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 .
Status of claims
This final office action on merits is in response to the communication received on 12/25/2025. Amendments to claims 1-2, and 7 are acknowledged and have been carefully considered. Claims 1-7 are pending and considered below.
Subject Matter Free of Art
Claims 1-7 include subject matter that is free of prior art. The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within independent claim 1. In particular, the cited prior art fails to expressly teach or suggest the specific combination of elements and ordered operations recited in these claims, including calculating multiple content matching degrees between different field identification elements and arranging elements within an integrated report based on a comparison between the calculated matching degrees.
For claim 1, the cited prior art of record fails to expressly teach or suggest, either alone or in combination, comparing matching degrees associated with multiple candidate field identification elements and ordering the placement of the field identification elements in the integrated report based on the relative magnitude of the matching degrees.
The closest prior art of record includes Reicher et al. (U.S. Publication 2019/0042703 A1), referred to hereinafter as Reicher, in view of Rahme (U.S. Publication 2023/0410990 A1), referred to hereinafter as Rahme.
Reicher teaches generating mappings between report language and report sections and inserting content into a selected section of a structured report template based on the mapping. However, Reicher fails to teach or suggest calculating multiple content matching degrees between a reference field identification element and multiple candidate field identification elements and ordering the candidate elements relative to each other in a generated report based on a comparison of the calculated matching degrees. Instead, Reicher selects a destination section based on a best match determination and does not disclose ranking multiple elements or determining relative ordering within a generated integrated report using comparative similarity values.
Rahme teaches generating a medical document by merging medical images and medical reports into a single document. However, Rahme fails to teach or suggest determining placement order of report elements based on calculated similarity relationships between multiple candidate elements, and instead combines information from multiple sources without performing similarity based prioritization or ordering of report sections.
Accordingly, the prior art of record does not teach or suggest the claimed similarity-comparison-based ordering of field identification elements within an integrated report, and the rejection under 35 U.S.C. §103 is withdrawn.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1-7 are drawn to a method. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Step 2A Prong One
Claim 1 recites the limitations of automatically identifying, mapping regions between a first type report and a second type report different from the first type report; automatically generating, an integrated report on the basis of the identified mapping regions; and adding, at least some information included in non-mapping regions, which are regions of the first type report and the second type report other than the mapping regions, to the integrated report, wherein the adding comprises; identifying, additional positions for adding non- mapping regions which are not mapped between the first type report and the second type report to the integrated report; identifying, whether any region in the non-mapping regions is additionally mapped; extracting content keywords associated with a first field identification element, a second field identification element, and a third field identification element; calculating content matching degree between first content keywords corresponding to the first field identification element and second content keywords corresponding to the second field identification element; calculating content matching degree between the first content keywords and third content keywords corresponding to the third field identification element; and when the content matching degree between the first content keywords and the second content keywords is higher than the content matching degree between the first content keywords and the third content keywords, placing the second field identification element prior to the third field identification element in the integrated report. These limitations, as drafted, are processes that, under their broadest reasonable interpretations, cover performance of the limitations in the mind or by using a pen and paper. But for the “by the processor” or “by the machine learning processor” language, the claim encompasses a user reviewing two reports, determining corresponding sections, comparing relevant information, and arranging the information in an ordered combination report in their mind or by using a pen and paper. The mere nominal recitation of a “by the processor” or “by the machine learning processor” do not take the claim limitations out of the mental processes grouping. Thus, the claim recites a mental process which is an abstract idea.
