DETAILED ACTION
Notice to Applicant
This communication is in response to the amendment submitted January 26, 2026. The present application claims priority under 35 U.S.C. 119 to European Patent Application Number 21193589.5, filed August 21, 2021. Claims 1 and 14 – 16 are amended. Claims 2 and 4 were previously cancelled. Claims 22 – 23 are new. Claims 1, 3, and 5 – 23 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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, 3, and 5 – 23 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.
Step One
Claims 1, 3, and 5 – 23 are drawn to a method, system, and non-transitory computer-readable medium, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1, 14, and 15 recite obtaining a medical imaging dataset of a current examination of a patient; determining, based on at least one image included in the medical imaging dataset, one or more diagnostic cues, the one or more diagnostic cues being associated with patient- specific diagnostic findings; pose the one or more diagnostic cues to a user after generating a current medical report for the current examination and during validation of a workflow providing a validation toolset to the user for refining the current medical report; obtain user feedback to at least one or more of the diagnostic cues, and edit the current medical report based on the user feedback.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that various examples of the disclosure “pertain to a workflow for drawing up a medical report” (paragraph 2 of published specification). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “a need for advanced techniques of assisting medical practitioners in drawing up medical reports” (paragraph 4 of published specification). The present application states “techniques are disclosed that pose one or more diagnostic cues to the user as part of a workflow for drawing up a medical report (paragraph 4 of published specification). This indicates that one or more diagnostic cues can assist the user in drawing up the medical report.. Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including:
Claim 1: “computer implemented”, “processing algorithm”, “user interface”,
“providing a validation toolset”
Claims 3, 7, 16, 21 – 23: “computer implemented”
Claims 5, 18: “computer implemented”, “natural language processing algorithm”, “machine readable”
Claim 6: “computer implemented”, “machine readable”
Claims 8, 19: “computer implemented”, “processing algorithm is machine learned and includes multiple encoder branches configured to determine respective latent features”, “different ones of the multiple encoder branches are associated with the at least one prior medical report and the medical imaging dataset of the current examination”
Claim 9: “computer implemented”, “wherein at least one encoder branch, of the multiple encoder branches, associated with the at least one prior medical report includes an attention layer for determining shortcuts between the respective latent features and a respective input to the processing algorithm”
Claims 10, 20: “computer implemented”, “merging, via the processing algorithm, the respective latent features to obtain merged latent features, wherein the processing algorithm includes a decoder branch for reconstructing the one or more diagnostic cues based on the merged latent features”
Claim 11: “computer implemented”, “the processing algorithm includes a multiple-instance pooling layer to merge latent features obtained from a respective encoder branch, of the multiple encoder branches, used to encode each one of the multiple prior medical reports”
Claim 12: “computer implemented”, “the multiple-instance pooling layer”
Claim 13: “computer implemented”, “user interface”
Claim 14: “device”, “at least one processor configured to execute computer readable instructions”, “user interface”
Claim 15: “non-transitory computer readable medium storing program code”, “processing algorithm”, “user interface”
Claim 17: “computer implemented”, “the different input channels are associated with different encoder branches of the processing algorithm”
These features are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B
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 integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The published specification supports this conclusion as follows:
[0019] Some examples of the present disclosure generally provide for a plurality of circuits or other electrical devices. All references to the circuits and other electrical devices and the functionality provided by each are not intended to be limited to encompassing only what is illustrated and described herein. While particular labels may be assigned to the various circuits or other electrical devices disclosed, such labels are not intended to limit the scope of operation for the circuits and the other electrical devices. Such circuits and other electrical devices may be combined with each other and/or separated in any manner based on the particular type of electrical implementation that is desired. It is recognized that any circuit or other electrical device disclosed herein may include any number of microcontrollers, a graphics processor unit (GPU), integrated circuits, memory devices (e.g., FLASH, random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), or other suitable variants thereof), and software which co-act with one another to perform operation(s) disclosed herein. In addition, any one or more of the electric devices may be configured to execute a program code that is embodied in a non-transitory computer readable medium programmed to perform any number of the functions as disclosed.
