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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Acknowledgement is made of applicant’s claim for foreign priority to 19 October 2020 under 35 U.S.C. 119(a)-(d).
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 25 February 2026 has been entered.
Response to Amendment
Claims 20-34 & 36-38 were previously pending in this application. The amendment filed 09 June 2025 has been entered and the following has occurred: Claim 20 has been amended. No claims have been cancelled. Claims 39-40 have been added.
Claims 20-34 & 36-40 remain pending in the application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 25 February 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the Examiner in this Office Action.
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 20-34 & 36-40 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.
The claims recite subject matter within a statutory category as a process (claims 20-34 & 39-40), machine (claims 36), and manufacture (claims 37-38) which recite steps of:
selecting, in response to obtaining medical image-related data of the patient, from a plurality of program modules, at least one program module having an input requirement matching the medical image-related data of the patient;
using the at least one program module selected in step (a) to obtain a medical finding based on the medical image-related data of the patient;
selecting, from the plurality of program modules, at least one program module having an input requirement matching the obtained medical finding;
using the at least one program module selected in step (c) to obtain a medical finding based on the previously obtained medical finding; and
generating the medical report of the patient, the medical report comprising at least one of the obtained medical findings,
wherein the method further comprises:
storing each of the obtained medical findings as a node of a graph in a graph database, wherein
the graph stores medical findings of the patient for whom the medical report is generated, wherein
the medical report is generated based on the graph, and wherein the program modules infer new parts of the graph based on other parts of the graph, thereby adding a new node and edge to the graph; wherein
upon adding the new node to the graph, the computer system queries a module database based on the current status of the graph, receives a list comprising at least one program module having an input requirement matching an input requirement specified by the added new node, selects the at least one program module, and automatically triggers execution of the selected program module.
These steps of selecting a plurality of program modules having an input requirement matching the medical image-related data of the patient, using the at least one program module selected to obtain a medical finding, selecting at least one program having an input requirement matching the obtained medical finding, using the at least one program module selected to obtain a medical finding based on the previously obtained medical finding, generating a medical report of the patient comprising the obtained medical findings, and selecting a program module based on user input requirements for respective program modules, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. MPEP 2106.04(a)(2)(II) describes various methods of organizing human activity, such as fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The steps recited above amount to managing personal behavior or relationships or interactions between people such as following rules or instructions. More specifically, the steps recited above set forth instructions for a user to follow for generating a medical report of a patient, such as selecting a plurality of program modules having an input requirement matching the medical image-related data of the patient, using the at least one program module selected to obtain a medical finding, selecting at least one program having an input requirement matching the obtained medical finding, using the at least one program module selected to obtain a medical finding based on the previously obtained medical finding, and generating a medical report of the patient comprising the obtained medical findings, and merely utilizing a computer or computer program based on various user input. Even further, the steps at least set forth efforts of managing the behavior of a user for generating a medical report of a patient by the system effectively managing the typical behaviors or findings regarding outputting a medical report for a patient. Therefore these steps, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 21-34 & 38-40, reciting particular aspects of how selecting a program module, determining medical findings, and generating a medical report may be performed in the mind but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of a program module(s), at least one processor, at least one memory, a non-transitory computer program product, computer readable recording media amounts to invoking computers as a tool to perform the abstract idea, see Applicant’s associated Patent Application Publication (US2023/0386629) Par [0095] for program module(s); Par [0071] for a processor; Par [0072] for a memory; Par [0057] for a non-transitory computer program product; Par [0057] for a computer readable recording media, see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation of obtaining medical image-related data of a patient, obtaining medical findings based on the received medical image-related data of the patient, obtaining a medical finding based on previously obtained medical finding, storing each of the obtained medical findings as a node of a graph in a graph database and for the medical report generated, receiving a list comprising at least one program module having an input requirement matching an input requirement specified by the added new node, amounts to mere data gathering; recitation of selecting at least one program module matching the medical image-related data of the patient, determining a medical finding based on medical image-relate data, determining a medical finding based on previously obtained medical findings, and determining/creating a medical report of the patient comprising the one or more medical findings, generating a medical report based on the graph, infer new parts of the graph based on other parts of the graph amounts to selecting a particular data source or type of data to be manipulated; recitation of generating/outputting a medical report comprising at least one or more obtained medical findings, inferring new parts of the graph based on the other parts of the graph and medical findings, storing one or more data, findings, etc., such as in a medical report or memory, selecting at least one program module, triggering execution of the selected program module amounts to insignificant application, see MPEP 2106.05(g));
generally link the abstract idea to a particular technological environment or field of use (such as recitation of the steps being generally applied to medical imaging data/reports, and/or implementation of one or more program modules, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 21-34 & 38-40, which recite limitations relating to a program module, artificial intelligence modules, user interface modules, and a memory, additional limitations which amount to invoking computers as a tool to perform the abstract idea, see Applicant’s associated Patent Application Publication (US2023/0386629) [0095] for one or more program module; [0023] for artificial intelligence (AI) modules; [0047] for user interface (UI) modules; [0072] for a memory, see MPEP 2106.05(f); claims 21, 23-24, 28, & 38, which recite limitations relating to obtaining medical image-related data of the patient, including a region of interest, a property of the ROI, and/or a medical finding, inputting a predetermined subset of obtained medical findings or all of the obtained medical findings, obtaining different types of medical findings, requesting a user input defining the medical finding, storing each of the obtained medical findings as a node of a graph in a graph database, storing one or more computer readable recording media and data/a computer program product recorded thereon, additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claims 22, 25, 27, & 30-34, which recite limitations relating to performing the actions of selection of at least one program module and using the program module at least once, determining the medical finding based on medical image-related data, and based on previously obtained medical findings, using an ensemble of AI modules to obtain a medical finding, training an artificial intelligence module with medical findings obtained using the one or more UI modules, displaying a visualization of the medical finding, selection of the at least one program modules by using an AI, additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated; claims 26, 28-29, 33, & 38-40, which recite limitations relating to the program modules being one or more generic AI or UI modules and/or said modules being automatically triggered or executed at the same time and/or the computer program product being stored on one or more computer readable recording media additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as obtaining medical image-related data of a patient, obtaining medical findings based on the received medical image-related data of the patient, obtaining a medical finding based on previously obtained medical finding, storing obtained medical findings as a node of a graph in a graph database, receives a list comprising at least one program module having an input requirement matching an input requirement specified by the added new node, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); selecting at least one program module matching the medical image-related data of the patient, determining a medical finding based on medical image-relate data, determining a medical finding based on previously obtained medical findings, using one or more program modules to obtain a medical finding and determining/creating a medical report of the patient comprising the one or more medical findings, inferring new parts of the graph based on the other parts of the graph and medical findings, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); generating and maintaining one or more medical reports of the patient comprising one or more medical findings, which under BRI, includes generating an electronic record for patient medical recordkeeping purposes, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing one or more obtained medical data/findings of the patient, storing one or more program modules, storing a medical report of the patient, storing each of the obtained medical findings as a node of a graph in a graph database, the graph stores medical findings of the patient for whom the medical report is generated, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); obtaining a medical finding based on previously obtained medical findings, which under BRI, includes determining previously obtained medical findings from electronic scanning or parsing of patient documents/medical records, storing each of obtained medical findings as a node of a graph in a graph database, wherein the medical report is generated based on the graph, the graph stores medical findings of the patient for whom the medical report is generated, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v); using one or more program modules to obtain a medical finding, which under BRI includes simple selection/operation of a program module or UI for generating the medical finding, selecting at least one program module and triggering execution of the selected program module, e.g., a web browser’s back and forward button functionality, Internet Patent Corp., MPEP 2106.05(d)(II)(ii)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 21-34 & 38-40, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields; claims 21, 23-24, & 28, which recite limitations relating to obtaining medical image-related data of the patient, including a region of interest, a property of the ROI, and/or a medical finding, inputting a predetermined subset of obtained medical findings or all of the obtained medical findings, obtaining different types of medical findings, requesting a user input defining the medical finding, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 22, 25, 27, & 30-34, which recite limitations relating to performing the actions of selection of at least one program module and using the program module at least once, determining the medical finding based on medical image-related data, and based on previously obtained medical findings, using an ensemble of AI modules to obtain a medical finding, training an artificial intelligence module with medical findings obtained using the one or more UI modules, displaying a visualization of the medical finding, selection of the at least one program modules by using an AI, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 21-34 & 38-40, which recite limitations relating to storing computerized instructions for performing a computerized method, storing one or more program modules, storing medical findings and a graph in a database, storing one or more computer program products for execution simultaneously and/or automatically triggered, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); claims 23, 25, 27, 30-32, & 34, which recites limitations relating to obtaining medical findings, which under BRI, could include electronically scanning or extracting said findings from medical records or previous medical reports, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v); claims 23, 28, & 40, which recite limitations relating to receiving one or more user inputs, such as selection at a user interface or button means, executing one or more program modules, e.g., a web browser’s back and forward button functionality, Internet Patent Corp., MPEP 2106.05(d)(II)(ii)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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.
Claims 20-34 & 36-40 are rejected under 35 U.S.C. 103 as being unpatentable by Tao et al. (U.S. Patent Publication No. 2020/0303062), hereinafter “Tao”, in view of Sorenson et al. (U.S. Patent Publication No. 2019/0392943), hereinafter “Sorenson”.
Claim 20 –
Regarding Claim 20, Tao discloses a computer-implemented method of generating a medical report of a patient (See Tao Par [0043] which discloses a medical image aided diagnosis system that includes various computerized modules/programs for performing image recognition and/or report generation), the method comprising:
(a) selecting in response to obtaining medical image-related data of the patient, from a plurality of program modules, at least one program module having an input requirement matching the medical image-related data of the patient (See Tao Par [0164] which discloses one or more modules to be employed, and specifically discloses the use of a knowledge graph establishment module that is configured to establish an image semantic representation knowledge graph according to a standardized dictionary library in the field of images and historical accumulated medical image report analysis, thereby requiring input that matches medical image-related data of the patient to populate said database/library; furthermore, see Tao Par [0158]-[0162] which discloses upon adding a new type of focus to the knowledge graph described throughout, along with the attributes and the corresponding focus local image, will be recorded and added into a temporary focus image library of the new type of focus, and submitted to other experts for cross-validation, i.e. input requirement for the program module to proceed, and upon said input requirement being artificially confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample );
(b) using the at least one program module selected in step (a) to obtain a medical finding based on the medical image-related data of the patient (See Tao Par [0164] which discloses using one or more of the plurality of modules in order to generate a structured report related to the ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation);
(c) selecting, from the plurality of program modules, at least one program module having an input requirement matching the obtained medical finding (See Tao Par [0164] which discloses one or more modules to be employed, and specifically discloses the use of a knowledge graph establishment module that is configured to establish an image semantic representation knowledge graph according to a standardized dictionary library in the field of images and historical accumulated medical image report analysis, thereby requiring input that matches medical image-related data of the patient to populate said database/library);
(d) using the at least one program module selected in step (c) to obtain a medical finding based on the previously obtained medical finding (See Tao Par [0164] which discloses one or more modules to be employed, and specifically discloses the use of a knowledge graph establishment module that is configured to establish an image semantic representation knowledge graph according to a standardized dictionary library in the field of images and historical accumulated medical image report analysis, thereby requiring input that matches medical image-related data of the patient to populate said database/library; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample); and
(e) generating the medical report of the patient, the medical report comprising at least one of the obtained medical findings (See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to the ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample), wherein,
the method further comprises:
storing each of the obtained medical findings as a node of a graph in a graph database (See Tao Par [0164] which discloses one or more modules to be employed, and specifically discloses the use of a knowledge graph establishment module that is configured to establish an image semantic representation knowledge graph according to a standardized dictionary library in the field of images and historical accumulated medical image report analysis, thereby requiring input that matches medical image-related data of the patient to populate said database/library; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample), wherein the graph stores medical findings of the patient for whom the medical report is generated (See Tao Par [0156], [0160], [0162], & [0165] which discloses a new discovered medical focus/discovery, i.e. medical finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample; See Tao Par [0157] & [0164] which discloses a report generation module configured to generate a structured report related to the ROI/medical focus/discovery, i.e. medical finding of the medical image of the patient);
wherein the medical report is generated based on the graph (While not a “node” of the graph per se, see Tao Par [0078]-[0080] which discloses characteristic description text specification for each named entity obtained being transformed into the image semantics representation based on the expert knowledge, and then the image semantic representation knowledge graph of the medical image is created by each named entity, in cooperation with the image and the image semantics representation corresponding to the named entity, such that the image semantics representation for the associated knowledge graph represents a structured description of text and data including labels corresponding to image spatial attributes, gray distributions and texture structure descriptions and relations therebetween), wherein
the program modules infer new parts of the graph based on other parts of the graph, thereby adding a new node and edge to the graph (See Tao Par [0159]-[0162] which discloses a new discovery being artificially confirmed (i.e. by the software/program modules)and subsequently, the corresponding new knowledge region being added to the image semantic representation knowledge graph, and Tao Par [0163] specifically states that the system itself may generate similar samples based on the research and other knowledge of such new samples by performed learning), wherein
upon adding the new node to the graph, the computer system queries a module database based on the current status of the graph, receives a list comprising at least one program module having an input requirement matching an input requirement specified by the added new node, selects the at least one program module, and automatically triggers execution of the selected program module (While not “nodes” per se, see Tao Par [0158]-[0162] which discloses upon adding a new type of focus to the knowledge graph described throughout, along with the attributes and the corresponding focus local image, will be recorded and added into a temporary focus image library of the new type of focus, and submitted to other experts for cross-validation, i.e. input requirement for the program module to proceed, and upon said input requirement being artificially confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample, i.e. the program module for adding the focus to the knowledge graph is executed after the input requirement is satisfied, albeit not recited for explicit “selection” of said module following these steps).
