 10-14 and 16 are rejected as dependent upon a rejected claim and failing to include additional elements which amount to significantly more.
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 1-16 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.
Claim 1 recites “obtaining a plurality of training examples … for each respective location in the given input image, an indication that the given input image contains an object of interest at the respective location,”. The Examiner is unable to find support for this limitation. While the Specification teaches having a heat map during the inference, but doesn’t teach a heat map/pointwise input or in particular “for each respective location in the given input image, an indication that the given input image contains an object of interest at the respective location” during training. At best it discloses in paragraphs 26 & 28 images having/not having feature of interest anywhere.
Claim 1 before last limitation and last limitation recites “improve an objective performance threshold thereof” AND “stopping the training after the objective performance threshold satisfies a condition”. Paragraph 24-25 defines threshold/thresholding as “[024] A "threshold" is defined as a level, point, or value above which something is true or will take place and below which it is not or will not, such levels, points, or values include probabilities, sizes in pixels, and values representing pixel brightness. [025] "Thresholding" is defined as modifying pixels that contain a characteristic either above or below a selected threshold value.”
The original disclosure fails to disclose these claimed limitations. A threshold is only disclosed in paragraphs 5, 35, 37 40 and 77. None in the same context as the specification. The word “stop” does not appear in the disclosure and there does not appear to be any disclosure of terminating the training step.
Claim 2 recites “a mathematical model of the object of interest”. While the disclosure does support heat maps and point-wise outputs, it does not expressly use the phrase “mathematical model”, nor does it support anything broader than the heat map or point-wise outputs.
Claim 9 recites “A diagnostic product for diagnosing a disease condition in a patient, wherein the diagnostic product is stored on a non-transitory computer readable medium and is manufactured by a process comprising:”1. This limitation is not supported by the original disclosure.
Claim 9 is rejected under similar grounds as claim1.
Claims 2-8 and 10-16 are rejected as dependent upon a rejected claim.
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 1-16 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.
Claim 1 first two limitations are completely disconnected from the actual training method. It is unclear whether training is performed on using the accessed data or an unrelated set of indications.
Claim 1 before last limitation and last limitation recites “improve an objective performance threshold thereof” AND “stopping the training after the objective performance threshold satisfies a condition”. Paragraph 24-25 defines threshold/thresholding as “[024] A "threshold" is defined as a level, point, or value above which something is true or will take place and below which it is not or will not, such levels, points, or values include probabilities, sizes in pixels, and values representing pixel brightness. [025] "Thresholding" is defined as modifying pixels that contain a characteristic either above or below a selected threshold value.”
It is not clear how one “improves” a threshold. As applicant has defined, which is consistent with the plain meaning of the term, a threshold is a point, level or value. One can update a metric which is compared to a threshold and when the metric reaches the threshold stop training.
Additionally the original disclosure fails to disclose these claimed limitations. A threshold is only disclosed in paragraphs 5, 35, 37 40 and 77. None in the same context as the specification. The word “stop” does not appear in the disclosure
Claims 9 is rejected under similar grounds as claim 1.
Claims 2-8, 10-16 are rejected as dependent upon a rejected claim.
Claim Rejections - 35 USC § 102
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)(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) 1-16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Abramoff (PGPub 2013/0301889).
Abramoff discloses 1. (Original) A method for training a diagnostic model for diagnosing a disease condition in a patient, the method comprising:
accessing a plurality of input images, each input image including a portion of body of a patient selected from a plurality of patients; accessing a label for each input image that indicates whether the selected patient in the input image has a disease condition; (Abramoff, “[0079] In direct sampling, the target lesions (e.g., typical lesions and positive lesion confounders) can be annotated on a training dataset. As an example, the annotation comprises an indication of the center of the lesions, or segments the lesions. In an aspect, a candidate lesion detector can be used to find a center of the lesion within the segmented region. As an example, a set of sample images can represent all images and all local image variation that the proposed filter framework may encounter to optimize the framework.”)
obtaining a plurality of training examples, each training example corresponding to a given input image and comprising:for each respective location in the given input image, an indication that the given input image contains an object of interest at the respective location, wherein the object of interest is indicative of a disease, and the label of the given input image; and(Abramoff, “[0079] In direct sampling, the target lesions (e.g., typical lesions and positive lesion confounders) can be annotated on a training dataset. As an example, the annotation comprises an indication of the center of the lesions, or segments the lesions. In an aspect, a candidate lesion detector can be used to find a center of the lesion within the segmented region. As an example, a set of sample images can represent all images and all local image variation that the proposed filter framework may encounter to optimize the framework.”)
