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 .
DETAILED ACTION
In the amendments filed on 27 January 2026, the following has occurred: claims 11, 8, and 15 have been amended; claims 7, 14 and 21 have been canceled.
Now claims 1-6, 8-14 and 15-20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8-14 and 15-20 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.
Claims 1, 8 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a computer-implemented method, a system and computer program product stored on non-transitory storage medium. The limitations of:
Claim 1, which is representative of claims 8 and 15
a. [… obtaining …], first medical data of a patient comprising medical image data of the patient and additional available information related to at least one of the medical image data or the patient, b. providing the first medical data in machine-readable form a input data of an […] algorithm for automatically selecting, launching and orchestrating […] algorithms through iterative, dynamic adjustment of building blocks of the structured medical report and by influencing the results of a first […] algorithm based on results of a second […] algorithm, and executing, […], the […] algorithm in an automated manner wherein each iteration of the […] algorithm includes the steps of: c. the […] selecting from a collection of report modules which have a […] structure a report module and including the selected module in the structured medical report, wherein the at least one report module has a […] nature and wherein the selection is made based on the input data, d. the […] selecting from a collection of […] Algorithms an […]-algorithm for supporting the interpretation of the medical image data, wherein the selection is made based on at least one of the input data or the selected report module, e. the […] supplying the selected […]-Algorithm with the input data as input parameters, and executing the selected […]-Algorithm thereby producing output data, f. the […] filling in the selected report module based on the output data, g. the […] deriving machine-readable second medical data from the produced output data, the second medical data comprising at least one of a finding, a segmentation, a measurement, a classification, a risk score, or a longitudinal descriptor of change relative to one or more prior studies; h. the […] generating updated machine-readable input data by adding the second medical data to the input data or by replacing the input data or parts thereof by the second medical data, i. the […] determining whether machine-readable stop criteria are met that depend on the updated input data, the stop criteria comprising presence or absence of new findings and/or exhaustion of guideline-recommended reporting steps; and j. the […] I. upon determining that the stop criteria are not met, performing a further iteration of the iterative loop in which steps c. to j. are repeated using the updated input data as the input data for the further iteration, thereby re-selecting a report module and an […] algorithm for the further iteration, or II. upon determining that the stop criteria are met, terminating and exiting the iterative loop, and automatically generating the medical report based on the report module selected, included, and filled in during execution of the iterative loop.
, as drafted, is a method, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via the recitation of generic computer components. That is by a human user interacting with a computer processor (claim 1), a receiving unit and medical report generating unit on non-transitory computer readable medium, an input-output unit comprising a display device, an user input device and a processor (claim 8), and a non-transitory storage medium with a processor (claim 15), the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, by human users interacting with a computer (claim 1), a receiving unit and medical report generating unit on non-transitory computer readable medium, an input-output unit comprising a display device, an user input device and a processor (claim 8), and a non-transitory storage medium with a processor (claim 15), the claim encompasses collection of data, organization of the collected data to provide selections for a human user workflow, and providing an output or the organized data to a human user for human user interaction with a human user workflow. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally, as drafted is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a computer processor (claim 1), a receiving unit and medical report generating unit on non-transitory computer readable medium, an input-output unit comprising a display device, an user input device and a processor (claim 8), and a non-transitory storage medium with a processor (claim 15), the claim encompasses a user making selections for inclusion of data in creation of a medical report. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computer processor (claim 1), a receiving unit and medical report generating unit on non-transitory computer readable medium, an input-output unit comprising a display device, an user input device and a processor (claim 8), and a non-transitory storage medium with a processor (claim 15), which implements the abstract idea. The computer processor (claim 1), a receiving unit and medical report generating unit on non-transitory computer readable medium, an input-output unit comprising a display device, an user input device and a processor (claim 8), and a non-transitory storage medium with a processor (claim 15) is recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 3, page 13) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “receiving… by a computer”, use of an iterative loop algorithm, use of IA algorithms and a module with a hierarchical nature/structure. The “receiving…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The use of an iterative loop algorithm is recited at a high-level of generality (i.e., using a generic off-the shelf iterative model with respect to a threshold) and amounts to generally linking the abstract idea to a particular technological environment. The use of IA algorithms is recited at a high-level of generality (i.e., using a generic off-the shelf model) and amounts to generally linking the abstract idea to a particular technological environment. The a module with a hierarchical nature/structure is recited at a high-level of generality (i.e., use of software that utilizes a tree) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that 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 elements of a computer (claim 1), a receiving unit and medical report generating unit on non-transitory computer readable medium, an input-output unit comprising a display device, an user input device and a processor (claim 8), and a non-transitory storage medium with a processor (claim 15) to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “receiving…”, use of an iterative loop algorithm, use of IA algorithms and a module with a hierarchical nature/structure were considered generally linking the abstract idea to particular technological environment and/or extra-solution activity. The “receiving…” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The use of an iterative loop algorithm been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Xu (2021/0074427): see below but at least Figure 1, paragraph [0041]-[0042]; He (20210327563): see below but at least figures 1-2, paragraphs [0085]-[0092]; Bogonoi (20190066343): Figures 11-13, paragraph [0068]; use of iterative loop algorithms is well-understood, routine and conventional. The use of IA algorithms been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Xu (2021/0074427): see below but at least paragraph [0047]; Qian (2012/0035963): see below but at least paragraph [0034]; Gooben (20210134465): paragraph [0043]; use of IA algorithms is well-understood, routine and conventional. The module with a hierarchical nature/structure been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Liu (2004/0168119): see below but at least paragraph [0042]; Mukherjee (20210110912): paragraph [0044]; Paik (20210216822): paragraph [0210]; use of a hierarchical module is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-6, 9-13 and 16-20 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2, 9 and 16 recite the additional element of a hierarchical ontology, however hierarchical data was already considered above and is incorporated herein, nevertheless arguendo, the hierarchical ontology is recited at a high-level of generality (i.e., a mapping of data) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a hierarchical ontology were considered generally linking the abstract idea to a particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Qian (20120035963): see below but at least paragraph [0035]; Sevenster (20170154156): paragraph [0015]; Paik (20210216822): paragraph [0096]; use of a hierarchical ontology is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 3, 5, 10, 12, 17 and 19 further describe the IA algorithm, however the IA algorithm was already considered above and is incorporated herein.
Claims 4, 11 and 18 recite the additional element of use of a decision tree, however it is recited at a high-level of generality (i.e., a data storage structure) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a decision tree were considered generally linking the abstract idea to a particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Liu (2004/0168119): see below but at least paragraph [0042]; Mukherjee (20210110912): paragraph [0044]; Paik (20210216822): paragraph [0210]; use of decision trees is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 6, 13 and 20 further describe user input, however do not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
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.
The factual inquiries 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(s) 1, 3-8, 10-15, and 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2021/0074427 (hereafter “Xu”), in view of U.S. Patent Pub. No. 20210327563 (hereafter “He”), further in view of U.S. Patent Pub. No. 2004/0168119 (hereafter “Liu”), further in view of U.S. Patent Pub. No. 20190066343 (hereafter “Bogonoi”).
Regarding (Currently Amended) claim 1, Xu teaches a computer-implemented method for generating a structured medical report (Xu: paragraph [0002], “a computer system and method for creating medical reports for imaging studies”), the method including the steps of:
a. receiving, by a computer processor, first medical data of a patient comprising medical image data of the patient and additional available information related to at least one of the medical image data or the patient (Xu: Figures 1-2, paragraphs [0007]-[0008], “pre-processing medical images, collecting findings, and automatically creating reports. The system can be used in all medical imaging modalities, such as CT, MRI, etc… The finding collecting software displays the medical image with an interactive interface”, paragraphs [0042]-[0044], “For studies that involve multiple medical images, all images are pre-processed… Picture Archiving and Communication System (PACS) 210 is a repository that stores medical images from healthcare providers… includes a hardware processor coupled to a memory that stores program instructions executed by the hardware processor”, paragraph [0056], “meta-data of the image”. The PACS in which the systems retrieves and stores image and meta data teaches the claimed data under the broadest reasonable interpretation),
b. providing the first medical data in machine-readable form a input data of an iterative loop algorithm for automatically selecting, launching and orchestrating image-analysis (IA) algorithms through iterative, dynamic adjustment of building blocks of the structured medical report and by influencing the results of a first IA algorithm […], and executing, by the processor, the IA algorithm in an automated manner (Xu: Figure 1, paragraphs [0034]-[0035], “software tools for pre-processing medical images, collecting findings, and automatically generating readable medical reports… The pre-processing software 100 pre-processes a medical image in step 101, which generates an anatomical segmentation and/or computer-aided diagnosis.”, paragraphs [0041]-[0042], “Steps 111-117 are repeated until the physician describes all the findings in the image”, paragraphs [0046]-[0047], “The pre-processing software 100 (FIG. 1) generates anatomical segmentations and/or computer-aided diagnoses for the image with computer vision algorithms”. The Examiner notes that “for automatically selecting, launching and orchestrating image-analysis (IA) algorithms through iterative, dynamic adjustment of building blocks of the structured medical report” is an intended use of the providing data to an iterative loop that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the providing data to an iterative loop. The image data is used as input data to generate a report, which is an iterative process, in which results from a first run (i.e., a second IA algorithm) are used to influence subsequent runs (i.e., subsequent iterations of the loop, or a first IA algorithm) and teaches what is required of the claim under the broadest reasonable interpretation) wherein each iteration of the iterative loop algorithm includes the steps of:
c. the processor selecting from a collection of report modules […] a report module and including the selected module in the structured medical report, […] wherein the selection is made based on the input data (Xu: Figure 1, paragraphs [0008]-[0009], “The finding collecting software displays the medical image with an interactive interface. The software captures mouse clicks (and/or other types of interactions such as touch screen taps and gestures) from the physician on the location of interest, and prompts the physician to select a template to fill in for describing the finding, from a list of possible templates… the physician is prompted with a list of templates that are related to the findings around the selected (e.g., clicked on) location according to a computer-aided diagnosis”, paragraph [0029], “a template refers to a pre-formatted form that serves as the starting point for describing a finding”, paragraphs [0032]-[0033], “the physician is prompted to select a template from a list of templates related to the anatomy”, paragraphs [0039]-[0041], “The finding collecting software 110 may generate the filtered list(s) of templates using pre-defined template classifications. Specifically, each template may be stored in computer memory in association with (1) one or more anatomy identifiers, and/or (2) one or more diagnosis identifiers. To generate the filtered list in the semi-enhanced mode, the system may identify and select, from a master set of templates, all templates having an anatomy identifier that matches the anatomy classification of the currently selected image”. Also see, paragraph [0037]. The Examiner notes the filtering of the templates reads on the computer performing a selection of at least one report module under the broadest reasonable interpretation),
d. the processor selecting from a collection of image-analysis (IA) Algorithms an IA-algorithm for supporting the interpretation of the medical image data, wherein the selection is made based on at least one of the input data or the selected report module (Xu: Figure 1, paragraphs [0035]-[0037], “generates an anatomical segmentation and/or computer-aided diagnosis… the system is being used in the semi-enhanced mode versus the enhanced mode. In some implementations, the physician can select between these two modes; in other implementations, the finding collecting software 110 may only implement one of these two modes”, paragraph [0040], “multiple computer-aided diagnoses”, paragraph [0047], “The pre-processing software may use computer vision algorithms that are known in the art”, paragraphs [0054]-[0055], “In step 802, the software determines if the template is triggered in the enhanced mode… compare findings by different… computer-aided diagnosis algorithms”. Also see, paragraph [0008]-[0009], [0030]-[0033], [0046]. The Examiner notes image segmentation and and image CAD are both image-analysis algorithms, additionally multiple algorithms CAD algorithms are taught and a selection is made which teaches what is required under the broadest reasonable interpretation),
e. the processor supplying the selected IA-Algorithm with the input data as input parameters, and executing the selected IA-Algorithm thereby producing output data (Xu: paragraphs [0008]-[0009], “the software also generates a computer-aided diagnosis for the study”, paragraphs [0030]-[0033], “Computer-aided diagnosis: a computer-aided diagnosis refers to the separation of locations of interest for the diagnostic purpose. Each pixel of a medical image can be labeled according to whether there is a positive finding or not”, paragraph [0040], “multiple computer-aided diagnoses may have been generated for the currently selected region”, paragraphs [0046]-[0047], “The pre-processing software 100 (FIG. 1) generates anatomical segmentations and/or computer-aided diagnoses for the image with computer vision algorithms”, paragraph [0054], “In step 802, the software determines if the template is triggered in the enhanced mode… more options in the template are prefilled by referring to the associated computer-aided diagnosis”. The selected algorithm is executed on the input data and reads on what is required under the broadest reasonable interpretation),
f. the processor filling in the selected report module based on the output data (Xu: Figures 1, 6-8, paragraphs [0041], “before displaying the selected template, it is first prefilled according to the physician's preset preferences (if any), and/or based on any computer-aided diagnosis”, paragraphs [0052]-[0054], “The template window 600 is pre-filled based on the computer-aided diagnosis generated by the pre-processing software… the measurement options 620 are pre-filled by the system by computing the number of pixels with the parenchymal bleed label and the pixel thickness. The Size option 610 is prefilled with medium, by comparing the measurements with predefined thresholds for size levels. The Number of Foci option 630 is pre-filled with 2, because two separate clusters of bleed are detected from the diagnosis. The With Edema option 640 is pre-filled to "yes," because edema labels are found near the finding in the diagnostic segmentation”. The filled in template (i.e., selected report module) contain second medical data under the broadest reasonable interpretation),
g. the processor deriving machine-readable second medical data from the produced output data, the second medical data comprising at least one of a finding, a segmentation, a measurement, a classification, a risk score, or a longitudinal descriptor of change relative to one or more prior studies; h. the processor generating updated machine-readable input data by adding the second medical data to the input data or by replacing the input data or parts thereof by the second medical data (Xu: paragraphs [0036]-[0037], “If a diagnosis has been generated for the image by the pre-processing software… templates include predefined entries and options specifically designed according to the finding type and anatomy”, paragraph [0046], “The pre-processing software 100 (FIG. 1) generates anatomical segmentations and/or computer-aided diagnoses for the image with computer vision algorithms… The anatomical segmentation generated from the pre-processing software is shown as 310, where it is displayed as a mask over the original image… A computer-aided diagnosis generated from the pre-processing software is shown as 320, where it is displayed as a mask over the original study”, paragraph [0050], “The software collects all findings from the computer-aided diagnosis”, paragraph [0056], “report generating software, which automatically generates a readable report from all templates filled for the study… All the information can be drawn from the meta-data of the image. The report generating software then iterates through all the filled templates of the image in step 901”. The Examiner notes displaying a mask of the produced output data (i.e., a segmentation) over the original study (i.e., first input data), teaches what is required under the broadest reasonable interpretation),
i. the […] determining whether […] stop criteria are met that depend on the updated input data, the stop criteria comprising presence or absence of new findings and/or exhaustion of guideline-recommended reporting steps; and j. the processor I. upon determining that the stop criteria are not met, performing a further iteration of the iterative loop in which steps […] are repeated […] for the further iteration, thereby re-selecting a report module and an IA algorithm for the further iteration (Xu: Figure 1, paragraphs [0041]-[0042], “Steps 111-117 are repeated until the physician describes all the findings in the image. The physician may indicate completion of the diagnosis task in step 118 by, for example, selecting an associated user interface element. Then, the report generating software 120 collects all the filled templates, and automatically converts them into a medical report, as in step 121”. The Examiner notes the computer decides to repeat until a user element is selected, which teaches what is required under the broadest reasonable interpretation), or
II. upon determining that the stop criteria are met, terminating and exiting the iterative loop, and automatically generating the medical report based on the report module selected, included, and filled in during execution of the iterative loop ((Xu: Figure 1, paragraphs [0041]-[0042], “The physician may indicate completion of the diagnosis task in step 118 by, for example, selecting an associated user interface element. Then, the report generating software 120 collects all the filled templates, and automatically converts them into a medical report, as in step 121”. The Examiner notes a decision is made to either repeat for all findings or generate a report which teaches what is required of the deciding under the broadest reasonable interpretation).
