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
Claims 1-4 are objected to because of the following informalities:
Claim 1 recites “method or system” in line 1; the Examiner suggests Applicant select the method because the claim has no structure, i.e. processor connected to memory for a system claim. For the purposes of Examiner, the claim is treated as such.
Claim 2 recites “method or system” in line 1; the Examiner suggests Applicant select the method because the claim has no structure, i.e. processor connected to memory for a system claim. For the purposes of Examiner, the claim is treated as such.
Claim 3 recites “method or system” in line 1; the Examiner suggests Applicant select the method because the claim has no structure, i.e. processor connected to memory for a system claim. For the purposes of Examiner, the claim is treated as such.
Claim 4 recites “method or system” in line 1; the Examiner suggests Applicant select the method because the claim has no structure, i.e. processor connected to memory for a system claim. For the purposes of Examiner, the claim is treated as such.
Appropriate correction is required.
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 1-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract without significantly more.
Claim 1
Step 1 Analysis: Claim is directed to a method/system, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1, in part recites “de-identified images collected from patients in association with de-identified data collected as part of medical care to train
The step of “de-identified images collected from patients in association with de-identified data collected as part of medical care to train” of the claim encompass for e.g. using a mask to over a person face or an electronic mask for the case when the image is electronic. This step falls under mental observations and these, mental observations or evaluations fall within the “mental processed” grouping of abstract ideas.
Step 2A Prong 2 Analysis: The claim does include any additional elements that amount to an integration of the judicial exceptions into a practical application. In particular, the claim in part recites the additional elements –
machine vision,
deep learning,
other algorithms that associate an outcome variable,
an input image or video image(s).
The “machine vision” is used to generally apply the abstract idea without limiting how the trained deep learning model functions. The machine learning model is described at a high level such that it amounts to using a computer with a generic deep learning model to apply the abstract idea.
The “deep learning” is used to generally apply the abstract idea without limiting how the trained deep learning model functions. The machine learning model is described at a high level such that it amounts to using a computer with a generic deep learning model to apply the abstract idea.
The “other algorithms that associate an outcome variable” limitation only recite the outcome of “other algorithms” without any details about how the outcomes are accomplished.
. The claim element of “an input image or video image(s)” impart additional element, the additional elements merely constitute pre-solution activating involving receiving of input data.
Step 2B Analysis: The claim does not include additional elements that are
sufficient to amount to significantly more than the judicial exception. For the reasons
given in Step 2A Prong 2. Thus, the claim is not patent eligible.
Claim 2
Step 1 Analysis: Claim is directed to a method/system, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 2, in part recites “method or system to store de-identified images or video images for use in
The step of “method or system to store de-identified images or video images for use in machine vision,” of the claim encompass for e.g. using a mask to over a person face or an electronic mask for the case when the image is electronic. This step falls under mental observations and these, mental observations or evaluations fall within the “mental processed” grouping of abstract ideas.
Step 2A Prong 2 Analysis: The claim does include any additional elements that amount to an integration of the judicial exceptions into a practical application. In particular, the claim in part recites the additional elements –
store
machine vision,
deep learning,
other algorithms that associate an outcome variable,
an input image or video image(s).
The “store” is used to generally apply the abstract idea without limiting how the trained deep learning model functions. The machine learning model is described at a high level such that it amounts to using a computer with a generic deep learning model to apply the abstract idea.
The “machine vision” is used to generally apply the abstract idea without limiting how the trained deep learning model functions. The machine learning model is described at a high level such that it amounts to using a computer with a generic deep learning model to apply the abstract idea.
The “deep learning” is used to generally apply the abstract idea without limiting how the trained deep learning model functions. The machine learning model is described at a high level such that it amounts to using a computer with a generic deep learning model to apply the abstract idea.
The “other algorithms that associate an outcome variable” limitation only recite the outcome of “other algorithms” without any details about how the outcomes are accomplished.
. The claim element of “an input image or video image(s)” and “store” impart additional elements, the additional elements merely constitute pre-solution and post-solution activities involving receiving of input data and storing the processed image in memory.
Claim 3
Step 1 Analysis: Claim is directed to a method/system, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 3, in part recites “method or system to use unsupervised learning based on video images and data collected as part of medical care to train
The step of “method or system to use unsupervised learning based on video images and data collected as part of medical care to train” of the claim encompass for e.g. using a mask to over a person face or an electronic mask for the case when the image is electronic. This step falls under mental observations and these, mental observations or evaluations fall within the “mental processed” grouping of abstract ideas.