[0166] In addition, or alternative, to that discussed above, units and/or devices according to one or more example embodiments may be implemented using hardware, software, and/or a combination thereof. For example, hardware devices may be implemented using processing circuity such as, but not limited to, a processor, Central Processing Unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a System-on-Chip (SoC), a programmable logic unit, a microprocessor, or any other device capable of responding to and executing instructions in a defined manner. Portions of the example embodiments and corresponding detailed description may be presented in terms of software, or algorithms and symbolic representations of operation on data bits within a computer memory. These descriptions and representations are the ones by which those of ordinary skill in the art effectively convey the substance of their work to others of ordinary skill in the art. An algorithm, as the term is used here, and as it is used generally, is conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of optical, electrical, or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.
[0171] For example, when a hardware device is a computer processing device (e.g., a processor, Central Processing Unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a microprocessor, etc.), the computer processing device may be configured to carry out program code by performing arithmetical, logical, and input/output operations, according to the program code. Once the program code is loaded into a computer processing device, the computer processing device may be programmed to perform the program code, thereby transforming the computer processing device into a special purpose computer processing device. In a more specific example, when the program code is loaded into a processor, the processor becomes programmed to perform the program code and operations corresponding thereto, thereby transforming the processor into a special purpose processor.
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 3, 5 – 13, and 16 – 23 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 7 – 16, and 20 – 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Buckler et al., herein after Buckler (U.S. Publication Number 2019/0159737 A1) in view of Reiner (U.S. Publication Number 2012/0221347 A1) further in view of Dhoble (U.S. Publication Number 2011/0119290 A1).
Claim 1 (Currently Amended). Buckler teaches a computer-implemented method, comprising:
obtaining a medical imaging dataset of a current examination of a patient (Figure 1; paragraph 151 discloses imaging features from one or more acquired images of a patient);
determining, based on at least one image included in the medical imaging dataset, one or more diagnostic cues using a processing algorithm, the one or more diagnostic cues being associated with patient- specific diagnostic findings (paragraph 47 discloses Coronary computed tomography angiography (CCTA) utilized in tandem with quantitative analysis software, indicating image data; paragraph 59 discloses incorporating computerized image analysis and data fusion algorithms with patient clinical chemistry and blood biomarker data; paragraph 61 discloses a hierarchical analytics framework comprised of a first level of algorithms which measure biological properties capable of being objectively validated against a truth standard independent of imaging, followed by a second set of algorithms to determine medical or clinical conditions based on the measured biological properties which is applicable to a number of distinct biological properties in an "and/or" fashion including, but not limited to, angiogenesis, neovascularization, inflammation, and stenosis; paragraph 61 discloses paragraph 136 discloses utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes; paragraph 62 discloses CAP (computer aided phenotyping) which may apply a hierarchical inference incorporating computerized image analysis and data fusion algorithms to patient clinical chemistry and blood biomarker data to provide a multi-factorial panel or "profile" of measurements that may be used to distinguish between different subtypes of disease that would be treated differently. paragraph 137 discloses utilizing radiological imaging to provide surrogate measures for predicting clinical outcome or guiding treatment).
Buckler fails to explicitly teach the following limitations met by Reiner as cited:
controlling a user interface to pose the one or more diagnostic cues to a user after generating a current medical report for the current examination and during validation of a workflow providing a validation toolset to the user for refining the current medical report (paragraph 43 discloses a bi-directional communication between the medical reconciliation system and the information systems (e.g. HIS, RIS, PACS), and allows the system to generate desired reports and/or other information; paragraph 80 discloses the program would track the degree to which the requested data is successfully integrated into the reports, thereby providing an objective method for determining data completeness and compliance with community or institutional standards, where the completeness and compliance is interpreted as a validation toolset; paragraph 188 discloses any modifications made are recorded by the program in the report database (and time stamped) along with highlighted changes to the final report (refining the current medical report)).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Buckler to further include electronic multi-disciplinary tools, which have the ability to record, track, analyze, and provide feedback to practitioners in a context and user-specific manner as disclosed by Reiner.
One of ordinary skill in the art at the time of the invention, before the effective filing date of the claimed invention, would have been motivated to expand the method of Buckler in this way since as the data reconciliation tool can track data which is stored, between any two data sources within the healthcare continuum, and can be reconciled at different steps using different databases to impact healthcare delivery at the point of care, for the combined purposes of improving timeliness, quality, and patient safety (Reiner: paragraph 85).
Buckler and Reiner fail to explicitly teach the following limitations met by Dhoble as cited:
the one or more diagnostic cues being questions for a user (paragraph 31 discloses a questionnaire completed by the patient/interviewer of the patient);
obtaining, from the user interface, user feedback to at least one of the one or more diagnostic cues (paragraph 40 discloses the feedback may take the form of answers to a questionnaire which test the knowledge of the user after presentation of the media to the patient, the feedback may also take the form of responses to survey questions designed to evaluate the condition of the user); and
editing the current medical report based on the user feedback (Figure 6C discloses updating user medical record(s) based on system user action(s); paragraph 76 discloses upon determining that the user has provided sufficient feedback, the healthcare management server(s) may secure access to the medical records database(s) and update (edit) the user’s medical history records; paragraph 509 discloses updating (editing) the medical history record of the user based on the modified treatment plan).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Buckler and Reiner to further include a prescription optimization processor-implemented method, comprising: obtaining a coordinated patient management trigger for a user and a medical history record of the user using the obtained trigger; analyzing the obtained medical history record of the user and identifying, based on the analysis of the medical history record, a current treatment plan for the user; generating a user feedback request based on the identified current treatment plan; providing the user feedback request to a user device of the user; obtaining user feedback in response to providing the user feedback request; analyzing the obtained user feedback based on the current treatment plan for the user; generating a modified treatment plan for the user based on the analysis of the obtained user feedback; and providing the modified treatment plan for the user as discloses by Dhoble.
One of ordinary skill in the art at the time of the invention, before the effective filing date of the claimed invention, would have been motivated to expand the method of Buckler and Reiner in this way to maximize personal understanding of health information and coach individuals toward better health outcomes (Dhoble: paragraph 6).
System and storage claims 14 -15 repeat the subject matter of claim 1. As the underlying processes of claims 14 -15 have been shown to be fully disclosed by the teachings of Buckler, Reiner, and Dhoble in the above rejections of claim 1; as such, these limitations (14 -15) are rejected for the same reasons given above for claim 1 and incorporated herein.
Claim 3 (Previously Presented). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 1.
Buckler fails to explicitly teach the following limitations met by Reiner as cited:
wherein said editing of the current medical report based on the user feedback comprises: refining the current medical report (paragraph 43 discloses a bi-directional communication between the medical reconciliation system and the information systems (e.g. HIS, RIS, PACS), and allows the system to generate desired reports and/or other information; paragraph 188 discloses any modifications made are recorded by the program in the report database (and time stamped) along with highlighted changes to the final report (refining the current medical report)).
The motivation to combine the teachings of Buckler, Reiner, and Dhoble is discussed in the rejection of claim 1, and incorporated herein.
Claim 7 (Original). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 1. Buckler discloses a method further comprising: obtaining at least one prior medical imaging dataset of at least one prior examination (paragraph 152 discloses non-imaging inputs including data from laboratory systems, patient reported symptoms, or patient history), and
wherein the determining determines the one or more diagnostic cues based on the medical imaging dataset and the at least one prior medical imaging dataset of the at least one prior examination (paragraph 136 discloses utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes; paragraph 137 discloses utilizing radiological imaging to provide surrogate measures for predicting clinical outcome or guiding treatment).
Claim 8 (Original). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 1. Buckler discloses a method further comprising: obtaining at least one prior medical report of at least one prior examination of the patient (paragraph 152 discloses non-imaging inputs including data from laboratory systems, patient reported symptoms, or patient history), wherein the processing algorithm is machine-learned and includes multiple encoder branches configured to determine respective latent features, and different ones of the multiple encoder branches are associated with the at least one prior medical report and the medical imaging dataset of the current examination (paragraph 66 discloses convolutional neural networks (CNNs) may be utilized for building a classifier in an approach that can be characterized as transfer-learning with fine-tuning approach, the CNNs are trained on a large compendium of imaging data on a powerful computational platform can be used to classify images that have not been annotated in the network training, where the latent features are interpreted as the images that have not been interpreted).
Claim 9 (Original). Buckler and Reiner teach the computer-implemented method of claim 8. Buckler teaches a method wherein at least one encoder branch, of the multiple encoder branches, associated with the at least one prior medical report includes an attention layer for determining shortcuts between the respective latent features and a respective input to the processing algorithm (paragraph 68 discloses the fully connected one or two last layers act as classifiers (e.g. softmax layers); paragraph 146 discloses a hierarchical analytics framework, wherein a one or more intermediary sets of data points are utilized as an intermediary processing layer or an intermediary transformation between initial set of data points and an end set of data points, which is similar to the concept of deep learning or hierarchical learning wherein algorithms are used to model higher level abstractions using multiple processing layers or otherwise utilizing multiple transformations such as multiple non-linear transformations).