Xu may not explicitly teach (Underlined below for clarity):
a. receiving, by a computer processor, first medical data of a patient comprising medical image data of the patient and additional available information related to at least one of the medical image data or the patient,
j. the processor I. upon determining that the stop criteria are not met, performing a further iteration of the iterative loop in which steps c. to j. are repeated using the updated input data as the input data for the further iteration, thereby re-selecting a report module and an IA algorithm for the further iteration, or II. upon determining that the stop criteria are met, terminating and exiting the iterative loop, and automatically generating the medical report based on the report module selected, included, and filled in during execution of the iterative loop.
He teaches a. receiving, by a computer processor, first medical data of a patient comprising medical image data of the patient and additional available information related to at least one of the medical image data or the patient (He: Figures 1-3, 12, paragraph [0118]-[0119], “planar medical imaging data 30a or volumetric medical imaging data 40a may be received”, paragraph [0179], “access to medical imaging data 30”),
j. the processor I. upon determining that the stop criteria are not met, performing a further iteration of the iterative loop in which steps c. to j. are repeated using the updated input data as the input data for the further iteration, thereby re-selecting a report module and an IA algorithm for the further iteration, or II. upon determining that the stop criteria are met, terminating and exiting the iterative loop, and automatically generating the medical report based on the report module selected, included, and filled in during execution of the iterative loop (He: Figures 1-2, 9-10, paragraphs [0073]-[0077], “a medical imaging interaction 34 with the artificial intelligence engine to mask or revise medical imaging 30, or a feature assessment interaction 35 to vary relevancy thresholds for the features of medical imaging 40 or hypothesize alternative feature assessments 31 of medical imaging 30… the clinician may select prediction/classification y via the GUI to see the features that are most relevant to prediction/classification y of medical imaging 30 as visualized by salient image(s) 31 illustrative of prediction/classification y of medical imaging 30”, paragraphs [0082]-[0084], “trained to render a feature assessment of medical imaging data 30a/30b for one or more particular medical procedures or one or more particular type of medical imaging machines whereby the machine learning model 43 may be further trained for one or more particular types of patient… AI engine 40 may include […] a plurality of similar and/or dissimilar machine learning models”, paragraphs [0085]-[0092], “salient image manager 43 directs data pre-processor 41 to perform a data masking of planar medical image data 30a or volumetric medical image data 30b as directed by data masking specification 34a as previously described for salient manipulation stage S26 of FIG. 1, or a data revision of planar medical image data 30a or volumetric medical image data 30b as directed by data masking specification 34a as previously described for salient manipulation stage S26 of FIG. 1. Subsequently, stage S144 is repeated to render a new feature assessment 31a based on the data masking or the data revisions, and stage S146 is repeated to generate new salient image data 36a representative of the relevance of each feature of masked/revised medical imaging data 30a/30b to the new feature assessment 31a”. The Examiner notes the steps repeated include pre-processing, which in combination with the repeated steps of Xu teach repeating all of claimed steps under the broadest reasonable interpretation).
One of ordinary skill in the art before the effective filing date would have found it obvious to include generating and using updated first data to be used in the interactive loop as taught by He within the interactive loop as taught by Xu with the motivation of improving clinician trust (He: paragraphs [0002]-[0004]).
Xu and He may not explicitly teach (Underlined below for clarity):
c. the processor selecting from a collection of report modules which have a hierarchical structure a report module and including the selected module in the structured medical report, wherein the at least one report module has a hierarchical nature and wherein the selection is made based on the input data,
Liu teaches c. the processor selecting from a collection of report modules which have a hierarchical structure a report module and including the selected module in the structured medical report, wherein the at least one report module has a hierarchical nature and wherein the selection is made based on the input data (Liu: paragraph [0032], “the Report Template 212 is presented by the Template Designer 210 as a hierarchical collection of nodes referred to as a decision tree, wherein each node of the Report Template 212 represents a group/classification of related items or specific alternatives regarding observations of the subject of the report”, paragraph [0042], “the Report Template 212 is represented as a hierarchical model of decision tree nodes that may be selected by the application user”),
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a hierarchical natured reporting module as taught by Liu with the use of report modules to generate a report as taught by Xu and He with the motivation of “providing an improved technique that allows users to generate written reports” (Liu: paragraph [0009]).
Xu, He and Liu may not explicitly teach (Underlined below for clarity):
b. providing the first medical data in machine-readable form a input data of an iterative loop algorithm for automatically selecting, launching and orchestrating image-analysis (IA) algorithms through iterative, dynamic adjustment of building blocks of the structured medical report and by influencing the results of a first IA algorithm based on results of a second IA algorithm, and executing, by the processor, the IA algorithm in an automated manner wherein each iteration of the iterative loop algorithm includes the steps of:
i. the processor determining whether machine-readable stop criteria are met that depend on the updated input data, the stop criteria comprising presence or absence of new findings and/or exhaustion of guideline-recommended reporting steps;
Bogonoi teaches b. providing the first medical data in machine-readable form a input data of an iterative loop algorithm for automatically selecting, launching and orchestrating image-analysis (IA) algorithms through iterative, dynamic adjustment of building blocks of the structured medical report and by influencing the results of a first IA algorithm based on results of a second IA algorithm, and executing, by the processor, the IA algorithm in an automated manner (Bogonoi: paragraph [0063], “Selective reconstruction may be performed using techniques, including but not limited to, iterative reconstruction, filtered back projection, etc. The selective reconstruction may be performed to zoom-in on or target the identified region of interest instead of capturing the entire width or length of the patient's body. The identified region of interest may occupy a substantially larger area or volume in the resulting second image than the first image (i.e. reconstructed at a higher spatial resolution) … Another iteration of steps 404 and 406 may result in a third set of images that focus on multiple locations of the kidney where lesions are automatically or manually specified”, paragraph [0068], “Each of the selectively reconstructed images may be iteratively analyzed to better identify candidate locations or refine the region of interest. In the context of a CAD system, such iterations may greatly improve the confidence and classification of the list of candidates and yield a reduction in false positives”) wherein each iteration of the iterative loop algorithm includes the steps of:
i. the processor determining whether machine-readable stop criteria are met that depend on the updated input data, the stop criteria comprising presence or absence of new findings and/or exhaustion of guideline-recommended reporting steps (Bogonoi: paragraph [0078], “until a stop criterion is satisfied, upon which step 4 is then performed”, paragraph [0084], “reconstruction engine 30… Steps 2 and 3 may then be iterated to further refine the identification (e.g., segmentation) of the anatomical structure or localization of smaller regions of interest (e.g., suspicious candidates) within the anatomical structure based on the reconstructed images 1310 until a stop criterion is satisfied, upon which step 4 is then performed”. This criterion check is performed by a computer);
One of ordinary skill in the art before the effective filing date would have found it obvious to use a computer based criterion and iteratively using results of previous iterations to influence the results of future iterations as taught by Bogonoi within the iterative image processing as taught by Xu, He and Liu with the motivation of “such iterations may greatly improve the confidence and classification of the list of candidates and yield a reduction in false positives” (Bogonoi: paragraph [0068]).
Regarding (Previously Presented) claim 4, Xu, He, Liu and Bogonoi teach the limitations of claim 1, and further teach wherein the at least one report module comprises decision tree elements each of which corresponds to a clinical reporting task (Liu: paragraph [0032], “the Report Template 212 is presented by the Template Designer 210 as a hierarchical collection of nodes referred to as a decision tree, wherein each node of the Report Template 212 represents a group/classification of related items or specific alternatives regarding observations of the subject of the report”, paragraph [0042], “the Report Template 212 is represented as a hierarchical model of decision tree nodes that may be selected by the application user.”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 5, Xu, He, Liu and Bogonoi teach the limitations of claim 1, and further teaches wherein the collection of IA-algorithms comprises at least one of computer-aided detection (CADe) algorithms or computer-aided diagnosis (CADx) algorithms (Xu: paragraphs [0030]-[0033], “Computer-aided diagnosis: a computer-aided diagnosis refers to the separation of locations of interest for the diagnostic purpose”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 6, Xu, He, Liu and Bogonoi teach the limitations of claim 1, and further teaches wherein filling in the selected at least one report module is additionally based on at least one of one of the first input data or user input (Xu: Figures 1, 6-8, paragraphs [0009]-[0010], “the templates selected are also preferably prefilled with information from the computer-aided diagnosis for saving time. The physician can edit or confirm the template with the pre-filled text… there are preferably entries and options for describing a finding, and the physician can preferably fill in the template by one or more types of human-machine interactions such as mouse clicks, screen taps, typing, speaking into a microphone, and/or dragging with a mouse or touch gesture. Once the physician finishes describing all the findings, the report generating software converts the filled templates into a medical report”, paragraphs [0041], “before displaying the selected template, it is first prefilled according to the physician's preset preferences (if any), and/or based on any computer-aided diagnosis”, paragraphs [0052]-[0054], “The template window 600 is pre-filled based on the computer-aided diagnosis generated by the pre-processing software”. The filled in template (i.e., selected report module) contains data, based user input data).