for a diagnostic model, the diagnostic model comprising a machine learning model that is configured to output a diagnosis of a disease condition based on an input of indications of whether there is an object of interest at each location within a sample image (Abramoff, “[0122] The methods and systems can employ Artificial Intelligence techniques such as machine learning and iterative learning. Examples of such techniques include, but are not limited to, expert systems, case based reasoning, Bayesian networks, behavior based AI, neural networks, fuzzy systems, evolutionary computation (e.g. genetic algorithms), swarm intelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g. Expert inference rules generated through a neural network or production rules from statistical learning).”):
training the diagnostic model by repeatedly applying a training example from the plurality of training examples to the diagnostic model and updating parameters of the diagnostic model to improve an objective performance threshold thereof, and stopping the training after the objective performance threshold satisfies a condition. (Abramoff, “[0098] In an aspect, probability maps can be generated with different optimal filters chosen by Sequential Forward Selection (SFS) for each iteration. As an example, the metric can evaluate the probability maps and give the AUC for the ROC curve for the whole set of probability maps. As a further example, SFS first selects out one filter with the highest AUC, then adds a new filter from the remaining filters such that the two filters have the highest AUC. In an aspect, SFS can add a new filter from the remaining filters to give the highest AUC until the stop criteria of the feature selection is met. As an example, the feature selection stops when the number of selected filters reaches the maximal number of filters, or the AUC starts to decline.”)
Abramoff discloses 2. (Currently amended) The method of claim 1, wherein the indication that the given input image contains an object of interest for each of one or more locations comprises a mathematical model of the retinal object of interest. (Abramoff, “[0064] As an example, in the expert-driven approach (e.g., mathematical modeling 104), a modeler can be configured to translate verbal descriptions with clinicians, as well as intensity distributions in a limited number of samples selected by the clinicians, into mathematical equations. In an aspect, the mathematical equations can be based upon a continuous model of the intensity distribution of the modeled lesions, and were defined for typical lesions, for positive lesion confounders if any are identified, and for negative lesion confounders, if any are identified. As a further example, the intensity profile of the lesions can be modeled by generalized Gaussian functions of normalized intensity, for example, such as the following function:
profile(r;.beta.,.delta.)=.delta.e.sup.-r.sub..beta.”)
Abramoff discloses 3. (Original) The method of claim 1, wherein the input of indications of whether there is an object of interest at each location within a sample image comprises a heat map indicating the likelihood that the sample image contains an object of interest for each location in the sample image.(Abramoff, paragraph 89)
Abramoff discloses 4. (Original) The method of claim 1, wherein the input of indications of whether there is an object of interest at each location within a sample image comprises a point-wise output corresponding to indications that the sample image contains an object of interest at each location in the sample image. (Abramoff, paragraph 89)
Abramoff discloses 5. (Original) The method of claim 1, wherein one or more of the objects of interests is indicative of disease. (Abramoff, paragraph 89)
Abramoff discloses 6. (Currently amended) The method of claim 1, wherein the portion of the patient's body includes at least a portion of the patient's eye, and the determined diagnosis of a disease condition in the patient comprises a diagnosis of a disorder manifesting in a retina. (Abramoff, paragraph 2-3)
Abramoff discloses 7. (Original) The method of claim 6, wherein one or more of the object of interests is selected from a group consisting of: a microaneurysm, a dot hemorrhage, a flame-shaped hemorrhage, a sub-intimal hemorrhage, a sub-retinal hemorrhage, a pre-retinal hemorrhage, a micro-infarction, a cotton-wool spot, and a yellow exudate.(Abramoff, paragraph 39)
Abramoff discloses 8. (Original) The method of claim 1, wherein the input image is obtained by at least one of: computed tomography (CT), magnetic resonance imaging (MRI), computed radiography, magnetic resonance, angioscopy, optical coherence tomography, color flow Doppler, cystoscopy, diaphanography, echocardiography, fluorescein angiography, laparoscopy, magnetic resonance angiography, positron emission tomography, single-photon emission computed tomography, x- ray angiography, nuclear medicine, biomagnetic imaging, colposcopy, duplex Doppler, digital microscopy, endoscopy, fundoscopy, laser surface scanning, magnetic resonance spectroscopy, radiographic imaging, thermography, and radio fluoroscopy.(Abramoff, paragraph 3)
Claims 9-16 are rejected under similar grounds as claims 1-8 as shown above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached at 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GANDHI THIRUGNANAM/ Primary Examiner, Art Unit 2672
1 MANUFACTURED definition | Cambridge English Dictionary - to produce goods in large numbers, usually in a factory using machines: