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 .
Status of the Claims
The office action is in response to the claims filed on June 16, 2025 for the application filed June 16, 2025 which claims priority to a foreign application filed on June 24, 2024. Claims 1-17 are currently pending and have been examined.
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 7-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Eligibility Step 1:
Under step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, claims 7-10 are directed towards a computer implemented method (i.e. a process), which is a statutory category. Claim 10 is directed towards a device (i.e. a machine), which is a statutory category. Claims 11-17 are directed towards a system (i.e. a machine), which is a statutory category. Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea). In the instant application, the claims are directed towards an abstract idea.
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, dependent claims 7 and independent claims 10-11 are determined to be directed to an judicial exception because an abstract idea is recited in the claims which fall within the subject matter groupings of abstract ideas.
The abstract idea (identified in bold) recited in claim 7 is identified as:
A computer implemented method for training a neural network for determining a case complexity index, CCI, the method comprising:
receiving training data, the training data comprising pairs of: a medical image of a set of medical images and a CCI for the medical image; and
training the neural network with the training data to determine a respective CCI for a respective medical image by adjusting weights and biases of the neural network such that a loss function is minimized; and
calculating a case complexity index for an input dataset comprising at least one medical image and a related report for the at least one medical image using the trained neural network.
The abstract idea (identified in bold) recited in claim 10 is identified as:
A device configured to calculate a case complexity index, CCI, the device comprising:
an input interface configured for receiving an input dataset;
an output interface configured to provide a calculated case complexity index;
a memory for storing a trained neural network configured to calculate a case complexity index for an input dataset comprising at least one medical image and a related report for the at least one medical image using the trained neural network.
The abstract idea (identified in bold) recited in claim 11 is identified as:
A system for use in medical technology for applying a case complexity index, CCI, for an image-related medical processing task, the system comprising:
an input interface configured for receiving an input dataset comprising at least one medical image and a related report;
a calculator interface for a CCI-calculator device configured for calculating the case complexity index using a trained neural network when provided the input dataset; and
a control interface configured for controlling the image-related medical processing task based on the calculated CCI.
The identified limitations fall within the subject matter grouping of mental processes. Calculating case complexity index based on medical image and a related report can is merely a mental calculation based on observations, evaluations, judgments and opinions. Controlling a task based on the CCI encompasses determining assignment, priority, order, etc. of tasks, which can also be performed mentally using observations, evaluations, judgments and opinions. If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea
The identified limitation of “controlling the image-related medical processing task based on the calculated CCI” also falls within the subject matter grouping of certain methods of organizing human activity related and the sub grouping of managing personal behavior or relationships or interactions between people, (including following rules or instructions) Controlling task based on task complexity is a method for organizing the human tasks, such as by follow rules related to the complexity of a task when controlling the task. For example, this encompasses a manager assigning imaging tasks to radiologist based on imaging task complexity.
Accordingly, claims 7, 10 and 11 recite an abstract idea under step 2A, prong one.
Eligibility Step 2A, Prong Two:
Under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the identified abstract ideas are integrated into a practical application. After evaluation, there is no indication that any additional elements or combination of elements integrate the abstract idea into a practical application, such as through: an additional element that reflects an improvement to the functioning of a computer, or an improvements to any other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element that implements the judicial exception with, or uses the judicial exception in connection with, a particular machine or manufacture that is integral to the claim; an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. As shown below, the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than a recitation of: generally linking the abstract idea to a particular technological environment or field of use; insignificant extra-solution activity to the judicial exception; and/or adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as evidenced below.
The additional elements recited in claim 7 are identified in italics as:
A computer implemented method for training a neural network for determining a case complexity index, CCI, the method comprising:
receiving training data, the training data comprising pairs of: a medical image of a set of medical images and a CCI for the medical image; and
training the neural network with the training data to determine a respective CCI for a respective medical image by adjusting weights and biases of the neural network such that a loss function is minimized; and
calculating a case complexity index for an input dataset comprising at least one medical image and a related report for the at least one medical image using the trained neural network.
The additional elements recited in claim 10 are identified in italics as:
A device configured to calculate a case complexity index, CCI, the device comprising:
an input interface configured for receiving an input dataset;
an output interface configured to provide a calculated case complexity index;
a memory for storing a trained neural network configured to calculate a case complexity index for an input dataset comprising at least one medical image and a related report for the at least one medical image using the trained neural network.