Step 2A Prong 2 Analysis: The claim does include any additional elements that amount to an integration of the judicial exceptions into a practical application. In particular, the claim in part recites the additional elements –
machine vision,
deep learning,
other algorithms that associate an outcome variable,
an input image or video image(s).
The “machine vision” is used to generally apply the abstract idea without limiting how the trained deep learning model functions. The machine learning model is described at a high level such that it amounts to using a computer with a generic deep learning model to apply the abstract idea.
The “deep learning” is used to generally apply the abstract idea without limiting how the trained deep learning model functions. The machine learning model is described at a high level such that it amounts to using a computer with a generic deep learning model to apply the abstract idea.
The “other algorithms that associate an outcome variable” limitation only recite the outcome of “other algorithms” without any details about how the outcomes are accomplished.
. The claim element of “an input image or video image(s)” impart additional element, the additional elements merely constitute pre-solution activating involving receiving of input data.
Claim 4
Step 1 Analysis: Claim is directed to a method/system, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 4, in part recites “method or system to use unsupervised learning based on de-identified video images and de-identified data collected as part of medical care to train
The step of “method or system to use unsupervised learning based on de-identified video images and de-identified data collected as part of medical care to train,” of the claim encompass for e.g. using a mask to over a person face or an electronic mask for the case when the image is electronic. This step falls under mental observations and these, mental observations or evaluations fall within the “mental processed” grouping of abstract ideas.
Step 2A Prong 2 Analysis: The claim does include any additional elements that amount to an integration of the judicial exceptions into a practical application. In particular, the claim in part recites the additional elements –
machine vision,
deep learning,
other algorithms that associate an outcome variable,
an input image or video image(s).
The “machine vision” is used to generally apply the abstract idea without limiting how the trained deep learning model functions. The machine learning model is described at a high level such that it amounts to using a computer with a generic deep learning model to apply the abstract idea.
The “deep learning” is used to generally apply the abstract idea without limiting how the trained deep learning model functions. The machine learning model is described at a high level such that it amounts to using a computer with a generic deep learning model to apply the abstract idea.
The “other algorithms that associate an outcome variable” limitation only recite the outcome of “other algorithms” without any details about how the outcomes are accomplished.
. The claim element of “an input image or video image(s)” impart additional element, the additional elements merely constitute pre-solution activating involving receiving of input data.
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.
Claims 1-4 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by
Rao et al (Pub No.: 20220253592).
Regarding independent claim 1, Rao discloses a method or system to use de-identified images collected from patients (de-identified medical scans of medical scans received from the medical picture archive system 2620 – see [p][0170]) in association with de-identified data (de-identification system 2800 can be utilized to implement the de-identification system 2608 of FIGS. 8A-8F. In some embodiments, the de-identification system 2800 can be utilized by other subsystems to de-identify image data, medical report data, private fields of medical scan entries 352 such as patient identifier data 431, and/or other private fields stored in databases of the database memory device 340. – see [p][0223]) collected as part of medical care to train machine learning (the annotation data and/or de-identified medical scans can be sent to the central server system 2640, and the central server system can train on this information to produce new and/or updated model parameters for transmission back to the medical picture archive integration system 2600 for use on subsequently received medical scans – see [p][0170]), machine vision (convolutional neural network – see [p][0386]), deep learning, or other algorithms that associate an outcome variable (inference function -see [p][0386]) with an input image or video image(s) (a DICOM image, received from a medical picture archive system – see [p][0170]).
Regarding independent claim 2, Rao discloses a method or system to store (stored in medical scan analysis function database 346 – see [p][0401]) de-identified images (de-identified medical scans of medical scans received from the medical picture archive system 2620 – see [p][0170]) or video images for use in machine vision (the annotation data and/or de-identified medical scans can be sent to the central server system 2640, and the central server system can train on this information to produce new and/or updated model parameters for transmission back to the medical picture archive integration system 2600 for use on subsequently received medical scans – see [p][0170]), machine learning, neural network (convolutional neural network – see [p][0386]), deep learning, or other algorithms (inference function -see [p][0386]) that associate an outcome variable with an input image or video image(s) (a DICOM image, received from a medical picture archive system – see [p][0170]).
