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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 23, 2024, January 14, 2025, February 10, 2025, March 25, 2025, May 9, 2025, May 23, 2025, July 9, 2025, December 8, 2025, March 24, 2026 and April 8, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 29, 32 and 36-37 are objected to because of the following informalities:
Claim 29 states, “at least one of findings, CAD marks, and lesions.” The examiner notes that “at least one of A, B and C” reads that one of each of the categories must be included (i.e. one A, one B AND one C). However, based on the examiner’s understanding of the specification and claims, it appears the applicant intends to claim “at least one of A, B or C” where only one of the entire group ABC is required.
Similar issue in claims 32, 36 and 37
For the sake of examination, the examiner will interpret the claims as “at least one of A, B or C”
Appropriate correction is required.
Claim Rejections - 35 USC § 103
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 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.
Claim(s) 21-38 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2015/0347693 to Lam et al. (hereinafter Lam), and further in view of U.S. Publication No. 2018/0286504 to Trovato (hereinafter Trovato).
Regarding independent claim 21, Lam discloses A method of analyzing medical image data (abstract, “There is described a computer-implemented method for selecting readers for analyzing a medical image”), the method comprising:
receiving, from an X-ray imaging system, mammographic exam data for a patient (paragraph 0090, “At step 34, if it is determined that the modality corresponding to the radiology order is mammography, then the radiology breast subspecialty is assigned to the radiology order. ”),
wherein the mammographic exam data includes breast image data including one or more X-ray images of the patient's breast tissue (paragraph 0090, “At step 34, if it is determined that the modality corresponding to the radiology order is mammography, then the radiology breast subspecialty is assigned to the radiology order;” mammography is known to include breast image data);
processing the breast image data to determine one or more image factors (paragraph 0090, “At step 34, if it is determined that the modality corresponding to the radiology order is mammography, then the radiology breast subspecialty is assigned to the radiology order;” modality analysis of the image is read as an image factor);
Lam fails to explicitly disclose as further recited. However, Trovato discloses providing the mammographic exam data and the determined one or more image factors to a predictive model (paragraph 0042, “In the illustrative embodiment the prospective challenge level assessment component 60 used only data available on the PACS 10. This information includes current examination information 62 stored on the PACS for the current radiology examination, such as the reason for examination, the imaging modality of the examination, and/or the number of RVU points for the examination.” Figure 1, element 62 input into element 80 which determines the weight and further the challenge level);
determining, by the predictive model, correlations between the one or more image factors and image complexity factors (Figure 1, element 80 and 88), wherein determining the correlations comprises evaluating the determined one or more image factors against a complexity index (Figure 1, element 80 and 88), wherein the complexity index comprises machine learning-generated mappings of the image complexity factors (Figure 1, element 80 is read as learning the weights (i.e. mappings); paragraph 0055, “With continuing reference to FIG. 1, more generally the prospective challenge level assessment component 60 may compute the challenge value as a weighted aggregation 80 of various data values, with challenge value components computed from the data being weighted by respective weights 82, which may be general weights (the same weight being used for computing the challenge value for all radiologists) or radiologist-specific weights (different weights used to account for different skills/preferences of different radiologists).”), wherein the image complexity factors include a number of regions of interest associated with a particular set of image data (Figure 1, element 70);
determining a complexity label for the mammographic exam data based on the correlations (Figure 1, element 88, “prospective challenge level”);
receiving a selection of the mammographic exam data for the patient (paragraph 0034, “ The radiologist will then move on to view the work list display 32D on the display device 20 (which may be automatically brought up in response to filing the radiology report for the last examination, and/or may be brought up by a suitable activation operation performed by the radiologist such as clicking on the entry) ”); and
in response to receiving the selection of the mammographic exam data, displaying the complexity label in association with the mammographic exam data (Figure 2, element 50 and 52).