The additional elements recited in claim 11 are identified in italics as:
A system for use in medical technology for applying a case complexity index, CCI, for an image-related medical processing task, the system comprising:
an input interface configured for receiving an input dataset comprising at least one medical image and a related report;
a calculator interface for a CCI-calculator device configured for calculating the case complexity index using a trained neural network when provided the input dataset; and
a control interface configured for controlling the image-related medical processing task based on the calculated CCI.
The additional limitations of “computer implement”, “a device configured to”, “an input interface configured for”, “an output inface configured for”, “a memory for storing”, “a calculator interface for a CCI-calculator device configured for” and “a control interface configured for” are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). These components are recited at a high level of generality and merely used in their ordinary capacity in order to perform the abstract idea of calculating a CCI and control task based on the CCI. Therefore, these additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or no more than mere instructions to implement an abstract idea or other exception on a computer or no more than merely using a computer as a tool to perform an abstract idea. Similarly, “using the trained neural network” and “a trained neural network configured to” are also determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). The trained neural network is used to generally apply the abstract idea without placing any limits on how the trained neural network functions. Rather, these limitations only recite the outcome of “calculating a CCI” and do not include any details about how the “calculating” is accomplished.
The additional limitations of “receiving…. data…”, “receiving… dataset…” “provide… index” and “are determined to be no more than the insignificant extra-solution activity to the judicial exception of mere necessary data gathering and data outputting under MPEP §2106.05(g).
Accordingly, claims 7, 10 and 11 do not recite additional elements which integrate the abstract idea into a practical application.
Eligibility Step 2B:
Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations are mere instructions to apply an abstract idea under MPEP §2106.05(f) and insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g), which is do not amount to significantly more than the abstract idea. The additional element of “training the neural network with the training data to determine a respective CCI for a respective medical image by adjusting weights and biases of the neural network such that a loss function is minimized” is determined to be well-understood, routine and conventional in the field of machine learning as neural networks must be trained and all training of a neural network involves adjusting weights and biases of the neural network such that a loss function is minimized. Evidence for this assertion is provided by Lindemer et al. (U.S. Patent No. 11,282,196) at columns 18-19, spanning paragraph. Furthermore, evidence that receiving and outputting data is well-understood, routine and conventional is provided by MPEP 2606.05(d), subsection II.
Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept.
Dependent Claims:
The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. None of these limitations are deemed to integrate the claims into a practical application or to amount to significantly more than the abstract idea as detailed below.
Claims 8-9 merely recite intended use of the CCI which does not change the analysis.
Claims 12 follows the same analysis as claim 7.
Claims 13-15 merely define the training data and neural network and does not change the analysis with respect to claim 7.
Claims 16-17 are directed to merely define the controlled task and use of the CCI, which also does not change the analysis of claim 11.
Therefore, whether taken individually or as an ordered combination, 7-17 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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)(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 and 6-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kshirsagar et al. (U.S. Pub. No. 2021/0098120).
Regarding claim 1, Kshirsagar discloses a computer implemented method for training a neural network for determining a case complexity index, CCI, the method comprising: receiving training data, the training data comprising pairs of: a medical image of a set of medical images and a CCI for the medical image (Paragraph [0048], Example method 400 begins at operation 402, where a first set of data is collected. In aspects, a data collection component, such as data collection engine 202, may collect or receive a first set of data from one or more data sources. The first set of data may comprise or relate to 2D and/or 3D breast image data, image evaluation data, and/or image reader information. In at least one aspect, the first set of data may comprise labeled and/or unlabeled training data.); and
training the neural network with the training data to determine a respective CCI for a respective medical image by adjusting weights and biases of the neural network such that a loss function is minimized (Paragraph [0049], At operation 404, a predictive model is trained using the first set of data. In aspects, the first set of data may be provided to an evaluation component, such as processing engine 204. The evaluation component may be, comprise, or have access to one or more predictive models. The first set of data may be provided as input to a predictive model to train the predictive model to generate one or more outputs. Example outputs include estimated complexity ratings or a complexity system/component for reading or interpreting mammographic exam data. As a particular example, the first set of data may comprise a labeled or annotated breast image, a reported amount of time for reading the image, a reported or suggested complexity rating for reading the image, and profile information for the reader of the image. The first set of data may be provided to a predictive model. The predictive model may use one or more data correlation techniques to determine correlations between the reported/suggested complexity rating and the other factors/data points in the first set of data. Paragraph [0027], For example, processing system 108 may train an artificial neural network, a support vector machine (SVM), a linear reinforcement model, a random decision forest, or a similar ML technique. Training a neural network inherently involves adjusting weights and biases of the neural network such that a loss function is minimized.).
Regarding claim 2, Kshirsagar discloses wherein the training data further comprises a related report for the medical image (Paragraph [0048], Examples of breast image data may include, but are not limited to, pixel image data and image header data. Image header data may provide information such as the type of study (e.g., screening, diagnostic, etc.) performed, the image resolution, the type of hardware system used to collect the images, the image processing method used, etc. Examples of image evaluation data may include, but are not limited to, study (e.g., mammographic exam reading session) open and close times, type of reading tools used (e.g., magnifier, notation tool, measurement tool, etc.), hanging protocol, workstation hardware/software configuration, study reading times, number and/or type of studies performed, previous patient report data, etc.. Paragraph [0038], The reader may annotate one or more images based on specific areas of interest and determine a finding or a result. The finding or result may then be recorded in a report using standardizes methodology or categories, such as BI-RADS.).
Regarding claim 3, Kshirsagar discloses wherein the training data further comprises at least one of: clinical data for a respective patient, what the medical image refers to, operational data, and/or guideline data (Paragraph [0048], Examples of breast image data may include, but are not limited to, pixel image data and image header data. Pixel image data may be used to derive various attributes of a patient's breast, such as tissue patterns, density, complexity, thickness, volume, and abnormalities. Image header data may provide information such as the type of study (e.g., screening, diagnostic, etc.) performed, the image resolution, the type of hardware system used to collect the images, the image processing method used, etc. Examples of image evaluation data may include, but are not limited to, study (e.g., mammographic exam reading session) open and close times, type of reading tools used (e.g., magnifier, notation tool, measurement tool, etc.), hanging protocol, workstation hardware/software configuration, study reading times, number and/or type of studies performed, previous patient report data, etc. Examples of image reader information may include, but are not limited to, a reader's experience, expertise, certifications, title/classification, workload/status, proficiency rating, and age. Also see paragraph [0021].).
Regarding claim 4, Kshirsagar discloses wherein the set of medical images comprises current images, prior images, or current images and prior images of a same procedure, a same patient, or the same procedure for the same patient (Paragraph [0048], the first set of data may comprise or relate to 2D and/or 3D breast image data, image evaluation data, and/or image reader information. In at least one aspect, the first set of data may comprise labeled and/or unlabeled training data. Examples of breast image data may include, but are not limited to, pixel image data and image header data. Pixel image data may be used to derive various attributes of a patient's breast, such as tissue patterns, density, complexity, thickness, volume, and abnormalities. Image header data may provide information such as the type of study (e.g., screening, diagnostic, etc.) performed, the image resolution, the type of hardware system used to collect the images, the image processing method used, etc. Examples of image evaluation data may include, but are not limited to, study (e.g., mammographic exam reading session) open and close times, type of reading tools used (e.g., magnifier, notation tool, measurement tool, etc.), hanging protocol, workstation hardware/software configuration, study reading times, number and/or type of studies performed, previous patient report data, etc. Examples of image reader information may include, but are not limited to, a reader's experience, expertise, certifications, title/classification, workload/status, proficiency rating, and age. Also see paragraph [0021], preexisting patient data (e.g., patient history records/reports and previously collected patient image data and paragraph [0023], training predictive models based on preexisting patient data and paragraph [0029].).
Regarding claim 6, Kshirsagar discloses wherein the neural network comprises at least one of a vision language model, a large language model, or an image processing model (Paragraph [0021], A model may be based on, or incorporate, one or more rule sets, machine learning (ML), a neural network, or the like. Paragraph [0048], Examples of breast image data may include, but are not limited to, pixel image data and image header data. Pixel image data may be used to derive various attributes of a patient's breast, such as tissue patterns, density, complexity, thickness, volume, and abnormalities.).