Claim 10 (Original). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 8. Buckler discloses a method further comprising: merging, via the processing algorithm, the respective latent features to obtain merged latent features, wherein the processing algorithm includes a decoder branch for reconstructing the one or more diagnostic cues based on the merged latent features (paragraph 66 discloses classifying images that have not been annotated in network training, thus the trained convolution layers can be tweaked, to include the latent (or unclassified features; paragraph 79 discloses merging disparate typing systems where the class map may have changed).
Claim 11 (Original). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 8. Buckler teaches a method wherein the processing algorithm includes a multiple-instance pooling layer to merge latent features obtained from a respective encoder branch, of the multiple encoder branches, used to encode each one of the multiple prior medical reports (paragraph 66 discloses convolutional neural networks (CNNs) may be utilized for building a classifier in an approach that can be characterized as transfer-learning with fine-tuning approach, the CNNs are trained on a large compendium of imaging data on a powerful computational platform can be used to classify images that have not been annotated in the network training, where the latent features are interpreted as the images that have not been interpreted).
Buckler fails to explicitly teach the following limitations met by Reiner as cited:
further comprising: obtaining multiple prior medical reports of multiple prior examinations of the patient (paragraph 82 discloses date/time of the data recorded by the program in the reconciliation database; paragraph 84 discloses the date/time (of examination) would be recorded by the program in the reconciliation database).
The motivation to combine the teachings of Buckler, Reiner, and Dhoble is discussed in the rejection of claim 1, and incorporated herein.
Claim 12 (Original). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 11. Buckler teaches a method wherein the multiple-instance pooling layer comprises attention weighting for the multiple prior medical reports (paragraph 157 discloses the training module may rely on reference annotations to derive weights or models).
Claim 13 (Original). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 1. Buckler teaches a method wherein the determining determines multiple diagnostic cues sequentially (paragraph 60 discloses employing a hierarchical inference scheme including intermediary steps of determining spatially resolved image features and time resolved kinetics at multiple levels of biologically objective component of which are subsequently utilized to draw clinical inferences),
Buckler fails to explicitly teach the following limitations met by Reiner as cited:
wherein the computer-implemented method further includes obtaining user feedback associated with the multiple diagnostic cues (paragraph 76 discloses annotation or electronic marks (user feedback) is converted into a report finding (editing); paragraph 79 discloses the feedback (textual data) provided by the user is expanded upon and the radiologist will approve (completing and/or validating the report),
wherein said controlling of the user interface, said determining of the multiple diagnostic cues, and said obtaining of the user feedback is implemented in an entangled manner, so that a subsequent diagnostic cue of the multiple diagnostic cues depends on the user feedback associated with a preceding diagnostic cue among the multiple diagnostic cues (paragraph 76 discloses annotation or electronic marks (user feedback) is converted into a report finding (editing); paragraph 79 discloses the feedback (textual data) provided by the user is expanded upon and the radiologist will approve (completing and/or validating the report).
The motivation to combine the teachings of Buckler, Reiner, and Dhoble is discussed in the rejection of claim 1, and incorporated herein.
Claim 16 (Currently Amended). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 1. Buckler discloses a method wherein the determining determines the one or more diagnostic cues based on the medical imaging dataset and the current medical report (paragraph 136 discloses utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes; paragraph 137 discloses utilizing radiological imaging to provide surrogate measures for predicting clinical outcome or guiding treatment).
Claim 20 (Original). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 9. Buckler teaches a method further comprising: merging, via the processing algorithm, the respective latent features of the multiple encoder branches to obtain merged latent features (paragraph 66 discloses classifying images that have not been annotated in network training, thus the trained convolution layers can be tweaked, to include the latent (or unclassified features; paragraph 79 discloses merging disparate typing systems where the class map may have changed), wherein the processing algorithm includes a decoder branch for reconstructing the one or more diagnostic cues based on the merged latent features (paragraph 165 discloses post-acquisition reconstructions).