Lam is directed toward a computer-implemented method for selecting readers for analyzing a medical image (abstract). Trovato is directed toward generating a prospective challenge level for radiology examination reading tasks (abstract). As such, both Lam and Trovato are directed toward similar methods of endeavor of optimizing radiologist workflow. Further, Trovato discloses that radiologists are expected to maintain high throughput (paragraph 0002), and complete a certain number of RVU points per shift (paragraph 0005) while maintaining efficiency (paragraph 0004). It can be easily conceived that all hospitals want to operate as efficiently as possible, especially when considering patient's health. Thus, it would have been obvious to a person having ordinary skill in the art at the time of the claimed invention to incorporate the teaching of Trovato in order to allow for increased efficiency and allowing users to understand their challenge levels to perform most accurately (see paragraph 0017).
Regarding dependent claim 22, the rejection of claim 21 is incorporated herein. Additionally, Lam in the combination further discloses further comprising balancing workloads for one or more clinical professionals using the complexity label, wherein each of the workloads comprises at least one mammographic exam data (paragraph 0189, “In one embodiment, the distribution engine is further adapted to monitor the workload capacity of the readers and detect overloaded situations. A reader's workload capacity may be measured in terms of RVU throughput rates, which relates to the amount of work in terms of order RVU values that a radiologist is capable of reading over a given time period such as an hour.;” paragraph 0190, “If the workload is greater than their remaining capacity, a reader is considered overloaded. Other measures for overload detection can be thresholds for maximum STAT orders workload compared to routine orders workload in either RVU or ERT terms;” paragraph 0191, “In one embodiment, the detection of overload work conditions for readers can improve the performance of the distribution engine by better balancing excessive workloads across additional readers who have capacity to analyze further orders. This may be accomplished by reassigning orders from overloaded readers to other non-overloaded readers who have capacity.”).
Regarding dependent claim 23, the rejection of claim 22 is incorporated herein. Additionally, Lam in the combination further discloses wherein balancing the workloads comprises prioritizing a first case with a first complexity label over a second case with a second complexity label, wherein the first complexity label is associated with greater complexity than the second complexity label (paragraph 0141, “In the same or another embodiment, the order expected reading time depends on at least one of the following parameters: the order relative value units (RVUs) value, the order subspecialty(ies), the reader experience in years, the reader subspecialty(ies), a measure of the reader subspecialties match with the order subspecialties, and/or the like. ” paragraph 0176, “Examples of parameters that may be used for the ranking of the readers comprise the total expected reading time for the reader schedule, the average expected reading time, the minimum expected reading time, the maximum expected reading time, the variance in expected reading time, the total RVU value for the reader schedule, the average RVU value, the minimum RVU value, the maximum RVU value,…the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like”).
Regarding dependent claim 24, the rejection of claim 23 is incorporated herein. Additionally, Lam in the combination further discloses wherein balancing the workloads further uses reader information associated with the one or more clinical professionals (paragraph 0141, “In the same or another embodiment, the order expected reading time depends on at least one of the following parameters: the order relative value units (RVUs) value, the order subspecialty(ies), the reader experience in years, the reader subspecialty(ies), a measure of the reader subspecialties match with the order subspecialties, and/or the like. ” paragraph 0190, “Given a reader's workload capacity expressed in either RVU and/or ERT values, conditions where a reader may be overloaded with work can be detected. The workload from the outstanding orders contained in a reader schedule can be measured and compared against their remaining work capacity for a shift. If the workload is greater than their remaining capacity, a reader is considered overloaded. Other measures for overload detection can be thresholds for maximum STAT orders workload compared to routine orders workload in either RVU or ERT terms.;” paragraph 0191, “In one embodiment, the detection of overload work conditions for readers can improve the performance of the distribution engine by better balancing excessive workloads across additional readers who have capacity to analyze further orders.” Paragraph 0192, “In one embodiment, the reassignment of orders from overloaded readers to non-overloaded readers can be performed according to at least one optimization criteria. ”).
Regarding dependent claim 25, the rejection of claim 24 is incorporated herein. Additionally, Lam in the combination further discloses wherein the reader information includes one or more of a reader's experience, expertise, certifications, title, classification, workload, status, proficiency rating, reading time opinions, complexity opinions, and age (paragraph 0141, “In the same or another embodiment, the order expected reading time depends on at least one of the following parameters: the order relative value units (RVUs) value, the order subspecialty(ies), the reader experience in years, the reader subspecialty(ies), a measure of the reader subspecialties match with the order subspecialties, and/or the like;” Given a reader's workload capacity expressed in either RVU and/or ERT values, conditions where a reader may be overloaded with work can be detected. The workload from the outstanding orders contained in a reader schedule can be measured and compared against their remaining work capacity for a shift. If the workload is greater than their remaining capacity, a reader is considered overloaded. Other measures for overload detection can be thresholds for maximum STAT orders workload compared to routine orders workload in either RVU or ERT terms.).
Regarding dependent claim 26, the rejection of claim 24 is incorporated herein. Additionally, Lam in the combination further discloses wherein the first case is assigned to a first clinical professional associated with a first reader information and the second case is assigned to a second clinical professional associated with a second reader information (paragraph 0175, “The reader that occupies the first position in the ranking is then selected as being the most adequate reader for the new order which is inserted in the schedule of the selected reader at the previously determined insertion position;” orders are assigned based on the rankings), wherein the first reader information is associated with greater experience than the second reader information (paragraph 0141, “In the same or another embodiment, the order expected reading time depends on at least one of the following parameters: the order relative value units (RVUs) value, the order subspecialty(ies), the reader experience in years, the reader subspecialty(ies), a measure of the reader subspecialties match with the order subspecialties, and/or the like;” paragraph 0175, “In another embodiment, the method 100 further comprises a step 108 of ranking and/or scoring the selected readers in order to determine the most adequate reader for analyzing the new order, as illustrated in FIG. 5. The reader that occupies the first position in the ranking is then selected as being the most adequate reader for the new order which is inserted in the schedule of the selected reader at the previously determined insertion position;” paragraph 0176, “ Examples of parameters that may be used for the ranking of the readers comprise the total expected reading time for the reader schedule, the average expected reading time, the minimum expected reading time, the maximum expected reading time, the variance in expected reading time, the total RVU value for the reader schedule, the average RVU value, the minimum RVU value, the maximum RVU value, the variance in RVU value;” experience is related to ERT, which is then used for ranking; the more experience correlates to a lower ERT thus the lower ERT would take a higher ranking than a higher ERT).
Regarding dependent claim 27, the rejection of claim 23 is incorporated herein. Additionally, Trovato in the combination further discloses wherein the first complexity label is associated with a greater number of regions of interest than the second complexity label (paragraph 0053, “The imaging modality of the current examination can be further used in assessing the challenge value. For example, the average reading time varies for different modalities due to differing numbers of images (e.g. a typical x-ray examination has only a few images while an MRI or CT examination may have hundreds of slices or images) and the complexity of the image content (again, an MRI image is usually more complex than an x-ray image);” additional images is read as including additional ROIs).
One of ordinary skill in the art before the effective filing date would easily understand it takes more time and energy to review 1000 images as opposed to one x-ray. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Trovato in order to ensure review images with more images are given higher complexity to account for the additional review time needed.
Regarding dependent claim 28, the rejection of claim 22 is incorporated herein. Additionally, Lam in the combination further discloses wherein balancing the workloads comprises assigning a plurality of mammographic exam data to each workload such that each plurality is associated with a mix of complexity labels (paragraph 0190, “If the workload is greater than their remaining capacity, a reader is considered overloaded. Other measures for overload detection can be thresholds for maximum STAT orders workload compared to routine orders workload in either RVU or ERT terms;” paragraph 0191, “In one embodiment, the detection of overload work conditions for readers can improve the performance of the distribution engine by better balancing excessive workloads across additional readers who have capacity to analyze further orders. This may be accomplished by reassigning orders from overloaded readers to other non-overloaded readers who have capacity.”).
Regarding dependent claim 29, the rejection of claim 21 is incorporated herein. Additionally, Trovato in the combination further discloses wherein the regions of interest include at least one of findings, CAD marks, and lesions (paragraph 0034, “ a current tumor image”).
Reviewing medical image data often includes reviewing various types of data, and lesions are of utmost importance to a reviewer for diagnosis. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Trovato in order to ensure the most important data is considered for review and diagnosis.
Regarding dependent claim 30, the rejection of claim 21 is incorporated herein. Additionally, Trovato in the combination further discloses wherein the image complexity factors further comprise image factors derived from a training set of mammographic exam data (paragraph 0053, “The imaging modality of the current examination can be further used in assessing the challenge value. For example, the average reading time varies for different modalities due to differing numbers of images (e.g. a typical x-ray examination has only a few images while an MRI or CT examination may have hundreds of slices or images) and the complexity of the image content (again, an MRI image is usually more complex than an x-ray image);” the training data is read as the time associated with the modality).
One simple way to predict further complexity is known to be based on past history. For example, if in the past analysis has proven to be time consuming and difficult as related to a specific modality, or imaged area, it would be obvious to one of ordinary skill in the art before the effective filing date to ensure the future orders that contain similar features are provided a similar complexity. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Trovato in order to ensure data of similar characteristics is assigned similar complexity labels.
Regarding dependent claim 31, the rejection of claim 30 is incorporated herein. Additionally, Trovato in the combination further discloses wherein the image complexity factors are further determined based at least in part on mammographic exam data for one or more patients and evaluation data for one or more exam readers (paragraph 0041, “However, it is also contemplated for the prospective challenge level assessment component 60 to generate the prospective challenge levels for the radiology examination reading tasks using data stored on the PACS 10, which may include historical images and reports, as well as data from another source such as an Electronic Medical (or Health) Record (EMR or EHR);” paragraph 0050, “Various components of these various data 62, 64, 66, 68, 70, and/or other data, may be available on the PACS 10 for assessing the challenge level of any particular selected radiology examination reading task. The prospective challenge level assessment component 60 may be configured to collect and use any portion, or all, of the available data;” paragraph 0052, “Data 68 from past radiology reports may be used to estimate the prospective challenge value in various ways. ”).
One simple way to predict further complexity is known to be based on past history. For example, if in the past analysis has proven to be time consuming and difficult as related to a specific modality, or imaged area, it would be obvious to one of ordinary skill int eh art before the effective filing date to ensure the future orders that contain similar features are provided a similar complexity. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Trovato in order to ensure data of similar characteristics is assigned similar complexity labels.
Regarding dependent claim 32, the rejection of claim 21 is incorporated herein. Additionally, Trovato in the combination further discloses wherein the complexity index is generated using at least one of an index creation algorithm, a data mapping utility, and a data correlation algorithm (abstract, “A challenge level assessment component (60) generates prospective challenge levels (88) for radiology examination reading tasks;” paragraph 0025, “ Patient data available on the PACS but not shown on the work list, such as the modality of the radiology examination to be read, and/or the reason for the radiology examination, and/or patient demographic data (both that shown on the work list and optionally additional patient demographic data that may be available on the PACS but not included in the work list) may be leveraged as relevant data for the prospective challenge level assessment;” paragraph 0041, “With reference back to FIG. 1, the challenge level indicators are generated by a prospective challenge level assessment component 60 comprising an electronic processor programmed to generate a prospective challenge level for a radiology examination reading task prior to the radiology workstation receiving entry of the radiology report for the radiology examination reading task.”).
As noted above, the goal is to determine a correlation between image features and complexity for radiology review. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date to incorporate the teaching of Trovato in order to ensure the complexity level is determined based on other data from the image data.
Regarding dependent claim 33, the rejection of claim 21 is incorporated herein. Additionally, Trovato in the combination further discloses wherein the complexity label is used to determine an estimated reading time (paragraph 0018, “the RVU points should be proportional to the expected difficulty of the reading task, which roughly translates to an expected reading time (intuitively, a more difficult task should take longer and be more highly compensated);” paragraph 0056, “n estimated examination reading time 92 is determined from the prospective challenge level 88. In the examples of FIGS. 2 and 3 this is straightforward: the “simple” challenge level translates to an estimated reading time of 2 min or less; the “moderate” challenge level translates to an estimated reading time of 2-10 min; and the “difficult” challenge level translates to an estimated reading time of greater than 10 min.”).
One of ordinary skill in the art before the effective filing date would easily understand it takes more time and energy to review 1000 images as opposed to one x-ray; additionally, more difficult cases take a longer time to review. Further, a reviewer only has a specific allotted time to review data in one shift. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Trovato in order to ensure there is a coordination between more complex images and the time to read them, so that a user can review the specific amount of data per shift.
Regarding dependent claim 34, the rejection of claim 33 is incorporated herein. Additionally, Trovato in the combination further discloses wherein the complexity label is used by a reading time predictive model to determine the estimated reading time (paragraph 0018, “the RVU points should be proportional to the expected difficulty of the reading task, which roughly translates to an expected reading time (intuitively, a more difficult task should take longer and be more highly compensated);” paragraph 0056, “n estimated examination reading time 92 is determined from the prospective challenge level 88. In the examples of FIGS. 2 and 3 this is straightforward: the “simple” challenge level translates to an estimated reading time of 2 min or less; the “moderate” challenge level translates to an estimated reading time of 2-10 min; and the “difficult” challenge level translates to an estimated reading time of greater than 10 min.”).
One of ordinary skill in the art before the effective filing date would easily understand it takes more time and energy to review 1000 images as opposed to one x-ray; additionally, more difficult cases take a longer time to review. Further, a reviewer only has a specific allotted time to review data in one shift. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Trovato in order to ensure there is a coordination between more complex images and the time to read them, so that a user can review the specific amount of data per shift.
Regarding dependent claim 35, the rejection of claim 21 is incorporated herein. Additionally, Trovato in the combination further discloses wherein the complexity index comprises a standalone executable file or utility (paragraph 0041, “With reference back to FIG. 1, the challenge level indicators are generated by a prospective challenge level assessment component 60 comprising an electronic processor programmed to generate a prospective challenge level for a radiology examination reading task prior to the radiology workstation receiving entry of the radiology report for the radiology examination reading task;” the challenge level assessment component is read as a utility).
One of ordinary skill in the art before the effective filing date of the claimed invention would be easily aware there may be a need for tools for analysis to be independent to limit processing needs or execution operations. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date to incorporate the teaching of Trovato in order to ensure the utility can be ran independently for the specific task at hand.
Regarding dependent claim 36, the rejection of claim 21 is incorporated herein. Additionally, Trovato in the combination further discloses wherein the complexity index is integrated into at least one of a service, application, or system (paragraph 0012, “FIG. 1 diagrammatically illustrates a radiology workstation including a prospective challenge level assessment component as disclosed herein. Naturally, this could be performed as a client-server system, where the user interface is remote from the physical computer.”).
One of ordinary skill in the art before the effective filing date of the claimed invention would be easily aware there may be a need for tools for analysis to be independent to limit processing needs or execution operations and only connect to data which is necessary (see paragraph 0041 of Trovato). Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date to incorporate the teaching of Trovato in order to ensure the utility can be ran independently for the specific task at hand and connected to the PACS where data is pulled from
Regarding dependent claim 37, the rejection of claim 21 is incorporated herein. Additionally, Trovato in the combination further discloses further comprising:
displaying the machine learning-generated mappings of the image complexity factors (paragraph 0015, “FIG. 5 diagrammatically illustrates a method for updating radiologist-specific weights of the prospective challenge level assessment component of FIG. 1” in order for a user to update the mapping logic they must be able to see the logic/mappings somewhere);
and modifying, in response to a received user input, at least one of mappings, mapping logic, classifications, and category values of the complexity index (mapping logic: paragraph 0015, “FIG. 5 diagrammatically illustrates a method for updating radiologist-specific weights of the prospective challenge level assessment component of FIG. 1”).
Automated learning methods may not always be accurate in terms of how a user would like data to be analyzed. It would be obvious to a person having ordinary skill in the art to allow a user to manipulate the weights as needed, based on what they prefer. Thus it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Trovato in order to ensure the system is using a method the reviewer agrees with.
Regarding dependent claim 38, the rejection of claim 37 is incorporated herein. Additionally, Trovato in the combination further discloses further comprising assigning a weight, in response to a received user input, to one or more of the image complexity factors (paragraph 0015, “FIG. 5 diagrammatically illustrates a method for updating radiologist-specific weights of the prospective challenge level assessment component of FIG. 1”).
Automated learning methods may not always be accurate in terms of how a user would like data to be analyzed. It would be obvious to a person having ordinary skill in the art to allow a user to manipulate the weights as needed, based on what they prefer. Thus it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Trovato in order to ensure the system is using a method the reviewer agrees with.
Regarding independent claim 40, the rejection of claim 1 applies directly. Additionally, Lam further discloses A system for analyzing medical image data (abstract, “There is described a computer-implemented method for selecting readers for analyzing a medical image”), the system comprising:
at least one processor (paragraph 0033, “According to a second broad aspect, there is provided a computer program product for identifying readers adequate for analyzing a medical image to be analyzed within a time limit, the computer program product comprising a computer readable memory storing computer executable instructions thereon that when executed by a processing unit perform the steps of the above-described method.”); and
a memory in communication with the at least one processor and including instructions which, when executed by the at least one processor (paragraph 0033, “According to a second broad aspect, there is provided a computer program product for identifying readers adequate for analyzing a medical image to be analyzed within a time limit, the computer program product comprising a computer readable memory storing computer executable instructions thereon that when executed by a processing unit perform the steps of the above-described method.”), cause the at least one processor to:
receive, from an X-ray imaging system, mammographic exam data for a patient (paragraph 0090, “At step 34, if it is determined that the modality corresponding to the radiology order is mammography, then the radiology breast subspecialty is assigned to the radiology order. ”),
wherein the mammographic exam data includes breast image data including one or more X-ray images of the patient's breast tissue (paragraph 0090, “At step 34, if it is determined that the modality corresponding to the radiology order is mammography, then the radiology breast subspecialty is assigned to the radiology order. ”);
process the breast image data to determine one or more image factors (paragraph 0090, “At step 34, if it is determined that the modality corresponding to the radiology order is mammography, then the radiology breast subspecialty is assigned to the radiology order;” modality analysis of the image is read as an image factor);
Lam fails to explicitly disclose as further recited. However, Trovato further discloses provide the mammographic exam data and the determined one or more image factors to a predictive model (paragraph 0042, “In the illustrative embodiment the prospective challenge level assessment component 60 used only data available on the PACS 10. This information includes current examination information 62 stored on the PACS for the current radiology examination, such as the reason for examination, the imaging modality of the examination, and/or the number of RVU points for the examination.” Figure 1, element 62 input into element 80 which determines the weight and further the challenge level);
determine, by the predictive model, correlations between the one or more image factors and image complexity factors (Figure 1, element 80 and 88), wherein determining the correlations comprises evaluating the determined one or more image factors against a complexity index (Figure 1, element 80 and 88), wherein the complexity index comprises machine learning-generated mappings of the image complexity factors (Figure 1, element 80 is read as learning the weights (i.e. mappings); paragraph 0055, “With continuing reference to FIG. 1, more generally the prospective challenge level assessment component 60 may compute the challenge value as a weighted aggregation 80 of various data values, with challenge value components computed from the data being weighted by respective weights 82, which may be general weights (the same weight being used for computing the challenge value for all radiologists) or radiologist-specific weights (different weights used to account for different skills/preferences of different radiologists).”), wherein the image complexity factors include a number of regions of interest associated with a particular set of image data (Figure 1, element 70);
determine a complexity label for the mammographic exam data based on the correlations (Figure 1, element 88, “prospective challenge level”);
receive a selection of the mammographic exam data for the patient (paragraph 0034, “ The radiologist will then move on to view the work list display 32D on the display device 20 (which may be automatically brought up in response to filing the radiology report for the last examination, and/or may be brought up by a suitable activation operation performed by the radiologist such as clicking on the entry) ”); and
in response to receiving the selection of the mammographic exam data, display the complexity label in association with the mammographic exam data (Figure 2, element 50 and 52).
Lam is directed toward a computer-implemented method for selecting readers for analyzing a medical image (abstract). Trovato is directed toward generating a prospective challenge level for radiology examination reading tasks (abstract). As such, both Lam and Trovato are directed toward similar methods of endeavor of optimizing radiologist workflow. Further, Trovato discloses that radiologists are expected to maintain high throughput (paragraph 0002), and complete a certain number of RVU points per shift (paragraph 0005) while maintaining efficiency (paragraph 0004). It can be easily conceived that all hospitals want to operate as efficiently as possible, especially when considering patient's health. Thus, it would have been obvious to a person having ordinary skill in the art at the time of the claimed invention to incorporate the teaching of Trovato in order to allow for increased efficiency and allowing users to understand their challenge levels to perform most accurately (see paragraph 0017).
Claim(s) 39 is rejected under 35 U.S.C. 103 as being unpatentable over Lam further in view of Trovato as applied to claim 21 above, and further in view of U.S. Patent No. 7,889,896 to Roehrig et al. (hereinafter Roehrig).
Regarding dependent claim 39, the rejection of claim 21 is incorporated herein. Additionally, Lam discloses wherein the image complexity factors further comprise one or more of a BI-RADS mammographic density classification and an estimated reading time (paragraph 0010, “the step of receiving an order expected reading time comprises determining the order expected reading time for each one of the readers.”).
However, Lam and Trovato in the combination fail to explicitly disclose as further recited. However, Roehrig discloses wherein the image complexity factors further comprise one or more of a BI-RADS mammographic density classification (column 5, line 45, “ CAD-computed metrics such as, but not limited to: a total number of CAD markers metric, a breast density metric, a breast size metric, a maximum overall suspiciousness metric, a maximum calcification suspiciousness metric, and a maximum mass suspiciousness metric. ”)
As noted above, Lam and Trovato are directed towards methods of optimizing radiologists workload and workflow (see claim 21 analysis). Roehrig is directed toward methods of managing a patient worklist in a radiology environment (abstract). As such, Lam, Trovato and Roehrig are directed toward similar endeavors of managing various workflows in a radiology department. Further, Roehrig discloses analysis of specifically breast data, which is of importance when analyzing mammographic data Thus, it would have been obvious to a person having ordinary skill in the art at the time of the claimed invention to incorporate the teaching of Roehrig in order to allow for the most relevant data as related to mammography to be considered in relation to mammographic images.
Double Patenting
Non-statutory
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 21 and 40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 20 of U.S. Patent No. 12,119,107 (hereinafter US ‘107). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application is more broad in scope than US ‘107.
Claim 21: Regarding claim 21, claim 21 compares to claim 1 of the US ‘107 patent as indicated below:
Instant Application
U.S. Patent No. 12,119,107
Notes
A method of analyzing medical image data, the method comprising:
A method of analyzing medical image data, the method comprising:
Verbatim the same
receiving, from an X-ray imaging system, mammographic exam data for a patient, wherein the mammographic exam data includes breast image data including one or more X-ray images of the patient's breast tissue;
receiving, from an X-ray imaging system, mammographic exam data for a patient, wherein the mammographic exam data includes breast image data including one or more X-ray images of the patient's breast tissue;
Verbatim the same
processing the breast image data to determine one or more image factors;
processing the breast image data to determine one or more image factors;
Verbatim the same
providing the mammographic exam data and the determined one or more image factors to a predictive model;
providing the mammographic exam data and the determined one or more image factors to a predictive model;
Verbatim the same
determining, by the predictive model, correlations between the one or more image factors and image complexity factors,
determining, by the predictive model, correlations between the one or more image factors and training factors, wherein the training factors are determined based at least in part on mammographic exam data for one or more patients and evaluation data for one or more exam readers,
Instant application more broad
wherein determining the correlations comprises evaluating the determined one or more image factors against a complexity index,
wherein determining the correlations comprises evaluating the determined one or more image factors against a complexity index,
Verbatim the same
wherein the complexity index comprises machine learning-generated mappings of the image complexity factors, wherein the image complexity factors include a number of regions of interest associated with a particular set of image data;
wherein the complexity index comprises machine learning-generated mappings of a plurality of factors that affect an amount of time required to interpret the mammographic exam data, wherein the plurality of factors include image factors derived from a training set of mammographic exam data and reader factors derived from a training set of evaluation data;
Instant application more broad
determining an expected reading time for the mammographic exam data based on the correlations;
Instant application more broad
determining a complexity label for the mammographic exam data based on the correlations;
assigning a complexity label to the breast image data based on at least one of the expected reading time, the correlations, and a portion of the breast image data;
Instant application more broad
receiving a selection of the mammographic exam data for the patient; and
receiving a selection of the mammographic exam data for the patient; and
Verbatim the same
in response to receiving the selection of the mammographic exam data, displaying the complexity label in association with the mammographic exam data.