Regarding claim 7, Kshirsagar discloses further comprising: calculating a case complexity index for an input dataset comprising at least one medical image and a related report for the at least one medical image using the trained neural network (Abstract, The first set of data may be used to train a predictive model to predict/estimate an expected reading time and/or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. Paragraph [0052], the second set of data may comprise or relate to breast image data, image evaluation data, and/or image reader information. In some examples, however, the second set of data may not include labeled or unlabeled training data. Also see paragraph [0022].).
Regarding claim 8, Kshirsagar discloses wherein the calculated CCI is used for configuring a software-based downstream task on the medical image (Construed as intended use and not given patentable weight.).
Regarding claim 9, Kshirsagar discloses wherein the software-based downstream task comprises an image annotation task, wherein the calculated CCI is used with respect to estimated reading time, and/or clinician skill level (Construed as intended use and not given patentable weight.).
Regarding claim 10, Kshirsagar discloses a device configured to calculate a case complexity index, CCI (Abstract and figure 6), the device comprising:
an input interface configured for receiving an input dataset (Figure 2, Data collection engine 202 and user interface 203 and Figure 6, input devices 618 and communication connection(s) 612. Paragraph [0029], Data collection engine 202 may be configured to access and/or collect a set of data. Paragraph [0030], User interface 203 may be configured to collect user input from or relating to one or more healthcare professionals.);
an output interface configured to provide a calculated case complexity index (Figure 2, User interface 203 and Output Engine 206. Figure 6, output devices 614. Paragraph [0036], output engine 206 may generate a complexity rating for an image in the received information based on a set of factors or a complexity index. Also see Figure 5B.);
a memory for storing a trained neural network configured to calculate a case complexity index for an input dataset comprising at least one medical image and a related report for the at least one medical image using the trained neural network (Paragraph [0058], In its most basic configuration, operating environment 600 typically includes at least one processing unit 602 and memory 604. Depending on the exact configuration and type of computing device, memory 604 (storing, instructions to perform the X-ray tube roll off detection techniques disclosed herein) may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.), or some combination of the two. Paragraph [0031], . In some aspects, the received information may be used by processing engine 204 (or an alternate component of input processing system 200) to train the AI processing algorithms or models. The trained AI processing algorithms or models may then be used to evaluate received information in order to determine correlations between the received information and the training data used to train the AI processing algorithms or models. Abstract, The first set of data may be used to train a predictive model to predict/estimate an expected reading time and/or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. Paragraph [0052], the second set of data may comprise or relate to breast image data, image evaluation data, and/or image reader information. In some examples, however, the second set of data may not include labeled or unlabeled training data. Also see paragraph [0022].)
Regarding claim 11, Kshirsagar discloses a system for use in medical technology for applying a case complexity index, CCI, for an image-related medical processing task (Abstract), the system comprising:
an input interface configured for receiving an input dataset comprising at least one medical image and a related report (Figure 2, Data collection engine 202 and user interface 203 and Figure 6, input devices 618 and communication connection(s) 612. Paragraph [0029], Data collection engine 202 may be configured to access and/or collect a set of data. Paragraph [0030], User interface 203 may be configured to collect user input from or relating to one or more healthcare professionals. Abstract, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. Paragraph [0052], the second set of data may comprise or relate to breast image data, image evaluation data, and/or image reader information.);
a calculator interface for a CCI-calculator device configured for calculating the case complexity index using a trained neural network when provided the input dataset (Abstract, Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. Paragraph [0021], A model may be based on, or incorporate, one or more rule sets, machine learning (ML), a neural network, or the like. Figure processing engine 204.); and
a control interface configured for controlling the image-related medical processing task based on the calculated CCI (Abstract, The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists. Paragraph [0040], the output may be used to automate the balancing or optimization of the workloads of available clinical professionals. For instance, the output of output engine 206 may be provided to a workload management system/service that is configured to dynamically create/update clinical professional workloads. The workload management system/service may balance the workloads of two clinical professionals such that the first clinical professional is assigned ten mammography exam readings per day, each categorized as having “Fast” reading times, and the second clinical professional is assigned five mammography exam readings per day, each categorized as having “Slow” reading times. Despite the different number of mammography exam readings assigned to the first and second clinical professional, their respective workloads may require approximately the same amount of time to complete. Alternately, the workload management system/service may balance the workloads of two clinical professionals such that each clinical professional is assigned the same number and mix of complex mammography exam readings or such that the clinical professional having the most experience is assigned a proportionately higher number of complex and/or “Slow” reading time mammography exam readings.).