Claim 21 (Previously Presented): Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 1.
Buckler and Reiner fail to explicitly teach the following limitations met by Dhoble as cited:
wherein the diagnostic cues are user prompts in the form of sentences to be displayed to a user (Figure 7G illustrates a user prompt in the form of a question displayed on the user’s device; paragraph 51 discloses the user device, operating in conjunction with the client applications, may display the personalized interactive rich media prescription objects to the patient, which may require the user to provide PRO-feedback and/or other forms of user feedback).
The motivation to combine the teachings of Buckler, Reiner, and Dhoble is discussed in the rejection of claim 1, and incorporated herein.
Claim 22 (New). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 1.
Buckler and Reiner fail to explicitly teach the following limitations met by Dhoble as cited:
further comprising: storing the edited current medical report in a medical repository of a hospital for subsequent retrieval by at least one of the user or a patient (Figure 6C discloses updating user medical record(s) based on system user action(s); paragraph 76 discloses upon determining that the user has provided sufficient feedback, the healthcare management server(s) may secure access to the medical records database(s) and update (edit) the user’s medical history records, indicating storage; paragraph 509 discloses updating (editing) the medical history record of the user based on the modified treatment plan).
The motivation to combine the teachings of Buckler, Reiner, and Dhoble is discussed in the rejection of claim 1, and incorporated herein.
Claim 23 (New). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 1.
Buckler and Reiner fail to explicitly teach the following limitations met by Dhoble as cited:
further comprising: providing the edited current medical report to at least one of the user or a patient (paragraph 76 discloses upon determining that the user has provided sufficient feedback, the healthcare management server(s) may secure access to the medical records database(s) and update (edit) the user’s medical history records).
The motivation to combine the teachings of Buckler, Reiner, and Dhoble is discussed in the rejection of claim 1, and incorporated herein.
Claim(s) 5 – 6 and 17 – 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Buckler et al., herein after Buckler (U.S. Publication Number 2019/0159737 A1) in view of Reiner (U.S. Publication Number 2012/0221347 A1) further in view of Dhoble (U.S. Publication Number 2011/0119290 A1) and further in view of Alpert et al., herein after Alpert (U.S. Patent Number 11,911,200 B1).
Claim 5 (Original). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 1. Buckler discloses a method further comprising:
obtaining at least one prior medical report of at least one prior examination of the patient (paragraph 152 discloses non-imaging inputs including data from laboratory systems, patient reported symptoms, or patient history).
Buckler, Reiner, and Dhoble fail to explicitly teach the following limitations met by Alpert as cited:
analyzing, using a natural language processing algorithm, the at least one prior medical report to obtain a machine-readable representation of the at least one prior medical report (column 2, lines 47 – 59 discloses automatically generating a report as the user dictates information to the system, the system enables automatic selection and inclusion of cropped medical images in a report based on natural language processing of user-provided information, and enables a user to provide report generation instructions),
wherein the determining determines the one or more diagnostic cues based on the medical imaging dataset and the machine-readable representation of the at least one prior medical report (column 10, lines1 – 20 discloses a module for advance language, speech, and image analysis services (analytics module); column 12, lines 9 – 24 discloses an analytics module which may include additional algorithms including computer vision, natural language, and other algorithms for processing image, speech, text, and other data; column 10, 62 through column 11, line 5 discloses the reporting module may be configured to receive, edit, and produce reports, indicating receipt of a prior medical report (editing)).
It would have been obvious to one of ordinary skill at the time of the invention to expand the method of Buckler, Reiner, and Dhoble to further include systems and processes by which a medical report can be generated by medical images, by aiding medical personnel in analyzing, annotating, and generating easy to understand medical reports as disclosed by Alpert.
One of ordinary skill in the art at the time of the invention would have been motivated to expand the method of Buckler, Reiner, and Dhoble in this way since generating medical reports that is intuitive and simple for a user to provide information and automatically generate a report as the user dictates to the system provides an improved user experience (Alpert: column 2, lines 47 – 51).
Claim 6 (Original). Buckler, Reiner, Dhoble and Alpert teach the computer-implemented method of claim 5.