in response to receiving the selection of the mammographic exam data, displaying the complexity label in association with the mammographic exam data.
Verbatim the same
As can be seen above, claim 21 of the current application is more broad in scope than claim 1 of the US ‘107 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 1 of the US ‘107 patent.
Claim 40: Regarding claim 40, claim 40 compares to claim 20 of the US ‘107 patent as indicated below:
Instant Application
U.S. Patent No. 12,119,107
Notes
A system for analyzing medical image data, the system comprising:
A system for analyzing medical image data and determining a complexity of the medical image data, the system comprising:
Instant application more broad
at least one processor; and
a processor; and
Instant application more broad
a memory in communication with the at least one processor and including instructions which, when executed by the at least one processor, cause the at least one processor to:
memory coupled to the processor, the memory comprising computer executable instructions that, when executed by the processor, performs a method comprising:
Substantially the same
receive, from an X-ray imaging system, mammographic exam data for a patient, wherein the mammographic exam data includes breast image data including one or more X-ray images of the patient's breast tissue;
receiving, from an X-ray imaging system, mammographic exam data for a patient, wherein the mammographic exam data includes breast image data including one or more X-ray images of the patient's breast tissue;
Verbatim the same
process the breast image data to determine one or more image factors;
processing the breast image data to determine one or more image factors;
Substantially the same
provide the mammographic exam data and the determined one or more image factors to a predictive model;
providing the mammographic exam data and the determined one or more image factors to a predictive model;
Verbatim the same
determine, by the predictive model, correlations between the one or more image factors and image complexity factors
determining, by the predictive model, correlations between the one or more image factors and training factors,
Instant application more broad
wherein determining the correlations comprises evaluating the determined one or more image factors against a complexity index,
wherein the training factors are determined based at least in part on mammographic exam data for one or more patients and evaluation data for one or more exam readers, wherein determining the correlations comprises evaluating the one or more image factors against a complexity index
Instant application more broad
wherein the complexity index comprises machine learning-generated mappings of the image complexity factors, wherein the image complexity factors include a number of regions of interest associated with a particular set of image data;
wherein the complexity index comprises machine learning-generated mappings of a plurality of factors that affect an amount of time required to interpret the mammographic exam data,
Instant application more broad
wherein the plurality of factors include image factors derived from a training set of mammographic exam data and reader factors derived from a training set of evaluation data;
Instant application more broad
determining an expected reading time for the mammographic exam data based on the correlations;
Instant application more broad
determine a complexity label for the mammographic exam data based on the correlations;
assigning a complexity label to the breast image data based on at least one of the expected reading time, the correlations, and a portion of the breast image data;
Instant application more broad
receive a selection of the mammographic exam data for the patient; and
receiving a selection of the mammographic exam data for the patient; and
Verbatim the same
in response to receiving the selection of the mammographic exam data, display the complexity label in association with the mammographic exam data.
in response to receiving the selection of the mammographic exam data, displaying the complexity label in association with the mammographic exam data.
Verbatim the same
As can be seen above, claim 40 of the current application is more broad in scope than claim 20 of the US ‘107 patent. Therefore, any patent granted on the current application would result in the unjustifiable timewise extension of the monopoly granted on claim 20 of the US ‘107 patent.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Publication No. 2009/0138318 to Hawkins et al. discloses, “Certain embodiments provide an adaptive clinical workflow management system. The system includes a workflow engine establishing a workflow based on a worklist including a plurality of tasks to be performed (abstract).”
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