While Tao generally discloses a program module for adding the focus to the knowledge graph being executed after an input requirement is satisfied, Tao does not recite explicit selection of said program module from a plurality of program modules following the input requirement.
However, Sorenson discloses selection of said program module from a plurality of program modules following the input requirement (See Sorenson Par [0227] which discloses an engine or engine of engines, such that the engine or engine of engines, i.e. program modules, determines the organs, body parts or even features found in the body parts or organs and even classifiers of these features, i.e. input requirements for determination of appropriate program module to choose, and uses this information to select pertinent other engines that can be run on all of these, and combinations of the images to provide precision-application of engines to image data; See also Sorenson Par [0245] which discloses machine learning efforts for selecting one or more optimized workflows, to improve display protocols and/or application of one or more modules). The disclosure of Sorenson is directly applicable to the disclosure of Tao, because both disclosures share limitations and capabilities, such as being directed towards analysis of medical images for medical findings, anomalies, or other determinations.
It would have been obvious to one of ordinary skill in the art to modify the disclosure of Tao regarding a program module for adding the focus to the knowledge graph being executed after an input requirement is satisfied, to further include selection of said program module from a plurality of program modules following the input requirement, because this allows for certain input information to be used in order to select specifically-pertinent engines/programs to provide precision-application of engines/programs to image data based on attributes/characteristics of the image data (See Sorenson Par [0227]).
Claim 21 –
Regarding Claim 21, Tao and Sorenson disclose the computer-implemented method of Claim 20 in its entirety. Tao further discloses a computer-implemented method, wherein:
the medical image-related data of the patient comprises at least one of a medical image of the patient, a region of interest, ROI, in the medical image of the patient (See Tao Par [0164] which discloses one or more modules to be employed, and specifically discloses the use of a region of interest determination module configured to determine an ROI of the medical image of the patient; See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to an ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation), a property of the ROI (See Tao Par [0077]-[0079] which discloses the medical image report containing state descriptions of various organs and some local focus descriptions, such that the report clearly describes the position, nature and grade of a lesion, i.e. ROI, in an image, and can further include spatial attributes, gray distributions and texture structure descriptions, thereby forming the image semantic representation knowledge graph in a medical ontology involved in an image report), and a medical finding derived from the medical image of the patient (See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to an ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample).
Claim 22 –
Regarding Claim 22, Tao and Sorenson disclose the computer-implemented method of Claim 20 in its entirety. Tao further discloses a computer-implemented method, wherein:
steps (c) and (d) are repeated at least once prior to generating the medical report in step (e) (See MPEP 2144.05(II)(A) which discloses optimizations within prior art conditions or through routine experimentation, where the conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation; therefore, by Tao Par [0164] disclosing the use of a knowledge graph establishment module that is configured to establish an image semantic representation knowledge graph according to a standardized dictionary library in the field of images and historical accumulated medical image report analysis, thereby requiring input that matches medical image-related data of the patient to populate said database/library and Tao Par [0160] & [0162] disclosing a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample, and as seen in Tao Fig. 1 that these steps are a loop, i.e. iterative, as indicated by the feedback arrow, the steps are indeed repeated and MPEP 2144.05(II)(A) describing that specifying that the steps are repeated at least once is not inventive, this claim is considered met in its entirety by Tao).
Claim 23 –
Regarding Claim 23, Tao and Sorenson disclose the computer-implemented method of Claim 22 in its entirety. Tao further discloses a computer-implemented method, wherein:
the at least one program module selected in step (c) has an input requirement matching a predetermined subset of the obtained medical findings or all of the obtained medical findings (See Tao Par [0164] which discloses one or more modules to be employed, and specifically discloses the use of a knowledge graph establishment module that is configured to establish an image semantic representation knowledge graph according to a standardized dictionary library in the field of images and historical accumulated medical image report analysis, thereby requiring input that matches medical image-related data of the patient to populate said database/library; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample; See Tao Par [0128] which discloses the system calculating a matching degree between the ROI and the known focus type to derive a possible focus or lesion region corresponding to the ROI, i.e. the ROI matches previously obtained medical findings to a degree acceptable enough to categorize the ROI as a possible focus or lesion region).
Claim 24 –
Regarding Claim 24, Tao and Sorenson disclose the computer-implemented method of Claim 20 in its entirety. Tao further discloses a computer-implemented method, wherein:
some or all of the selected at least one program module are used to obtain different types of medical findings (See Tao Par [0164] which discloses one or more modules to be employed, and specifically discloses the use of a knowledge graph establishment module that is configured to establish an image semantic representation knowledge graph according to a standardized dictionary library in the field of images and historical accumulated medical image report analysis, thereby requiring input that matches medical image-related data of the patient to populate said database/library; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample).
Claim 25 –
Regarding Claim 25, Tao and Sorenson disclose the computer-implemented method of Claim 20 in its entirety. Tao further discloses a computer-implemented method, wherein:
one or more of the at least one program module selected in step (a) are configured to autonomously determine the medical finding based on the obtained medical image-related data (See Tao Par [0164] which discloses one or more modules to be employed, and specifically discloses the use of a knowledge graph establishment module that is configured to establish an image semantic representation knowledge graph according to a standardized dictionary library in the field of images and historical accumulated medical image report analysis, thereby requiring input that matches medical image-related data of the patient to populate said database/library; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample; See Tao Par [0128] which discloses the system calculating a matching degree between the ROI and the known focus type to derive a possible focus or lesion region corresponding to the ROI, i.e. the ROI matches previously obtained medical findings to a degree acceptable enough to categorize the ROI as a possible focus or lesion region; See Tao Par [0165]); and/or
one or more of the at least one program module selected in step (c) are configured to autonomously determine the medical finding based on the previously obtained medical finding (See Tao Par [0164] which discloses one or more modules to be employed, and specifically discloses the use of a knowledge graph establishment module that is configured to establish an image semantic representation knowledge graph according to a standardized dictionary library in the field of images and historical accumulated medical image report analysis, thereby requiring input that matches medical image-related data of the patient to populate said database/library; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample; See Tao Par [0128] which discloses the system calculating a matching degree between the ROI and the known focus type to derive a possible focus or lesion region corresponding to the ROI, i.e. the ROI matches previously obtained medical findings to a degree acceptable enough to categorize the ROI as a possible focus or lesion region).
Claim 26 –
Regarding Claim 26, Tao and Sorenson disclose the computer-implemented method of Claim 25 in its entirety. Tao further discloses a computer-implemented method, wherein:
the one or more of the at least one selected program module is an artificial intelligence, AI, module (See Tao Par [0131]-[0132] which discloses an image semantic representation knowledge graph and a variety of machine learning, deep learning, and reinforcement learning, being combined to perform medical image recognition, which are all understood to be species of artificial intelligence, such that ROI, focus type description/determination, and lesion region determination are automatically determined, see MPEP 2131.02(I), which states a species will anticipate a claim to a genus).
Claim 27 –
Regarding Claim 27, Tao and Sorenson disclose the computer-implemented method of Claim 26 in its entirety. Tao further discloses a computer-implemented method, wherein:
if a plurality of AI modules providing a same type of medical finding are selected in step (a) or (c), combining the plurality of AI modules in an ensemble and using the ensemble to obtain the medical finding (See Tao Par [0131]-[0132] which discloses an image semantic representation knowledge graph and a variety of machine learning, deep learning, and reinforcement learning, being combined to perform medical image recognition, which are all understood to be species of artificial intelligence, such that ROI, focus type description/determination, and lesion region determination are automatically determined, see MPEP 2131.02(I), which states a species will anticipate a claim to a genus; See Tao Par [0164]-[0165] which discloses one or more modules for generating a medical finding and associated report for the medical finding, such that one or more AI or trained ML modules are utilized to produce various aspects for the report, such as the lesion region determination module including a focus type determination unit and a lesion region determination unit, i.e. one or more modules being combined into an ensemble to obtain the medical finding; See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to the ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample).
Claim 28 –
Regarding Claim 28, Tao and Sorenson disclose the computer-implemented method of Claim 20 in its entirety. Tao further discloses a computer-implemented method, wherein:
one or more of the at least one program module selected in step (a) are user interface, UI, modules that, in step (b), request a user input defining the medical finding (See Tao Par [0042] which discloses the image semantic representation corresponding to the ROI is input and send to other experts for verification when a candidate focus does not match the ROI; See Tao Par [0157]-[0159] which discloses an expert needing to manually input and send the image semantic representation corresponding to the ROI for verification when there is no matching candidate focus option for the ROI, i.e. that there is no matching semantic representation/algorithm step to proceed the automated diagnosis due to the ROI not being automatically recognized, and thereby requiring UI input from an expert; See Tao Par [0119] which specifically discloses an expert manually clicking on a computer or through an image recognition algorithm, i.e. UI, for the determination of the ROI); and/or
one or more of the at least one program module selected in step (c) are user interface, UI, modules that, in step (d), request a user input defining the medical finding (See Tao Par [0042] which discloses the image semantic representation corresponding to the ROI is input and send to other experts for verification when a candidate focus does not match the ROI; See Tao Par [0157]-[0159] which discloses an expert needing to manually input and send the image semantic representation corresponding to the ROI for verification when there is no matching candidate focus option for the ROI, i.e. that there is no matching semantic representation/algorithm step to proceed the automated diagnosis due to the ROI not being automatically recognized, and thereby requiring UI input from an expert); See Tao Par [0119] which specifically discloses an expert manually clicking on a computer or through an image recognition algorithm, i.e. UI, for the determination of the ROI).
Claim 29 –
Regarding Claim 29, Tao and Sorenson disclose the computer-implemented method of Claim 28 in its entirety. Tao further discloses a computer-implemented method, wherein:
if the plurality of program modules does not comprise a program module that is configured to autonomously determine the medical finding, the at least one selected program module is the one or more UI modules (See Tao Par [0042] which discloses the image semantic representation corresponding to the ROI is input and send to other experts for verification when a candidate focus does not match the ROI; See Tao Par [0157]-[0159] which discloses an expert needing to manually input and send the image semantic representation corresponding to the ROI for verification when there is no matching candidate focus option for the ROI, i.e. that there is no matching semantic representation/algorithm step to proceed the automated diagnosis due to the ROI not being automatically recognized, and thereby requiring UI input from an expert); See Tao Par [0119] which specifically discloses an expert manually clicking on a computer or through an image recognition algorithm, i.e. UI, for the determination of the ROI).
Claim 30 –
Regarding Claim 30, Tao and Sorenson disclose the computer-implemented method of Claim 28 in its entirety. Tao further discloses a computer-implemented method, further comprising:
training an artificial intelligence, AI, module of the plurality of program modules with the medical finding obtained using the one or more UI modules (See Tao Par [0042] which discloses the image semantic representation corresponding to the ROI is input and send to other experts for verification when a candidate focus does not match the ROI; See Tao Par [0157]-[0159] which discloses an expert needing to manually input and send the image semantic representation corresponding to the ROI for verification when there is no matching candidate focus option for the ROI, i.e. that there is no matching semantic representation/algorithm step to proceed the automated diagnosis due to the ROI not being automatically recognized, and thereby requiring UI input from an expert); See Tao Par [0160] & [0162]-[0163] which discloses that once the new discovery is confirmed, the knowledge will be added into the focus image library and added to a training set as a new training sample for the learning algorithm; See Tao Par [0119] which specifically discloses an expert manually clicking on a computer or through an image recognition algorithm, i.e. UI, for the determination of the ROI).
Claim 31 –
Regarding Claim 31, Tao and Sorenson disclose the computer-implemented method of Claim 28 in its entirety. Tao further discloses a computer-implemented method, wherein:
one or more of the at least one program module selected in step (a) are configured to autonomously determine the medical finding based on the obtained medical image-related data (See Tao Par [0131]-[0132] which discloses an image semantic representation knowledge graph and a variety of machine learning, deep learning, and reinforcement learning, being combined to perform medical image recognition, which are all understood to be species of artificial intelligence, such that ROI, focus type description/determination, and lesion region determination are automatically determined, see MPEP 2131.02(I), which states a species will anticipate a claim to a genus; See Tao Par [0164]-[0165] which discloses one or more modules for generating a medical finding and associated report for the medical finding, such that one or more AI or trained ML modules are utilized to produce various aspects for the report, such as the lesion region determination module including a focus type determination unit and a lesion region determination unit, i.e. one or more modules being combined into an ensemble to obtain the medical finding; See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to the ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample); and/or
one or more of the at least one program module selected in step (c) are configured to autonomously determine the medical finding based on the previously obtained medical finding (See Tao Par [0131]-[0132] which discloses an image semantic representation knowledge graph and a variety of machine learning, deep learning, and reinforcement learning, being combined to perform medical image recognition, which are all understood to be species of artificial intelligence, such that ROI, focus type description/determination, and lesion region determination are automatically determined, see MPEP 2131.02(I), which states a species will anticipate a claim to a genus; See Tao Par [0164]-[0165] which discloses one or more modules for generating a medical finding and associated report for the medical finding, such that one or more AI or trained ML modules are utilized to produce various aspects for the report, such as the lesion region determination module including a focus type determination unit and a lesion region determination unit, i.e. one or more modules being combined into an ensemble to obtain the medical finding; See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to the ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample); and
further comprising:
if a module is selected in step (a) that is configured to autonomously determine the medical finding, displaying a visualization of the medical finding determined by the selected module that is configured to autonomously determine the medical finding (See MPEP 2111.04(II) which states that the broadest reasonable interpretation of a method (or process) claim having contingent limitations (e.g. a method claim that requires step A if a first condition happens and step B if a second condition happens, if the claimed invention may be practiced without either the first or second condition happening, then neither step A or B is required by the broadest reasonable interpretation of the claim) requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met, therefore this limitation does not have to be met by Tao to read on the entirety of this claim; See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to the ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample), and
if a UI module providing a same type of medical finding as the selected module that is configured to autonomously determine the medical finding is also selected in step (a), hiding the visualization (See MPEP 2111.04(II) which states that the broadest reasonable interpretation of a method (or process) claim having contingent limitations (e.g. a method claim that requires step A if a first condition happens and step B if a second condition happens, if the claimed invention may be practiced without either the first or second condition happening, then neither step A or B is required by the broadest reasonable interpretation of the claim) requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met, therefore this limitation does not have to be met by Tao to read on the entirety of this claim); and/or if a module is selected in step (c) that is configured to autonomously determine the medical finding, displaying a visualization of the medical finding determined by the selected module that is configured to autonomously determine the medical finding (See MPEP 2111.04(II) which states that the broadest reasonable interpretation of a method (or process) claim having contingent limitations (e.g. a method claim that requires step A if a first condition happens and step B if a second condition happens, if the claimed invention may be practiced without either the first or second condition happening, then neither step A or B is required by the broadest reasonable interpretation of the claim) requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met, therefore this limitation does not have to be met by Tao to read on the entirety of this claim; See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to the ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample), and
if a UI module providing a same type of medical finding as the selected module that is configured to autonomously determine the medical finding is also selected in step (c), hiding the visualization (See MPEP 2111.04(II) which states that the broadest reasonable interpretation of a method (or process) claim having contingent limitations (e.g. a method claim that requires step A if a first condition happens and step B if a second condition happens, if the claimed invention may be practiced without either the first or second condition happening, then neither step A or B is required by the broadest reasonable interpretation of the claim) requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met, therefore this limitation does not have to be met by Tao to read on the entirety of this claim).
Claim 32 –
Regarding Claim 32, Tao and Sorenson disclose the computer-implemented method of Claim 31 in its entirety. Tao further discloses a computer-implemented method, wherein:
the selected module that is configured to autonomously determine the medical finding is an artificial intelligence, AI, module (See Tao Par [0131]-[0132] which discloses an image semantic representation knowledge graph and a variety of machine learning, deep learning, and reinforcement learning, being combined to perform medical image recognition, which are all understood to be species of artificial intelligence, such that ROI, focus type description/determination, and lesion region determination are automatically determined, see MPEP 2131.02(I), which states a species will anticipate a claim to a genus; See Tao Par [0164]-[0165] which discloses one or more modules for generating a medical finding and associated report for the medical finding, such that one or more AI or trained ML modules are utilized to produce various aspects for the report, such as the lesion region determination module including a focus type determination unit and a lesion region determination unit, i.e. one or more modules being combined into an ensemble to obtain the medical finding; See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to the ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation; See Tao Par [0160] & [0162] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample), and wherein
the method further comprises training the selected AI module with the medical finding obtained with the selected UI module providing the same type of medical finding as the selected AI module (See Tao Par [0131]-[0132] which discloses an image semantic representation knowledge graph and a variety of machine learning, deep learning, and reinforcement learning, being combined to perform medical image recognition, which are all understood to be species of artificial intelligence, such that ROI, focus type description/determination, and lesion region determination are automatically determined, see MPEP 2131.02(I), which states a species will anticipate a claim to a genus; See Tao Par [0164]-[0165] which discloses one or more modules for generating a medical finding and associated report for the medical finding, such that one or more AI or trained ML modules are utilized to produce various aspects for the report, such as the lesion region determination module including a focus type determination unit and a lesion region determination unit, i.e. one or more modules being combined into an ensemble to obtain the medical finding; See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to the ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation; See Tao Par [0160] & [0162]-[0163] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample).
Claim 33 –
Regarding Claim 33, Tao and Sorenson disclose the computer-implemented method of Claim 20 in its entirety. Tao further discloses a computer-implemented method, wherein:
the selection of the at least one program module in step (a) and/or (c) is performed by an artificial intelligence, AI, selection module (See Tao Par [0131]-[0132] which discloses an image semantic representation knowledge graph and a variety of machine learning, deep learning, and reinforcement learning, being combined to perform medical image recognition, which are all understood to be species of artificial intelligence, such that ROI, focus type description/determination, and lesion region determination are automatically determined, see MPEP 2131.02(I), which states a species will anticipate a claim to a genus; See Tao Par [0164]-[0165] which discloses one or more modules for generating a medical finding and associated report for the medical finding, such that one or more AI or trained ML modules are utilized to produce various aspects for the report, such as the lesion region determination module including a focus type determination unit and a lesion region determination unit, i.e. one or more modules being combined into an ensemble to obtain the medical finding; See Tao Par [0049] which discloses a report generation module configured to generate a structured report related to the ROI of the medical image of the patient according to the divided lesion region and corresponding image semantic representation; See Tao Par [0160] & [0162]-[0163] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample).
Claim 34 –
Regarding Claim 34, Tao and Sorenson disclose the computer-implemented method of Claim 33 in its entirety. Tao further discloses a computer-implemented method, wherein:
training the each of the obtained medical findings as a node of a graph in a graph database, wherein the medical report is generated based on the graph (See Tao Par [0164] which discloses the system including a knowledge graph establishment module, such that an image semantic representation knowledge graph according to a standardized dictionary library in the field of images and historically accumulated medical image report analysis; See Tao Par [0160] & [0162]-[0163] which discloses a new discovered focus/discovery, i.e. finding, being made and confirmed, the corresponding knowledge will be added into the image semantic representation knowledge graph, and the focus image will be added to the corresponding focus image library and added to a training set as a new training sample).
Claim 36 –
Regarding Claim 36, Tao and Sorenson disclose an apparatus comprising:
at least one processor and at least one memory (See Tao Par [0116] which discloses the image diagnosis system being contained in a computer, which is understood to include an associated process and memory), the at least one memory containing instructions executable by the at least one processor such that the apparatus is operable to perform the method of claim 20 (See Tao Par [0116] which discloses the image diagnosis system being contained in a computer, which is understood to include an associated process and memory; See Tao Par [0164] which discloses one or more modules, i.e. programs or instructions, for performing the methods described throughout Tao).
Claim 37 –
Regarding Claim 37, Tao and Sorenson disclose a non-transitory computer program product comprising:
program code portions for performing the method of claim 20 when the computer program product is executed on one or more processors (See Tao Par [0164] which discloses one or more modules, i.e. programs or instructions, for performing the methods described throughout Tao; See Tao Par [0116] & [0119]-[0120] which discloses the image diagnosis system being contained in a computer, which is understood to include an associated process and memory).
Claim 38 –
Regarding Claim 38, Tao and Sorenson disclose the non-transitory computer program product of claim 37 in its entirety. Tao further discloses a computer program product, wherein:
the product is stored on one or more computer readable recording media (See Tao Par [0164] which discloses one or more modules, i.e. programs or instructions, for performing the methods described throughout Tao; See Tao Par [0116] & [0119]-[0120] which discloses the image diagnosis system being contained in a computer, which is understood to include an associated process and memory, and can utilize computer readable recording media for performing the methods, such that the image recognition algorithm that is performed is understood to possibly represent computer readable recording media).
Claim 39 –
Regarding Claim 39, Tao and Sorenson disclose the computer-implemented method of claim 20 in its entirety. Tao further discloses a computer program product, wherein:
the program module is automatically triggered when at least one input requirement of the program module is available in the graph (See Tao Par [0117] which discloses the system upon receiving characteristics, as well as the semantic representation knowledge graph and the ROI, i.e. at least one input requirement available in the graph, the medical image aided diagnosis system may automatically trigger a program module to automatically pop up a list of options after preliminary calculation, including a plurality of description options based on the possibility, that is, candidate focus options of the patient).
Claim 40 –
Regarding Claim 40, Tao and Sorenson disclose the computer-implemented method of claim 20 in its entirety. Tao further discloses a computer program product, wherein:
two or more of the program modules are executed at the same time (See Tao Par [0041] which discloses that the structured report contains a hyperlink of image semantic representation corresponding to a determined lesion region related to the lesion region, and the lesion region displayed in the image and the image semantic representation corresponding to the lesion region can be viewed simultaneously by clicking the hyperlink, and therefore, the modules that produce the structured report, the lesion region displayed in the image, and the image semantic representation; See Tao Par [0166] which discloses one or more modules being combined and used at the same time regarding image recognition and report editing).
Response to Arguments
Applicant's arguments filed 25 February 2026 have been fully considered but they are not persuasive:
Regarding 35 U.S.C. 101 rejections of claims 20-34 & 36-40, Applicant argues on p. 6-7 that claim 20 as amended recites a machine-implemented inference architecture in which, execution is conditioned on a dynamically evolving graph state which it would not be possible for a human to perform. Applicant further argues that the features do not relate to methods of organizing human activity and provide no opportunity for user decision making as the execution of the selected program module(s) is automatic. Examiner respectfully disagrees with Applicant’s arguments. The steps recited in the independent claims set forth instructions for a user to follow for generating a medical report of a patient, such as selecting a plurality of program modules having an input requirement matching the medical image-related data of the patient, using the at least one program module selected to obtain a medical finding, selecting at least one program having an input requirement matching the obtained medical finding, using the at least one program module selected to obtain a medical finding based on the previously obtained medical finding, and generating a medical report of the patient comprising the obtained medical findings, and merely utilizing a computer or computer program based on various user input. Even further, the steps at least set forth efforts of managing the behavior of a user for generating a medical report of a patient by the system effectively managing the typical behaviors or findings regarding outputting a medical report for a patient. These aspects heavily relate to various methods of organizing human activity, at least by managing various user behaviors and/or operation of systems based on various user inputs/activity. While Examiner does not necessarily contend against a machine-implemented inference architecture in which, execution is conditioned on a dynamically evolving graph state which being possible for a human to perform, Examiner does contend that this is generally not relevant in the considerations of whether steps recite certain methods of organizing human activity, which is how the steps have been characterized by Examiner. As such, claims 20-34 & 36-40 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of claims 20-34 & 36-40, Applicant argues on p. 7-8 of Arguments/Remarks that the present application provides a technical contribution sufficiently patentable under 35 U.S.C. 101, for example by using the graph architecture, fast updating of the medical report is achieved and by ensuring that the selected program modules are capable of providing the medical finding based on the input data a “smooth execution” without interruptions is achieved, and at least implicitly prevents unnecessary execution of program modules that would be inappropriate based on the input data, saving execution cycles of the processor. As such, Applicant argues that the 35 U.S.C. 101 rejections of claims 20-34 & 36-40 should be withdrawn. Examiner respectfully disagrees with Applicant’s arguments. These aspects argued by Applicant represent improvements to the already-characterized abstraction versus a technological improvement over prior art systems and/or are not sufficiently disclosed in Applicant’s disclosure regarding technological/prior art shortcomings. That is, an improvement to an abstraction does not constitute a practical application under the Alice/Mayo test, Step 2A. Improvements to fast updating of the medical report and/or ensuring selected program modules are capable are aspects that are substantially abstract. Furthermore, regarding preventing unnecessary execution of program modules that would be inappropriate based on the input data, saving execution cycles of the processor, Applicant does not specifically express this as a shortcoming in prior art systems that needs to be solved. Furthermore, the inventive concept of the invention is not directed towards saving execution cycles of the processor, but rather generation of a medical report and discovery of new medical knowledge. That is, the invention, as a whole, is not directed towards said improvement, but rather merely contains features that achieve said aspects of saving execution cycles of the processor as a byproduct or unintended effect of the abstract idea being performed. Furthermore, assuming arguendo that Applicant’s invention is directed towards said aspects of saving execution cycles of the processor, Examiner contends that these problems and associated solutions are not explicitly laid out in Applicant’s disclosure for supporting said conclusions under 35 U.S.C. 101. That is, there are no substantial details or explicit mention of how said aspects are achieved, other than that they simply are indeed achieved, and while the specification need not explicitly set forth the improvement, it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology (See MPEP 2106.04(d)(1)). As such, claims 20-34 & 36-40 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of claims 20-34 & 36-40, Applicant argues on p. 8 of Arguments/Remarks that program modules may be easily presented, selected, automatically executed, and even replaced with improved versions while maintaining graph structure, facilitating reconfiguration of a processing system, and the graph architecture enables software developed by different organizations to interoperate more easily, therefore constituting technical improvements over prior art systems. Examiner respectfully disagrees with Applicant’s arguments. These aspects argued by Applicant represent improvements that are not related to the inventive concept of the instantly claimed invention. That is, the inventive concept of the invention is not directed towards facilitating reconfiguration of a processing system, and/or enables software developed by different organizations to interoperate more easily, but rather generation of a medical report and discovery of new medical knowledge. That is, the invention, as a whole, is not directed towards said improvement, but rather merely contains features that achieve said aspects of s facilitating reconfiguration of a processing system, and/or enables software developed by different organizations to interoperate more easily as a byproduct or unintended effect of the abstract idea being performed, as similarly described for embodiments discussed above. While Applicant further argues in view of newly added dependent claims 39 & 40, due to event-driven triggering of program modules and simultaneous triggering of program modules, these aspects do not necessarily reflect a practical application or significantly more than the recited abstract idea. As such, claims 20-34 & 36-40 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 102 rejections of claims 20-34 & 36-38, Applicant generally argues on p. 9-12 of Arguments/Remarks that Tao alone does not effectively disclose the entirety of amended, independent claim 20. Applicant further argues that therefore the 35 U.S.C. 102 rejections for claims 20-34 & 36-38 should be withdrawn. Examiner agrees with Applicant’s arguments. Therefore, the previous 35 U.S.C. 102 rejections for claims 20-34 & 36-38 have been withdrawn. However, upon further consideration, a new ground of rejection has been made under 35 U.S.C. 103 over Tao, in view of Sorenson. Sorenson is relied upon for teaching aspects regarding selecting one or more program modules amongst a plurality of program modules to be chosen from based on user input and/or data characteristics that the program module(s) is/are being applied to, as found in amended, independent claim 20. Therefore, claims 20-34 & 36-38 remain rejected under 35 U.S.C. 103. Furthermore, newly added claims 39 & 40 were determined to also be met by Tao, in view of Sorenson, and therefore also remain rejected under 35 U.S.C. 103.
Regarding 35 U.S.C. 102 rejections of claims 20-34 & 36-40, Applicant argues on p. 9 & 10-11 of Arguments/Remarks that Tao does not disclose graph inference, i.e. graphs for nodes and edges, and/or Tao combines information sampled from a larger number of patients, as opposed to representing information from one patient as claimed, and that the graph structure is not a patient-specific graph structure corresponding to medical findings related to one patient. Applicant further argues on p. 10-11 of Arguments/Remarks that training data on the information from the just one patient would not be attempted in the context of Tao. Examiner respectfully disagrees with Applicant’s arguments. While Tao may not make explicit mention of nodes and edges, Tao substantially describes and provides support for a knowledge graph, which is substantially understood and described throughout the prior art to contain nodes and edges representing knowledge and findings therein. For example, Examiner maintains that knowledge graphs that are well-known in prior art systems are understood to contain “edges” and “nodes”. If additional evidence is required (and while not being relied upon for art-based rejections) see Pujara et al. (“Knowledge Graph Identification” – NPL – 2013) p. 544, “3 Motivation: Knowledge Graph Identification” which states “a knowledge graph contains entities that are nodes, categories are labels associated with each node, and relations are directed edges between the nodes”; see Petkova et al. (“Why Graph Databases Make a Better Home for Interconnected Data Than the Relational Databases?” – NPL – 2016), p. 3, “Relationships as paths of (machine) understanding: Graph Databases” which discloses “graph databases use graph structures (knowledge graphs). A graph is comprised of interconnected nodes (i.e. things) and edges (relationships between things)”; Giannulli et al. (U.S. Patent No. 11,636,350) Abstract and Figs. 3-8 disclose generating a knowledge graph of clinical information for use as a reference model to semantically represent relevant information from clinical encounters, such that the knowledge graphs include one or more nodes and relationships, i.e. lines/edges, therebetween. As such, it is understood by Examiner that one of ordinary skill in the art before the effective filing date of the claimed invention would recognize that knowledge graphs are well-known in the prior art to contain “edges” and “nodes”. Furthermore, Tao Par [0079] specifically describes “the image semantic representation knowledge graph, in addition to structured descriptions of text and data, includes labeled samples of images (most of them being local images) corresponding to each named entity (including easy-to-recognize basic components and focuses) type. While Applicant argues that Tao does not give any detail on how such implicit “edges” and “nodes” should be generated, Examiner points to newly cited Tao Par [0159]-[0162] which discloses a new discovery being artificially confirmed (i.e. by the software/program modules) and subsequently, the corresponding new knowledge region being added, i.e. generated, to the image semantic representation knowledge graph, and Tao Par [0163] specifically states that the system itself may generate similar samples based on the research and other knowledge of such new samples by performed learning. As such, Tao effectively discloses knowledge graph embodiments, which are understood to include “edges” and nodes” and generation thereof, based on new knowledge/connections being identified by the system. Furthermore, regarding Tao combining information sampled from a larger number of patients, as opposed to representing information from one patient, Examiner points to Tao Par [0157] & [0164] which discloses a report generation module configured to generate a structured report related to the ROI/medical focus/discovery, i.e. medical finding of the medical image of the patient. That is, it is explicitly mentioned in Tao Par [0157] & [0164] that said embodiments can be configured to acquire a medical image of a patient and that ROI determination may determine an ROI and subsequent medical findings on the medical image of a/the patient, and therefore would constitute representing information from a singular or one patient. Therefore, claims 20-34 & 36-40 remain rejected under 35 U.S.C. 103.
