Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
In the response filed on 11 June 2026, the following has occurred: claims 1 and 7-8 have been amended; claim 4 has been canceled.
Now claims 1-2, and 5-8 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11 June 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.
Claims 1-2, and 5-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite treatment support apparatus (i.e., a system), a treatment support method (i.e., a method) and non-transitory computer-readable storage medium (i.e., a system), for performing the limitations of:
Claim 1, which is representative of claims 7 and 8
acquiring a target image [… of …] a treatment target patient who is to be treated; searching for a first similar case from among a plurality of reference cases, each of the plurality of reference cases including a diagnosed image [… of …] a diagnosed patient before treatment, a post-treatment image [… of …] the diagnosed patient after treatment, and a diagnosis log describing a treatment method performed on the diagnosed patient for whom the diagnosed image and post-treatment image are acquired and a treatment result obtained by the treatment method, the first similar case being searched based on differences […] of the target image and […] the diagnosed images and having a similar feature to the target image; searching for a second similar case from among the plurality of reference cases, wherein the post-treatment image included in the first similar case is used as a new image query to perform the search for the second similar case based on differences […] of the post-treatment image of the first similar case and […] the post-treatment image of the reference cases, the second similar case being at least a portion of the reference cases other than the first similar case and having a similar feature to the post-treatment image included in the first similar case; acquire environment information including a facility such as a hospital and a doctor that give treatment to the treatment target patient; patient information including a patient name of the treatment target patient and a diagnosis result; and the treatment method described in a search diagnosis log that is the diagnosis log included in each of the first similar case and the second similar case which are directed to the diagnosed patients, to derive an expected treatment result of the target image when the treatment target patient is treated in accordance with the treatment method corresponding to the first and second similar cases described in the search diagnosis log; wherein the expected treatment result is derived through a […] model by inputting the environment information, the patient information, and the treatment method corresponding to each of the first and second similar cases described in the search diagnosis log, wherein the […] model is constructed […] using the plurality of reference cases as training data in which treating-doctor information, patient information, treatment methods and treatment results corresponding to the treatment methods serve as training information and the treatment results described in the diagnosis logs serve as ground-truth data to [… preform math …] between an expected treatment result output of the […] model and the ground-truth data corresponding to the treatment results corresponding to the diagnosed patient, the […] model being configured to output, as the expected treatment result, an expected post-treatment blood-vessel diameter corresponding to the treatment method corresponding to each of the first and second similar cases described in the search diagnosis log; in response to determining that the expected treatment result of the target image is derived, generate a presentation screen by using contents of a description of the search diagnosis log and the expected treatment result, wherein the first similar case and the second similar case are classified by a unit of the post-treatment image included in the first similar case, which is referred to for searching for the second similar case, and the contents of the description of the search diagnosis log and the expected treatment result is generated for each of the classified groups; and [… outputting …] contents of the description of the search diagnosis log, and the expected treatment result of the treatment target patient, wherein the presentation screen [… output …] correspond to the treatment target patient and to each of the diagnosed patients for whom the first and second similar cases are acquired, [… output …] correspond to the environment information, the patient information, the treatment method, the grouping result and the treatment result, wherein the treatment result for each of the diagnosed patients for whom the first and second similar cases.