Regarding claim 12, Kshirsagar discloses wherein the trained neural network is trained with training data to determine a respective CCI for a respective medical image by adjusting weights and biases of the trained neural network such that a loss function is minimized, the training data comprising sets of: a medical image of a set of medical images, a related report for the medical image, and a CCI for the medical image (Paragraph [0049], At operation 404, a predictive model is trained using the first set of data. In aspects, the first set of data may be provided to an evaluation component, such as processing engine 204. The evaluation component may be, comprise, or have access to one or more predictive models. The first set of data may be provided as input to a predictive model to train the predictive model to generate one or more outputs. Example outputs include estimated complexity ratings or a complexity system/component for reading or interpreting mammographic exam data. As a particular example, the first set of data may comprise a labeled or annotated breast image, a reported amount of time for reading the image, a reported or suggested complexity rating for reading the image, and profile information for the reader of the image. The first set of data may be provided to a predictive model. The predictive model may use one or more data correlation techniques to determine correlations between the reported/suggested complexity rating and the other factors/data points in the first set of data. Paragraph [0027], For example, processing system 108 may train an artificial neural network, a support vector machine (SVM), a linear reinforcement model, a random decision forest, or a similar ML technique. Training a neural network inherently involves adjusting weights and biases of the neural network such that a loss function is minimized ).
Regarding claim 13, Kshirsagar discloses wherein the training data further comprises at least one of: clinical data for a respective patient, what the medical image refers to, operational data, and/or guideline data (Paragraph [0048], Examples of breast image data may include, but are not limited to, pixel image data and image header data. Pixel image data may be used to derive various attributes of a patient's breast, such as tissue patterns, density, complexity, thickness, volume, and abnormalities. Image header data may provide information such as the type of study (e.g., screening, diagnostic, etc.) performed, the image resolution, the type of hardware system used to collect the images, the image processing method used, etc. Examples of image evaluation data may include, but are not limited to, study (e.g., mammographic exam reading session) open and close times, type of reading tools used (e.g., magnifier, notation tool, measurement tool, etc.), hanging protocol, workstation hardware/software configuration, study reading times, number and/or type of studies performed, previous patient report data, etc. Examples of image reader information may include, but are not limited to, a reader's experience, expertise, certifications, title/classification, workload/status, proficiency rating, and age. Also see paragraph [0021].).
Regarding claim 14, Kshirsagar discloses wherein the set of medical images comprises current images, prior images, or current images and prior images of a same procedure, a same patient, or the same procedure for the same patient (Paragraph [0048], the first set of data may comprise or relate to 2D and/or 3D breast image data, image evaluation data, and/or image reader information. In at least one aspect, the first set of data may comprise labeled and/or unlabeled training data. Examples of breast image data may include, but are not limited to, pixel image data and image header data. Pixel image data may be used to derive various attributes of a patient's breast, such as tissue patterns, density, complexity, thickness, volume, and abnormalities. Image header data may provide information such as the type of study (e.g., screening, diagnostic, etc.) performed, the image resolution, the type of hardware system used to collect the images, the image processing method used, etc. Examples of image evaluation data may include, but are not limited to, study (e.g., mammographic exam reading session) open and close times, type of reading tools used (e.g., magnifier, notation tool, measurement tool, etc.), hanging protocol, workstation hardware/software configuration, study reading times, number and/or type of studies performed, previous patient report data, etc. Examples of image reader information may include, but are not limited to, a reader's experience, expertise, certifications, title/classification, workload/status, proficiency rating, and age. Also see paragraph [0021], preexisting patient data (e.g., patient history records/reports and previously collected patient image data and paragraph [0023], training predictive models based on preexisting patient data and paragraph [0029].).
Regarding claim 15, Kshirsagar discloses wherein the trained neural network comprises at least one of a vision language model, a large language model, or an image processing model (Paragraph [0021], A model may be based on, or incorporate, one or more rule sets, machine learning (ML), a neural network, or the like. Paragraph [0048], Examples of breast image data may include, but are not limited to, pixel image data and image header data. Pixel image data may be used to derive various attributes of a patient's breast, such as tissue patterns, density, complexity, thickness, volume, and abnormalities.).