The motivation to combine is the same as in claim 1, incorporated herein.
REGARDING CLAIM(S) 8 and 15
Claim(s) 8 and 15 is analogous to Claim(s) 1, thus Claim(s) 8 and 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
REGARDING CLAIM(S) 10-13 and 17-20
Claim(s) 10-13 and 17-20 are analogous to Claim(s) 3-6, thus Claim(s) 10-13 and 17-20 are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3-6.
Response to Arguments
Applicant's arguments filed on 27 January 2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed on 27 January 2026.
Rejections under 35 U.S.C. § 101
Regarding the rejection of claims 1-21, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
In view of the recent updates in the Office's policy for examining and interpreting claims under 35 U.S.C. § 101 (see above) and in view of the amendments to Applicant's claims, Applicant respectfully submits that the Office's analysis and allegations are moot and that the additional elements of the claims are sufficient to render the claims patent-eligible…. As noted in Applicant's previous response, Applicant's disclosure sets forth these improvements and explicitly characterizes these improvements as resulting from the hierarchical nature of the report module… Applicant's disclosure at ,i [0054]… By improving memory usage and system performance, the claims result in the improved functioning of a computer… By providing for report elements whose meaning is unambiguous, the claims result in improved medical reports… implements the iterative loop algorithm (e.g., reduced memory usage, improved system performance). So long as the claims reflect the disclosed improvement, then, the claims are patenteligible… As such, Applicant's claims reflect the disclosed improvement. Accordingly, the claims provide a technical improvement sufficient to integrate the alleged judicial exception into a practical application thereof ( at Step 2A, Prong One) and to amount to significantly more than the alleged judicial exception itself (at Step 2B).
The Examiner respectfully disagrees.
It is respectfully submitted, none of the claimed additional elements, provide any of the alleged technical improvements argued by the Applicant. With respect to paragraphs [0014], [0054]–[0056], the paragraphs at best describe improvements to a manual time-intensive human activity not an improvement in the functionality of a computer, the section provides no details, as to how any of the additional elements are actually providing any such improvement, instead at best this is merely application of the abstract idea on the generic computer components which are not particular, they are generic off-the shelf hardware (see Applicant’s Specification Figure 3, page 13), and which as stated in 2106.05(f)(2) “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA). Applicants claimed additional elements are generic high-level well-known algorithms and processes that one of ordinary skill in the art may find improves upon the human activity of problem of generating a medical report (i.e., an abstract idea), nevertheless an improved abstract idea is still an abstract idea, as the claims as drafted do not recite any additional elements that improve the performance of the computer nor do they recite technical solutions to a technical problem recited in Applicant’s specification, as such the argument is unpersuasive.