, as drafted, is a system that under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with at least one processor and a display (claim 1), a display (claim 7), a computer and a display (claim 8), the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, but for at least one processor and a display (claim 1), a display (claim 7), a computer and a display (claim 8), the claim encompasses a doctor imaging a patient and providing the image as a search query (i.e. collecting data), to organize the collected data to perform searches for additional data (i.e., organization of data) to provide a human user an output for the treatment of their patient (i.e., output data), the claims collect, organize and output a result for a human user for the human user to use in their workflow. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of at least one processor and a display (claim 1), a display (claim 7), a computer and a display (claim 8). The at least one processor and a display (claim 1), a display (claim 7), a computer and a display (claim 8) is recited at a high level of generality (i.e., general purpose computers with processors and memory, performing/ implementing generic computer functions; see applicant’s specification Figures 1-3, paragraphs [0018]-[0028]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claims recite the additional elements of “imaging a treatment target patient… by imaging…”, comparison of pixel values, “trained machine learning model… constructed by machine learning using”, “minimize a loss function” and “presenting, on a display the presentation screen… wherein the presentation screen displays a table in which rows… and columns”. The “imaging a treatment target patient… by imaging…” steps are recited at a high-level of generality (i.e., as a general means of capturing a medical image) and amounts to the capture of an image which amounts to generally linking the abstract idea to a particular technological environment. The comparison of pixel value steps are recited at a high-level of generality (i.e., as a general means of comparing images) and amounts to the comparison of images which amounts to generally linking the abstract idea to a particular technological environment. The “trained machine learning model… constructed by machine learning using” steps are recited at a high level of generality (training and using a generic machine learning to perform generic machine learning determinations) and amounts to generally linking the abstract idea to a particular technological environment. The “minimize a loss function” steps are recited at a high-level of generality (i.e., as a general means of performing math) and amounts to the performance of math which amounts to generally linking the abstract idea to a particular technological environment. The “presenting, on a display the presentation screen… wherein the presentation screen displays a table in which rows… and columns” steps are recited at a high level of generality (simply outputting data on a generic display) and amounts to simply output of data, which is a form of post/extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed toward an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of at least one processor and a display (claim 1), a display (claim 7), a computer and a display (claim 8), to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “imaging a treatment target patient… by imaging…”, comparison of pixel value, “trained machine learning model… constructed by machine learning using”, “minimize a loss function” and “presenting, on a display the presentation screen… wherein the presentation screen displays a table in which rows… and columns” were considered post/extra-solution activity and/or generally linking to a particular technological environment. The “imaging a treatment target patient… by imaging…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Hibbard (20190192880): paragraph [0063]; De Francisco (20200082930): paragraph [0075]; Han (20170100078): paragraph [0007]. The comparison of pixel value has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Hisanaga (20100232661): paragraph [0022]; Min (20210209757): paragraphs [0159]-[0161]; Sakata (20220148180): paragraph [0057]; comparison of pixels for image comparison is well-understood, routine and conventional. The “trained machine learning model… constructed by machine learning using” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Peng (2021/0019342): see below but at least paragraphs [0013]; Bogonoi (20160321427): see below but at least paragraphs [0036]; training and applying a machine learning model is well-understood, routine and conventional. The “minimize a loss function” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Hibbard (20190192880): paragraph [0134]; Eswaran (20200019617): paragraph [0102]; use of math to perform an optimization is well-understood, routine and conventional. The “presenting, on a display the presentation screen… wherein the presentation screen displays a table in which rows… and columns” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in, Peng (2021/0019342): see below but at least Fig. 10, 15, paragraphs [0008], [0011]-[0012], [0049]; Moriya (2009/0080734): see below but at least Figures 16-18; Yee (20150161175): Fig. 7, paragraphs [0065]-[0072]; Kanada (20160055394): Fig. 16, paragraph [0130]; displaying treatment results on a generic user interface in a table with rows and columns is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2 and 5-6 are similarly rejected because the claims either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claim 2 further describes doctor and patient information, however does not recite any additional elements sufficient for practical application and/or significantly more.
Claim 5 further describes repeating, however does not recite any additional elements sufficient for practical application and/or significantly more.
Claim 6 further describes looking at stenosis in a coronary artery, however does not recite any additional elements sufficient for practical application and/or significantly more.
Response to Arguments
Applicant's arguments filed on 11 June 2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed on 11 June 2026.