Regarding claim 16, Kshirsagar discloses wherein the calculated CCI is used for configuring a software-based downstream task by the control interface (Abstract, The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists. Paragraph [0040], the output may be used to automate the balancing or optimization of the workloads of available clinical professionals. For instance, the output of output engine 206 may be provided to a workload management system/service that is configured to dynamically create/update clinical professional workloads. The workload management system/service may balance the workloads of two clinical professionals such that the first clinical professional is assigned ten mammography exam readings per day, each categorized as having “Fast” reading times, and the second clinical professional is assigned five mammography exam readings per day, each categorized as having “Slow” reading times. Despite the different number of mammography exam readings assigned to the first and second clinical professional, their respective workloads may require approximately the same amount of time to complete. Alternately, the workload management system/service may balance the workloads of two clinical professionals such that each clinical professional is assigned the same number and mix of complex mammography exam readings or such that the clinical professional having the most experience is assigned a proportionately higher number of complex and/or “Slow” reading time mammography exam readings.).
Regarding claim 17, Kshirsagar discloses wherein the software-based downstream task comprises an image annotation task, wherein the calculated CCI is used with respect to estimated reading time, and/or clinician skill level. (Abstract, The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists. Paragraph [0022], he complexity rating may indicate the difficulty or complexity of reading a mammographic exam or images thereof. The difficulty or complexity of reading a mammographic exam may be based on factors, such breast type, breast density, number of CAD marks, etc. The complexity rating may infer or be correlated with a time for reading a mammographic exam. For instance, the complexity rating and the reading time for a mammographic exam may be related such that the reading time increases as the complexity rating increases. Paragraph [0040], the output may be used to automate the balancing or optimization of the workloads of available clinical professionals. For instance, the output of output engine 206 may be provided to a workload management system/service that is configured to dynamically create/update clinical professional workloads. The workload management system/service may balance the workloads of two clinical professionals such that the first clinical professional is assigned ten mammography exam readings per day, each categorized as having “Fast” reading times, and the second clinical professional is assigned five mammography exam readings per day, each categorized as having “Slow” reading times. Despite the different number of mammography exam readings assigned to the first and second clinical professional, their respective workloads may require approximately the same amount of time to complete. Alternately, the workload management system/service may balance the workloads of two clinical professionals such that each clinical professional is assigned the same number and mix of complex mammography exam readings or such that the clinical professional having the most experience is assigned a proportionately higher number of complex and/or “Slow” reading time mammography exam readings. Paragraph [0038], The reader may view a 2D image synthesized from two or more tomography images and investigate the CAD marks indicated by the tomography images. The reader may view various features in the images, such as calcifications, areas of skin thickening, features that are associated with types of cancers, speculated masses, etc. The reader may annotate one or more images based on specific areas of interest and determine a finding or a result.)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kshirsagar et al. (U.S. Pub. No. 2021/0098120) in view of Park et al. (U.S. Pub. No. 2023/0214994).
Regarding claim 5, Kshirsagar discloses does not appear to explicitly disclose, but Park teaches that it was old and well known in the art of medical imaging at the time of the filing to include calibrating the received training data using a calibration module with respect to different images, reports, or different images and reports (Park, paragraph [0032], For example, the training data A 310 may include the patient information 330 and the medical records 332 that correspond to a plurality of labeled medical image studies. This patient and procedure information can be pulled from data stored in or available through the information repository 110 using natural language processing (NLP) techniques. For example, NLP techniques can be used to pull and standardize (e.g., categorize) relevant information (e.g., a normal, benign, or malignant finding) from image study reports stored in a RIS.) to provide more accurate difficulty metrics than other systems (Park, paragraph [0032]).
Therefore, it would have been obvious to one of ordinary skill in the art of medical imaging at the time of the filing to modify the method of Kshirsagar to include calibrating the received training data using a calibration module with respect to different images, reports, or different images and reports, as taught by Park, in order to provide more accurate difficulty metrics than other systems.
Conclusion
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/DEVIN C HEIN/Examiner, Art Unit 3686
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