Rejections under 35 U.S.C. § 103
Regarding the rejection of claims 1, 3-8, 10-15 and 17--21, the Examiner has considered the applicant’s arguments; however, the arguments are not persuasive as addressed herein. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons:
Applicant argues:
Applicant respectfully submits that the combined teachings of the cited references do not disclose or suggest several of the features recited in Applicant's claims. As will be discussed in more detail below, the combination of Xu with He, Liu, and Bogonoi does not provide a prima facie case of unpatentability for the claimed inventio… The claimed invention recites an iterative algorithm in which an IA-algorithm and a report module a re-selected in each iteration… Thus, Xu lacks the claimed re-selection of an IA-algorithm per iteration. Importantly, Xu does not even disclose a step of an automated selection of an IA algorithm by a computer at all. Thus, even if the pre-processing of Xu was to be repeated… With respect to He: He does not perform the claimed re-selection in each iteration either. Rather, He's repeated stages are data pre-processing, feature assessment, and salient image generation… Even if, for the sake of argument, the template selection discussed in Xu was to be considered a selection of a report module, this selection is performed by the physician and not by the computer in an automated manner… The claimed invention recites the automated iteration over steps c to j based on machine-readable stop criteria depending on the updated input data… Xu's repetition is explicitly user-driven and terminated by the user… Bogonoi uses stop criteria; however, this use is limited to selective reconstruction, not for reporting workflows nor for datasets updated with second medical data… The claimed invention recites that the processor derives second medical data (finding, segmentation, measurement, classification, risk score, longitudinal descriptor) from IA outputs and generates "updated input data" by adding/replacing with this second medical data… Xu does not disclose deriving "second medical data" and folding it back into the dataset to create "updated input data" that governs automated loop control. It is noted that the mask overlays discussed by Xu (Xu et al., paragraph [0046]) equal the claimed dataset augmentation to be used as input data in further iterations… 1. Automated repetition that uses the updated input data to re-select both the report module and the IA algorithm for the further iteration (steps c, d, and j .I), and automated termination with report generation upon completion (step j .11). 2. Machine-evaluated stop criteria that depend on the updated input data (step i), comprising presence/absence of new findings and/or exhaustion of guideline-recommended steps. 3. Deriving second medical data from IA output data and generating "updated input data" by adding/replacing with the second medical data (steps g/h).
The Examiner respectfully disagrees.
It is respectfully submitted, the argued limitations are taught by the combination of Bogonoi and He within Xu, in particular Xu explicitly teaches an iterative loop that uses an IA algorithm in each loop, and teaches selection of a model for the loop in a preprocessing step (see above but at least paragraphs [0032]-[0033] and [0043]-[0043]), one of ordinary skill in the art would find it prima facie obvious to include a preprocessing step in the iterative loop and using the generated data in combination with the original as taught by He within the iterative loop as taught by Xu with the motivation of improving clinical trust (He: paragraph [0003]). Additionally, Bogonoi explicitly teaches analyzing images (i.e., a first IA algorithm) and iteratively reanalyzing the images by zooming in on selected regions (i.e., a second IA algorithm), which in combination with the teachings of Xu and He teach what is required of the claim under the broadest reasonable interpretation.
In addition, the Examiner respectfully notes that the cited reference was never applied as a reference under 35 U.S.C. 102 against the pending claims. As such, the Examiner respectfully submits that the issue at hand is not whether the applied prior art specifically teaches the claimed features, per se, but rather, whether or not the prior art, when taken in combination with the knowledge of average skill in the art, would put the artisan in possession of these features. Regarding this issue, it is well established that references are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969). The issue of obviousness is not determined by what the references expressly state but by what they would reasonably suggest to one of ordinary skill in the art, as supported by decisions in In re DeLisle 406 Fed 1326, 160 USPQ 806; In re Kell, Terry and Davies 208 USPQ 871; and In re Fine, 837 F.2d 1071, 1074, 5 USPQ 2d 1596, 1598 (Fed. Cir. 1988) (citing In re Lalu, 747 F.2d 703, 705, 223 USPQ 1257, 1258 (Fed. Cir. 1988)). Further, it was determined in In re Lamberti et al, 192 USPQ 278 (CCPA) that:
(i) obviousness does not require absolute predictability;
(ii) non-preferred embodiments of prior art must also be considered; and
(iii) the question is not express teaching of references, but what they would suggest.
According to In re Jacoby, 135 USPQ 317 (CCPA 1962), the skilled artisan is presumed to know something more about the art than only what is disclosed in the applied references. In In re Bode, 193 USPQ 12 (CCPA 1977), every reference relies to some extent on knowledge of persons skilled in the art to complement that which is disclosed therein.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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