Buckler, Reiner, and Dhoble fail to explicitly teach the following limitations met by Alpert as cited:
wherein the machine-readable representation of the at least one prior medical report classifies multiple sections of the at least one prior medical report (column 2, lines 47 – 59 discloses automatically generating a report as the user dictates information to the system, the system enables automatic selection and inclusion of cropped medical images in a report based on natural language processing of user-provided information, and enables a user to provide report generation instructions), and
wherein different ones of the multiple sections are provided to different input channels of the processing algorithm (column 1, lines 63 – 66 discloses real-time generation of medical reports combining image data from an imaging device and speech input from an input device; column 10, line 66 through column 11, line 5 discloses the reporting module receives input from the input sensors as well as the analytics module and image data to generate a report, indicating different input channels).
The motivation to combine the teachings of Buckler, Reiner, Dhoble, and Alpert is discussed in the rejection of claim 5, and incorporated herein.
Claim 17 (Original). Buckler, Reiner, Dhoble and Alpert teach the computer-implemented method of claim 6. Buckler teaches a method wherein the different input channels are associated with different encoder branches of the processing algorithm (paragraph 66 discloses convolutional neural networks (CNNs) may be utilized for building a classifier in an approach that can be characterized as transfer-learning with fine-tuning approach, the CNNs are trained on a large compendium of imaging data on a powerful computational platform can be used to classify images that have not been annotated in the network training, where the latent features are interpreted as the images that have not been interpreted).
Claim 18 (Previously Presented). Buckler, Reiner, and Dhoble teach the computer-implemented method of claim 3. Buckler teaches a method further comprising:
obtaining at least one prior medical report of at least one prior examination of the patient (Figure 1; paragraph 151 discloses imaging features from one or more acquired images of a patient); and
Buckler, Reiner, and Dhoble fail to explicitly teach the following limitations met by Alpert as cited:
analyzing, using a natural language processing algorithm, the at least one prior medical report to obtain a machine-readable representation of the at least one prior medical report (column 2, lines 47 – 59 discloses automatically generating a report as the user dictates information to the system, the system enables automatic selection and inclusion of cropped medical images in a report based on natural language processing of user-provided information, and enables a user to provide report generation instructions),
wherein the determining determines the one or more diagnostic cues based on the medical imaging dataset and the machine-readable representation of the at least one prior medical report (column 10, lines1 – 20 discloses a module for advance language, speech, and image analysis services (analytics module); column 12, lines 9 – 24 discloses an analytics module which may include additional algorithms including computer vision, natural language, and other algorithms for processing image, speech, text, and other data; column 10, 62 through column 11, line 5 discloses the reporting module may be configured to receive, edit, and produce reports, indicating receipt of a prior medical report (editing)).
The motivation to combine the teachings of Buckler, Reiner, Dhoble, and Alpert is discussed in the rejection of claim 5, and incorporated herein.
Claim 19 (Original). Buckler, Reiner, Dhoble and Alpert teach the computer-implemented method of claim 5. Buckler discloses a method further comprising: obtaining at least one prior medical report of at least one prior examination of the patient (paragraph 152 discloses non-imaging inputs including data from laboratory systems, patient reported symptoms, or patient history), wherein the processing algorithm is machine-learned and includes multiple encoder branches configured to determine respective latent features, and different ones of the multiple encoder branches are associated with the at least one prior medical report and the medical imaging dataset of the current examination (paragraph 66 discloses convolutional neural networks (CNNs) may be utilized for building a classifier in an approach that can be characterized as transfer-learning with fine-tuning approach, the CNNs are trained on a large compendium of imaging data on a powerful computational platform can be used to classify images that have not been annotated in the network training, where the latent features are interpreted as the images that have not been interpreted).
Response to Arguments
Applicant's arguments filed March 15, 2025 have been fully considered but they are not persuasive. The Applicant’s arguments have been addressed in the order in which they were presented.