Regarding independent claim 3, Rao discloses a method or system to use unsupervised learning (unsupervised learning model – see [p][0149]) based on video images (see [p][0564]) and data collected (de-identification system 2800 can be utilized to implement the de-identification system 2608 of FIGS. 8A-8F. In some embodiments, the de-identification system 2800 can be utilized by other subsystems to de-identify image data, medical report data, private fields of medical scan entries 352 such as patient identifier data 431, and/or other private fields stored in databases of the database memory device 340. – see [p][0223]) as part of medical care to train machine learning (the annotation data and/or de-identified medical scans can be sent to the central server system 2640, and the central server system can train on this information to produce new and/or updated model parameters for transmission back to the medical picture archive integration system 2600 for use on subsequently received medical scans – see [p][0170]), machine vision, deep learning (convolutional neural network – see [p][0386]), or other algorithms (inference function -see [p][0386]) that associated an outcome variable with an input image or video image(s) (a DICOM image, received from a medical picture archive system – see [p][0170]).
Regarding independent claim 4, Rao discloses a method or system to use unsupervised learning (unsupervised learning model – see [p][0149]) based on de-identified video images (de-identified medical scans of medical scans received from the medical picture archive system 2620 – see [p][0170]) and de-identified data collected as part of medical care to train machine learning (the annotation data and/or de-identified medical scans can be sent to the central server system 2640, and the central server system can train on this information to produce new and/or updated model parameters for transmission back to the medical picture archive integration system 2600 for use on subsequently received medical scans – see [p][0170]), machine vision, deep learning, or other algorithms (inference function -see [p][0386]) that associated an outcome variable with an input image or video image(s) (a DICOM image, received from a medical picture archive system – see [p][0170]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lyman et al (Pub No.: 20240161035 ) - A medical scan viewing system is configured to: generate inference data via at least one inference function, based the at least one medical scan and further based on receiver operating characteristic (ROC) parameters that include at least one ROC set point; present for display, via an interactive user interface, medical image data corresponding to the at least one medical scan, the inference data and a ROC adjustment tool; generate, in response to user interaction with the ROC adjustment tool, at least one adjusted ROC set point; generate updated inference data via the at least one inference function, based the at least one medical scan and further based on the at least one adjusted ROC set point; and present for display, via the interactive user interface, the medical image data corresponding to the at least one medical scan and the updated inference data.
Lyman et al (Pub No. 20200357117 :) - A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of medical labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the medical labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Preliminary heat map visualization data can be generated for transmission to a client device based on the probability matrix data. Heat map visualization data can be generated via a post-processing of the preliminary heat map visualization data to mitigate heat map artifacts.
Marks et al (Pub No.: 20230112302 ) - Present disclosure discloses an image processing system and method for manipulating two-dimensional (2D) images of three-dimensional (3D) objects of a predetermined class (e.g., human faces). A 2D input image of a 3D object of the predetermined class is manipulated by manipulating physical properties of the 3D object, such as a 3D shape of the 3D input object, an albedo of the 3D input object, a pose of the 3D input object, and lighting illuminating the 3D input object. The physical properties are extracted from the 2D input image using a neural network that is trained to reconstruct the 2D input image. The 2D input image is reconstructed by disentangling the physical properties from pixels of the 2D input image using multiple subnetworks. The disentangled physical properties produced by the multiple subnetworks are combined into a 2D output image using a differentiable renderer.
Ferer et al (Pub No.: 20210141926) - In one embodiment, a method includes accessing a first machine-learning model trained to generate a feature representation of an input data, a second machine-learning model trained to generate a desired result based on the feature representation, and a third machine-learning model trained to generate an undesired result based on the feature representation, and training a fourth machine-learning model by generating a secured feature representation by processing a first output of the first machine-learning model using the fourth machine-learning model, generating a second output and a third output by processing the secured feature representation using, respectively, the second and third machine-learning models, and updating the fourth machine-learning model according to an optimization function configured to optimize a correctness of the second output and an incorrectness of the third output.
Koby et al (Pub No.: 20200395105) - A system monitors a health facility while de-identifying certain objects, such as individuals and patient information, people and sensitive information. A platform associates de-identified sensor data with electronic medical data (EMD). Data captured by one or more intelligent sensors can be de-identified and used to detect events within the health facility and associated with an EMD record. For example, data from the one or more intelligent sensors can be used to detect progress of a procedure being performed on a patient having an EMD record associated with the health facility. As events during the procedure are detected based on processing of the intelligent sensor data, actions may be taken to further facilitate the procedure or optimize an outcome in a safe and efficient manner.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRAE S ALLISON whose telephone number is (571)270-1052. The examiner can normally be reached on Monday-Friday 9am-5pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns, can be reached on (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDRAE S ALLISON/Primary Examiner, Art Unit 2673
August 20, 2026
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