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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 16 September 2026 has been entered.
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.
Claim(s) 1,3-8,10-15 and 17-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 recites:
A system comprising:
a memory configured to store instructions; and
one or more processors configured to execute the instructions to:
receive a radiology report including unstructured natural language text corresponding to a finding of a radiologist with respect to a region of interest of a subject;
generate, using a first artificial intelligence (AI) model, structured data in a predetermined format including a set of predetermined fields and a set of corresponding text values that are extracted from the radiology report and that correspond to the set of predetermined fields by inputting the radiology report into the first Al model that is configured to extract the set of corresponding text values;
select, using the first Al model, a medical image dataset of the subject corresponding to the radiology report from among a plurality of medical image datasets of the subject stored in a medical image database by inputting the structured data and respective metadata of the plurality of medical image datasets into the first Al model that is configured to match the text values with the respective metadata;
segment, using a second Al model, a set of regions of each medical image of the medical image dataset by inputting each medical image of the medical image dataset into the second Al model that is configured to segment the set of regions of each medical image into a set of segmented structures of each medical image;
classify, using the second Al model, each segmented structure of the set of segmented structures of each medical image by inputting each medical image including the set of segmented structures into the second Al model that is configured to determine a classification of each segmented structure;
label, using the second Al model, the set of regions of each medical image of the medical image dataset by inputting each medical image including the set of segmented structures and the classification of each segmented structure into the second Al model that is configured to label the set of regions of each medical image, wherein each label identifies a segmented structure depicted in each medical image and the classification of the segmented structure;
select, using the first Al model, a medical image of the medical image dataset that depicts the region of interest of the subject by inputting the structured data, the labelled and segmented set of regions, and the classification of each segmented structure into the first Al model that is configured to determine, for each medical image of the medical image dataset, a score based on a number of matches between the set of corresponding text values and labels associated with the labelled and segmented set of regions, wherein each match of the number of matches is a match between a text value of the set of corresponding text values and one of the segmented structure or the classification of the segmented structure identified by a label of the labels, and to select the medical image of the medical image dataset having a greatest score; and
display the radiology report and the medical image that depicts the region of interest of the subject via a user interface of a user device.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the steps of selecting an image based on the image’s relevance to the patient’s region of interest are traditionally performed by a physician when treating a patient, i.e. managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a generic computer recited with a high level of generality in a post hoc manner to implement the abstract idea, the steps of processing data may be performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
The different categories of abstract ideas are being considered together as one single abstract idea. MPEP 2106.04(II)(B)
Dependent claim(s) recite(s) additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim(s) 3-6 reciting limitations further defining the abstract idea, which may be performed in the mind but for recitation of generic computer components, and/or may be a method of managing relationship or interactions between people).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
a memory configured to store instructions; and
one or more processors configured to execute the instructions to:
receive a radiology report including unstructured natural language text corresponding to a finding of a radiologist with respect to a region of interest of a subject;
using a first artificial intelligence (AI) model by inputting the radiology report into the first Al model that is configured to extract the set of corresponding text values;
using the first Al model by inputting the structured data and respective metadata of the plurality of medical image datasets into the first Al model that is configured;
using a second Al model by inputting each medical image of the medical image dataset into the second Al model that is configured to;
using the second Al model inputting each medical image including the set of segmented structures into the second Al model that is configured t;
using the second Al model by inputting each medical image including the set of segmented structures and the classification of each segmented structure into the second Al model that is configured;
using the first Al model by inputting the structured data, the labelled and segmented set of regions, and the classification of each segmented structure into the first Al model that is configured; and
display the radiology report and the medical image that depicts the region of interest of the subject via a user interface of a user device.
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
Regarding the memory, the Specification as originally filed discloses generic memory (page 7 paragraph 0027).
Regarding the processor, the Specification as originally filed discloses a generic computer (page 7 paragraph 0025).
Regarding the use of AI models, these limitations amount to invoking a generic computer to implement the abstract idea.
Accordingly, these limitation amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
Regarding the step of receiving a radiology report, segmenting and displaying an image, this limitation merely add(s) insignificant extra-solution activity to the abstract idea (mere data gathering). MPEP 2106.05(g))
Dependent claim(s) recite(s) additional subject matter which amount to limitation(s) consistent with the additional element(s) in the independent claims (such as claim(s) 3 reciting storing data in a database, claim(s) 7 reciting generating an AI prompt, additional limitation(s) which add(s) insignificant extra-solution activity to the abstract idea).
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.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
Regarding the step of receiving a radiology report, this limitation amount(s) to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
Regarding the step of segmenting an image, Rao (20030120134) discloses that image segmentation amounts to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (page 4 paragraph 0041).