Claim Rejections - 35 USC § 101
Claim 1:
The Applicant argues claim 1 provides an improvement to the technology of radiological diagnosis. The Examiner respectfully disagrees. The Examiner respectfully disagrees. The Applicant’s specification states “Some examples of the present disclosure generally provide for a plurality of circuits or other electrical devices. All references to the circuits and other electrical devices and the functionality provided by each are not intended to be limited to encompassing only what is illustrated and described herein. While particular labels may be assigned to the various circuits or other electrical devices disclosed, such labels are not intended to limit the scope of operation for the circuits and the other electrical devices. Such circuits and other electrical devices may be combined with each other and/or separated in any manner based on the particular type of electrical implementation that is desired. It is recognized that any circuit or other electrical device disclosed herein may include any number of microcontrollers, a graphics processor unit (GPU), integrated circuits, memory devices (e.g., FLASH, random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), or other suitable variants thereof), and software which co-act with one another to perform operation(s) disclosed herein. In addition, any one or more of the electric devices may be configured to execute a program code that is embodied in a non-transitory computer readable medium programmed to perform any number of the functions as disclosed” (paragraph 19 of the published specification) and “In addition, or alternative, to that discussed above, units and/or devices according to one or more example embodiments may be implemented using hardware, software, and/or a combination thereof. For example, hardware devices may be implemented using processing circuity such as, but not limited to, a processor, Central Processing Unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a System-on-Chip (SoC), a programmable logic unit, a microprocessor, or any other device capable of responding to and executing instructions in a defined manner. Portions of the example embodiments and corresponding detailed description may be presented in terms of software, or algorithms and symbolic representations of operation on data bits within a computer memory. These descriptions and representations are the ones by which those of ordinary skill in the art effectively convey the substance of their work to others of ordinary skill in the art. An algorithm, as the term is used here, and as it is used generally, is conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of optical, electrical, or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.” (paragraph 166 of the published specification). The generic computer cited by the Applicant is a general link to execute the abstract idea. The processor, as used in the recited claims, is at best the equivalent of merely adding the words “apply it” to the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept. Thus, Applicant’s argument is not persuasive and the rejection is maintained.
The Applicant argues the features of claim 1 integrate any alleged abstract idea into a practical application. The Examiner respectfully disagrees. The additional elements of the present claims fail to integrate the exception into a practical application of the exception. The 2019 PEG defines the phrase “integration into a practical application” to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. For example, the 2019 PEG guidelines recite limitations that are indicative of integration into a practical application when recited in a claim with a judicial exception include:
Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
The present claims fail to demonstrate an improvement to the functioning of a computer or to any other technology or technical field. Thus, Applicant’s argument is not persuasive, and the rejection is maintained.
Claims 8 and 9:
The Applicant argues claim 8 reflects a technical improvement that integrates any alleged abstract idea into a practical application. The Examiner submits the Applicant’s argument has been addressed in the response to claim 1, and incorporated herein.
Claim Rejections - 35 USC § 103
Claim 1:
The Applicant argues the cited art (Buckler, Reiner, and Dew), alone or in combination, fail to disclose or suggest, at least, "determining, based on the medical imaging dataset, one or more diagnostic cues using a processing algorithm, the one or more diagnostic cues being associated with patient-specific diagnostic findings, and the one or more diagnostic cues being questions for a user," as recited independent claim 1. The Examiner respectfully disagrees. The Examiner submits Reiner discloses medical (radiological) applications may be implemented using the system designed to interface with existing information systems such as a Hospital Information System (HIS), a Radiology Information System (RIS), a radiographic device, and/or other information systems that may access a computed radiography (CR) cassette or direct radiography (DR) system (paragraph 42), indicating medical imaging datasets. Reiner discloses utilizing a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes (paragraph 136). Dhoble recites generating a user feedback request based on the identified current treatment plan, providing the user feedback request to a user device of the user, obtaining user feedback in response to providing the user feedback request, analyzing the obtained user feedback based on the current treatment plan for the user, generating a modified treatment plan for the user based on the analysis of the obtained user feedback, and providing the modified treatment plan (paragraph 11). Dhoble discloses the diagnostic cues being associated with patient-specific diagnostic findings, thus Applicant’s argument is not persuasive and the rejection is maintained.
Claim 7:
The Applicant argues Buckler fails to disclose or suggest, at least, “obtaining at least one prior medical imaging dataset of at least one prior examination, and wherein the determining determines the one or more diagnostic cues based on the medical imaging dataset and the at least one prior medical imaging dataset of the at least one prior examination.” The Examiner respectfully disagrees. The Examiner submits Buckler discloses non-imaging inputs including data from laboratory systems, patient reported symptoms, or patient history, where patient history can include prior imaging of the patient (paragraph 152). Thus, Applicant’s argument is not persuasive and the rejection is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/K.K.R/Examiner, Art Unit 3682
/ROBERT A SOREY/Primary Examiner, Art Unit 3682
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