Under Step 2A Prong Two
The claimed limitations, as per method claim 1, include the steps of:
automatically identifying, by the processor, mapping regions between a first type report and a second type report different from the first type report;
automatically generating, by the processor, an integrated report on the basis of the identified mapping regions; and
adding, by the processor, at least some information included in non-mapping regions, which are regions of the first type report and the second type report other than the mapping regions, to the integrated report, wherein the adding comprises:
generating and training, by the machine learning processor, a neural network model:
identifying, by the machine learning processor, additional positions for adding non- mapping regions which are not mapped between the first type report and the second type report to the integrated report;
identifying, by the machine learning processor, whether any region in the non-mapping regions is additionally mapped;
extracting content keywords associated with a first field identification element, a second field identification element, and a third field identification element;
calculating content matching degree between first content keywords corresponding to the first field identification element and second content keywords corresponding to the second field identification element;
calculating content matching degree between the first content keywords and third content keywords corresponding to the third field identification element; and
when the content matching degree between the first content keywords and the second content keywords is higher than the content matching degree between the first content keywords and the third content keywords, placing the second field identification element prior to the third field identification element in the integrated report.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of organizing and correlating information from multiple reports based on a similarity determination in a computer environment. The claimed computer components (i.e., by the processor, by the machine learning processor, and generating and training a neural network model) are recited at a high level of generality and are merely invoked as tools to perform an existing process of reviewing documents, determining correspondence between information, and arranging the information accordingly. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of organizing and correlating information from multiple reports based on a similarity determination in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is not patent eligible.
Claims 3 and 5 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 2, 4, and 6-7 recite the additional elements of by the machine learning processor (claims 2 and 7), outputting a relation report corresponding to the non-mapping regions when the non-mapping regions in each of the first type report and the second type report exceed a reference ratio (claim 4), the outputting of the relation report comprises transforming field identification elements of the non-mapping regions of each of the first type report and the second type report on the basis of the relation report (claim 6), by the processor (claim 6), and outputting candidate identification elements corresponding to the field identification elements of for the non-mapping regions on the basis of the plurality of relation reports. However, this additional element amounts to implementing an abstract idea on a generic computing device or displaying or outputting data (i.e., an insignificant extra-solution activity). As such, these additional elements, when considered individually or in combination with the prior devices, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claim is rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Claim Rejections - 35 USC § 103
Regarding the rejection of claim 1-7, the Examiner has considered Applicant’s arguments in light of the present amendments and withdraws the prior art rejection.
Response to Arguments
Applicant’s arguments and amendments, see Remarks/Amendments submitted on 12/25/2025 with respect to the rejection of the claims have been carefully considered and is addressed below.
Claim Rejections - 35 USC § 101
Applicant states that amended claim 1 is not directed to a mental process because the claim now recites interworking hardware elements and operations performed by a machine learning processor, including generating and training a neural network model and identifying additional mapping positions. This argument is not persuasive.
The eligibility analysis does not depend on whether a human could practically perform the recited operations with the same speed, but rather whether the claimed steps are directed to a type of reasoning that can be performed conceptually in the human mind. Under the broadest reasonable interpretation, the claim recites reviewing two reports, determining corresponding sections, evaluating similarity of information, and arranging information based on that evaluation. The recitation of a “processor,” “machine learning processor,” or “neural network model” only identifies a tool used to perform this analysis and does not change the recited steps. Thus, the amended limitations are within the mental processes grouping of abstract ideas.
Applicant also states that extracting keywords, calculating matching degrees, and ordering elements integrates the judicial exception into a practical application because the mapping is deep learning based and dynamically adaptable. However, the claim does not recite any improvement to computer functionality, machine learning technology, or another technological field. The neural network is invoked at a high level of generality and is used only to evaluate relationships between pieces of information and organize a report accordingly. Overall, producing a better organized report is considered an improvement in the content of information rather than an improvement in computer technology. Therefore, the additional elements merely apply the abstract idea using generic computing components and do not integrate the exception into a practical application.
Therefore, the rejection under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 103
Regarding the rejection of claim 1-7, the Examiner has considered Applicant’s arguments in light of the present amendments and withdraws the prior art rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
Cohen-Solal et al. (U.S. Publication 2014/0379378 A1) teaches a computer implemented system that processes a patient report by identifying predefined sections, extracting relevant patient data from those sections, and generating summary information based on the extracted data.
Schuurman et al. (Schuurman et al., A method to map heterogeneity between near but non-equivalent semantic attributes in multiple health data registries, 2008, Health Informatics Journal, Vol 14(1): 39–57 (Year: 2008)) teaches an OWL encoded ontology and ontology based metadata system that formalizes and contextualizes semantically heterogenous terms across health registries, enabling researchers to address uncertainty in data integration through a web-based GUI for precise mapping and ad hoc comparison.
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.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685