Rejections under 35 U.S.C. § 101
Regarding the rejection of claims 1-2 and 4-8, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
Applicant respectfully submits that the amended claim 1 is not directed to an abstract idea… Here amended claim I does not merely recite use of a generic AI model to perform generalized prediction for providing treatment support or presenting medical information. Rather, amended claim I recites a specifically integrated technical architecture in which medical-image-derived similarity processing, recursive post-treatment-image querying, and machine-learning-based treatment-result cooperate together as a coordinated medical image processing pipeline… amended claim I recites a specific machine-learning training architecture… claimed invention recites a specific technological implementation for recursive medical-image retrieval and machine learning-based treatment-result derivation… Applicant respectfully submits that the amended claim integrates the abstract idea into a practical application… Even if amended claim I were considered to recite an abstract idea, Applicant respectfully submits that the amended claim I integrates any alleged abstract idea into a practical application directed to improving computerized medical-image retrieval and treatment-result derivation systems… The claimed invention addresses a specific technical problem identified in the specification: conventional similar-case search systems merely retrieve treatment results from similar cases and fail to provide information that sufficiently reflects the effects of various treatment methods on treatment outcomes. See, e.g., paragraph [0004] of the published application. In particular, conventional systems are unable to effectively correlate pretreatment image similarity with post-treatment outcome similarity… the claimed trained machine learning model is not merely appended as a field-of use limitation or generic prediction engine. Rather, the trained machine learning model is technically integrated into a specialized recursive medical-image retrieval architecture that automatically generates treatment-result derivation inputs based on staged image-similarity processing operations. The claimed invention therefore improves operation of AI-based medical-image analysis systems themselves. In particular, the claimed recursive image-query architecture improves how machine-learning-based treatment-result derivation systems generate and utilize training-relevant similarity relationships…. This recursive staged similarity-search processing improves the quality and structure of machine-learning inputs used for treatment-result derivation… Accordingly, the claimed invention improves the technological operation of AI-based medical-image retrieval and treatment-result derivation systems themselves by improving recursive similarity-search functionality, AI input generation, and AI-based treatment-result prediction reliability for post-treatment physiological outcomes… Accordingly, the claimed invention records a specific and particular way for solving technical problems and achieving improvements in the technical field.
The Examiner respectfully disagrees.
It is respectfully submitted, that the claims under the broadest reasonable interpretation are directed toward the organization of human activity between a medical provider and a patient, in particular, the claims encompass a medical provider (i.e., a human user) imaging a patient (i.e., another human user) to collect data, and the human user using the collected data as input via interaction with generic computer components to search for similar data to organize a result for the medical provider to use/interact with in their treatment of the patient in a human activity workflow, which as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. The claim is directed toward the certain method of organizing human activity grouping of abstract ideas.
The claimed additional elements do not recite a technical problem recited in Applicant’s specification, the only argued portions of Applicant’s specification is paragraph [0004], looking to the argued paragraph, at best the paragraph describes a human activity problem of providing information to a human user for the human user to comprehend, and provides no technical details on any technical computer related problems as argued, and it provides no details of any of the alleged technical problems of similar case searching systems. The Examiner notes a doctor searching for similar data to use for the planning of their treatment of a patient is not a technical problem rooted in computer hardware technology, it is a human activity problem of a medical provider treatment problem which is not technical problem rooted in computer hardware technology, the claims use a human activity solution to improve upon the abstract idea however an improved abstract idea is still an abstract idea. Finally, with respect to the machine learning model, no technical details of the training or use of the machine learning model is claimed, the claimed amendments amount to generic training and use of an off-the-shelf machine learning model using labeled training data, however the labels used to describe data are not additional elements, the claims merely generally link the abstract idea to a particular technological environment without reciting any technical details that actually improve the machine learning model (i.e., improve the computer) nor do the claims recite a technical solution to a technical problem recited in Applicant’s specification, as such the claims are not subject matter eligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew E Lee whose telephone number is (571)272-8323. The examiner can normally be reached M-Th 9-5:00 PM.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