Regarding the step of displaying an image, Knoplioch (20200311912) discloses displaying data in a manner that is WURC (page 1 paragraph 0003)
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claim(s) 3 reciting storing data in a database, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);; claim 7 reciting an AI prompt, Singh (20240296316) disclosing that an AI prompt is WURC (page 4 paragraph 0040)). 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.
The claim is not patent eligible.
Claim(s) 8,10-15 and 17-20 recite(s) substantially similar limitations as those of claim(s) 1, 3-7 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim(s) 1,3-8,10-15 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang (20230343454) in view of Eswaran (20200019617).
Claim 1: Chang discloses:
A system (Title illustrating a system) comprising:
a memory configured to store instructions (page 16 paragraph 0168 illustrating a memory containing software thereon); and
one or more processors configured to execute the instructions (page 16 paragraph 0168 illustrating a processor) to:
receive a radiology report including unstructured natural language text corresponding to a finding of a radiologist with respect to a region of interest of a subject (page 5 paragraph 0055 illustrating a radiology report with radiologist annotated text regarding findings [considered to be a “region of interest”]);
generate, using a first artificial intelligence (AI) model, structured data in a predetermined format including a set of predetermined fields and a set of corresponding text values that are extracted from the radiology report and that correspond to the set of predetermined fields by inputting the radiology report into the first Al model that is configured to extract the set of corresponding text values (Figure 2A illustrating processing the natural language of the radiology report to generate a plurality of patient data values with an AI model);
select, using the first Al model, a medical image dataset of the subject corresponding to the radiology report from among a plurality of medical image datasets of the subject stored in a medical image database by inputting the structured data and respective metadata of the plurality of medical image datasets into the first Al model that is configured to match the text values with the respective metadata (page 6 paragraph 0060 illustrating using AI to select the patient’s images);
segment, using a second Al model, a set of regions of each medical image of the medical image dataset by inputting each medical image of the medical image dataset into the second Al model that is configured to segment the set of regions of each medical image into a set of segmented structures of each medical image (page 6 paragraph 0060 illustrating processing the image to classify the image [considered to be a form of “segment”]);
select, using the first Al model, a medical image of the medical image dataset that depicts the region of interest of the subject by inputting the structured data, the labelled and segmented set of regions, and the classification of each segmented structure into the first Al model (page 6 paragraph 0060 illustrating using AI to select the patient’s images)
display the radiology report and the medical image that depicts the region of interest of the subject via a user interface of a user device (page 6 paragraph 0059-0060, page 16-17 claim 4 illustrating displaying the patient’s image and medical record).
Chang does not disclose:
classify, using the second Al model, each segmented structure of the set of segmented structures of each medical image by inputting each medical image including the set of segmented structures into the second Al model that is configured to determine a classification of each segmented structure;
label, using the second Al model, the set of regions of each medical image of the medical image dataset by inputting each medical image including the set of segmented structures and the classification of each segmented structure into the second Al model that is configured to label the set of regions of each medical image, wherein each label identifies a segmented structure depicted in each medical image and the classification of the segmented structure;
that is configured to determine, for each medical image of the medical image dataset, a score based on a number of matches between the set of corresponding text values and labels associated with the labelled and segmented set of regions, wherein each match of the number of matches is a match between a text value of the set of corresponding text values and one of the segmented structure or the classification of the segmented structure identified by a label of the labels, and to select the medical image of the medical image dataset having a greatest score.
Eswaran discloses:
classify, using the second Al model, each segmented structure of the set of segmented structures of each medical image by inputting each medical image including the set of segmented structures into the second Al model that is configured to determine a classification of each segmented structure (page 1 paragraph 0007 illustrating a classifier, page 2 paragraph 0015 illustrating processing the region of interest and according sub-metadata for the ROI);
label, using the second Al model, the set of regions of each medical image of the medical image dataset by inputting each medical image including the set of segmented structures and the classification of each segmented structure into the second Al model that is configured to label the set of regions of each medical image, wherein each label identifies a segmented structure depicted in each medical image and the classification of the segmented structure (page 8 paragraph 0108 illustrating a labeler labeling anatomical parts of an image);
that is configured to determine, for each medical image of the medical image dataset, a score based on a number of matches between the set of corresponding text values and labels associated with the labelled and segmented set of regions, wherein each match of the number of matches is a match between a text value of the set of corresponding text values and one of the segmented structure or the classification of the segmented structure identified by a label of the labels, and to select the medical image of the medical image dataset having a greatest score (page 9 paragraph 0124 illustrating selecting the highest similarity score indicating the best match of the image to the supporting annotations).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the matching algorithm of Eswaran within the image processing system of Chang with the motivation of improving patient care by helping physicians locate clinically similar images to help diagnose the patient currently under treatment (Eswaran; page 1 paragraph 0008).
Claim 3: Chang discloses:
The system of claim 1, as discussed above and incorporated herein.