Regarding 35 U.S.C. 102 rejections of claims 20-34 & 36-40, Applicant argues on p. 9-10 of Arguments/Remarks that Tao does not explicitly disclose “obtaining a medical finding” to then select another program module for execution within a graph-based structure, and various other embodiments related to said program module selection/execution and associated branched structure for determining said program module selection/execution based on the outcome of the elements from steps (a) to (d). Examiner agrees with Applicant’s arguments. However, as discussed above, a new ground of rejection has been made under 35 U.S.C. 103 over Tao, in view of Sorenson. Sorenson is relied upon for teaching aspects regarding selecting one or more program modules amongst a plurality of program modules to be chosen from based on user input and/or data characteristics that the program module(s) is/are being applied to, as found in amended, independent claim 20. As such, the combination of Tao and Sorenson now read on said program module selection/execution and related steps thereof with associated branched structure. Therefore, claims 20-34 & 36-40 remain rejected under 35 U.S.C. 103.
Regarding 35 U.S.C. 102 rejections of claims 20-34 & 36-40, Applicant argues on p. 11-12 of Arguments/Remarks that Tao does not disclose “wherein the program modules infer new parts of the graph based on other parts of the graph, thereby adding a new node and edge to the graph” because there is no specific disclosure that the “knowledge graph” creates new nodes and edges based on existing nodes and edges. Applicant further argues that Tao does not recite the newly amended limitation regarding “wherein upon adding the new node to the graph… triggers execution of the selected program module” as recited. Examiner agrees with Applicant’s arguments. However, as addressed above, Sorenson is relied upon for teaching aspects regarding selecting one or more program modules amongst a plurality of program modules to be chosen from based on user input and/or data characteristics that the program module(s) is/are being applied to, i.e. the newly amended limitation found in amended, independent claim 20. Furthermore, regarding Tao not disclosing “wherein the program modules infer new parts of the graph based on other parts of the graph, thereby adding a new node and edge to the graph” because there is no specific disclosure that the “knowledge graph” creates new nodes and edges based on existing nodes and edges, Examiner contends that knowledge graphs that are well-known in prior art systems are understood to contain “edges” and “nodes”. As such, it is understood by Examiner that one of ordinary skill in the art before the effective filing date of the claimed invention would recognize that knowledge graphs are well-known in the prior art to contain “edges” and “nodes”. Furthermore, Tao Par [0079] specifically describes “the image semantic representation knowledge graph, in addition to structured descriptions of text and data, includes labeled samples of images (most of them being local images) corresponding to each named entity (including easy-to-recognize basic components and focuses) type. While Applicant argues that Tao does not give any detail on how such implicit “edges” and “nodes” should be generated, Examiner points to newly cited Tao Par [0159]-[0162] which discloses a new discovery being artificially confirmed (i.e. by the software/program modules) and subsequently, the corresponding new knowledge region being added, i.e. generated, to the image semantic representation knowledge graph, and Tao Par [0163] specifically states that the system itself may generate similar samples based on the research and other knowledge of such new samples by performed learning. As such, Tao effectively discloses knowledge graph embodiments, which are understood to include “edges” and nodes” and generation thereof, based on new knowledge/connections being identified by the system. Furthermore, regarding Tao combining information sampled from a larger number of patients, as opposed to representing information from one patient, Examiner points to Tao Par [0157] & [0164] which discloses a report generation module configured to generate a structured report related to the ROI/medical focus/discovery, i.e. medical finding of the medical image of the patient. That is, it is explicitly mentioned in Tao Par [0157] & [0164] that said embodiments can be configured to acquire a medical image of a patient and that ROI determination may determine an ROI and subsequent medical findings on the medical image of a/the patient, and therefore would constitute representing information from a singular or one patient. Therefore, claims 20-34 & 36-38 remain rejected under 35 U.S.C. 103. Furthermore, newly added claims 39 & 40 were determined to also be met by Tao, in view of Sorenson, and therefore also remain rejected under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Goldberg et al. (U.S. Patent No. 11,636,949) discloses a system for generating and storing a medical hybrid knowledge graph, such that the system is designed to process a patient condition data and to process a hybrid knowledge graph ,and select one or more pathways on the knowledge graph to generate one or more differential diagnoses for suspected disorders;
Stevens et al. (U.S. Patent No. 11,176,326) discloses a system for generating a knowledge graph based in part on a clinical trial, and one or more therapies in the knowledge graph are selected based on a patient profile and the confidence level assigned to each of the criteria applicable to a trial;
Stevens et al. (U.S. Patent Publication No. 2020/185098) discloses a system for implementing one or more plans and dynamically modifying said plans based on treatment stage and treatment options defined in a knowledge graph.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUNTER J RASNIC whose telephone number is (571)270-5801. The examiner can normally be reached M-F 8am-5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached on (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.R./Examiner, Art Unit 3684 /Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
Prosecution Insights