Chang further discloses:
wherein the one or more processors are further configured to generate a data entry that correlates the radiology report with the medical image that depicts the region of interest of the subject, and store the data entry in a database (page 6 paragraph 0060 illustrating storing the patient’s annotated record into the computerized patient records database).
Claim 4: Chang discloses:
The system of claim 1, as discussed above and incorporated herein.
Chang further discloses:
wherein the selecting the medical image dataset of the subject comprises selecting the medical image dataset based on a text value of an imaging modality field included in the structured data (Figure 2A illustrating selecting the patient’s image based on the AI’s processing of the text annotations).
Claim 5: Chang discloses:
The system of claim 1, as discussed above and incorporated herein.
Chang further discloses:
wherein the selecting the medical image dataset of the subject comprises selecting the medical image dataset based on a text value of a date field included in the structured data (page 11 paragraph 0119 illustrating selecting the image based on the value of a date).
Claim 6: Chang discloses:
The system of claim 1, as discussed above and incorporated herein.
Chang further discloses:
wherein the selecting the medical image of the medical image dataset that depicts the region of interest comprises selecting the medical image of the medical image dataset that depicts the region of interest based on a finding field included in the structured data and a label associated with the labelled and segmented set of regions (page 6 paragraph 0060 illustrating using AI to select the patient’s image based on the annotated findings found on the patient’s radiology report).
Claim 7: Chang discloses:
The system of claim 1, as discussed above and incorporated herein.
Chang further discloses:
wherein the selecting the medical image dataset of the subject comprises selecting the medical image dataset based on generating a prompt for the first AI model that identifies the plurality of medical image datasets (Figure 2A-D, page 1 paragraph 0008 illustrating prompts used to instruct the AI to perform the operations as discussed above, and incorporated herein).
Claim(s) 8,10-15 and 17-20 recite(s) substantially similar limitations as those of claim(s) 1, 3-7 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Response to Arguments
In the Remarks filed on 16 September 2026, Applicant makes numerous arguments. Examiner will address these arguments in the order presented.
On page 15 Applicant argues that the claims do not recite interaction between two or more people.
The number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings. MPEP 2106.04(a)(2)(II)
While Applicant’s arguments have been carefully considered, they are not found persuasive for at least the reasons discussed in the section above (because human beings analyze radiological images when diagnosing and treating patients), and incorporated herein.
On page 15 Applicant argues that the AI operations render the claims non-abstract.
While Applicant’s arguments have been carefully considered, they are not found persuasive because the AI has been found to be part of the additional elements; the functionality performed by the AI is still found to be directed towards an abstract idea.
On page 16 Applicant argues that the Office characterized the claims at a high level of abstraction.
While Applicant’s argument has been carefully considered, it is not found persuasive because the limitations have been specifically considered in the section above and provided with reasoned rationale why the highlighted portions would be considered to be certain enumerated categories of abstract idea, and incorporated herein.
On page 17-18 Applicant argues that the AI functions are not Mental Processes.
While Applicant’s argument has been carefully considered, it is not found persuasive because the argued limitations merely specify an outcome of the data processing without any technical limitations performed by the AI, and is still considered to be directed towards Mental Processes.
In response to applicant's argument, it is noted that the features upon which applicant relies (i.e., large numbers of images and dataset) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Examiner submits that a simpleton embodiment with two images would mee the claimed scope, and would certainly be practically performed in the human mind.
Applicant’s on page 18-19 attempt to correlate the claims to image processing; however, after careful consideration, the argument is not found persuasive because the instant pending claims do not recite any technical image processing, such as pixel-processing or other forms of technical processing.
Instead, the claims merely recite abstract concepts deployed on a generic AI model without any technical details regarding how the AI would implement the claimed functions, i.e. “apply it”.
On page 19-20 Applicant argues the technical problem of selecting images from a plurality of images.
While Applicant’s argument has been carefully considered, it is not found persuasive because selecting an image for physician review is not a technology or related field, as recognized by one of ordinary skill in the art.
Applicant’s arguments on page 20-22 merely rehash the arguments previously addressed above, and incorporated herein. Namely, the argued limitations are part of the abstract idea and cannot provide technical improvement.
On page 22-23 Applicant argues that Rao is not adequate Berkheimer art for the argued limitations.
While Applicant’s argument has been carefully considered, it is not found persuasive because Applicant argues limitations that are part of the abstract idea, and therefore need not be disclosed by Rao.
Applicant’s arguments with respect to claim(s) 1 on page 23-25 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Based on the evidence presented above, Applicant’s arguments are not found persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shinagawa (20170221204) discloses overlaying text onto an image (Figure 2) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Liu (20170337329) discloses generating annotation for a medical image (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRAN N NGUYEN whose telephone number is (571)272-0259. The examiner can normally be reached Monday-Friday 9AM-5PM Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KAMBIZ ABDI can be reached on (571)272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.N.N./ Examiner, Art Unit 3685 /KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
